CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit and priority of United States provisional application 62/554,427, filed on September 5, 2017.

FIELD OF THE INVENTION

The present invention relates to the field of human medicine and specifically to the diagnosis of cognitive disorder including Alzheimer disease. This invention relates to a biomarker for cognitive disorders, in particular Alzheimer's disease, consisting of circulating non-coding RNAs (ncRNA) such as microRNA (miRNA) and long non-coding RNAs (IncRNA), or their combination in a peripheral body fluid and representing a non-invasive method for diagnosing the cognitive disorder in particular Alzheimer's disease or for monitoring its development or progression using this biomarker. It further relates to associated kits, methods, protocols and transmittable forms of information for diagnosis purposes and/or for other human medicine applications including the patient stratification in clinical trials and/or monitoring the efficacy of therapeutic strategies using this biomarker and peripheral body fluids from patients. The present invention will thus have a strong impact on the quality of life of patients and a significant impact on the healthcare system and economy. BACKGROUND ART

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive loss of cognitive function and pathologically by extracellular deposition of amyloid-beta peptide (Αβ) and intracellular deposition of hyper-phosphorylated tau protein in neurofibrillary tangles in the brain, associated with progressive neuronal degeneration (Marcus et al. J Neurogenet. 2011; 25(4):127-33; Gotz et al, Br J Pharmacol. 2012; 165(5):1246-59).

AD is the most common form of dementia. Approximately 46.8 million people worldwide currently live with AD or other type of dementia. With an ageing population, this number is estimated to increase to 131.5 million by 2050 (World Alzheimer Report, 2015: http://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf). As such, AD is becoming an increasingly important burden on the affected individuals and their families as well as economic and social costs on medical and healthcare resources in both developed and emerging countries.

Currently, there is no cure for AD. Symptomatic treatments exist for this disease all trying to counterbalance neurotransmitter's disturbance. Although a number of new potential disease- modifying therapeutic candidates are currently being studied in clinical trials, none has been approved yet. From 2002 to 2012, there was a failure of 99.6% of AD clinical trials that were 289 Phases 2 and 3 trials on symptomatic agents (36.6%), disease-modifying small molecules (35.1%) and disease-modifying immunotherapies (18%) (Cummings et al., Expert Rev Clin Pharmacol. 2014;7(2):161-5). Among the strategies proposed by worldwide experts in the AD field and by pharmaceutical industries to improve the success rate for AD drug development, are: a) intervening earlier in the disease process before neurodegeneration begins, b) identifying and developing accurate biomarkers for early diagnosis, non-invasive and suitable for stratification of subject populations and for longitudinal monitoring of drug efficacy in clinical trials. The AD pathological features are defined in post-mortem histopathological analysis. The presence of amyloid plaques remains an absolutely required feature for the definitive diagnosis of AD, as accepted by the American Academy of Neurology, American Psychiatric Association (DSM-IV) and both the CERAD and NIA-Reagan Institute neuropathological criteria.

Current diagnosis of the disease remains uncertain (Dubois et al Lancet Neurol. 2014;13(6):614-29) and it is based on combination of battery of clinical and neuropsychological tests and neuroimaging, such as structural imaging using Magnetic Resonance Imaging (MRI) and/or glucose metabolism using Positron emission tomography (PET) of fluorodeoxyglucose F18 ( ¹⁸ F-FDG), with an accuracy of less than 70%-85% depending on the method and the severity stage of the disease (Frisoni et al., Nat Rev Neurol. 2010; 6(2): 67-77; Tahmasian et al., J Nucl Med. 2016 ;57(3):410-5).

Sometimes, detection of AD-associated biomarkers Αβ42 and tau or phosphorylated-tau in cerebrospinal fluid (CSF) is additionally performed (Sunderland et al., JAMA. 2003;289(16):2094- 2103), but CSF tests request a lumbar puncture, which is an invasive procedure and often requires hospitalization of subjects. In addition, the accuracy of these CSF tests remains insufficient as up to 30% of subjects can be misdiagnosed and the test cannot be longitudinally practiced to be followed up for confirmation of diagnostic purposes.

Longitudinal confirmatory diagnosis test is essential for applications to early detection of the preclinical stages of AD and mild cognitive impairment (MCI; defined as an early stage during which the first subtle symptoms manifest) and thus detecting early and calculating the risk of conversion of MCI into AD. Due to its limits (invasive), a CSF test is not rarely used as a biomarker for early diagnosis in preclinical AD stages before symptoms appear, nor it is suitable as a companion biomarker repeatedly practiced at several time points in same subjects recruited in longitudinal clinical trials.

Recently, specific neuroimaging methods relevant for AD pathology are being developed, notably with ligands for in vivo amyloid-beta (Αβ) ligands for Positron Emission Tomography (PET) neuroimaging including F-18 florbetapir and F-18 flutemetamol which have been approved by FDA and/or EMA (Choi et al., 2009 and Wong et al., 2010). However, their practice is costly and very limited (available only in some hospitals of large cities equipped with PET technology, still mainly for research purposes as not reimbursed by health insurances) and their diagnosis accuracy is still under studies for further understanding. Thus, the use of these Αβ PET neuroimaging methods remains very limited and the vast majority of patients do not profit from such tests even in rich countries.

There is a need for better definition of AD patient population to be included for the drug-development trials: PET imaging using Αβ ligands showed that up to 30% of subjects diagnosed with AD show a negative Αβ scan and that up to 35% of subjects with normal cognition status show a positive Αβ scan (Gael Chetelat et al, Neurolmage Clinical 2013; 2:356-65). Thus, PET Αβ scan is being used to guide for recruitment of subjects in the desired cohorts in some clinical trials by the large pharmaceutical companies. Anti-Αβ antibody e.g. Solanezumab tested in patients with mixed mild to moderate AD failed to show clear efficacy, however with some encouraging data in only mild AD patients subgroup. In mild AD patients selected based on CSF biomarker profile, the results showed a promising efficacy (Carlson et al., Alzheimers Dement (Amst). 2016; 2:75-85; Siemers et al., Alzheimers Dement. 2016;12(2):110-20). However, the use of CSF biomarker is invasive, dramatically limiting its use as a biomarker for drug development. For example, it is not suitable for repeated use to monitor the stability and/or the progression of the disease, nor it is suitable to monitor in clinical longitudinal trials the efficacy of disease-modifying therapeutic drug candidates or preventive strategies. Overall, the existing tests either lack an easy accessibility and simplicity for use for diagnosis of the large AD population and/or lack accuracy (sensitivity and specificity). This represents a major impediment and bottleneck to develop reliable and rapid diagnosis test for AD. Another impediment is the identification of a biomarker that does not require invasive sample collecting, such as a spinal tap. The lack of such an accessible, sensitive and specific biomarker that could be validated by cellular, animal model, pre-clinical models, and human testing impedes the development of therapies and drugs for AD or for the studies on pathological processes triggering AD or involved in the progression of AD.

Today, clearly there is an unmet need for an accurate and non-invasive peripheral biomarker test for diagnosis of AD including preclinical and early AD and for applications in drug development (patient stratification and monitoring drug efficacy in clinical trials). ncRNAs include small microRNAs (miRNAs) and long non-coding RNAs (IncRNAs).

- miRNAs are small non-coding RNA molecules (18-23 nucleotide) that modulate gene expression negatively at the post-transcriptional level. About 70% of the currently identified miRNAs are expressed in the brain. miRNAs play a major role in neural development, differentiation and synaptic plasticity. Some specific miRNAs are aberrantly expressed in AD brain, CSF and blood (for review: Hu et al., 2016, Front Aging Neurosci. 9(8): 13), revealing their potential value in the diagnosis of AD.

- IncRNAs are typically defined as transcripts longer than 200 nucleotides and expressed in a tissue- specific manner. In the brain, IncRNAs can regulate gene expression at epigenetic, transcriptional, and posttranscriptional levels of proteins with diverse functions including neuronal transmission and synaptic plasticity. Recent studies identified IncRNA candidates in AD postmortem brain tissue; some IncRNA candidates directly or indirectly regulate the formation of the neurotoxic Αβ, synaptic activity or the neuronal DNA repair (for review: Luo K and Chen Y, Clin Interv Aging 2016; 11: 867- 872).

Expression levels of miRNAs may have potential as diagnostic biomarkers as they are known to circulate and tissue specific profiles can be identified in a number of body fluids such as plasma, CSF and urine. Recent developments in deep sequencing technology present a viable approach to develop biomarker pipelines in order to profile peripheral miRNA signatures specific to neurodegenerative diseases.

We previously showed that the IncRNAs expressed or highly enriched in tissues such as cardiac tissue or brain tissue, can be released into the peripheral circulation and be easily quantifiable by classical RT-PCR in different peripheral samples (PCT/EP2018/065492). In addition of the RT-PCR, we performed also total RNA sequencing on peripheral samples and quantified significant proportion of IncRNAs in the peripheral samples such as serum, plasma and Paxgene-RNA-tube collected whole blood (PCT/EP2018/065492).

According to the invention, new miRNA signatures and IncRNA signatures specific of neurodegenerative diseases, especially of AD, have been identified, using a method set up combining most recent technology and samples collected in non-invasively manner, including Paxgene whole blood tube, serum and plasma.

BRIEF DESCRIPTION OF THE FIGURES Figure 1 shows the expression level of miR.99a.5p, MiR.1229.3p, miR.378d and miR.6880.5p on human plasma lithium heparin samples from patients with early to moderate Alzheimer (AD) and age-matched healthy controls. Y axis: mean +/- SD of the mean of concentration of the miRNAs in log2 of the normalized number of reads. X axis: AD: Alzheimer patient group, HV: Healthy control group.

Figure 2 shows a predictive model using random forest for the diagnosis of AD patients. Three miRNAs were used in this example of results and show a good AUC of 0.839 and an accuracy of 0.818.

Figure 3 shows the expression level of IncRNA candidates lnc-TPPP-l:2, lnc-TMEM185B-12:7, lnc- NAXD-6:5 and lnc-HECA-6:l on human serum samples from patients with early to moderate Alzheimer (AD) and age-matched healthy controls. Y axis: mean +/- standard error of the mean of concentration of the IncRNAs in CPM (count per million). X axis: AD: Alzheimer patient group, HV: Healthy control group. Figure 4 shows Volcano plot of the comparison. The volcano plot shows the 1008 IncRNAs that are differentially expressed in AD group versus HV (healthy control) group with statistical significance (p<0.05) above the line. 33 IncRNAs show both p value of <0.05 and fold change of >2 or <0.5. The IncRNAs that are differentially expressed but do not reach a statistical significance are shown below the line.

Figure 5 shows a predictive modelling based on the random forest algorithm enabling the identification of the signature of the 13-top ranked IncRNAs candidates selected out of 19867 IncRNAs.

Figure 6 shows a predictive modelling based on the random forest algorithm enabling the identification of the signature of the 7-top ranked IncRNAs candidates out of 90 IncRNAs with a p value of <0.05 and fold change of > 2 or < 0.5 or an AUC of > 0.85 or < 0.15.

Figure 7 shows a predictive modelling based on the random forest algorithm enabling identification of the signature of the 12 top IncRNAs candidates selected out of the 90 IncRNAs (with a p value of <0.05 and _. fold change of > 1,6 or < 0.6 and an AUC of > 0.85 or ^0.15. Figure 8 shows a predictive modelling based on the random forest algorithm enabling identification of the signature of the 3 top IncRNA candidates selected out of the IncRNAs with a p value of <0.05 and a good correlation (Pearson) with scores of neurocognitive tests including MMSE and/or MoCA out of 7 neuropsychological tests performed. Figure 9 shows a predictive modelling based on the random forest algorithm enabling the identification of the signature of the 7 top IncRNA candidates selected out of IncRNAs with a p value of <0.05 and a good correlation (Pearson) with neuroimaging scores (volume of brain structures of relevance for cognition and memory such as the mediotemporal area, left and right hippocampus, left and right amygdala, entorhinal cortex out of more than 120 structures measured.

Figure 10 shows a predictive modelling based on the random forest algorithm enabling the identification of the signature of the 18 top IncRNA candidates selected out of IncRNAs that show a p value of <0.05 and a good correlation (Pearson) with CSF biomarkers Αβ42 and tau (total tau or phosphorylated tau).

Figure 11 shows examples of results on correlation between the level of IncRNAs and the volumetric neuroimaging score of brain structures implicated in cognition such as the mediotemporal area and the hippocampus. Figure 12 shows examples of results on correlation between IncRNAs and CSF biomarkers Αβ42 or Tau.

DESCRIPTION OF THE INVENTION The invention relates to a method for the diagnosis of a cognitive disorder including but not limited to Alzheimer disease in a subject at risk of having or developing mild cognitive impairment or a cognitive disorder, comprising:

(a) isolating a biological sample from the subject;

(b) detecting a level of expression in a ncRNA (miRNA and/or IncRNA) signature, the ncRNA signature comprising or consisting of at least one ncRNA (miRNA and/or IncRNA) selected from 406 miRNAs listed in Table 1 and 1008 IncRNAs listed in Table 5 in the biological sample from said subject;

(c) comparing the level of expression of miRNA and/or IncRNA in the sample to a level of expression in a reference,

wherein an increased or decreased level of expression in the sample compared to the level in the reference identifies the subject having a cognitive disorder or being at risk of developing a cognitive disorder.

In some embodiments, the ncRNA signature comprises or consists of ncRNAs selected from the group consisting of:

(i) miR.99a.5p;

7 miRNAs of miR.99a.5p, miR.378d, miR.100.5p, miR.193b.3p, miR.34a. miR.1306.5p, and miR.1229.3p;

(iii) 3 miRNAs of miR1220.3p, miR378d and miR99a.5p;

11 mRNAs of miR.99a.5p, miR.1229.3p, miR.100.5p, miR.378d, miR.193b.3p, miR.1306.5p, miR.195.5p, miR.532.3p, miR.125b.5p, miR.34a.5p, and miR.6880.5p;

15 miRNAs of miR.23a.5p, miR.6880.5p, miR.7111.5p, miR.6812.5p, miR.6738.5p, miR.5196.5p, miR.6894.5p, miR.8085, miR.4463, miR.2392, miR.144.3p, miR.192.5p, miR.193b.3p, miR.194.5p, and miR.122.5p;

13 IncRNAs of lnc-DLG5-l:l, lnc-EBLNl-l:4, lnc-FATl-7:2, lnc-PRR5-5:l, lnc-RBKS 6:1, lnc-FOXD4L5-35:l, lnc-TENM3-3:3, lnc-FAM133B-2:l, lnc-ZNF726-l:3, lnc AP3M1-1:!, lnc-DUSP10-6:l, lnc-TPPP-l:2, and LINC01206:20;

7 IncRNAs of LINC02345:11, lnc-EBLNl-l:4, lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-FATl- 7:2, lnc-DKKl-5:3, and lnc-TACSTD2-2:4;

12 lnc RNAs of lnc-TPPP-l:2, ARRDC3-AS1:7, lnc-TENM3-3:5, lnc-TENM3-3:4, lnc QRFP-5:1, lnc-CRYBBl-l:l, lnc-MGST3-l:3, lnc-FAM49B-8:l, HAND2-AS1:70, lnc TMEM185B-12:7, lnc-CNDPl-7: l, and lnc-C21orf58-l:2;

3 IncRNAs of lnc-TENM3-3:3, lnc-MARCH4-2:7, and lnc-LRRCl-5:2; 7 IncRNAs of lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-TMEM185B-12:7, lnc-NAXD-6:5, lnc- HECA-6:1, lnc-COMMD6-10:l, and MIR29B2CHG:46;

(xi) 18 IncRNAs of lnc-TPPP-l:2, LINC02345:11, lnc-ZNF273-4:4, lnc-TACC2-8:6, LINC01206:20, lnc-C5orf67-3:l, HAND2-AS1:58, lnc-PRDM9-20:l, lnc-CLKl-l:7, lnc-

DNALIl-5:4, R0RB-AS1:6, lnc-TPPP-l:3, lnc-BMSl-2:l, lnc-ADRBl-4:l, lnc-XXYLTl- 5:1, MIR99AHG:104, LINC01748:17, and Inc-AKR1E2-I5:l;

28 miRNAs listed in Table 2;

74 miRNAs listed in Table 3;

33 IncRNAs listed in Table 6;

60 IncRNAs listed in Table 7;

32 IncRNAs listed in Table 8;

(xvii) 84 IncRNAs listed in Table 9; and

(xviii) 94 IncRNAS listed in Table 10. The invention also relates to a method for monitoring the development or progression of a cognitive disorder in a subject suffering from a loss or impairment of cognitive function or dementia, said method comprising the following steps:

(a) isolating a biological sample from the subject;

(b) detecting a level of expression in a ncRNA signature, the ncRNA signature comprising or consisting of at least one ncRNA (miRNA and/or IncRNA) selected from 406 miRNAs listed in Table

1 and 1008 IncRNAs listed in Table 5 in the biological sample;

(c) comparing the level of expression of miRNA and/or IncRNA in the sample to a level of expression in a reference,

wherein an increased or decreased level of expression in the sample compared to the level in the reference identifies the subject having a cognitive disorder or being at risk of developing a cognitive disorder, and

(d) designing the therapeutic treatment according to the said identified cognitive disorder.

In some embodiments, the ncRNA signature comprises or consists of ncRNAs selected from the group consisting of:

(i) miR.99a.5p;

(ϋ) 7 miRNAs of miR.99a.5p, miR.378d, miR.100.5p, miR.193b.3p, miR.34a. miR.1306.5p, and miR.1229.3p;

(iii) 3 miRNAs of miR1220.3p, miR378d and miR99a.5p;

(iv) 11 mRNAs of miR.99a.5p, miR.1229.3p, miR.100.5p, miR.378d, miR.193b.3p, miR.1306.5p, miR.195.5p, miR.532.3p, miR.125b.5p, miR.34a.5p, and miR.6880.5p;

(v) 15 miRNAs of miR.23a.5p, miR.6880.5p, miR.7111.5p, miR.6812.5p, miR.6738.5p, miR.5196.5p, miR.6894.5p, miR.8085, miR.4463, miR.2392, miR.144.3p, miR.192.5p, miR.193b.3p, miR.194.5p, and miR.122.5p; (vi) 13 IncRNAs of lnc-DLG5-l:l, lnc-EBLNl-l:4, lnc-FATl-7:2, lnc-PRR5-5:l, lnc-RBKS- 6:1, lnc-FOXD4L5-35:l, lnc-TENM3-3:3, lnc-FAM133B-2:l, lnc-ZNF726-l:3, lnc- AP3M1-1:1, lnc-DUSP10-6:l, lnc-TPPP-l:2, and LINC01206:20;

(vii) 7 IncRNAs of LINC02345:11, lnc-EBLNl-l:4, lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-FATl- 7:2, lnc-DKKl-5:3, and lnc-TACSTD2-2:4;

(viii) 12 lnc RNAs of lnc-TPPP-l:2, ARRDC3-AS1:7, lnc-TENM3-3:5, lnc-TENM3-3:4, lnc- QRFP-5:1, lnc-CRYBBl-l:l, lnc-MGST3-l:3, lnc-FAM49B-8:l, HAND2-AS1:70, lnc- TMEM185B-12:7, lnc-CNDPl-7: l, and lnc-C21orf58-l:2;

(ix) 3 IncRNAs of lnc-TENM3-3:3, lnc-MARCH4-2:7, and lnc-LRRCl-5:2;

(x) 7 IncRNAs of lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-TMEM185B-12:7, lnc-NAXD-6:5, lnc- HECA-6:1, lnc-COMMD6-10:l, and MIR29B2CHG:46;

(xi) 18 IncRNAs of lnc-TPPP-l:2, LINC02345:11, lnc-ZNF273-4:4, lnc-TACC2-8:6, LINC01206:20, lnc-C5orf67-3:l, HAND2-AS1:58, lnc-PRDM9-20:l, lnc-CLKl-l:7, lnc- DNALIl-5:4, R0RB-AS1:6, lnc-TPPP-l:3, lnc-BMSl-2:l, lnc-ADRBl-4:l, lnc-XXYLTl- 5:1, MIR99AHG:104, LINC01748:17, and Inc-AKR1E2-I5:l;

(xii) 28 miRNAs listed in Table 2;

(xiii) 74 miRNAs listed in Table 3;

(xiv) 33 IncRNAs listed in Table 6;

(xv) 60 IncRNAs listed in Table 7

(xvi ) 32 IncRNAs listed in Table 8

(xvii) 84 IncRNAs listed in Table 9; and

(xviii) 94 IncRNAS listed in Table 10.

A biological sample may be a small part of a subject obtained from a body fluid, e.g. blood, plasma, serum, urine or cerebrospinal fluid. Suitably the biological sample are plasma or serum or Paxgene- RNA-tube.

By cognitive disorder is meant e.g. progressive impairment or progressive loss of a cognitive function, for example AD.

For diagnosis of cognitive impairment or dementia:

The "reference" may be suitable control sample such as for example a sample from a normal, healthy subject having no cognitive impairment symptoms and no abnormal neuroimaging findings and being age-matched to the patient to be diagnosed with the method of the present invention. The reference may be a sample from the same subject prior to demonstration of disorder or disease symptoms or prior to diagnosis of the impairment or loss of cognitive function or dementia. The reference may be a standardized sample, e.g. a sample comprising material or data from several samples of healthy subjects who have no cognitive impairment symptoms and no abnormal neuroimaging findings. For differential diagnosis of a specific dementia type or its mild cognitive impairment (MCI) versus other dementia types and their MCI:

Dementia types and subtypes are numerous and include e.g.

Alzheimer (AD) and MCI of AD type

Frontotemporal dementia (FTD) and MCI of FTD type

Dementia with Lewy body (DLB) and MCI of DLB type

Parkinson's disease dementia (PDD) and MCI of PDD type

Vascular dementia

Progressive supranuclear palsy (PSP),

Corticobasal degeneration (CBD)

For differential diagnosis of one dementia subtype, the reference sample may be sample(s) from patient(s) suffering dementia of other type(s). For example, for differential diagnosis of AD or MCI of AD type, the sample(s) shall be from patient(s) with MCI/dementia of non-AD type (i.e. sample(s) from subject(s) with FTD and/or DLB and/or PDD and/or vascular dementia and/or PSP and/or CBD and/or other dementia),

or from subject(s) who may suffer another disease but have no cognitive impairment or dementia.

The invention also relates to a method of diagnosing a cognitive disorder including but not limited to Alzheimer disease and monitoring its development or progression in a subject suffering from progressive impairment or loss of cognitive function or dementia, comprising:

(a) determining the presence of a cognitive disorder using a biological sample of the said subject using a method according to the invention, and

(b) adapting a therapeutic treatment in function of the results of step (a).

The invention also relates to a method of diagnosing a cognitive disorder including but not limited to early MCI and very mild Alzheimer disease in a subject with no apparent and/or no measurable symptoms when using the available methods such as MMSE, MoCA and other neurocognitive tests, but with abnormal changes of biomarkers of the present invention preceding the symptoms, comprising:

(a) determining the risk of developing a cognitive disorder using a biological sample of the said subject using a method according to the invention, and

(b) adapting a therapeutic treatment in function of the results of step (a). The invention further relates to a method of predictive diagnosing a cognitive disorder including but not limited to early MCI and very mild Alzheimer disease in a subject with no apparent and/or no measurable abnormality by neuroimaging methods as MRI and/or CT scans, comprising:

(a) determining the risk of developing a cognitive disorder using a biological sample of the said subject using a method according to the invention, and

(b) adapting a therapeutic treatment in function of the results of step (a). The invention also relates to a method for treating a subject suffering from a progressive impairment or loss of cognitive function or dementia, said method comprising:

(a) diagnosing the cognitive disorder associated to the progressive impairment or loss of cognitive function or dementia in said patient using the method according to the invention, and

(b) adapting the therapeutic treatment to the results obtained in step (a).

Examples of therapeutic treatment of cognitive disorders may comprise the administration of: a drug already approved for treatment of cognitive disorders in particular Alzheimer's disease, e.g. cholinesterase inhibitors, for example donenepezil, rivastigmine or galantamine, or a NMDA receptor antagonist, e.g. memantine, or a combination of drugs, for example donepezil and memantine, and/or

a novel therapeutic candidate or combination under clinical development, for example as currently tested in the anti-amyloid field, e.g. beta-secretase inhibitors and anti-beta-amyloid monoclonal antibodies, and anti-tau approaches, for example as currently tested in the anti- tau field, e.g. modulators of kinases or phosphatases that regulate tau phosphorylation status and anti-tau antibodies; and drug candidates modulating the molecular pathways such as neurodegeneration, synaptic plasticity, oxidative stress, autophagy, mitochondrial dysfunction and immuno-inflammation pathways where ncRNAs of the present invention are implicated. Furthermore, the invention comprises a kit for diagnosing and/or monitoring a cognitive disorder including but not limited to AD, comprising at least one reagent for the determination of a ncRNA expression profile comprising or consisting of at least one ncRNA (miRNA and/or IncRNA) selected from 406 miRNAs listed in Table 1 and 1008 IncRNAs listed in Table 5 in the biological sample. In some embodiments, the ncRNA expression profile comprises or consists of ncRNAs selected from the group consisting of:

(i) miR.99a.5p;

(ii) 7 miRNAs of miR.99a.5p, miR.378d, miR.100.5p, miR.193b.3p, miR.34a.5p, miR.1306.5p, and miR.1229.3p;

(iii) 3 miRNAs of miR1220.3p, miR378d and miR99a.5p;

(iv) 11 mRNAs of miR.99a.5p, miR.1229.3p, miR.100.5p, miR.378d, miR.193b.3p, miR.1306.5p, miR.195.5p, miR.532.3p, miR.125b.5p, miR.34a.5p, and miR.6880.5p;

(v) 15 miRNAs of miR.23a.5p, miR.6880.5p, miR.7111.5p, miR.6812.5p, miR.6738.5p, miR.5196.5p, miR.6894.5p, miR.8085, miR.4463, miR.2392, miR.144.3p, miR.192.5p, miR.193b.3p, miR.194.5p, and miR.122.5p;

(vi) 13 IncRNAs of lnc-DLG5-l:l, lnc-EBLNl-l:4, lnc-FATl-7:2, lnc-PRR5-5:l, lnc-RBKS- 6:1, lnc-FOXD4L5-35:l, lnc-TENM3-3:3, lnc-FAM133B-2:l, lnc-ZNF726-l:3, lnc- AP3M1-1:!, lnc-DUSP10-6:l, lnc-TPPP-l:2, and LINC01206:20; (νϋ) 7 IncRNAs of LINC02345:11, lnc-EBLNl-l:4, lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-FATl- 7:2, lnc-DKKl-5:3, and lnc-TACSTD2-2:4;

(viii) 12 lnc RNAs of lnc-TPPP-l:2, ARRDC3-AS1:7, lnc-TENM3-3:5, lnc-TENM3-3:4, lnc- QRFP-5:1, lnc-CRYBBl-l:l, lnc-MGST3-l:3, lnc-FAM49B-8:l, HAND2-AS1:70, lnc- TMEM185B-12:7, lnc-CNDPl-7: l, and lnc-C21orf58-l:2;

(ix) 3 IncRNAs of lnc-TENM3-3:3, lnc-MARCH4-2:7, and lnc-LRRCl-5:2;

(x) 7 IncRNAs of lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-TMEM185B-12:7, lnc-NAXD-6:5, lnc- HECA-6:1, lnc-COMMD6-10:l, and MIR29B2CHG:46;

(xi) 18 IncRNAs of lnc-TPPP-l:2, LINC02345:11, lnc-ZNF273-4:4, lnc-TACC2-8:6, LINC01206:20, lnc-C5orf67-3:l, HAND2-AS1:58, lnc-PRDM9-20:l, lnc-CLKl-l:7, lnc- DNALIl-5:4, R0RB-AS1:6, lnc-TPPP-l:3, lnc-BMSl-2:l, lnc-ADRBl-4:l, lnc-XXYLTl- 5:1, MIR99AHG:104, LINC01748:17, and Inc-AKR1E2-I5:l;

(xii) 28 miRNAs listed in Table 2;

(xiii) 74 miRNAs listed in Table 3;

(xiv) 33 IncRNAs listed in Table 6;

(xv) 60 IncRNAs listed in Table 7;

(xvi) 32 IncRNAs listed in Table 8;

(xvii) 84 IncRNAs listed in Table 9; and

(xviii) 94 IncRNAS listed in Table 10.

The kit of the invention allows performing the measurement of the miRNA signature and/or the IncRNA signature of the invention, wherein the kit comprises at least one reagent for measuring at least one ncRNA (miRNA and/or IncRNA) as indicated above.

By "reagent" is meant a reagent which specifically allows the determination of the miRNA/gene expression profile, i.e. a reagent specifically intended for the specific determination of the expression level of the miRNA/gene present in the miRNA/gene expression profile. Examples include e.g. amplification primer pairs (forward and reward) and/or probes specific for the miRNA/gene present in the miRNA/gene expression profile. This definition excludes generic reagents useful for the determination of the expression level of any other miRNA/gene.

By "reagent" is also meant a reagent which specifically allows the determination of the IncRNA expression profile, i.e. a reagent specifically intended for the specific determination of the expression level of the IncRNA present in the IncRNA expression profile. Examples include e.g. amplification primer pairs (forward and reward) and/or probes specific for the IncRNA present in the IncRNA expression profile. This definition excludes generic reagents useful for the determination of the expression level of any other IncRNA.

In some embodiments, the kit for diagnosing and/or monitoring a cognitive disorder comprises one or more oligonucleotide probes specific for ncRNAs of interest and a reagent for purifying the probe- target nucleic acid complexes. The oligonucleotide probes comprise a sequence complementary to a region of the ncRNAs of interest. The oligonucleotide probes may be DNA or RNA. The oligonucleotide probes are preferably DNA. In a preferred embodiment, the oligonucleotide probes are biotinylated and the reagent for purifying the probe-target complexes is a streptavidin-coated substrate, e.g, a streptavidin-coated magnetic particle, e.g., Tl streptavidin coated magnetic bead. In a preferred embodiment, the ncRNAs of interest is IncRNAs. The length of oligonucleotide probes specific for IncRNAs may be from 30 to 80 nucleotides, e.g., from 40 to 70, from 40 to 60, or about 50 nucleotides.

A further embodiment of the invention relates to a targeted sequencing panel for next generation sequencing, comprising nucleic acids specific for at least one ncRNA (miRNA and/or IncRNA) selected from 406 miRNAs listed in Table 1 and 1008 IncRNAs listed in Table 5 in the biological sample.

In some embodiments, the at least one ncRNA (miRNA and/or IncRNA) comprises or consists of ncRNAs selected from the group consisting of:

(i) miR.99a.5p;

(ii) 7 miRNAs of miR.99a.5p, miR.378d, miR.100.5p, miR.193b.3p, miR.34a.5p, miR.1306.5p, and miR.1229.3p;

(iii) 3 miRNAs of miR1220.3p, miR378d and miR99a.5p;

(iv) 11 mRNAs of miR.99a.5p, miR.1229.3p, miR.100.5p, miR.378d, miR.193b.3p, miR.1306.5p, miR.195.5p, miR.532.3p, miR.125b.5p, miR.34a.5p, and miR.6880.5p;

(v) 15 miRNAs of miR.23a.5p, miR.6880.5p, miR.7111.5p, miR.6812.5p, miR.6738.5p, miR.5196.5p, miR.6894.5p, miR.8085, miR.4463, miR.2392, miR.144.3p, miR.192.5p, miR.193b.3p, miR.194.5p, and miR.122.5p;

(vi) 13 IncRNAs of lnc-DLG5-l:l, lnc-EBLNl-l:4, lnc-FATl-7:2, lnc-PRR5-5:l, lnc-RBKS- 6:1, lnc-FOXD4L5-35:l, lnc-TENM3-3:3, lnc-FAM133B-2:l, lnc-ZNF726-l:3, lnc- AP3M1-1:1, lnc-DUSP10-6:l, lnc-TPPP-l:2, and LINC01206:20;

(vii) 7 IncRNAs of LINC02345:11, lnc-EBLNl-l:4, lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-FATl- 7:2, lnc-DKKl-5:3, and lnc-TACSTD2-2:4;

(viii) 12 lnc RNAs of lnc-TPPP-l:2, ARRDC3-AS1:7, lnc-TENM3-3:5, lnc-TENM3-3:4, lnc- QRFP-5:1, lnc-CRYBBl-l:l, lnc-MGST3-l:3, lnc-FAM49B-8:l, HAND2-AS1:70, lnc- TMEM185B-12:7, lnc-CNDPl-7: l, and lnc-C21orf58-l:2;

(ix) 3 IncRNAs of lnc-TENM3-3:3, lnc-MARCH4-2:7, and lnc-LRRCl-5:2;

(x) 7 IncRNAs of lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-TMEM185B-12:7, lnc-NAXD-6:5, lnc- HECA-6:1, lnc-COMMD6-10:l, and MIR29B2CHG:46;

(xi) 18 IncRNAs of lnc-TPPP-l:2, LINC02345:11, lnc-ZNF273-4:4, lnc-TACC2-8:6, LINC01206:20, lnc-C5orf67-3:l, HAND2-AS1:58, lnc-PRDM9-20:l, lnc-CLKl-l:7, lnc- DNALIl-5:4, RORB-ASl:6, lnc-TPPP-l:3, lnc-BMSl-2:l, lnc-ADRBl-4:l, lnc-XXYLTl- 5:1, MIR99AHG:104, LINC01748:17, and Inc-AKR1E2-I5:l; (xii) 28 miRNAs listed in Table 2;

(xiii) 74 miRNAs listed in Table 3;

(xiv) 33 IncRNAs listed in Table 6;

(xv) 60 IncRNAs listed in Table 7

(xvi) 32 IncRNAs listed in Table 8

(xvii) 84 IncRNAs listed in Table 9; and

(xviii) 94 IncRNAS listed in Table 10.

The invention also relates to the applications of the method for development of new therapeutic strategies (drug candidates) tested in clinical trials for the treatment of a cognitive disorder including but not limited to Alzheimer disease, wherein the method can be used (a) before starting the treatment for the selection of patients who would then be recruited in clinical trial; thus the test will enhance the likelihood for treating patient population that benefits from the tested therapeutic strategy(ies) while avoiding recruitment of patient subpopulation patients who do not benefit from this tested new drug candidate(s) and/or (b) for monitoring the response(s) including efficacy of the tested therapeutic strategy(ies) once treatment with the new tested drug candidate starts.

In a further aspect, the invention also relates to methods of detection. For example, the invention contemplates a method for the detection of at least one ncRNA (miRNA and/or IncRNA). In one aspect, the method comprises:

(a) isolating a biological sample from the subject;

(b) detecting a level of expression in a ncRNA signature, the ncRNA signature comprising or consisting of at least one ncRNA selected from 406 miRNAs listed in Table 1 and 1008 IncRNAs listed in Table 5 in the biological sample from said subject.

In some embodiments, the ncRNA signature comprises or consists of ncRNAs selected from the group consisting of:

(i) miR.99a.5p;

(ii) 7 miRNAs of miR.99a.5p, miR.378d, miR.100.5p, miR.193b.3p, miR.34a.5p, miR.1306.5p, and miR.1229.3p;

(iii) 3 miRNAs of miR1220.3p, miR378d and miR99a.5p;

(iv) 11 mRNAs of miR.99a.5p, miR.1229.3p, miR.100.5p, miR.378d, miR.193b.3p, miR.1306.5p, miR.195.5p, miR.532.3p, miR.125b.5p, miR.34a.5p, and miR.6880.5p;

(v) 15 miRNAs of miR.23a.5p, miR.6880.5p, miR.7111.5p, miR.6812.5p, miR.6738.5p, miR.5196.5p, miR.6894.5p, miR.8085, miR.4463, miR.2392, miR.144.3p, miR.192.5p, miR.193b.3p, miR.194.5p, and miR.122.5p;

(vi) 13 IncRNAs of lnc-DLG5-l:l, lnc-EBLNl-l:4, lnc-FATl-7:2, lnc-PRR5-5:l, lnc-RBKS- 6:1, lnc-FOXD4L5-35:l, lnc-TENM3-3:3, lnc-FAM133B-2:l, lnc-ZNF726-l:3, lnc- AP3M1-1:1, lnc-DUSP10-6:l, lnc-TPPP-l:2, and LINC01206:20;

(vii) 7 IncRNAs of LINC02345:11, lnc-EBLNl-l:4, lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-FATl- 7:2, lnc-DKKl-5:3, and lnc-TACSTD2-2:4; (viii) 12 lnc RNAs of lnc-TPPP-l:2, ARRDC3-AS1:7, lnc-TENM3-3:5, lnc-TENM3-3:4, lnc- QRFP-5:1, lnc-CRYBBl-l:l, lnc-MGST3-l:3, lnc-FAM49B-8:l, HAND2-AS1:70, lnc- TMEM185B-12:7, lnc-CNDPl-7: l, and lnc-C21orf58-l:2;

(ix) 3 IncRNAs of lnc-TENM3-3:3, lnc-MARCH4-2:7, and lnc-LRRCl-5:2;

(x) 7 IncRNAs of lnc-TPPP-l:2, lnc-TENM3-3:3, lnc-TMEM185B-12:7, lnc-NAXD-6:5, lnc-

HECA-6:1, lnc-COMMD6-10:l, and MIR29B2CHG:46;

(xi) 18 IncRNAs of lnc-TPPP-l:2, LINC02345:11, lnc-ZNF273-4:4, lnc-TACC2-8:6, LINC01206:20, lnc-C5orf67-3:l, HAND2-AS1:58, lnc-PRDM9-20:l, lnc-CLKl-l:7, lnc- DNALIl-5:4, R0RB-AS1:6, lnc-TPPP-l:3, lnc-BMSl-2:l, lnc-ADRBl-4:l, lnc-XXYLTl- 5:1, MIR99AHG:104, LINC01748:17, and Inc-AKR1E2-I5:l;

(xii) 28 miRNAs listed in Table 2;

(xiii) 74 miRNAs listed in Table 3;

(xiv) 33 IncRNAs listed in Table 6;

(xv) 60 IncRNAs listed in Table 7

(xvi) 32 IncRNAs listed in Table 8

(xvii) 84 IncRNAs listed in Table 9; and

(xviii) 94 IncRNAS listed in Table 10.

The expression level of miRNAs and/or IncRNAs may be determined by any technology known by a man skilled in the art. In particular, the expression level of miRNAs and/or IncRNAs is determined by measuring the amount of nucleic acid transcripts of each miRNA or IncRNAs. The amount of nucleic acid transcripts can be measured by any technology known by a man skilled in the art. The measure may be carried out directly on an extracted RNA sample or on retrotranscribed complementary DNA (cDNA) prepared from extracted RNA by technologies well-known in the art. From the RNA or cDNA sample, the amount of nucleic acid transcripts may be measured using any technology known by a man skilled in the art, including nucleic acid microarrays, quantitative PCR, sequencing (e.g., next generation sequencing), FIMAP quantification, and hybridization with a labeled probe. In some embodiments, the expression level of miRNAs and/or IncRNAs is determined using sequencing, e.g., next generation sequencing. Sequencing may be carried out after converting extracted RNA to cDNA using reverse transcriptase or RNA molecules may be directly sequenced. In a particular embodiment, which should not be considered as limiting the scope of the invention, the measurement of the expression level using next generation sequencing may be performed as follows. Briefly, RNA is extracted from a sample (e.g., blood sample). After removing rRNA, RNA samples are then reverse transcribed into cDNA. To ensure strand specificity, single stranded cDNA is first synthetized using Super-Script II reverse transcriptase and random primers in the presence of Actinomycin D, and then converted to double stranded cDNA with the second strand marking mix that incorporates dUTP in place of dTTP. Resulting blunt ended cDNA are purified using AMPure XP magnetic beads. After a 3'end adenylation step, adaptor is attached to cDNA. So obtained cDNA (sequencing library) may be amplified by PCR. The sequencing libraries can be sequenced by any next generation sequencing technology known by a man skilled in the art.

In some embodiments, the measurement of the expression level of ncRNAs, e.g., by sequencing (e.g., next generation sequencing), is facilitated by capturing and enriching nucleic acids (RNA or cDNA) corresponding to ncRNAs of interest prior to the measurement. As used herein, enrichment refers to increasing the percentage of the nucleic acids of interest in the sample relative to the initial sample by selectively purifying the nucleic acids of interest. The enrichment of nucleic acids corresponding to ncRNAs of interest can be carried out on extracted RNA sample or cDNA sample prepared from extracted RNA. In some embodiments, nucleic acids corresponding to ncRNAs of interest are captured and enriched by hybridizing RNA or cDNA sample to oligonucleotide probes specific for ncRNAs of interest (e.g. oligonucleotide probes comprising a sequence complementary to a region of ncRNAs of interest) under conditions allowing for hybridization of the probes and target nucleic acids to form probe-target nucleic acid complexes. Probes may be DNA or RNA, preferably DNA. The probe-target nucleic acid complexes can be purified by any technology known by a man skilled in the art. In a preferred embodiment, probes are biotinylated. The biotinylated probe-target nucleic acid complexes can be purified by using a streptavidin-coated substrate, e.g, a streptavidin-coated magnetic particle, e.g., Tl streptavidin coated magnetic bead. In a preferred embodiment, the ncRNAs measured by this method are IncRNAs. The length of probes specific for IncRNAs may be from 30 to 80 nucleotides, e.g., from 40 to 70, from 40 to 60, or about 50 nucleotides.

In some embodiments, the expression level of miRNAs and/or IncRNAs may be determined using quantitative PCR. Quantitative, or real-time, PCR is a well-known and easily available technology for those skilled in the art and does not need a precise description. In a particular embodiment, which should not be considered as limiting the scope of the invention, the determination of the expression profile using quantitative PCR may be performed as follows. Briefly, the real-time PCR reactions are carried out using the TaqMan Universal PCR Master Mix (Applied Biosystems). 6 μΐ cDNA is added to a 9 μΐ PCR mixture containing 7.5 μΐ TaqMan Universal PCR Master Mix, 0.75 μΐ of a 20X mixture of probe and primers and 0.75μ1 water. The reaction consists of one initiating step of 2 min at 50 deg. C, followed by 10 min at 95 deg. C, and 40 cycles of amplification including 15 sec at 95 deg. C and 1 min at 60 deg. C. The reaction and data acquisition can be performed using the ABI 7900HT Fast Real- Time PCR System (Applied Biosystems). The number of template transcript molecules in a sample is determined by recording the amplification cycle in the exponential phase (cycle threshold or CQ or d), at which time the fluorescence signal can be detected above background fluorescence. Thus, the starting number of template transcript molecules is inversely related to C _T .

In some embodiments, the expression level of miRNAs and/or IncRNAs may be determined by the use of a nucleic acid microarray. A nucleic acid microarray consists of different nucleic acid probes that are attached to a substrate, which can be a microchip, a glass slide or a microsphere-sized bead. A microchip may be constituted of polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, or nitrocellulose. Probes can be nucleic acids such as cDNAs ("cDNA microarray") or oligonucleotides ("oligonucleotide microarray"). To determine the expression profile of a target nucleic acid sample, said sample is labelled, contacted with the microarray in hybridization conditions, leading to the formation of complexes between target nucleic acids that are complementary to probe sequences attached to the microarray surface. The presence of labelled hybridized complexes is then detected. Many variants of the microarray hybridization technology are available to the man skilled in the art.

In some embodiments, the expression level of miRNAs and/or IncRNAs may be determined by FIMAP quantification. Briefly, IncRNAs and miRNAs are amplified by PCR using primers with the same sequences as for qPCR that are chemically modified. Forward primers are phosphorylated in 5' and Reverse primers biotinylated in 5'. PCR products are digested with exonuclease to eliminate the phosphorylated strand and keep only the biotinylated ones. Biotinylated PCR products are incubated with coded silica microdisks coated with oligos complementary to the RNAs of interest (one code per target oligo), and hybridized products are revealed by addition of a fluorescent streptavidin conjugate. The expression level of miRNAs and/or IncRNAs is determined by measuring fluorescence signal.

In a still further aspect, the method of detection contemplates the use of a reagent to measure or detect the one or more miRNAs and/or one or more IncRNA. By "reagent" is meant a reagent that specifically allows the determination of the miRNA/gene or the IncRNA expression profile, i.e. a reagent specifically intended for the specific determination of the expression level of the miRNA/gene present in the miRNA/gene expression profile or the IncRNA present in the IncRNA expression profile. Examples include e.g. amplification primer pairs (forward and reward) and/or probes specific for the miRNA/gene present in the miRNA/gene expression profile and/or or the IncRNA present in the IncRNA expression profile. This definition excludes generic reagents useful for the determination of the expression level of any other miRNA/gene and generic reagents useful for the determination of the expression level of any other IncRNA. In yet a further aspect, the method of detection comprises detecting one or more miRNAs and/or IncRNA from a biological sample, wherein the biological sample is selected from blood, plasma, serum, urine, cerebrospinal fluid, tear, saliva. In some embodiments, the biological sample is blood.

By "treating" or "treatment" of a subject being at risk to develop or having a cognitive disorder, particularly a progressive impairment or loss of cognitive function or dementia, e.g. AD, is meant administering or administration of a regimen to the subject in need thereof such that at least one symptom of the disorder or disease is cured, alleviated, remedied or improved. According to the present invention, depending on the diagnosing results, the subject is submitted to an adapted therapeutic treatment and is further monitored. The monitoring of the subject based on the miRNA signature and/or IncRNA signature of the invention allows to further adapt or modify the therapeutic treatment, e.g. increasing the drug amount, administering a combination of drugs to obtain a synergy effect, or replacing the drug by an effective amount of another drug.

The invention also relates to the modification of a therapeutic strategy in subjects suffering from cognitive disorder who have been diagnosed and/or monitored using a method for (in vitro) diagnosis or monitoring of a progressive impairment or loss of cognitive function or dementia according to the invention.

According to the invention, the miRNAs and/or the IncRNAs of the invention may be used in combination with one or more biomarkers currently used for diagnosing a cognitive disorder including but not limited to AD. Examples of such biomarkers include without any limitation the biomarkers as disclosed in USP 9,377,472, USP 7,897,786 and in USP 6,703,212, the contents thereof being included herein by reference. The method of the invention combining the miRNAs and/or the IncRNAs of the invention with one or more known biomarkers may allow enhanced accuracy of diagnosis; or differential diagnosis; and a more efficient and successful drug development, e.g. enhanced accuracy for patient stratification before recruitment in clinical trials and monitoring of the efficacy of approved therapies or novel drugs under development.

According to the invention, the miRNAs and/or the IncRNAs of the invention may be used in combination with one or more tests currently used for diagnosing a cognitive disorder including but not limited to AD and/or for differential diagnostic purposes. Examples of such tests include without any limitation the neuropsychological tests, such as MMSE, MoCA, and the neuroimaging methods, in particular volumetric MRI. The method of the invention combining the miRNAs and/or the IncRNAs of the invention with one or more known biomarkers may allow enhanced accuracy of diagnosis; or differential diagnosis; and a more efficient and successful drug development, e.g. enhanced accuracy for patient stratification before recruitment in clinical trials and monitoring of the efficacy of approved therapies or novel drugs under development.

To identify miRNA signatures involved specifically in the pathogenesis of AD and mild cognitive impairment (MCI) of AD type, a total of 2083 miRNAs in samples from different groups, including a group of patients with AD or MCI of AD type and group of cognitively intact healthy controls with no brain imaging abnormalities, have been screened. Subsequently miRNA profiling in all samples, 406 miRNAs have been detected above the threshold. By comparison of miRNA-expression levels measured in the samples of the different groups, the miRNA differentially expressed in the samples of the control group as compared to the expression level in the samples of patient group diagnosed as having AD or cognitive impairment of AD type (at the Neurology department of clinical site, based on neurocognitive and neuropsychological tests and neuroimaging tests and on cerebrospinal fluid biomarkers: beta-amyloid peptide 1-42 (Αβ42) and tau (total and/or phosphorylated) were identified as miRNA biomarker candidates. To identify IncRNA signatures involved specifically in the pathogenesis of AD and mild cognitive impairment (MCI) of AD type, a total of 127802 IncRNAs in samples from different groups, including a group of patients with AD or MCI of AD type and group of cognitively intact healthy controls with no brain imaging abnormalities, have been screened. Subsequently IncRNA profiling in all samples, 19867 IncRNAs have been detected above the threshold. By comparison of IncRNA-expression levels measured in the samples of the different groups, the IncRNA differentially expressed in the samples of the control group as compared to the expression level in the samples of patient group diagnosed as having AD or cognitive impairment of AD type (at the Neurology department of clinical sites, based on neurocognitive and neuropsychological tests and neuroimaging tests and on cerebrospinal fluid biomarkers: beta-amyloid peptide 1-42 (Αβ42) and tau (total and/or phosphorylated) were identified as IncRNA biomarker candidates.

Expression levels were analyzed using a two-tailed Welch t test and/or Wilcoxon Mann-Whitney test between two groups. Significant differential expression was identified as p<0.05. Fold change and AUC (Area Under the Curve) are calculated for each miRNA and IncRNA and for each tested condition. miRNA candidates were also selected when differential expression was determined based on fold- changes > 1.49-fold or < 0.75-fold changes and/or AUC>0.6 or <0.4, are indicated in Tables 2, 3 and 4.

IncRNA candidates were also selected when differential expression was determined based on fold- changes > 1.6 (or < 0.6) and/or an AUC > 0.80 (or <0.20), are indicated in Tables 6 and 7.

Random Forest algorithm (Breimann 2001, Breiman and Cutler 2001) was used to build the model and also used to select the top miRNAs and/or the top IncRNAs. A predictive model based on the combination of the top 2-20 miRNAs and/or IncRNAs enables to predict the disease with an accuracy of > 80%.

Out of the 2083 miRNA measured, 406 miRNA showed expression levels above threshold. Sequences of the 406 miRNAs are shown in Table 1.

Table 1: Sequences of 406 miRNAs

miR.151a.3p CUAGACUGAAGCUCCUUGAGG miR.502.3p AAUGCACCUGGGCAAGGAUUCA miR.152.3p UCAGUGCAUGACAGAACUUGG miR.504.3p GGGAGUGCAGGGCAGGGUUUC miR.154.5p UAGGUUAUCCGUGUUGCCUUCG miR.505.3p CGUCAACACUUGCUGGUUUCCU miR.5196.5

miR.155.5p UUAAUGCUAAUCGUGAUAGGGGU AGGGAAGGGGACGAGGGUUGGG

P

miR.15a.5p UAGCAGCACAUAAUGGUUUGUG miR.532.3p CCUCCCACACCCAAGGCUUGCA miR.15b.5p UAGCAGCACAUCAUGGUUUACA miR.532.5p CAUGCCUUGAGUGUAGGACCGU miR.16.5p UAGCAGCACGUAAAUAUUGGCG miR.541.3p UGGUGGGCACAGAAUCUGGACU miR.17.3p ACUGCAGUGAAGGCACUUGUAG miR.543 AAACAUUCGCGGUGCACUUCUU miR.17.5p CAAAGUGCUUACAGUGCAGGUAG miR.548ak AAAAGUAACUGCGGUUUUUGA miR.548d.5 AAAAGUAAUUGUGGUUUUUGC miR.181a.5p AACAUUCAACGCUGUCGGUGAGU

P C

miR.551b.3

miR.181b.5p AACAUUCAUUGCUGUCGGUGGGU GCGACCCAUACUUGGUUUCAG

P

miR.5585.3

miR.181c.5p AACAUUCAACCUGUCGGUGAGU CUGAAUAGCUGGGACUACAGGU

P

miR.5587.3

miR.181d.5p AACAUUCAUUGUUGUCGGUGGGU GCCCCGGGCAGUGUGAUCAUC

P

CAAAGUUUAAGAUCCUUGAAG

miR.185.3p AGGGGCUGGCUUUCCUCUGGUC miR.561.3p

U

miR.185.5p UGGAGAGAAAGGCAGUUCCUGA miR.566 GGGCGCCUGUGAUCCCAAC miR.186.3p GCCCAAAGGUGAAUUUUUUGGG miR.5694 CAGAUCAUGGGACUGUCUCAG miR.186.5p CAAAGAAUUCUCCUUUUGGGCU miR.5703 AGGAGAAGUCGGGAAGGU miR.187.3p UCGUGUCUUGUGUUGCAGCCGG miR.5739 GCGGAGAGAGAAUGGGGAGC miR.187.5p GGCUACAACACAGGACCCGGGC miR.574.3p CACGCUCAUGCACACACCCACA

UGAGUGUGUGUGUGUGAGUGU

miR.18a.5p UAAGGUGCAUCUAGUGCAGAUAG miR.574.5p

GU

miR.18b.5p UAAGGUGCAUCUAGUGCAGUUAG miR.584.5p UUAUGGUUUGCCUGGGACUGAG miR.1908.5p CGGCGGGGACGGCGAUUGGUC miR.590.5p GAGCUUAUUCAUAAAAGUGCAG miR.191.5p UCUGCCCCCUCCGCUGCUGCCA miR.6069 GGGCUAGGGCCUGCUGCCCCC miR.1913 GGAGGGGUCCCGCACUGGGAGG miR.6085 AAGGGGCUGGGGGAGCACA miR.1914.3p CCCUGUGCCCGGCCCACUUCUG miR.6086 GGAGGUUGGGAAGGGCAGAG miR.1914.5p CCCCAGGGCGACGCGGCGGG miR.6088 AGAGAUGAAGCGGGGGGGCG

GCUGGGCAGGGCUUCUGAGCUC

miR.1915.3p CAACGGAAUCCCAAAAGCAGCUG miR.612

CUU

miR.192.5p CUGACCUAUGAAUUGACAGCC miR.6124 GGGAAAAGGAAGGGGGAGGA miR.193a.3p AACUGGCCUACAAAGUCCCAGU miR.6126 GUGAAGGCCCGGCGGAGA miR.193a.5p UGGGUCUUUGCGGGCGAGAUGA miR.6127 UGAGGGAGUGGGUGGGAGG miR.193b.3p AACUGGCCCUCAAAGUCCCGCU miR.6131 GGCUGGUCAGAUGGGAGUG miR.194.5p UGUAACAGCAACUCCAUGUGGA miR.6165 CAGCAGGAGGUGAGGGGAG miR.195.5p UAGCAGCACAGAAAUAUUGGC miR.625.5p AGGGGGAAAGUUCUAUAGUCC miR.197.3p UUCACCACCUUCUCCACCCAGC miR.629.5p UGGGUUUACGUUGGGAGAACU miR.6511b. CCUCACCACCCCUUCUGCCUGC miR.197.5p CGGGUAGAGAGGGCAGUGGGAGG

3p A

miR.1976 CCUCCUGCCCUCCUUGCUGU miR.652.3p AAUGGCGCCACUAGGGUUGUG miR.199a.3p ACAGUAGUCUGCACAUUGGUUA miR.654.5p UGGUGGGCCGCAGAACAUGUGC

GGCGGAGGGAAGUAGGUCCGUU

miR.199a.5p CCCAGUGUUCAGACUACCUGUUC miR.658

GGU

miR.19a.3p UGUGCAAAUCUAUGCAAAACUGA miR.660.5p UACCCAUUGCAUAUCGGAGUUG miR.19b.3p UGUGCAAAUCCAUGCAAAACUGA miR.663b GGUGGCCCGGCCGUGCCUGAGG

Out of the 406 detectable miRNAs analyzed, 28 miRNAs showed a statistically significant difference [Welch T-test p value <0.05), and among these candidates, 7 candidates showed a p value <0.01. The 28 miRNAs are shown in Table 2.

Table 2: List of 28 miRNAs useful based on their p value <0.05. SD: standard deviation

Among the 406 detected miRNA, a total of 74 miRNA candidates also showed an AUC value > 0.6 or < 0.4; of which a total of 11 candidates showed a good AUC value of > 0.65 or < 0.35; and 1 candidates showed a very good AUC value of > 0.7 or < 0.30. The 74 candidate miRNAs are shown in Table 3.

Table 3: List of 74 candidates also showed an AUC value of > 0.6 or <0.40

miRNA AUC miRNA AUC miRNA AUC

miR.99a.5p 0,268 miR.145.5p 0,383 miR.4800.5p 0,612

miR.1229.3p 0,301 miR.139.5p 0,384 miR.6746.5p 0,613

miR.100.5p 0,301 miR.150.5p 0,386 miR.4667.5p 0,614

miR.378d 0,303 miR.193a.3p 0,390 miR.4656 0,614

miR.193b.3p 0,307 miR.342.3p 0,391 miR.1914.3p 0,616

miR.1306.5p 0,328 miR.210.3p 0,392 miR.1275 0,618

miR.195.5p 0,333 miR.144.5p 0,392 miR.8089 0,619

miR.532.3p 0,334 miR.27b.3p 0,394 miR.5703 0,619 miR.125b.5p 0,341 miR.664a.3p 0,396 miR.6124 0,621

miR.34a.5p 0,342 miR.16.5p 0,396 miR.2392 0,621

miR.378i 0,351 miR.877.3p 0,397 miR.7111.5p 0,622

miR.532.5p 0,354 miR.29a.3p 0,400 miR.8085 0,622

miR.375 0,363 miR.130a.3p 0,400 miR.6716.5p 0,624

miR.660.5p 0,363 miR.6794.5p 0,601 miR.6778.5p 0,625

miR.194.5p 0,369 miR.1908.5p 0,601 miR.6797.5p 0,625

miR.141.3p 0,370 miR.6799.5p 0,602 miR.4534 0,633

miR.144.3p 0,372 miR.3141 0,604 miR.6775.5p 0,635

miR.1234.3p 0,373 miR.6738.5p 0,604 miR.4270 0,636

miR.378f 0,375 miR.6894.5p 0,604 miR.6812.5p 0,638

miR.181b.5p 0,376 miR.7851.3p 0,605 miR.6086 0,645

miR.502.3p 0,378 miR.4695.5p 0,608 miR.765 0,645

miR.192.5p 0,378 miR.23a.5p 0,609 miR.6769a.5p 0,647

miR.766.3p 0,378 miR.4463 0,610 miR.5196.5p 0,649

miR.122.5p 0,380 miR.382.5p 0,611 miR.6880.5p 0,655

miR.378a.3p 0,381 miR.4655.5p 0,611

Among the 406 detected miRNAs, a total of 15 candidates showed both an AUC value of >0.6 or < 0.4 and a fold change value of > 1.49 or < 0.75. The list of the 15 miRNAs are shown in Table 4.

Table 4: List of 15 candidates showing both an AUC value of >0.6 or < 0.4 and a fold change value of > 1.49 or < 0.75

Out of the 127802 IncRNAs sequenced, 19867 IncRNAs were selected based on their threshold expression level for statistical analysis. The comparison of the AD patient with healthy control populations showed that 1008 IncRNAs are differentially expressed with a statistical significance (p value <0.05, Wilcoxon test) (Table 5). The sequences of these 1008 IncRNAs are shown in the sequence listing included in this application.

Table 5: The Sequence number, p value, mean +/- SD, fold change and AUC of the 1008 IncRNAs with differential expression in AD group versus healthy control group (p value <0.05, Wilcoxon test) Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change

ADAMTS9-

SEQ0397 0,02049 0,852 0,778 Inc-LMX1A-2:1 SEQ0708 0,03324 0,786 0,757 AS2:12

ADNP-AS1:12 SEQ0340 0,01727 0,809 0,785 lnc-LONP2-6: 10 SEQ0280 0,01209 0,912 0,799

ADPGK-AS1:7 SEQ0858 0,0449 1,225 0,257 lnc-LRCHl-l:l SEQ0437 0,02049 0,793 0,778

APTR: 17 SEQ0260 0,01209 1,147 0,201 lnc-LRP12-4:3 SEQ0939 0,0449 1,165 0,257

ARF4-AS1:8 SEQ0859 0,0449 1,143 0,257 lnc-LRP5L-2:14 SEQ0940 0,0449 1,203 0,257

ARRDC3-AS1:7 SEQ0050 0,00232 0,441 0,854 lnc-LRRl-l:2 SEQ0813 0,03872 0,729 0,75

BISPR:24 SEQ0566 0,02842 0,579 0,764 lnc-LRRl-l:3 SEQ0814 0,03872 0,725 0,75

BLACAT1:3 SEQ0300 0,01449 1,717 0,208 lnc-LRRCl-5:2 SEQ0087 0,00364 0,649 0,84

BLACAT1:5 SEQ0341 0,01727 0,424 0,785 lnc-LRRC3B-l:3 SEQ0941 0,0449 0,463 0,743

CALML3-AS1:9 SEQ0136 0,00681 1,567 0,181 lnc-LRRC41-3:2 SEQ0942 0,0449 1,272 0,257

CASC15:51 SEQ0567 0,02842 1,273 0,236 lnc-LRRC4C-7:l SEQ0815 0,03872 0,818 0,75

CASC20:2 SEQ0860 0,0449 0,765 0,743 lnc-LRRFIP2-3:4 SEQ0617 0,02842 0,74 0,764

CERS3-AS1:5 SEQ0301 0,01449 1,251 0,208 lnc-LRRKl-3:4 SEQ0242 0,01004 0,79 0,806

CFAP58-AS1:4 SEQ0109 0,00556 1,464 0,174 lnc-LRRK2-l:10 SEQ0369 0,01727 0,86 0,785

CLRN1-AS1:1 SEQ0475 0,02418 0,793 0,771 lnc-LRRK2-l:9 SEQ0370 0,01727 0,86 0,785

CPB2-AS1:18 SEQ0476 0,02418 0,682 0,771 lnc-LRRTM4-3:2 SEQ0618 0,02842 0,865 0,764

CYTOR:18 SEQ0062 0,00291 1,278 0,153 lnc-LY9-3:l SEQ0207 0,00829 1,317 0,188

DARS-AS1:47 SEQ0178 0,00829 2,015 0,188 lnc-LYN-8:l SEQ0619 0,02842 1,516 0,236

DDX11-AS1:5 SEQ0861 0,0449 1,256 0,257 lnc-MAFB-l:4 SEQ0525 0,02418 0,839 0,771 lnc-MAMDC2-

DLEU2:26 SEQ0862 0,0449 1,076 0,257 SEQ0526 0,02418 0,828 0,771

1:1

DLEU2:45 SEQ0863 0,0449 0,882 0,743 lnc-MAML3-2:l SEQ0438 0,02049 0,768 0,778

DLGAP2-AS1:18 SEQ0656 0,03324 0,789 0,757 Inc-MAN1A1-1:3 SEQ0158 0,00681 0,722 0,819

DPH6-AS1:3 SEQ0342 0,01727 2,004 0,215 lnc-MAP9-6:l SEQ0281 0,01209 0,741 0,799

EGFR-AS1:4 SEQ0093 0,00451 0,81 0,833 lnc-MARCH4-2:7 SEQ0088 0,00364 2 0,16

EGOT:ll SEQ0110 0,00556 1,195 0,174 lnc-MARCKS-l:9 SEQ0527 0,02418 0,717 0,771

EIF3J-AS1:21 SEQ0261 0,01209 0,87 0,799 lnc-MASTL-2:l SEQ0943 0,0449 0,756 0,743

ERICH3-AS1:4 SEQ0864 0,0449 0,888 0,743 lnc-MB-3:l SEQ0944 0,0449 1,302 0,257

EXTL3-AS1:16 SEQ0568 0,02842 0,739 0,764 lnc-MBP-16:2 SEQ0208 0,00829 1,204 0,188

FAM66B:14 SEQ0063 0,00291 0,604 0,847 lnc-MC5R-5:l SEQ0945 0,0449 0,742 0,743

FLG-AS1:14 SEQ0752 0,03872 1,18 0,25 lnc-MC5R-6:2 SEQ0243 0,01004 0,626 0,806

FLVCR1-AS1:13 SEQ0137 0,00681 0,413 0,819 lnc-MDM4-8:l SEQ0816 0,03872 0,791 0,75

FRG1-DT:6 SEQ0865 0,0449 0,873 0,743 lnc-ME3-l:l SEQ0946 0,0449 1,353 0,257

GAS1RR:11 SEQ0302 0,01449 0,848 0,792 lnc-MED10-23:l SEQ0209 0,00829 1,295 0,188

GAS6-AS2:9 SEQ0303 0,01449 0,787 0,792 lnc-MED15-l:2 SEQ0620 0,02842 1,191 0,236

GPC5-AS1:7 SEQ0753 0,03872 0,623 0,75 lnc-MESD-6:l SEQ0947 0,0449 0,762 0,743

GPR158-AS1:1 SEQ0398 0,02049 0,775 0,778 lnc-MEST-6:l SEQ0124 0,00556 0,817 0,826 lnc-METTL22-

GRM5-AS1:1 SEQ0138 0,00681 0,83 0,819 SEQ0709 0,03324 0,747 0,757

11:1

HAND2-AS1:58 SEQ0094 0,00451 0,587 0,833 lnc-MFSD8-6:3 SEQ0528 0,02418 0,845 0,771

HAND2-AS1:59 SEQOlll 0,00556 0,592 0,826 lnc-MFSD8-6:8 SEQ0371 0,01727 0,794 0,785

HAND2-AS1:70 SEQ0039 0,00183 0,543 0,861 lnc-MGST3-l:3 SEQ0043 0,00183 1,664 0,139

HAND2-AS1:71 SEQ0076 0,00364 0,565 0,84 lnc-MIBl-l:l SEQ0159 0,00681 0,68 0,819 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change

HCG23:5 SEQ0754 0,03872 0,813 0,75 lnc-MNXl-10:l SEQ0322 0,01449 1,204 0,208

KAZN-AS1:4 SEQ0399 0,02049 0,732 0,778 lnc-MOGATl-3:2 SEQ0948 0,0449 0,703 0,743

KCNQ1-AS1:3 SEQ0569 0,02842 0,848 0,764 lnc-MPLKIP-3:l SEQ0710 0,03324 0,867 0,757

KCNQ10T1:8 SEQ0139 0,00681 0,908 0,819 lnc-MPP4-3:l SEQ0817 0,03872 0,813 0,75

LACTB2-AS1:1 SEQ0755 0,03872 1,206 0,25 lnc-MRC2-2:l SEQ0818 0,03872 0,835 0,75

LINC00158:5 SEQ0570 0,02842 0,748 0,764 lnc-MRGPRD-2:l SEQ0244 0,01004 0,79 0,806

LINC00200:6 SEQ0064 0,00291 1,29 0,153 lnc-MRGPRF-4:4 SEQ0105 0,00451 0,604 0,833

LINC00276:2 SEQ0866 0,0449 0,846 0,743 lnc-MROH7-2:l SEQ0529 0,02418 0,844 0,771

LINC00354:4 SEQ0867 0,0449 1,284 0,257 lnc-MRPL57-5:8 SEQ0621 0,02842 0,855 0,764 lnc-MRPS30-

LINC00458:19 SEQ0571 0,02842 0,789 0,764 SEQ0819 0,03872 0,814 0,75

13:1

LINC00460:13 SEQ0868 0,0449 0,752 0,743 lnc-MSH2-3:2 SEQ0711 0,03324 1,401 0,243 lnc-MTRNR2Ll-

LINC00472:21 SEQ0140 0,00681 1,389 0,181 SEQ0530 0,02418 1,325 0,229

3:1

LINC00554:2 SEQ0756 0,03872 0,766 0,75 lnc-MVB12B-6:l SEQ0531 0,02418 0,811 0,771

LINC00554:4 SEQ0757 0,03872 0,766 0,75 lnc-MYC-12:l SEQ0622 0,02842 1,418 0,236

LINC00574:13 SEQ0112 0,00556 0,771 0,826 lnc-MY018B-2:3 SEQ0323 0,01449 0,833 0,792

LINC00589:3 SEQ0869 0,0449 0,781 0,743 lnc-MY018B-3:3 SEQ0160 0,00681 0,762 0,819

LINC00649:23 SEQ0077 0,00364 0,619 0,84 lnc-MYOCOS-2:l SEQ0439 0,02049 0,891 0,778

LINC00698:2 SEQ0657 0,03324 0,85 0,757 lnc-NAA38-3:l SEQ0532 0,02418 0,854 0,771 lnc-NAALADL2-

LINC00707:9 SEQ0572 0,02842 1,304 0,236 SEQ0324 0,01449 0,864 0,792

8:1

LINC00839:18 SEQ0113 0,00556 0,874 0,826 lnc-NANOSl-3:l SEQ0712 0,03324 1,277 0,243

LINC00882:70 SEQ0028 0,00111 0,867 0,875 lnc-NAXD-6:5 SEQ0125 0,00556 0,479 0,826

LINC00882:71 SEQ0029 0,00111 0,867 0,875 lnc-NBPF14-l:2 SEQ0089 0,00364 0,815 0,84

LINC00887:15 SEQ0870 0,0449 0,925 0,743 lnc-NBPF14-3:l SEQ0623 0,02842 0,889 0,764 lnc-NCR3LGl-

LINC00895:3 SEQ0179 0,00829 1,491 0,188 SEQ0018 0,00066 0,837 0,889

3:1

LINC00927:21 SEQ0658 0,03324 0,619 0,757 lnc-NDFIP2-7: 13 SEQ0820 0,03872 0,775 0,75

LINC00927:22 SEQ0659 0,03324 0,619 0,757 lnc-NDFIP2-7: 14 SEQ0440 0,02049 0,77 0,778

LINC00927:24 SEQ0343 0,01727 0,772 0,785 lnc-NDRG2-5:l SEQ0949 0,0449 0,773 0,743 lnc-NDUFAlO-

LINC00938:6 SEQ0262 0,01209 0,811 0,799 SEQ0161 0,00681 0,712 0,819

6:1

LINC00963:67 SEQ0573 0,02842 1,379 0,236 lnc-NDUFB9-2:2 SEQ0713 0,03324 1,345 0,243 lnc-NDUFS6-

LINC01050:6 SEQ0114 0,00556 0,82 0,826 SEQ0950 0,0449 0,805 0,743

15:1

lnc-NEUROD2-

LINC01058:3 SEQ0871 0,0449 1,225 0,257 SEQ0071 0,00291 0,772 0,847

4:1

LINC01087:1 SEQ0141 0,00681 0,764 0,819 lnc-NKAIN2-5:l SEQ0821 0,03872 0,794 0,75

LINC01088:18 SEQ0660 0,03324 0,757 0,757 lnc-NKX6-l-2:l SEQ0282 0,01209 0,773 0,799

LINC01107:1 SEQ0304 0,01449 0,768 0,792 lnc-NOC2L-l:21 SEQ0245 0,01004 1,382 0,194

LINC01122:27 SEQ0574 0,02842 1,263 0,236 lnc-NOC2L-12:l SEQ0372 0,01727 1,479 0,215

LINC01146:14 SEQ0758 0,03872 0,774 0,75 lnc-NOS2-7:l SEQ0951 0,0449 1,203 0,257

LINC01153:1 SEQ0661 0,03324 1,169 0,243 lnc-NPBWRl-2:2 SEQ0952 0,0449 1,517 0,257

LINC01185:7 SEQ0575 0,02842 0,793 0,764 lnc-NPIPB12-l:l SEQ0822 0,03872 1,13 0,25 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change

LINC01197:26 SEQ0759 0,03872 0,847 0,75 lnc-NPY5R-4:l SEQ0823 0,03872 0,817 0,75

LINC01206:11 SEQ0662 0,03324 0,612 0,757 lnc-NR2El-4:4 SEQ0714 0,03324 0,688 0,757

LINC01206:20 SEQ0400 0,02049 1,94 0,222 lnc-NR5A2-l: 13 SEQ0441 0,02049 0,908 0,778

LINC01226:8 SEQ0663 0,03324 0,902 0,757 lnc-NRPl-4:l SEQ0953 0,0449 1,258 0,257

LINC01270:15 SEQ0477 0,02418 1,703 0,229 lnc-NUBl-4:l SEQ0126 0,00556 0,809 0,826

LINC01320:22 SEQ0576 0,02842 0,785 0,764 lnc-NUP35-4:2 SEQ0715 0,03324 0,858 0,757

LINC01322:20 SEQ0223 0,01004 0,561 0,806 lnc-NUTMl-14:4 SEQ0716 0,03324 0,638 0,757

LINC01355:9 SEQ0664 0,03324 0,854 0,757 lnc-NUTM2B-3:l SEQ0533 0,02418 0,801 0,771

LINC01376:10 SEQ0760 0,03872 0,934 0,75 lnc-NUTM2E-l:5 SEQ0127 0,00556 0,849 0,826

LINC01394:15 SEQ0401 0,02049 1,238 0,222 lnc-ODFl-5:2 SEQ0325 0,01449 0,847 0,792

LINC01394:26 SEQ0402 0,02049 1,238 0,222 lnc-ODFl-9:l SEQ0824 0,03872 0,806 0,75

LINC01394:50 SEQ0095 0,00451 0,797 0,833 lnc-OPRKl-2:l SEQ0717 0,03324 0,826 0,757 lnc-ORlOADl-

LINC01410:11 SEQ0023 0,00086 0,734 0,882 SEQ0624 0,02842 0,826 0,764

1:1

LINC01419:6 SEQ0872 0,0449 0,663 0,743 lnc-OR13C4-8:l SEQ0625 0,02842 0,806 0,764

LINC01426:10 SEQ0577 0,02842 0,871 0,764 lnc-OR4F21-7:9 SEQ0283 0,01209 0,761 0,799 lnc-OR4F29-

LINC01481:8 SEQ0478 0,02418 0,9 0,771 SEQ0044 0,00183 0,85 0,861

3:11

LINC01519:3 SEQ0305 0,01449 0,805 0,792 lnc-OR4F29-8:4 SEQ0210 0,00829 0,633 0,813

LINC01527:1 SEQ0578 0,02842 0,781 0,764 lnc-OR5H2-l:2 SEQ0326 0,01449 0,768 0,792

LINC01568:12 SEQ0479 0,02418 0,68 0,771 lnc-OR6Sl-2:l SEQ0373 0,01727 0,857 0,785

LINC01629:11 SEQ0579 0,02842 0,59 0,764 lnc-OR8G5-7:2 SEQ0626 0,02842 0,763 0,764

LINC01653:2 SEQ0580 0,02842 0,792 0,764 lnc-ORAOVl-3:3 SEQ0442 0,02049 0,895 0,778

LINC01684:16 SEQ0480 0,02418 0,907 0,771 lnc-OSBPL6-l:l SEQ0443 0,02049 1,366 0,222

LINC01697:9 SEQ0873 0,0449 0,874 0,743 lnc-OSBPL7-3:3 SEQ0534 0,02418 0,855 0,771

LINC01719:19 SEQ0761 0,03872 0,855 0,75 lnc-OSBPL7-3:4 SEQ0535 0,02418 0,855 0,771

LINC01748:17 SEQ0481 0,02418 0,791 0,771 lnc-OSBPL7-3:6 SEQ0536 0,02418 0,855 0,771

LINC01762:4 SEQ0874 0,0449 0,941 0,743 lnc-OSBPL7-5:l SEQ0162 0,00681 1,685 0,181

LINC01793:2 SEQ0344 0,01727 0,743 0,785 lnc-OST4-6:2 SEQ0954 0,0449 0,774 0,743

LINC01798:4 SEQ0482 0,02418 0,791 0,771 lnc-OXGRl-6:2 SEQ0374 0,01727 1,987 0,215

LINC01845:3 SEQ0115 0,00556 1,55 0,174 lnc-OXGRl-6:3 SEQ0163 0,00681 0,861 0,819

LINC01856:2 SEQ0762 0,03872 1,176 0,25 lnc-OXRl-l:l SEQ0627 0,02842 0,752 0,764

LINC01903:2 SEQ0345 0,01727 0,774 0,785 lnc-P2RY2-ll:l SEQ0537 0,02418 0,86 0,771

LINC01918:13 SEQ0763 0,03872 1,138 0,25 lnc-PABPCl-3:l SEQ0444 0,02049 0,814 0,778 lnc-PABPC4L-

LINC02104:7 SEQ0306 0,01449 0,82 0,792 SEQ0375 0,01727 1,485 0,215

21:3

lnc-PABPC4L-

LINC02177:6 SEQ0403 0,02049 0,768 0,778 SEQ0284 0,01209 0,626 0,799

5:7

LINC02177:8 SEQ0764 0,03872 0,494 0,75 lnc-PACRGL-5:l SEQ0718 0,03324 0,64 0,757

LINC02197:6 SEQ0483 0,02418 0,76 0,771 lnc-PAGRl-2:4 SEQ0719 0,03324 1,176 0,243

LINC02204:2 SEQ0142 0,00681 0,78 0,819 lnc-PALLD-3:l SEQ0164 0,00681 0,741 0,819

LINC02204:3 SEQ0143 0,00681 0,78 0,819 lnc-PAN3-3:l SEQ0955 0,0449 0,758 0,743

LINC02232:10 SEQ0665 0,03324 0,646 0,757 lnc-PANKl-7:l SEQ0211 0,00829 0,723 0,813

LINC02246:11 SEQ0346 0,01727 0,74 0,785 lnc-PAPD7-2:3 SEQ0956 0,0449 0,786 0,743 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change

LINC02252:3 SEQ0765 0,03872 0,881 0,75 lnc-PAPPA-l:3 SEQ0720 0,03324 2,192 0,243

LINC02323:8 SEQ0307 0,01449 0,622 0,792 lnc-PAPPA-l:4 SEQ0721 0,03324 0,814 0,757 lnc-PAPPA2-

LINC02334:6 SEQ0581 0,02842 0,737 0,764 SEQ0246 0,01004 0,753 0,806

1:10

LINC02345:11 SEQ0013 0,00066 1,181 0,111 lnc-PAPPA2-7:l SEQ0165 0,00681 0,839 0,819

LINC02432:9 SEQ0180 0,00829 0,776 0,813 lnc-PATE2-l:l SEQ0247 0,01004 0,728 0,806

LINC02473:3 SEQ0065 0,00291 0,786 0,847 lnc-PAX8-6:2 SEQ0538 0,02418 1,413 0,229

LINC02519:7 SEQ0875 0,0449 0,682 0,743 lnc-PAXIPl-8:l SEQ0957 0,0449 0,731 0,743 lnc-PCDHlO-

LINC02554:5 SEQ0404 0,02049 0,744 0,778 SEQ0958 0,0449 0,719 0,743

11:1

LINC02580:5 SEQ0484 0,02418 0,767 0,771 lnc-PCDH8-12:l SEQ0166 0,00681 0,742 0,819 lnc-AASDHPPT- lnc-PCOLCE2-

SEQ0409 0,02049 0,715 0,778 SEQ0959 0,0449 0,909 0,743 3:1 1:1

lnc-ABCAl-8:9 SEQ0589 0,02842 0,592 0,764 lnc-PCSK9-4:6 SEQ0327 0,01449 1,154 0,208 lnc-ABCA5-14:2 SEQ0881 0,0449 1,164 0,257 lnc-PCSK9-4:9 SEQ0328 0,01449 1,154 0,208 lnc-ABCA5-7:l SEQ0014 0,00066 1,309 0,111 lnc-PDLIMl-3:l SEQ0960 0,0449 0,837 0,743 lnc-ABCG2-3:5 SEQ0667 0,03324 0,758 0,757 lnc-PFKP-16:15 SEQ0628 0,02842 0,662 0,764 lnc-AC01-l:l SEQ0590 0,02842 0,759 0,764 lnc-PFKP-17:l SEQ0445 0,02049 0,834 0,778 lnc-PHF14- lnc-ACOT12-9:l SEQ0053 0,00232 0,727 0,854 SEQ0825 0,03872 0,678 0,75

14:19

lnc-ACTL7B-8:l SEQ0493 0,02418 0,697 0,771 lnc-PIGB-l:5 SEQ0539 0,02418 0,691 0,771 lnc-ADADl-3:l SEQ0774 0,03872 0,828 0,75 lnc-PIGM-4:l SEQ0248 0,01004 0,715 0,806 lnc-ADAMTS20-

SEQ0494 0,02418 1,219 0,229 lnc-PINXl-7:l SEQ0329 0,01449 1,307 0,208 3:1

lnc-PLA2G2F- lnc-ADAMTS5-l:l SEQ0882 0,0449 0,851 0,743 SEQ0285 0,01209 1,224 0,201

1:2

lnc-PLA2G4A- lnc-ADATl-l:l SEQ0495 0,02418 0,877 0,771 SEQ0446 0,02049 0,865 0,778

7:5

lnc-PLA2G4A- lnc-ADRA2A-4:l SEQ0883 0,0449 0,729 0,743 SEQ0826 0,03872 0,74 0,75

7:8

lnc-ADRBl-4:l SEQ0118 0,00556 0,858 0,826 lnc-PLAT-l:3 SEQ0961 0,0449 0,738 0,743

Inc-AFG1L-5:1 SEQ0263 0,01209 1,407 0,201 lnc-PLCBl-7:2 SEQ0827 0,03872 0,854 0,75 lnc-PLEKHA8- lnc-AG02-2:2 SEQ0119 0,00556 0,511 0,826 SEQ0962 0,0449 0,793 0,743

3:5

lnc-AG02-2:3 SEQ0668 0,03324 1,442 0,243 lnc-PLKl-l:6 SEQ0963 0,0449 0,663 0,743 lnc-AGR3-6:l SEQ0884 0,0449 1,156 0,257 lnc-PLN-2:l SEQ0722 0,03324 0,781 0,757 lnc-AHR-5:l SEQ0264 0,01209 0,792 0,799 lnc-PLSCR2-2:l SEQ0964 0,0449 0,813 0,743 lnc-AIGl-5:l SEQ0496 0,02418 0,862 0,771 lnc-POC5-3:l SEQ0629 0,02842 0,747 0,764 lnc-AKAP9-l:2 SEQ0775 0,03872 0,704 0,75 lnc-POLE4-3:l SEQ0330 0,01449 0,798 0,792 lnc-POU2AFl- lnc-AKIRINl-l:ll SEQ0591 0,02842 0,553 0,764 SEQ0447 0,02049 0,874 0,778

1:2

Inc-AKR1C2-3:17 SEQ0097 0,00451 0,768 0,833 lnc-PPIAL4F-3:2 SEQ0828 0,03872 1,302 0,25 lnc-AKRlDl-5:2 SEQ0098 0,00451 0,891 0,833 Inc-PPM1D-1:8 SEQ0540 0,02418 0,8 0,771 lnc-PPP2R3C- lnc-AKRlDl-8:3 SEQ0885 0,0449 0,797 0,743 SEQ0025 0,00086 0,879 0,882

4:1

Inc-AKR1E2-I5:l SEQ0410 0,02049 1,303 0,222 lnc-PPP5C-4:l SEQ0723 0,03324 0,757 0,757 lnc-AKR7A2-2:l SEQ0145 0,00681 1,339 0,181 lnc-PRDM9-19:2 SEQ0829 0,03872 0,709 0,75 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change lnc-AKTl-l: 15 SEQ0411 0,02049 0,825 0,778 lnc-PRDM9-20:l SEQ0376 0,01727 0,678 0,785 lnc-ALB-l:12 SEQ0146 0,00681 1,448 0,181 lnc-PRELID2-l:2 SEQ0541 0,02418 1,232 0,229 lnc-ALB-l:6 SEQ0886 0,0449 0,881 0,743 lnc-PRKACG-l:2 SEQ0724 0,03324 0,738 0,757 lnc-ALDH3B2-3:4 SEQ0412 0,02049 0,89 0,778 lnc-PRKACG-2:l SEQ0630 0,02842 1,186 0,236 lnc-ALG14-5:2 SEQ0350 0,01727 0,697 0,785 lnc-PRKCH-l:l SEQ0286 0,01209 0,904 0,799 lnc-ALS2CR12-

SEQ0351 0,01727 0,729 0,785 lnc-PRKN-8:l SEQ0212 0,00829 1,368 0,188 1:2

lnc-ANAPCll-2:6 SEQ0887 0,0449 1,836 0,257 lnc-PRND-2:l SEQ0287 0,01209 0,798 0,799 lnc-ANKRDl-l:6 SEQ0888 0,0449 0,77 0,743 lnc-PRRll-l:4 SEQ0288 0,01209 0,862 0,799 lnc-ANKRD26-l:3 SEQ0497 0,02418 0,852 0,771 lnc-PRR5-5:l SEQ0167 0,00681 0,703 0,819 lnc-ANKRD30BL-

SEQ0413 0,02049 0,747 0,778 lnc-PRSS54-2:2 SEQ0168 0,00681 0,769 0,819 2:2

lnc-ANKRD46-l:3 SEQ0669 0,03324 0,687 0,757 lnc-PSMBl-6:4 SEQ0331 0,01449 2,134 0,208 lnc-ANXA3-8:l SEQ0670 0,03324 0,838 0,757 lnc-PTDSSl-l:2 SEQ0448 0,02049 1,246 0,222 lnc-APBAl-5:l SEQ0592 0,02842 0,781 0,764 lnc-PTP4A2-l:2 SEQ0725 0,03324 1,223 0,243 lnc-PTPN14- lnc-APIP-l:13 SEQ0776 0,03872 0,807 0,75 SEQ0449 0,02049 1,341 0,222

11:1

lnc-APLP2-4:l SEQ0054 0,00232 0,786 0,854 lnc-PTPN4-l:l SEQ0631 0,02842 0,755 0,764 lnc-APOB-l:2 SEQ0777 0,03872 0,583 0,75 lnc-QRFP-5:l SEQ0019 0,00066 2,17 0,111 lnc-APPL2-l:2 SEQ0498 0,02418 1,133 0,229 lnc-RAB3B-l:l SEQ0965 0,0449 0,843 0,743 lnc-AQP8-2:7 SEQ0352 0,01727 1,254 0,215 lnc-RAB6C-3:l SEQ0966 0,0449 0,836 0,743 lnc-ARAP2-9:l SEQ0147 0,00681 0,795 0,819 lnc-RAI14-3:l SEQ0542 0,02418 0,835 0,771 lnc-ARHGAP15- lnc-RALGAPAl-

SEQ0353 0,01727 0,857 0,785 SEQ0726 0,03324 0,86 0,757 17:1 1:2

lnc-ARHGAP15- lnc-RALGAPA2-

SEQ0593 0,02842 0,846 0,764 SEQ0967 0,0449 0,805 0,743 22:1 2:4

lnc-ARHGAP21- lnc-RARRESl-

SEQ0309 0,01449 0,723 0,792 SEQ0632 0,02842 0,84 0,764 1:2 3:2

lnc-ARHGAP26- lnc-RASGRPl-

SEQ0189 0,00829 0,923 0,813 SEQ0968 0,0449 0,796 0,743 4:11 3:3

lnc-ARHGAP26-

SEQ0778 0,03872 0,71 0,75 lnc-RBFOXl-2:l SEQ0377 0,01727 1,269 0,215 4:33

lnc-ARHGAP26-

SEQ0889 0,0449 1,383 0,257 lnc-RBKS-6:l SEQ0128 0,00556 0,802 0,826 4:39

lnc-ARHGEF26-

SEQ0067 0,00291 1,399 0,153 lnc-RBMll-ll:l SEQ0450 0,02049 0,714 0,778 2:1

lnc-ARHGEF5-5:l SEQ0354 0,01727 0,712 0,785 lnc-RBM25-l:l SEQ0969 0,0449 0,89 0,743 lnc-ARID2-7:l SEQ0671 0,03324 0,924 0,757 lnc-RBM33-3:1 SEQ0727 0,03324 0,762 0,757 lnc-ARNTL-2:l SEQ0265 0,01209 0,61 0,799 lnc-RBM45-7:l SEQ0830 0,03872 0,792 0,75 lnc-ARRDC4-7:l SEQ0779 0,03872 0,876 0,75 lnc-RBMSl-7:l SEQ0249 0,01004 0,778 0,806 lnc-ART5-2:l SEQ0414 0,02049 0,787 0,778 lnc-RCSDl-4:l SEQ0831 0,03872 0,831 0,75 lnc-ATADl-5:2 SEQ0148 0,00681 0,738 0,819 lnc-RDH13-l:2 SEQ0250 0,01004 0,635 0,806 lnc-ATIC-2:8 SEQ0780 0,03872 0,554 0,75 lnc-RGMA-28:2 SEQ0378 0,01727 0,875 0,785 lnc-ATP12A-3:l SEQ0499 0,02418 0,763 0,771 lnc-RGS9-15:6 SEQ0970 0,0449 0,705 0,743 lnc-ATP13A4-2:4 SEQ0672 0,03324 1,64 0,243 lnc-RHN01-l:l SEQ0026 0,00086 1,248 0,118 lnc-ATP50-3:l SEQ0224 0,01004 1,358 0,194 lnc-RHOB-l:3 SEQ0451 0,02049 0,612 0,778 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change lnc-ATP6V0E2-

SEQ0500 0,02418 1,361 0,229 lnc-RHOB-21:l SEQ0971 0,0449 0,817 0,743 7:1

Inc-ATP6V1B2- lnc-RHOBTB2-

SEQ0594 0,02842 1,132 0,236 SEQ0213 0,00829 0,839 0,813 2:6 4:1

Inc-ATP6V1B2-

SEQ0595 0,02842 1,132 0,236 lnc-RIPPLY3-l:3 SEQ0832 0,03872 0,889 0,75 2:7

lnc-ATP8A2-l:l SEQ0355 0,01727 1,477 0,215 lnc-RIT2-5:l SEQ0289 0,01209 1,302 0,201 lnc-ATXN2-l:l SEQ0099 0,00451 0,792 0,833 lnc-RNF6-2:l SEQ0059 0,00232 0,692 0,854 lnc-ATXN7-ll:l SEQ0501 0,02418 1,319 0,229 lnc-RNFT2-l:5 SEQ0379 0,01727 0,8 0,785 lnc-ATXN7Ll-l:l SEQ0890 0,0449 1,32 0,257 lnc-RNLS-l:l SEQ0972 0,0449 0,829 0,743 lnc-AUH-2:7 SEQ0190 0,00829 0,584 0,813 lnc-ROB02-16:l SEQ0251 0,01004 0,799 0,806 lnc-AUH-2:9 SEQ0673 0,03324 1,35 0,243 lnc-RPE65-4:2 SEQ0129 0,00556 0,857 0,826 lnc-AUH-4:l SEQ0596 0,02842 0,79 0,764 lnc-RPIA-25:l SEQ0130 0,00556 0,866 0,826 lnc-BAG3-4:4 SEQ0891 0,0449 1,314 0,257 lnc-RPL10L-5:l SEQ0452 0,02049 0,77 0,778 lnc-BARHL2-4:4 SEQ0100 0,00451 0,736 0,833 lnc-RPL24-6:l SEQ0728 0,03324 1,245 0,243 lnc-BCHE-l:l SEQ0674 0,03324 0,741 0,757 lnc-RPL35-2:l SEQ0543 0,02418 0,812 0,771 lnc-BCL6-9:l SEQ0310 0,01449 0,885 0,792 lnc-RPL37-2:l SEQ0214 0,00829 1,309 0,188 lnc-BIRC2-5:5 SEQ0225 0,01004 0,831 0,806 lnc-RPRM-7:l SEQ0380 0,01727 0,679 0,785 lnc-BIRC6-l:4 SEQ0892 0,0449 0,829 0,743 lnc-RPS12-4:l SEQ0633 0,02842 0,806 0,764 lnc-BMSl-2:l SEQ0675 0,03324 1,124 0,243 lnc-RPS21-4:2 SEQ0027 0,00086 0,664 0,882 lnc-BNC2-5:l SEQ0191 0,00829 1,299 0,188 lnc-RRMl-2:5 SEQ0973 0,0449 0,934 0,743 lnc-BORA-31:l SEQ0192 0,00829 0,67 0,813 Inc-RSL1D1-2:1 SEQ0215 0,00829 0,762 0,813 lnc-BRDl-17:l SEQ0311 0,01449 0,669 0,792 lnc-RTLl-3:9 SEQ0544 0,02418 0,848 0,771 lnc-BRINPl-3:l SEQ0893 0,0449 0,894 0,743 lnc-RUBCN-l:l SEQ0833 0,03872 0,788 0,75 lnc-BRINP2-3:l SEQ0597 0,02842 0,878 0,764 Inc-S1PR1-I3:l SEQ0381 0,01727 0,686 0,785 lnc-C10orf90-2:2 SEQ0415 0,02049 1,687 0,222 lnc-SAMDll-l:l SEQ0729 0,03324 1,247 0,243 lnc-C12orf40-3:3 SEQ0894 0,0449 1,434 0,257 lnc-SAMD5-l:10 SEQ0090 0,00364 0,708 0,84 lnc-C12orf42-3:6 SEQ0598 0,02842 1,303 0,236 lnc-SC5D-4:l SEQ0730 0,03324 1,249 0,243 lnc-C15orf41-

SEQ0676 0,03324 1,649 0,243 lnc-SCD-7:l SEQ0974 0,0449 0,731 0,743 18:5

lnc-C15orf41-

SEQ0895 0,0449 1,561 0,257 Inc-SCGB1C2-8:1 SEQ0975 0,0449 0,776 0,743 18:6

lnc-C19orf57-l:l SEQ0896 0,0449 1,361 0,257 Inc-SCNN1B-3:1 SEQ0976 0,0449 1,155 0,257

Inc-C1QTNF9-4:1 SEQ0781 0,03872 1,282 0,25 lnc-SCTR-2:4 SEQ0634 0,02842 0,852 0,764 lnc-C21orf58-l:2 SEQ0033 0,00143 2,231 0,132 lnc-SEPT14-6:l SEQ0545 0,02418 0,878 0,771 lnc-C2CD4B-6:4 SEQ0193 0,00829 1,39 0,188 lnc-SERHL2-l:8 SEQ0546 0,02418 1,483 0,229 lnc-C2orf42-10:l SEQ0502 0,02418 1,28 0,229 lnc-SERINCl-8:3 SEQ0731 0,03324 0,827 0,757 lnc-C3orf58-7:l SEQ0599 0,02842 0,725 0,764 lnc-SERPl-4:6 SEQ0072 0,00291 0,719 0,847 lnc-C5orf30-10:l SEQ0266 0,01209 0,708 0,799 lnc-SERPl-4:8 SEQ0977 0,0449 0,831 0,743 lnc-SERPINIl- lnc-C5orf30-10:2 SEQ0600 0,02842 0,891 0,764 SEQ0978 0,0449 0,85 0,743

14:1

lnc-C5orf67-3:l SEQ0149 0,00681 0,876 0,819 lnc-SERTMl-l:l SEQ0290 0,01209 0,817 0,799 lnc-C7orf57-4:l SEQ0356 0,01727 1,461 0,215 lnc-SFPQ-2:l SEQ0169 0,00681 0,72 0,819 lnc-C9orf3-5:l SEQ0120 0,00556 0,782 0,826 lnc-SGCG-7:2 SEQ0453 0,02049 0,733 0,778 lnc-CA7-2:2 SEQ0677 0,03324 0,817 0,757 lnc-SGKl-3: 14 SEQ0979 0,0449 1,836 0,257 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change lnc-CAB39L-l:4 SEQ0267 0,01209 0,769 0,799 lnc-SGMSl-4:l SEQ0834 0,03872 0,793 0,75 lnc-SH3BGRL2- lnc-CAB39L-4:2 SEQ0601 0,02842 1,181 0,236 SEQ0980 0,0449 1,174 0,257

4:1

lnc-CACNAH-l:l SEQ0503 0,02418 0,813 0,771 lnc-SKIL-2:3 SEQ0981 0,0449 1,222 0,257 lnc-CACNA2Dl-

SEQ0897 0,0449 0,82 0,743 Inc-SLC1A3-I:l SEQ0547 0,02418 0,791 0,771 1:1

lnc-SLC22A23- lnc-CACNGl-l:l SEQ0602 0,02842 0,652 0,764 SEQ0732 0,03324 1,239 0,243

11:2

lnc-SLC25A21- lnc-CALML6-l:10 SEQ0898 0,0449 0,577 0,743 SEQ0733 0,03324 0,77 0,757

1:1

lnc-SLC25A24- lnc-CAPS2-l:l SEQ0357 0,01727 0,708 0,785 SEQ0548 0,02418 0,883 0,771

2:1

lnc-SLC25A30- lnc-CASC10-3:l SEQ0899 0,0449 0,744 0,743 SEQ0291 0,01209 1,539 0,201

2:4

lnc-CASP9-l:l SEQ0782 0,03872 1,246 0,25 lnc-SLC2A10-l:l SEQ0549 0,02418 0,788 0,771 lnc-SLC38A2- lnc-CAVIN2-2:l SEQ0416 0,02049 1,425 0,222 SEQ0252 0,01004 0,892 0,806

1:11

lnc-SLC38A2- lnc-CBLB-9:l SEQ0900 0,0449 0,788 0,743 SEQ0332 0,01449 1,425 0,208

1:15

lnc-CCDC102B- lnc-SLC39All-

SEQ0901 0,0449 0,816 0,743 SEQ0292 0,01209 4,38 0,201 7:1 10:11

lnc-CCDC167-5:l SEQ0417 0,02049 1,25 0,222 lnc-SLC46A3-7:l SEQ0550 0,02418 0,858 0,771 lnc-SLC06Al- lnc-CCDC177-6:l SEQ0783 0,03872 1,157 0,25 SEQ0635 0,02842 0,775 0,764

2:1

lnc-SLITRK5- lnc-CCDC192-3:l SEQ0902 0,0449 0,8 0,743 SEQ0293 0,01209 0,636 0,799

17:1

lnc-CCDC197-2:l SEQ0068 0,00291 0,672 0,847 lnc-SLTM-l:2 SEQ0982 0,0449 0,9 0,743 lnc-SMARCA5- lnc-CCDC61-4:l SEQ0194 0,00829 0,561 0,813 SEQ0253 0,01004 0,825 0,806

4:18

lnc-CCDC7-17:l SEQ0226 0,01004 1,341 0,194 lnc-SMIM14-4:l SEQ0454 0,02049 0,788 0,778 lnc-CCDC93-10:2 SEQ0603 0,02842 0,87 0,764 lnc-SMIM17-5:4 SEQ0734 0,03324 0,715 0,757 lnc-SNAPC3- lnc-CCLl-10:l SEQ0227 0,01004 1,307 0,194 SEQ0455 0,02049 1,387 0,222

12:5

Inc-CCNB1IP1-1:2 SEQ0903 0,0449 0,512 0,743 lnc-SNCA-3:l SEQ0551 0,02418 0,498 0,771 lnc-CCR8-2:l SEQ0904 0,0449 1,409 0,257 lnc-SNRPB2-2:4 SEQ0254 0,01004 1,727 0,194 lnc-CCSERl-2:l SEQ0678 0,03324 0,791 0,757 lnc-SNX10-6:l SEQ0636 0,02842 0,764 0,764 lnc-CCT8L2-28:l SEQ0418 0,02049 0,812 0,778 lnc-SNX13-2:6 SEQ0456 0,02049 1,576 0,222 lnc-CD47-ll:2 SEQ0905 0,0449 1,317 0,257 lnc-SNX16-6:l SEQ0983 0,0449 0,83 0,743 lnc-CD47-ll:4 SEQ0906 0,0449 1,139 0,257 lnc-SNX17-l: 13 SEQ0333 0,01449 1,985 0,208 lnc-CDADCl-l:l SEQ0907 0,0449 1,18 0,257 lnc-SNX17-l:8 SEQ0984 0,0449 0,754 0,743 lnc-CDH23-2:l SEQ0908 0,0449 0,757 0,743 lnc-SNX19-6:l SEQ0835 0,03872 1,249 0,25 lnc-CDK20-14:l SEQ0358 0,01727 1,274 0,215 lnc-SNX19-9:l SEQ0836 0,03872 0,837 0,75 lnc-CDK2APl-l:8 SEQ0909 0,0449 0,84 0,743 lnc-SNX20-8:5 SEQ0985 0,0449 0,933 0,743 lnc-CEACAM16-

SEQ0195 0,00829 0,708 0,813 lnc-SOXll-5:l SEQ0382 0,01727 1,216 0,215 2:1

lnc-CEBPD-ll:2 SEQ0784 0,03872 0,763 0,75 lnc-SOX14-2:l SEQ0045 0,00183 0,837 0,861 lnc-CELF4-15:l SEQ0785 0,03872 0,89 0,75 lnc-SPAG9-2:l SEQ0383 0,01727 0,851 0,785 lnc-CELSRl-2:3 SEQ0268 0,01209 0,762 0,799 lnc-SPAG9-2:2 SEQ0384 0,01727 0,851 0,785 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change

lnc-SPATA31A6- lnc-CEP170-9:2 SEQ0042 0,00183 0,828 0,861 SEQ0837 0,03872 0,804 0,75

10:1

lnc-SPATA31D4- lnc-CFAP36-3:2 SEQ0786 0,03872 0,69 0,75 SEQ0838 0,03872 0,718 0,75

1:6

Inc-CHD1L-5:13 SEQ0312 0,01449 0,37 0,792 lnc-SPPl-l:l SEQ0046 0,00183 0,846 0,861 lnc-CHMP2B-l:ll SEQ0679 0,03324 0,858 0,757 lnc-SPRYl-9:l SEQ0839 0,03872 0,767 0,75 lnc-CHNl-5: ll SEQ0419 0,02049 0,739 0,778 lnc-SPTSSA-5:2 SEQ0552 0,02418 1,31 0,229 lnc-CHRACl-l:l SEQ0680 0,03324 0,747 0,757 lnc-SRCINl-l:18 SEQ0553 0,02418 0,798 0,771 lnc-CHRACl-6:l SEQ0420 0,02049 1,247 0,222 lnc-SRSF2-2:5 SEQ0735 0,03324 0,633 0,757 lnc-CHRM2-l:l SEQ0121 0,00556 0,833 0,826 lnc-ST8SIA4-3:5 SEQ0457 0,02049 1,584 0,222 lnc-STARDlO- lnc-CHRM3-l:5 SEQ0681 0,03324 0,714 0,757 SEQ0840 0,03872 1,156 0,25

1:6

lnc-CHST2-6:2 SEQ0196 0,00829 0,763 0,813 lnc-STATl-2:3 SEQ0986 0,0449 0,832 0,743 lnc-CLDN10-5:l SEQ0313 0,01449 0,717 0,792 lnc-STK32B-2:l SEQ0170 0,00681 0,807 0,819 lnc-CLEC19A-3:l SEQ0787 0,03872 1,284 0,25 lnc-STOML3-6:l SEQ0038 0,00143 0,731 0,868 lnc-CLKl-l:7 SEQ0197 0,00829 0,602 0,813 lnc-STPGl-l:l SEQ0637 0,02842 0,82 0,764 lnc-CLVS2-2:5 SEQ0359 0,01727 0,818 0,785 lnc-STRADB-6:2 SEQ0987 0,0449 0,723 0,743 lnc-SUCLA2- lnc-CMPK2-34:4 SEQ0101 0,00451 0,651 0,833 SEQ0736 0,03324 1,22 0,243

13:2

lnc-SUCLA2- lnc-CMTM7-2:2 SEQ0269 0,01209 0,838 0,799 SEQ0737 0,03324 1,22 0,243

13:3

lnc-CMTRl-10:l SEQ0788 0,03872 0,868 0,75 lnc-SUGTl-3:l SEQ0131 0,00556 2,047 0,174

Inc-SULT1A4- lnc-CNBDl-4: 13 SEQ0682 0,03324 0,568 0,757 SEQ0841 0,03872 1,274 0,25

1:27

lnc-CNDPl-7:l SEQ0024 0,00086 0,534 0,882 Inc-SULT1C2-3:1 SEQ0842 0,03872 1,396 0,25 lnc-CNOT6-10:l SEQ0270 0,01209 1,19 0,201 lnc-SUSDl-l:5 SEQ0638 0,02842 0,798 0,764 lnc-COL6A6-2:l SEQ0910 0,0449 0,717 0,743 lnc-SYCPl-4:l SEQ0843 0,03872 1,204 0,25 lnc-COMMD6-

SEQ0079 0,00364 1,58 0,16 lnc-TAAR9-3:2 SEQ0738 0,03324 0,806 0,757 10:1

lnc-COX10-9:2 SEQ0911 0,0449 0,849 0,743 lnc-TAB2-l:4 SEQ0988 0,0449 0,846 0,743 lnc-CPEB3-2:l SEQ0789 0,03872 0,78 0,75 lnc-TACC2-8:6 SEQ0171 0,00681 0,869 0,819 lnc-TACSTD2- lnc-CPM-2:ll SEQ0912 0,0449 0,424 0,743 SEQ0020 0,00066 0,757 0,889

2:4

lnc-CPM-3:l SEQ0228 0,01004 0,831 0,806 lnc-TADA2B-6:l SEQ0554 0,02418 1,467 0,229 lnc-CRIPT-l:3 SEQ0790 0,03872 0,802 0,75 lnc-TAF9-10:l SEQ0639 0,02842 0,825 0,764 lnc-CRISPl-l:2 SEQ0271 0,01209 0,685 0,799 lnc-TASPl-ll:l SEQ0989 0,0449 1,217 0,257

Inc-TBC1D22A- lnc-CRYBAl-4:l SEQ0421 0,02049 0,693 0,778 SEQ0739 0,03324 0,969 0,757

4:12

Inc-TBC1D3H- lnc-CRYBBl-l:l SEQ0055 0,00232 0,487 0,854 SEQ0334 0,01449 0,729 0,792

1:1

lnc- Inc-TBC1D3H-

SEQ0791 0,03872 0,781 0,75 SEQ0294 0,01209 0,72 0,799

CSGALNACT2-2:3 3:1

lnc-TBLlXRl-

Inc-CSNK1A1-6:1 SEQ0030 0,00111 0,696 0,875 SEQ0458 0,02049 0,754 0,778

7:1

lnc-TCEANC2- lnc-CTIF-9:2 SEQ0504 0,02418 1,28 0,229 SEQ0106 0,00451 0,549 0,833

3:1

lnc-TCEANC2- lnc-CTNNA2-3: ll SEQ0198 0,00829 0,812 0,813 SEQ0385 0,01727 1,882 0,215

3:2 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change lnc-CTNNA3-l:2 SEQ0683 0,03324 0,82 0,757 lnc-TCF19-l:80 SEQ0740 0,03324 1,351 0,243 lnc-CTNND2-3:l SEQ0913 0,0449 0,797 0,743 lnc-TCF7-l:3 SEQ0990 0,0449 0,866 0,743 lnc-CTR9-7:l SEQ0914 0,0449 0,763 0,743 lnc-TCP10-2:l SEQ0991 0,0449 0,876 0,743 lnc-CYB5R2-3: 13 SEQ0199 0,00829 0,749 0,813 lnc-TCPll-2:3 SEQ0640 0,02842 0,67 0,764 lnc-CYBA-4:3 SEQ0150 0,00681 0,784 0,819 Inc-TCP11L2-1:4 SEQ0741 0,03324 0,74 0,757 lnc-CYP2El-l:l SEQ0684 0,03324 1,733 0,243 lnc-TD02-6:l SEQ0641 0,02842 0,758 0,764 lnc-CYTIP-2:l SEQ0200 0,00829 0,727 0,813 lnc-TDP2-l:l SEQ0335 0,01449 1,29 0,208 lnc-DAO-3:l SEQ0685 0,03324 1,109 0,243 lnc-TEAD4-l:l SEQ0992 0,0449 1,868 0,257 lnc-DAPPl-2: ll SEQ0505 0,02418 0,822 0,771 lnc-TEFM-10:2 SEQ0993 0,0449 0,736 0,743 lnc-DAZAP2-3:l SEQ0056 0,00232 0,797 0,854 lnc-TEKT3-3:l SEQ0844 0,03872 0,744 0,75 lnc-DDXl-3:l SEQ0915 0,0449 1,251 0,257 lnc-TEKT4-4:l SEQ0132 0,00556 1,332 0,174 lnc-DDX18-l:l SEQ0506 0,02418 0,721 0,771 lnc-TENM3-3:3 SEQ0001 0,00007 0,717 0,938 lnc-DDX18-l:7 SEQ0314 0,01449 0,755 0,792 lnc-TENM3-3:4 SEQ0008 0,00037 1,947 0,097 lnc-DEFB112-3:4 SEQ0422 0,02049 0,565 0,778 lnc-TENM3-3:5 SEQ0009 0,00037 1,993 0,097 lnc-DEK-4:l SEQ0507 0,02418 0,715 0,771 lnc-TENM4-4:l SEQ0742 0,03324 2,209 0,243 lnc-DEPTOR-5:3 SEQ0360 0,01727 0,846 0,785 lnc-TEX10-l:l SEQ0459 0,02049 0,814 0,778 lnc-DGCR2-5:l SEQ0423 0,02049 0,872 0,778 lnc-TEX29-3:l SEQ0172 0,00681 0,767 0,819 lnc-DGCR6-7:26 SEQ0792 0,03872 0,736 0,75 lnc-TEX49-4:l SEQ0994 0,0449 0,789 0,743 lnc-DHX37-18:l SEQ0229 0,01004 0,751 0,806 lnc-TF-4:l SEQ0743 0,03324 0,898 0,757 lnc-DHX38-25:l SEQ0230 0,01004 0,894 0,806 lnc-TFCP2Ll-6:l SEQ0460 0,02049 0,743 0,778 lnc-DKKl-5:3 SEQ0005 0,00027 0,739 0,91 lnc-TGM6-2:l SEQ0216 0,00829 0,741 0,813 lnc-DKKl-5:4 SEQ0793 0,03872 0,893 0,75 lnc-THAP12-l:5 SEQ0642 0,02842 1,427 0,236 lnc-DLG5-l:l SEQ0034 0,00143 0,773 0,868 lnc-THOC5-3:l SEQ0133 0,00556 0,717 0,826 lnc-DLX2-12:l SEQ0151 0,00681 0,747 0,819 lnc-THYl-3:l SEQ0995 0,0449 0,788 0,743 lnc-DMRTAl-17:l SEQ0201 0,00829 0,79 0,813 lnc-THYNl-l:l SEQ0461 0,02049 1,173 0,222 lnc-DNAH9-l:l SEQ0508 0,02418 0,812 0,771 lnc-TLDC2-4:l SEQ0555 0,02418 1,214 0,229 lnc-DNALIl-5:4 SEQ0102 0,00451 0,776 0,833 lnc-TLE4-7:l SEQ0255 0,01004 1,184 0,194 lnc-DOCK7-7:l SEQ0015 0,00066 0,843 0,889 lnc-TLKl-l:2 SEQ0556 0,02418 0,76 0,771 lnc-DTWD2-14:l SEQ0361 0,01727 0,811 0,785 lnc-TLNRDl-3:l SEQ0462 0,02049 0,829 0,778 lnc-TMEM126B- lnc-DUSP10-6:l SEQ0057 0,00232 0,675 0,854 SEQ0336 0,01449 0,508 0,792

2:3

lnc-TMEM126B- lnc-DUSP26-3:2 SEQ0031 0,00111 0,796 0,875 SEQ0643 0,02842 1,964 0,236

2:4

lnc-TMEM132C- lnc-DYNAP-l:l SEQ0604 0,02842 0,761 0,764 SEQ0996 0,0449 1,222 0,257

6:5

lnc-TMEM168- lnc-EAFl-2:l SEQ0509 0,02418 0,737 0,771 SEQ0073 0,00291 0,761 0,847

1:1

lnc-TMEM185B- lnc-EBF3-l:6 SEQ0686 0,03324 1,241 0,243 SEQ0173 0,00681 0,549 0,819

12:1

lnc-TMEM185B- lnc-EBLNl-l:4 SEQ0016 0,00066 0,725 0,889 SEQ0060 0,00232 1,694 0,146

12:7

lnc-TMEM185B- lnc-EDDM13-5: ll SEQ0231 0,01004 0,372 0,806 SEQ0386 0,01727 1,212 0,215

2:3

lnc-TMEM211- lnc-EDDM13-5:3 SEQ0035 0,00143 1,505 0,132 SEQ0644 0,02842 1,278 0,236

2:8 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change

lnc-TMEM242- lnc-EDEM3-7:3 SEQ0272 0,01209 0,86 0,799 SEQ0463 0,02049 0,879 0,778

6:1

lnc-TMEM248- lnc-EDRFl-l:5 SEQ0916 0,0449 0,728 0,743 SEQ0845 0,03872 0,842 0,75

4:11

lnc-EEFlAKMTl-

SEQ0202 0,00829 0,423 0,813 lnc-TMEM70-7:l SEQ0217 0,00829 1,404 0,188 3:6

lnc-EEF2-3:l SEQ0687 0,03324 1,301 0,243 lnc-TMX4-3:l SEQ0464 0,02049 0,776 0,778 lnc-TNFRSF19- lnc-EFR3B-7:2 SEQ0362 0,01727 0,821 0,785 SEQ0744 0,03324 1,177 0,243

2:1

lnc-EGFR-7:l SEQ0917 0,0449 1,181 0,257 lnc-TNFSF4-3:3 SEQ0337 0,01449 1,714 0,208 lnc-TOGARAM2- lnc-EIF2AK3-31:7 SEQ0918 0,0449 0,848 0,743 SEQ0645 0,02842 0,81 0,764

1:5

lnc-TOGARAM2- lnc-EIF2AK3-4:81 SEQ0688 0,03324 1,466 0,243 SEQ0646 0,02842 0,81 0,764

1:6

lnc-TP53TG3- lnc-ELFl-5:l SEQ0510 0,02418 0,799 0,771 SEQ0465 0,02049 1,227 0,222

65:1

lnc-TP53TG3D- lnc-ELFN2-l:3 SEQ0080 0,00364 0,764 0,84 SEQ0846 0,03872 0,9 0,75

2:1

lnc-TP53TG3F- lnc-EPB42-l:3 SEQ0689 0,03324 0,917 0,757 SEQ0647 0,02842 1,33 0,236

8:1

lnc-EPHA7-3:l SEQ0081 0,00364 0,763 0,84 lnc-TPPP-l:2 SEQ0002 0,00014 0,158 0,924 lnc-ERCC6L2-

SEQ0605 0,02842 0,927 0,764 lnc-TPPP-l:3 SEQ0134 0,00556 1,834 0,174 10:2

lnc-ERCC6L2-6:l SEQ0363 0,01727 1,784 0,215 lnc-TRAMl-l:l SEQ0074 0,00291 0,59 0,847 lnc-ERFE-l:l SEQ0919 0,0449 0,489 0,743 lnc-TRIB2-14:l SEQ0021 0,00066 0,788 0,889 lnc-ERGIC2-2:2 SEQ0364 0,01727 0,799 0,785 lnc-TRIM13-2:l SEQ0174 0,00681 0,739 0,819 lnc-TRIM26- lnc-ERH-l:l SEQ0424 0,02049 0,708 0,778 SEQ0745 0,03324 0,782 0,757

2:33

lnc-TRIM26- lnc-ERICHl-9:l SEQ0920 0,0449 0,85 0,743 SEQ0338 0,01449 1,231 0,208

2:74

lnc-TRIM27- lnc-ERV3-l-9:l SEQ0203 0,00829 1,32 0,188 SEQ0107 0,00451 1,227 0,167

10:2

lnc-ESRPl-2:4 SEQ0315 0,01449 0,542 0,792 lnc-TRIM37-2:l SEQ0997 0,0449 1,221 0,257 lnc-TRIM43B- lnc-ETSl-2:2 SEQ0082 0,00364 1,436 0,16 SEQ0387 0,01727 0,8 0,785

1:2

lnc-TRIM49Dl- lnc-EXOC2-21:6 SEQ0204 0,00829 1,528 0,188 SEQ0175 0,00681 1,36 0,181

4:1

lnc-EZH2-3:l SEQ0273 0,01209 0,79 0,799 lnc-TRIM77-7:l SEQ0847 0,03872 0,741 0,75 lnc-TRIML2- lnc-Fll-8:l SEQ0690 0,03324 0,876 0,757 SEQ0557 0,02418 1,296 0,229

11:1

lnc-F13Al-2:7 SEQ0691 0,03324 1,485 0,243 lnc-TRMTll-4:l SEQ0218 0,00829 0,773 0,813 lnc-FAM133B-2:l SEQ0036 0,00143 0,767 0,868 lnc-TRPM 1-3:1 SEQ0388 0,01727 0,83 0,785 lnc-TSC22D2- lnc-FAM171B-l:6 SEQ0205 0,00829 0,851 0,813 SEQ0466 0,02049 0,633 0,778

1:4

lnc-FAM19A3-6:3 SEQ0274 0,01209 0,75 0,799 lnc-TSHB-2:3 SEQ0295 0,01209 0,564 0,799 lnc-FAM217A-l:2 SEQ0032 0,00111 0,854 0,875 lnc-TSHB-2:4 SEQ0467 0,02049 1,766 0,222 lnc-FAM231B-2:l SEQ0511 0,02418 1,252 0,229 lnc-TSHB-2:5 SEQ0468 0,02049 1,766 0,222 lnc-FAM231B-2:2 SEQ0512 0,02418 1,252 0,229 lnc-TSHB-2:6 SEQ0296 0,01209 0,564 0,799 lnc-FAM236D-2:l SEQ0692 0,03324 0,601 0,757 lnc-TSR3-l:2 SEQ0339 0,01449 0,784 0,792 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change lnc-FAM46C-3:l SEQ0794 0,03872 0,755 0,75 lnc-TSTD2-4:3 SEQ0848 0,03872 1,229 0,25 lnc-FAM49B-8:l SEQ0010 0,0005 1,758 0,104 lnc-TTC26-9:l SEQ0558 0,02418 0,686 0,771 lnc-FAM71F2-5:l SEQ0425 0,02049 0,642 0,778 lnc-TTC38-7:l SEQ0849 0,03872 1,169 0,25 lnc-FAM72B-6:3 SEQ0606 0,02842 1,327 0,236 lnc-TTF2-4:l SEQ0998 0,0449 0,848 0,743 lnc-TUBAlC- lnc-FAM84A-5:l SEQ0607 0,02842 0,697 0,764 SEQ0999 0,0449 0,731 0,743

1:12

lnc-FAM84B-17:4 SEQ0608 0,02842 0,741 0,764 lnc-TUBEl-6:l SEQ0850 0,03872 1,141 0,25 lnc-TUBGCP3- lnc-FAM84B-4:3 SEQ0083 0,00364 0,803 0,84 SEQ0469 0,02049 0,885 0,778

11:1

lnc-FAP-3:l SEQ0206 0,00829 0,749 0,813 lnc-TUSC5-3:l SEQ1000 0,0449 1,345 0,257 lnc-FARSB-6:l SEQ0232 0,01004 1,292 0,194 lnc-TWSGl-2:l SEQ0047 0,00183 0,673 0,861 lnc-UBE2QLl- lnc-FATl-7:2 SEQ0017 0,00066 1,383 0,111 SEQ0648 0,02842 0,864 0,764

4:1

lnc-FAT4-6:l SEQ0609 0,02842 0,751 0,764 lnc-UBE3C-10:l SEQ0075 0,00291 1,627 0,153 lnc-FBRSLl-3:3 SEQ0610 0,02842 1,645 0,236 lnc-UBLCPl-2:6 SEQ0048 0,00183 0,755 0,861 lnc-FCGR3B-4:ll SEQ0513 0,02418 0,848 0,771 lnc-UCKl-2:l SEQ0851 0,03872 0,774 0,75 lnc-FCGR3B-4:12 SEQ0514 0,02418 0,848 0,771 lnc-UGCG-l:l SEQ0219 0,00829 0,802 0,813

Inc-FER1L6-2:1 SEQ0515 0,02418 0,779 0,771 Inc-UGT1A8-3:1 SEQ0649 0,02842 1,428 0,236 lnc-UGT2B28- lnc-FGD4-8:l SEQ0233 0,01004 0,688 0,806 SEQ1001 0,0449 0,818 0,743

1:2

lnc-UGT2B28- lnc-FGD4-9:l SEQ0084 0,00364 0,711 0,84 SEQ0559 0,02418 0,764 0,771

2:1

Inc-FILIP1L-3:1 SEQ0058 0,00232 0,867 0,854 lnc-UGT3A2-3:l SEQ0389 0,01727 0,855 0,785 lnc-UNC93Bl-

Inc-FNBP1L-I: ll SEQ0006 0,00027 0,743 0,91 SEQ1002 0,0449 0,806 0,743

1:5

lnc-FOXCl-6:2 SEQ0516 0,02418 0,736 0,771 lnc-UNCX-3:26 SEQ0470 0,02049 0,493 0,778 lnc-FOXD4L5-

SEQ0152 0,00681 1,878 0,181 lnc-UPK3B-7:l SEQ0297 0,01209 0,607 0,799 35:1

lnc-FOX01-2:8 SEQ0611 0,02842 1,402 0,236 lnc-USP16-15:l SEQ0650 0,02842 0,811 0,764 lnc-FRG2-13:3 SEQ0275 0,01209 0,908 0,799 lnc-USP16-9:3 SEQ0091 0,00364 0,834 0,84 lnc-FSIPl-6:4 SEQ0795 0,03872 1,401 0,25 lnc-USP17L7-l:l SEQ0746 0,03324 1,48 0,243 lnc-FSIP2-2:l SEQ0921 0,0449 0,76 0,743 lnc-USP24-2:3 SEQ0747 0,03324 0,694 0,757 lnc-FTCD-5:l SEQ0796 0,03872 0,722 0,75 lnc-USP31-2:3 SEQ0022 0,00066 0,685 0,889 lnc-FTMT-2:14 SEQ0517 0,02418 0,792 0,771 lnc-USP53-l:l SEQ0390 0,01727 0,824 0,785 lnc-USP6NL- lnc-GALC-9:8 SEQ0234 0,01004 0,557 0,806 SEQ0748 0,03324 1,209 0,243

13:1

lnc-GALNT2-l:l SEQ0426 0,02049 0,729 0,778 lnc-VGLL3-ll:l SEQ0220 0,00829 0,81 0,813 lnc-GALNTL5-3:l SEQ0693 0,03324 0,759 0,757 lnc-VPS8-2:4 SEQ0852 0,03872 0,742 0,75 lnc-VSTM2B- lnc-GCLC-l:13 SEQ0922 0,0449 0,597 0,743 SEQ0560 0,02418 0,771 0,771

5:12

lnc-GDPD5-6:l SEQ0153 0,00681 0,799 0,819 lnc-VWA5Bl-2:l SEQ0391 0,01727 1,479 0,215 lnc-GFHB-2:3 SEQ0797 0,03872 0,681 0,75 lnc-WDR4-2:6 SEQ1003 0,0449 0,929 0,743 lnc-GGH-3:l SEQ0798 0,03872 0,782 0,75 lnc-WDR63-6:2 SEQ0471 0,02049 0,655 0,778 lnc-GHR-l:l SEQ0003 0,0002 0,787 0,917 lnc-WDR70-7:6 SEQ1004 0,0449 0,929 0,743 lnc-GJCl-2:2 SEQ0799 0,03872 0,832 0,75 lnc-WDR7-ll:l SEQ0749 0,03324 1,151 0,243 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change

lnc-WDYHVl- lnc-GJCl-2:3 SEQ0800 0,03872 0,832 0,75 SEQ0561 0,02418 0,912 0,771

1:2

lnc-GLIPRl-3:2 SEQ0923 0,0449 0,923 0,743 lnc-WISPl-17:2 SEQ0135 0,00556 0,715 0,826 lnc-GLIPRlLl-2:3 SEQ0427 0,02049 1,26 0,222 lnc-WSBl-2:l SEQ0256 0,01004 1,293 0,194 lnc-GMDS-6:8 SEQ0694 0,03324 0,721 0,757 lnc-XCL2-4:l SEQ1005 0,0449 0,714 0,743 lnc-GNG5-8:l SEQ0695 0,03324 0,771 0,757 lnc-XXYLTl-5:l SEQ0221 0,00829 0,681 0,813 lnc-GOLGA4-4:7 SEQ0365 0,01727 0,855 0,785 lnc-YPEL5-5:l SEQ0750 0,03324 1,357 0,243 lnc-GOLGA6L6- lnc-ZC3H12D-

SEQ0069 0,00291 0,711 0,847 SEQ0392 0,01727 0,831 0,785 9:1 2:3

lnc-GOLGA8F-2:l SEQ0235 0,01004 0,756 0,806 lnc-ZC3H15-2:l SEQ0012 0,0005 0,765 0,896 lnc-GOLGA80-5:6 SEQ0428 0,02049 0,715 0,778 lnc-ZFAT-l:3 SEQ0562 0,02418 0,907 0,771 lnc-GPAT4-l:3 SEQ0316 0,01449 1,56 0,208 lnc-ZFAT-l:8 SEQ0393 0,01727 0,841 0,785 lnc-GPATCHll- lnc-ZFC3Hl-

SEQ0612 0,02842 0,703 0,764 SEQ0651 0,02842 0,789 0,764 1:1 16:2

lnc-GPATCH2L-

SEQ0317 0,01449 0,85 0,792 lnc-ZFP57-15:l SEQ0177 0,00701 0,402 0,826 2:1

lnc-GPC2-2:5 SEQ0518 0,02418 1,355 0,229 lnc-ZFP90-3:6 SEQ0563 0,02418 0,739 0,771 lnc-GPR157-6:l SEQ0924 0,0449 0,827 0,743 lnc-ZMYM2-7:l SEQ0853 0,03872 1,335 0,25 lnc-GPR161-4:l SEQ0070 0,00291 0,726 0,847 lnc-ZNF107-7:l SEQ0652 0,02842 0,735 0,764 lnc-GPR27-18:l SEQ0925 0,0449 0,774 0,743 lnc-ZNF124-l:2 SEQ0564 0,02418 0,827 0,771 lnc-GPR33-14:l SEQ0236 0,01004 0,828 0,806 lnc-ZNF189-2:l SEQ0751 0,03324 0,903 0,757 lnc-GPR37-l:l SEQ0926 0,0449 0,793 0,743 lnc-ZNF25-7:l SEQ0257 0,01004 1,37 0,194 lnc-GPR39-10:2 SEQ0154 0,00681 1,393 0,181 lnc-ZNF25-9:4 SEQ0298 0,01209 0,663 0,799 lnc-GPRC5A-4:l SEQ0613 0,02842 0,717 0,764 lnc-ZNF273-4:4 SEQ0092 0,00364 0,742 0,84 lnc-GPRC6A-2:l SEQ0927 0,0449 0,865 0,743 lnc-ZNF330-2:l SEQ0854 0,03872 0,833 0,75 lnc-ZNF33A- lnc-GPSMl-3:3 SEQ0928 0,0449 0,64 0,743 SEQ0049 0,00183 1,499 0,139

14:1

lnc-GRAMD2B-

SEQ0801 0,03872 0,879 0,75 lnc-ZNF33A-8:l SEQ1006 0,0449 1,115 0,257 4:1

lnc-ZNF33B- lnc-GRIPl-l:2 SEQ0429 0,02049 1,564 0,222 SEQ1007 0,0449 0,846 0,743

3:11

lnc-GRIPl-5:2 SEQ0519 0,02418 0,914 0,771 lnc-ZNF33B-6:l SEQ0394 0,01727 2,524 0,215 lnc-GRIPl-8:l SEQ0802 0,03872 1,339 0,25 lnc-ZNF33B-6:3 SEQ0299 0,01209 0,825 0,799 lnc-ZNF385C- lnc-GRMl-l:19 SEQ0929 0,0449 1,338 0,257 SEQ0472 0,02049 1,311 0,222

4:1

lnc-GRMl-l:31 SEQ0155 0,00681 0,864 0,819 lnc-ZNF423-3:3 SEQ1008 0,0449 1,258 0,257 lnc-GRMl-l:32 SEQ0156 0,00681 0,864 0,819 lnc-ZNF430-3:l SEQ0855 0,03872 0,797 0,75 lnc-GRM8-2:2 SEQ0430 0,02049 0,902 0,778 lnc-ZNF430-3:4 SEQ0258 0,01004 0,788 0,806 lnc-GSN-2:4 SEQ0930 0,0449 0,94 0,743 lnc-ZNF442-l:2 SEQ0061 0,00232 0,683 0,854 lnc-GSN-2:5 SEQ0803 0,03872 0,955 0,75 lnc-ZNF460-2:l SEQ0473 0,02049 0,842 0,778 lnc-GTDCl-28:5 SEQ0157 0,00681 0,725 0,819 lnc-ZNF506-5:2 SEQ0108 0,00451 0,684 0,833

Inc-GUCY1A3-I:l SEQ0520 0,02418 0,724 0,771 lnc-ZNF544-2:l SEQ0474 0,02049 0,805 0,778 lnc-GUSB-l:l SEQ0931 0,0449 0,829 0,743 lnc-ZNF573-2:2 SEQ0856 0,03872 0,823 0,75 lnc-HECA-3:13 SEQ0237 0,01004 0,8 0,806 lnc-ZNF624-4:3 SEQ0395 0,01727 1,241 0,215 lnc-HECA-6:l SEQ0007 0,00037 0,723 0,903 lnc-ZNF654-3:l SEQ0857 0,03872 0,869 0,75 lnc-HELT-6:l SEQ0932 0,0449 0,784 0,743 lnc-ZNF720-5:3 SEQ0222 0,00829 1,313 0,188 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change lnc-HHATL-2:l SEQ0933 0,0449 1,127 0,257 lnc-ZNF724-6:l SEQ0176 0,00681 0,729 0,819 lnc-HHLA2-2:l SEQ0696 0,03324 1,238 0,243 lnc-ZNF726-l:3 SEQ0259 0,01004 2,115 0,194 lnc-HJURP-7:l SEQ0431 0,02049 0,776 0,778 lnc-ZNF827-2:l SEQ0653 0,02842 0,924 0,764 lnc-HLCS-5:l SEQ0238 0,01004 0,866 0,806 lnc-ZNF99-3:2 SEQ0396 0,01727 0,751 0,785 lnc-HMG20A-l:2 SEQ0432 0,02049 0,513 0,778 lnc-ZRANB2-2:l SEQ0654 0,02842 0,801 0,764 lnc-ZSWIM2- lnc-HMGAl-2:3 SEQ0804 0,03872 1,288 0,25 SEQ0655 0,02842 0,696 0,764

15:1

lnc-HMGNl-2:l SEQ0433 0,02049 1,424 0,222 lnc-ZWINT-2:4 SEQ0565 0,02418 0,637 0,771 lnc-HOMEZ-4:l SEQ0434 0,02049 0,848 0,778 LRP4-AS1:5 SEQ0766 0,03872 1,288 0,25 lnc-HOXC4-l:3 SEQ0805 0,03872 0,845 0,75 LYRM4-AS1:17 SEQ0485 0,02418 0,893 0,771 lnc-HS3ST3Al-

SEQ0239 0,01004 0,75 0,806 MAPT-AS1:1 SEQ0767 0,03872 0,785 0,75 1:1

lnc-HS6STl-8:l SEQ0697 0,03324 1,344 0,243 MCPH1-AS1:2 SEQ0308 0,01449 1,321 0,208 lnc-HSCB-7:l SEQ0614 0,02842 0,742 0,764 MDC1-AS1:5 SEQ0486 0,02418 1,248 0,229 lnc-HSD17Bll-

SEQ0276 0,01209 0,792 0,799 MEF2C-AS1:25 SEQ0582 0,02842 0,907 0,764 2:1

lnc-IFRD2-6:l SEQ0806 0,03872 1,31 0,25 MIR155HG:7 SEQ0876 0,0449 0,606 0,743 lnc-IFT80-8:l SEQ0934 0,0449 0,916 0,743 MIR29B2CHG:33 SEQ0583 0,02842 0,624 0,764 lnc-IL6-8:4 SEQ0318 0,01449 0,77 0,792 MIR29B2CHG:46 SEQ0181 0,00829 1,522 0,188 lnc-IP05-7:l SEQ0521 0,02418 0,869 0,771 MIR31HG:9 SEQ0768 0,03872 0,835 0,75 lnc-IQCF6-2:3 SEQ0807 0,03872 2,026 0,25 MIR4290HG:1 SEQ0405 0,02049 0,773 0,778 lnc-IRF2BP2-ll:3 SEQ0103 0,00451 1,403 0,167 MIR9-3HG:29 SEQ0769 0,03872 0,557 0,75 lnc-IRF6-l:l SEQ0808 0,03872 0,894 0,75 MIR99AHG:104 SEQ0096 0,00451 0,871 0,833 lnc-IRSl-2:5 SEQ0615 0,02842 1,878 0,236 MIR99AHG:37 SEQ0584 0,02842 0,879 0,764 lnc-IRSl-6:l SEQ0037 0,00143 0,804 0,868 MIR99AHG:46 SEQ0877 0,0449 2,516 0,257 lnc-IRSl-7:3 SEQ0522 0,02418 1,153 0,229 NAV2-AS5:1 SEQ0051 0,00232 1,302 0,146 lnc-IRX2-10:l SEQ0698 0,03324 0,744 0,757 NAV2-AS5:2 SEQ0052 0,00232 1,302 0,146 lnc-ITGB8-2:8 SEQ0616 0,02842 0,805 0,764 NBAT1:11 SEQ0347 0,01727 1,738 0,215 lnc-JMJD4-2:l SEQ0435 0,02049 0,736 0,778 NIFK-AS1:25 SEQ0487 0,02418 0,846 0,771

NUTM2A- lnc-JRK-2:l SEQ0935 0,0449 1,162 0,257 SEQ0182 0,00829 0,633 0,813

AS1:49

NUTM2B- lnc-JRK-2:2 SEQ0699 0,03324 0,764 0,757 SEQ0348 0,01727 0,74 0,785

AS1:40

NUTM2B- lnc-KAT5-2:l SEQ0936 0,0449 0,769 0,743 SEQ0004 0,00027 0,655 0,91

AS1:53

lnc-KBTBD7-l:l SEQ0523 0,02418 0,742 0,771 PACRG-AS3:1 SEQ0666 0,03324 1,162 0,243 lnc-KBTBD8-4:3 SEQ0366 0,01727 1,457 0,215 PCBP1-AS1:250 SEQ0770 0,03872 1,266 0,25 lnc-KCNAl-l:23 SEQ0122 0,00556 0,633 0,826 PITRM1-AS1:10 SEQ0488 0,02418 1,274 0,229 lnc-KCND3-l:l SEQ0085 0,00364 0,808 0,84 PTPRG-AS1:14 SEQ0183 0,00829 0,625 0,813

Inc-KCNE1B-I5:l SEQ0319 0,01449 0,823 0,792 PURPL: 13 SEQ0585 0,02842 1,386 0,236 lnc-KCNS3-9:l SEQ0320 0,01449 1,265 0,208 RAPGEF4-AS1:1 SEQ0878 0,0449 0,824 0,743 lnc-KCTD13-3:l SEQ0700 0,03324 0,709 0,757 RFX3-AS1:22 SEQ0586 0,02842 0,819 0,764 lnc-KCTD19-l:l SEQ0701 0,03324 0,761 0,757 RORB-ASl:6 SEQ0116 0,00556 0,845 0,826 lnc-KDM3A-l:4 SEQ0104 0,00451 0,731 0,833 SCHLAP1:9 SEQ0144 0,00681 0,8 0,819 lnc-KDM8-3:l SEQ0436 0,02049 0,758 0,778 SEC24B-AS1:19 SEQ0406 0,02049 1,852 0,222 Fold Fold

IncRNA SEQ p value AUC IncRNA SEQ p value AUC change change lnc-KIAA0141-3:l SEQ0702 0,03324 1,15 0,243 SLC16A12-AS1:1 SEQ0771 0,03872 0,773 0,75 lnc-KIF21B-2:l SEQ0937 0,0449 0,873 0,743 SMILR:3 SEQ0040 0,00183 1,306 0,139 lnc-KIN-5:l SEQ0703 0,03324 0,835 0,757 SNCA-AS1:3 SEQ0041 0,00183 0,777 0,861 lnc-KLFll-l:8 SEQ0704 0,03324 0,286 0,757 SPATA41:13 SEQ0879 0,0449 1,607 0,257 lnc-KLF12-4:l SEQ0938 0,0449 0,806 0,743 SRGAP3-AS2:10 SEQ0349 0,01727 0,674 0,785 lnc-KLF12-7:l SEQ0240 0,01004 0,88 0,806 SUCLG2-AS1:14 SEQ0489 0,02418 0,92 0,771 lnc-KLHL24-2:l SEQ0321 0,01449 1,206 0,208 SUCLG2-AS1:9 SEQ0490 0,02418 0,92 0,771 lnc-KLK2-4:3 SEQ0524 0,02418 0,881 0,771 TCL6:24 SEQ0066 0,00291 1,448 0,153

TM4SF19- lnc-KLRGl-8:l SEQ0809 0,03872 1,222 0,25 SEQ0184 0,00829 1,84 0,188

AS1:10

lnc-KREMEN2- TM4SF19-

SEQ0705 0,03324 1,279 0,243 SEQ0491 0,02418 0,853 0,771 1:1 AS1:15

TMEM9B- lnc-KRRl-4:7 SEQ0706 0,03324 0,801 0,757 SEQ0185 0,00829 3,049 0,188

AS1:11

lnc-KY-4:l SEQ0123 0,00556 0,708 0,826 TPT1-AS1:26 SEQ0186 0,00829 0,763 0,813 lnc-L3MBTL2-l:l SEQ0367 0,01727 1,259 0,215 TRIM52-AS1:24 SEQ0492 0,02418 1,183 0,229

Inc-LARP1B-I:15 SEQ0277 0,01209 0,786 0,799 TRIM52-AS1:8 SEQ0587 0,02842 1,272 0,236

Inc-LARP1B-I:17 SEQ0278 0,01209 0,786 0,799 TTN-AS1:79 SEQ0187 0,00829 0,829 0,813

Inc-LARP1B-I:18 SEQ0279 0,01209 1,214 0,201 TTN-AS1:80 SEQ0188 0,00829 0,829 0,813 lnc-LARP4-6:l SEQ0368 0,01727 0,803 0,785 UCA1:7 SEQ0078 0,00364 0,697 0,84 lnc-LBH-4:l SEQ0707 0,03324 1,312 0,243 UGDH-AS1:10 SEQ0407 0,02049 0,541 0,778 lnc-LBXl-l:l SEQ0241 0,01004 0,788 0,806 WDR86-AS1:15 SEQ0588 0,02842 1,212 0,236 lnc-LEPROTLl-

SEQ0011 0,0005 0,749 0,896 WEE2-AS1:23 SEQ0408 0,02049 0,704 0,778 12:1

lnc-LIMS3-l:10 SEQ0086 0,00364 2,27 0,16 WT1-AS:9 SEQ0117 0,00556 0,542 0,826 lnc-LINC00675-

SEQ0810 0,03872 0,669 0,75 YEATS2-AS1:3 SEQ0772 0,03872 0,806 0,75 1:3

lnc-LINSl-2:l SEQ0811 0,03872 0,81 0,75 ZNF528-AS1:1 SEQ0773 0,03872 0,636 0,75 lnc-LMBRDl-5:17 SEQ0812 0,03872 0,574 0,75 ZNF529-AS1:21 SEQ0880 0,0449 0,819 0,743

Out of the 1008 IncRNAs differentially expressed with a statistical significance (p value <0.05), 33 IncRNAs showed a fold change of >2 or <0.5 and are shown in the Table 6.

5 Table 6: Mean +/-SD of the 33 IncRNAs that shows a differential expression with both a statistically significance (p<0.05, Wilcoxon test) and a fold change > 2 or <0.5

IncRNA p value Fold IncRNA p value Fold change change lnc-QRFP-5:l 0.00066 2.170 MIR99AHG:46 0.04490 2.516 lnc-C21orf58-l:2 0.00143 2.231 lnc-CPM-2:ll 0.04490 0.424

ARRDC3-AS1:7 0.00232 0.441 lnc-ERFE-l:l 0.04490 0.489 lnc-CRYBBl-l:l 0.00232 0.487 lnc-LRRC3B-l:3 0.04490 0.463 lnc-LIMS3-l:10 0.00364 2.270 LINC02177:8 0.03872 0.494 lnc-MARCH4-2:7 0.00364 2.000 lnc-KLFll-l:8 0.03324 0.286 lnc-SUGTl-3:l 0.00556 2.047 lnc-SNCA-3:l 0.02418 0.498

DARS-AS1:47 0.00829 2.015 lnc-UNCX-3:26 0.02049 0.493

TMEM9B-AS1:11 0.00829 3.049 BLACAT1:5 0.01727 0.424 lnc-ZNF726-l:3 0.01004 2.115 Inc-CHD1L-5:13 0.01449 0.370 lnc-SLC39All-10 : ll 0.01209 4.380 lnc-EDDM13-5: ll 0.01004 0.372 lnc-PSMBl-6:4 0.01449 2.134 lnc-EEFlAKMTl-3:6 0.00829 0.423

DPH6-AS1 :3 0.01727 2.004 FLVCR1-AS1 : 13 0.00681 0.413 lnc-ZNF33B-6: l 0.01727 2.524 lnc-NAXD-6:5 0.00556 0.479 lnc-PAPPA-l:3 0.03324 2.192 lnc-ZFP57-15 : l 0.00701 0.402 lnc-TENM4-4: l 0.03324 2.209 lnc-TPPP-l :2 0.00014 0.158 lnc-IQCF6-2:3 0.03872 2.026

Out of the 1008 IncRNAs differentially expressed with a statistical significance (p value <0.05), 60 IncRNAs showed an AUC of >0.85 and are shown in Table 7.

Table 7: 60 IncRNAs with a p value <0.05 and an individual AUC value AUC

The results from the predictive modelling based on the random forest algorithm to discriminate between AD patient and healthy control populations when using the selected miRNAs with a p value <0.05 comprising signatures of 2 or more miRNAs showed a good accuracy. For example, the signature comprising three miRNAs, miR1220.3p, miR378d and miR99a.5p, enabled a good AUC of 0.839 and an accuracy of 81.8%. The predictive modelling based on the random forest algorithm to discriminate between AD patient and healthy control populations when using the total of the 19867 IncRNAs analyzed, enabled to show that the AUC in function of the number of IncRNA reached a plateau with the following 13 IncRNAs. These 13 IncRNAs were used to construct the model. The results show that this IncRNA signature enabled a discrimination between the 2 populations (mild AD patient and healthy control populations) with an AUC value = 0.993, an accuracy = 95.8%, sensitivity = 100% and specificity = 91.7%.

Out of the 1008 IncRNAs differentially expressed with a statistical significance (p value <0.05), the 90 IncRNAs with a fold change of > 2 (or < 0.5) or an AUC of > 0.85 (or < 0.15) were used for the predictive modelling based on the random forest algorithm. The results show that with the following 7 top ranked candidates enabled a full discrimination between AD and HV groups, with an AUC value of 100% as well as 100% accuracy, 100% sensitivity and 100% specificity.

When applying another filter considering a list of the IncRNAs which have a fold change of > 1.6 and an AUC of > 0.85, the use of random forest algorithm enabled to select the following 12-top ranked IncRNAs to construct the model and this enabled to discriminate between the AD and HV populations with still an excellent AUC=0.979, excellent accuracy of 95.8% sensitivity of 91.7% and specificity of 100%.

lnc-CRYBBl-l:l 6

lnc-MGST3-l:3 7

lnc-FAM49B-8:l 8

HAND2-AS1:70 9

lnc-TMEM185B-12:7 10

lnc-CNDPl-7:l 11

lnc-C21orf58-l:2 12

Further to select the best IncRNA candidates, modeling using Random Forest algorithm was applied to a specific set of IncRNA candidates having both a statistically significant differential expression in AD patients versus healthy control populations and a good correlation (Pearson R coefficient) with scores of neurocognitive tests including MMSE and/or MOCA out of 7 neuropsychological tests performed (Table 8) : The results show that the signature of the following 3 top ranked IncRNAs enabled an excellent discrimination between AD patients and healthy control populations with AUC=0.953, accuracy, sensitivity and specificity of 91.7%, 91.7% and 91.7%.

Table 8: IncRNAs of the present invention that correlate with neurocognitive tests, MoCA, MMSE. R: correlation coefficient (Pearson). HV: healthy control group, AD: mild to moderate AD group

Cognitive test IncRNA R.HV+AD R_HV R_AD p value AUC

MoCA LINC00839:18 -0,80 0,29 -0,78 0,00556 0,83

MoCA LINC01087:1 -0,74 -0,13 -0,89 0,00681 0,82

MMSE LINC01087:1 -0,68 0,05 -0,75 0,00681 0,82

MMSE lnc-ANAPCll-2:6 0,65 0,26 0,77 0,04490 0,26

MoCA lnc-ANAPCll-2:6 0,59 -0,46 0,77 0,04490 0,26

MoCA lnc-ARHGAP26-4:33 -0,64 -0,66 -0,72 0,03872 0,75

MoCA lnc-CCDC197-2:l -0,14 -0,42 0,74 0,00291 0,85

MMSE lnc-CFAP36-3:2 -0,62 0,05 -0,75 0,03872 0,75

MoCA lnc-DHX38-25:l -0,69 -0,60 -0,77 0,01004 0,81

MMSE lnc-DHX38-25:l -0,65 -0,57 -0,71 0,01004 0,81

MoCA lnc-EZH2-3:l -0,69 0,18 -0,78 0,01209 0,80

MMSE lnc-GNG5-8:l -0,72 0,32 -0,71 0,03324 0,76

MoCA lnc-GPRC5A-4:l -0,65 -0,02 -0,74 0,02842 0,76

MMSE lnc-GRAMD2B-4:l -0,59 0,42 -0,72 0,03872 0,75

MMSE lnc-GRIPl-8:l -0,03 0,30 -0,73 0,03872 0,25

MoCA lnc-LYN-8:l 0,13 0,54 -0,71 0,02842 0,24

MMSE lnc-MAP9-6:l -0,73 -0,08 -0,74 0,01209 0,80

MMSE lnc-NRPl-4:l -0,11 0,27 -0,72 0,04490 0,26

MoCA lnc-RBFOXl-2:l 0,02 0,13 -0,75 0,01727 0,22

MoCA lnc-RPL37-2:l 0,62 -0,16 0,75 0,00829 0,19

MMSE lnc-RUBCN-l:l 0,11 0,33 0,72 0,03872 0,75

MMSE lnc-SNCA-3:l 0,08 0,12 0,76 0,02418 0,77

MMSE lnc-SRSF2-2:5 0,10 0,04 0,75 0,03324 0,76

MoCA lnc-SRSF2-2:5 -0,05 -0,32 0,73 0,03324 0,76

MMSE lnc-TMEM185B-12:7 0,06 -0,14 -0,73 0,00232 0,15

MoCA lnc-TRIB2-14:l -0,76 -0,45 -0,71 0,00066 0,89 MMSE lnc-TRIM43B-l:2 -0,74 -0,11 -0,76 0,01727 0,78

MoCA lnc-TRIM43B-l:2 -0,72 -0,12 -0,74 0,01727 0,78

MMSE lnc-ZNF189-2:l -0,70 -0,35 -0,74 0,03324 0,76

MoCA lnc-ZNF189-2:l -0,67 0,03 -0,76 0,03324 0,76

MMSE lnc-ZRANB2-2:l -0,65 -0,36 -0,82 0,02842 0,76

MoCA lnc-ZRANB2-2:l -0,59 0,16 -0,80 0,02842 0,76

Modeling using Random Forest algorithm was also applied to a specific set of IncRNA candidates from Table 9 having both a statistically significant differential expression in AD patients versus healthy control populations and a good correlation (Pearson) with neuroimaging scores (volume of brain structures of relevance for cognition and memory such as the mediotemporal area, left and right hippocampus, left and right amygdala, entorhinal cortex out of more than 120 structures measured): The results show that the signature of the following 7 top ranked IncRNAs enabled an excellent discrimination between AD patients and healthy control population with AUC=0.993, accuracy=95.8%, sensitivity=91.7% and specificity=100%.

Table 9: IncRNAs of the present invention that correlate with MRI (volumes of the brain structures). R: correlation coefficient (Pearson). HV: healthy control group, AD: mild to moderate AD group.

R_ R_ R_

MRI IncRNA HV+AD HV AD p value AUC

LeftAmygdala CYTOR:18 0,71 0,30 0,69 0,00291 0,15

LeftHippocampus DLGAP2-AS1:18 -0,71 -0,51 -0,76 0,03324 0,76

LeftHippocampus LINC00458:19 -0,79 -0,48 -0,78 0,02842 0,76

Mediotemporal LINC00458:19 -0,72 -0,48 -0,53 0,02842 0,76

LeftHippocampus LINC00938:6 -0,82 -0,48 -0,76 0,01209 0,80

Mediotemporal LINC00938:6 -0,76 -0,63 -0,46 0,01209 0,80

RightHippocampus LINC00938:6 -0,75 -0,56 -0,48 0,01209 0,80

LeftAmygdala LINC01629:11 -0,78 -0,53 -0,83 0,02842 0,76

LeftHippocampus lnc-AIGl-5:l -0,78 -0,64 -0,73 0,02418 0,77

LeftHippocampus lnc-AKRlDl-5:2 -0,74 -0,47 -0,69 0,00451 0,83

RightHippocampus lnc-AKRlDl-5:2 -0,71 -0,27 -0,73 0,00451 0,83

Mediotemporal lnc-AKRlDl-5:2 -0,71 -0,26 -0,72 0,00451 0,83

RightAmygdala lnc-C3orf58-7:l -0,73 -0,22 -0,69 0,02842 0,76

LeftHippocampus lnc-C5orf30-10:2 -0,73 -0,58 -0,58 0,02842 0,76

RightAmygdala lnc-CASP9-l:l 0,73 0,26 0,81 0,03872 0,25

RightHippocampus lnc-CHNl-5: ll -0,72 -0,44 -0,67 0,02049 0,78

RightAmygdala lnc-CLVS2-2:5 -0,74 -0,39 -0,75 0,01727 0,78

LeftHippocampus lnc-DAZAP2-3:l -0,72 -0,53 -0,58 0,00232 0,85 Mediotemporal lnc-DTWD2-14:l -0,75 -0,49 -0,79 0 01727 0 78

LeftAmygdala lnc-DTWD2-14:l -0,74 -0,46 -0,82 0 01727 0 78 lh_entorhinal lnc-DTWD2-14:l -0,74 -0,59 -0,61 0 01727 0 78

RightHippocampus lnc-ELFN2-l:3 -0,77 -0,26 -0,76 0 00364 0 84

LeftAmygdala lnc-ELFN2-l:3 -0,74 -0,27 -0,81 0 00364 0 84

Mediotemporal lnc-ELFN2-l:3 -0,74 -0,22 -0,73 0 00364 0 84

RightAmygdala lnc-ELFN2-l:3 -0,74 -0,37 -0,61 0 00364 0 84

LeftHippocampus lnc-FAM171B-l:6 -0,72 -0,28 -0,62 0 00829 0 81

LeftHippocampus lnc-FGD4-8:l -0,74 -0,49 -0,61 0 01004 0 81

Left.Amygdala lnc-FGD4-9:l -0,76 -0,49 -0,56 0 00364 0 84

RightHippocampus lnc-FGD4-9:l -0,74 -0,19 -0,64 0 00364 0 84

Mediotemporal lnc-FGD4-9:l -0,72 -0,28 -0,54 0 00364 0 84

LeftHippocampus lnc-FGD4-9:l -0,71 -0,33 -0,52 0 00364 0 84 lh_entorhinal lnc-GPR39-10:2 0,77 0,68 0,49 0 00681 0 18

Left.Amygdala lnc-GPR39-10:2 0,72 0,54 0,37 0 00681 0 18

LeftHippocampus lnc-GRMl-l:31 -0,76 -0,39 -0,72 0 00681 0 82

RightHippocampus lnc-GRMl-l:31 -0,71 -0,38 -0,56 0 00681 0 82

LeftHippocampus lnc-GRMl-l:32 -0,76 -0,39 -0,72 0 00681 0 82

RightHippocampus lnc-GRMl-l:32 -0,71 -0,38 -0,56 0 00681 0 82

RightHippocampus lnc-HECA-3:13 -0,72 -0,31 -0,67 0 01004 0 81

RightAmygdala lnc-HJURP-7:l -0,77 -0,46 -0,77 0 02049 0 78

LeftHippocampus lnc-HJURP-7:l -0,73 -0,33 -0,73 0 02049 0 78

RightHippocampus lnc-HJURP-7:l -0,73 -0,02 -0,93 0 02049 0 78

Mediotemporal lnc-HJURP-7:l -0,72 -0,20 -0,82 0 02049 0 78

LeftAmygdala lnc-IRSl-6:l -0,75 -0,35 -0,72 0 00143 0 87

RightHippocampus lnc-IRSl-6:l -0,73 -0,36 -0,56 0 00143 0 87

Mediotemporal lnc-IRSl-6:l -0,71 -0,34 -0,51 0 00143 0 87

RightHippocampus lnc-KDM3A-l:4 -0,81 -0,35 -0,80 0 00451 0 83

Mediotemporal lnc-KDM3A-l:4 -0,77 -0,36 -0,70 0 00451 0 83

LeftHippocampus lnc-KDM3A-l:4 -0,72 -0,26 -0,61 0 00451 0 83

LeftHippocampus Inc-LARP1B-I:15 -0,75 -0,56 -0,75 0 01209 0 80

RightAmygdala Inc-LARP1B-I:15 -0,75 -0,63 -0,71 0 01209 0 80

LeftHippocampus Inc-LARP1B-I:17 -0,75 -0,56 -0,75 0 01209 0 80

RightAmygdala Inc-LARP1B-I:17 -0,75 -0,63 -0,71 0 01209 0 80

RightAmygdala lnc-MAMDC2-l:l -0,75 -0,72 -0,70 0 02418 0 77

LeftHippocampus lnc-MAP9-6:l -0,79 -0,43 -0,87 0 01209 0 80

RightHippocampus lnc-NAALADL2-8:l -0,71 -0,28 -0,72 0 01449 0 79

RightHippocampus lnc-NBPF14-l:2 -0,80 -0,38 -0,82 0 00364 0 84

Mediotemporal lnc-NBPF14-l:2 -0,75 -0,37 -0,67 0 00364 0 84

RightAmygdala lnc-NBPF14-l:2 -0,73 -0,40 -0,60 0 00364 0 84

LeftHippocampus lnc-NBPF14-l:2 -0,72 -0,48 -0,52 0 00364 0 84

RightHippocampus lnc-PIGB-l:5 -0,73 -0,26 -0,79 0 02418 0 77

RightAmygdala lnc-PLN-2:l -0,77 -0,66 -0,72 0 03324 0 76

Mediotemporal lnc-PLN-2:l -0,76 -0,66 -0,67 0 03324 0 76

LeftHippocampus lnc-PLN-2:l -0,75 -0,67 -0,64 0 03324 0 76

RightHippocampus lnc-PLN-2:l -0,73 -0,54 -0,68 0 03324 0 76

LeftHippocampus lnc-PTPN4-l:l -0,74 -0,59 -0,69 0 02842 0 76

RightAmygdala lnc-SLC38A2-l:ll -0,76 -0,73 -0,57 0 01004 0 81

LeftAmygdala lnc-SLC38A2-l:ll -0,73 -0,57 -0,38 0 01004 0 81 RightAmygdala lnc-SMARCA5-4:18 -0,72 -0,35 -0,78 0,01004 0,81

Mediotemporal lnc-SMARCA5-4:18 -0,72 -0,29 -0,84 0,01004 0,81

LeftAmygdala lnc-SMARCA5-4:18 -0,71 -0,30 -0,92 0,01004 0,81 lh_entorhinal lnc-TMEM242-6:l -0,77 -0,64 -0,78 0,02049 0,78

Mediotemporal lnc-TMEM242-6:l -0,72 -0,62 -0,63 0,02049 0,78

RightHippocampus lnc-TPPP-l:2 -0,73 -0,03 -0,42 0,00014 0,92

LeftHippocampus lnc-TRIM43B-l:2 -0,75 -0,47 -0,72 0,01727 0,78 lh_entorhinal lnc-TRMTll-4:l -0,80 -0,86 -0,30 0,00829 0,81

Mediotemporal lnc-TRMTll-4:l -0,74 -0,73 -0,30 0,00829 0,81

Left.Amygdala lnc-TRMTll-4:l -0,73 -0,58 -0,35 0,00829 0,81

LeftHippocampus lnc-USP53-l:l -0,75 -0,24 -0,84 0,01727 0,78

LeftHippocampus lnc-ZNF33B-6:3 -0,78 -0,40 -0,81 0,01209 0,80

RightAmygdala lnc-ZNF33B-6:3 -0,74 -0,47 -0,65 0,01209 0,80

RightAmygdala MIR29B2CHG:46 0,74 0,35 0,82 0,00829 0,19 rh_entorhinal MIR29B2CHG:46 0,71 0,45 0,80 0,00829 0,19

Right.Amygdala PCBP1-AS1:250 0,73 0,20 0,71 0,03872 0,25

Mediotemporal PCBP1-AS1:250 0,71 0,19 0,70 0,03872 0,25

Modeling using Random Forest algorithm was also applied to a specific set of IncRNA candidates from Table 10 having both a statistically significant differential expression in AD patients versus healthy control populations and a good correlation (Pearson) with level of the CSF biomarkers of high relevance to AD pathology: Αβ42, tau or phosphorylated-tau: The results show that the signature of the following 18 top ranked IncRNAs enabled an excellent discrimination between AD patients and healthy control population with AUC=0.972 accuracy=0,917 sensitivity =0.83 and specificity=l.

Table 10: IncRNAs of the present invention that correlate with CSF biomarkers

MATERIALS AND METHODS

To identify miRNA in plasma or serum or whole blood samples of human subjects, miRNA profiling of 2083 miRNAs was performed using HTG EdgeSeq miRNA whole transcriptome V2 targeted sequencing assay (HTG WTA V2, HTG Molecular, Tuscon, United States). This technology is based on nuclease protection targeted RNA sequencing assay that uses an extraction free lysis process followed by a nuclease protection assay (NPA) to prepare a stoichiometric library of nuclease protection probes (NPP) for measurement.

To identify IncRNAs in serum, plasma or whole blood samples of human subjects, circulating total RNA was first extracted and sequencing libraries were prepared by removal of ribosomic RNA (RiboZero TruSeq library preparation kit, Illumina Inc. San Diego, USA) and sequenced on Illumina NextSeq500 with 2x75 bp read length. ncRNA (miRNA and IncRNA) were also measured by qPCR using specific primers. For this, circulating total RNA was first extracted, reverse transcription and real time PCR using specific primers were performed. Using FiMAP, a proprietary platform based on hybridization of PCR products on coated microdiscs with complementary oligonucleotide coupled to detection probes. Total RNA was extracted followed by reverse transcription and PCR step allowing multiplexing of several targets using specific primers to the ncRNAs. Quantification was therefore performed on the FiMAP platform using coded microdiscs specific of each ncRNA.

Patient population and samples

Serum or plasma samples were prepared from blood samples collected in lithium-heparin tubes and whole blood samples were collected in Paxgene RNA tubes. Samples were from healthy volunteers (HV), donors at the "Etablissement Francais du Sang" (EFS) of Mulhouse, France, and from cognitively intact healthy control subjects and patients with mild cognitive impairment or with different stages of Alzheimer's disease (AD) recruited according to the protocols of the Amoneta Diagnostics sponsored prospective studies registered to the Agence Nationale de Securite du Medicament et des Produits de Sante (ANSM) including ADKIT study under the ID RCB: 2015-A00118-41 on Jan 22, 2015, the chronobiological study under the ID RCB: 2016-A200227-44 on Feb 4, 2016.

Method for miRNAs

HTG EdgeSeq run

HTG whole transcriptome miRNA (WTA) kit was used (HTG WTA V2, HTG Molecular, Tuscon, United States). Samples were prepared accordingly to the following protocol: 15 μΐ of plasma lysis buffer and 15 μΐ of plasma sample and 3 μΐ of Proteinase K are mixed and incubated at 50°C for 60 min with orbital shaking. 25 μΐ of the mix is transferred to the HTG sample plate and loaded into the HTG processor to perform the nuclease protection assay and prepare the stoichiometric NPP. Molecular Barcoding and Adapter Addition

For plasma samples, barcoding is performed using Hemo KlenTaq (M0332S, NEB, Evry, France) enzyme. For each sample, we mix 2.4 μΐ of Hemo KlenTaq, 0.6 μΐ of dNTPs (10 nM) (NEB, N0447S), 6 μΐ of OneTaq PCR GC Buffer 5X (B9023S, NEB, Evry, France), 3 μΐ of Forward and Reverse Primers (HTG WTA, HTG Molecular, Tuscon, United States), 3 μΐ of sample preparation and 12 μΐ of H20. PCR step was performed on ABI 2720 Thermocycler using the following cycling profile: 95°C for 4 min followed by 16 cycles of 95°C for 15 sec, 55°C for 45 sec and 68°C for 45 sec. Protocol is ended with 68°C for 10 min.

PCR clean up In order to remove excess of primer from the library, Agentcour AMPure XP beads (A63880, Beckmancoulter, Villepinte, France) were used. For each sample, 37.5 μΐ of AMPure XP beads are used in combination with 15 μΐ of the PCR product. After mixing 10 times with pipette, the solution is incubated for 5 min at room temperature and then placed on the magnetic stand. After 2 minutes to separate beads, the cleared solution is carefully removed without disturbing the beads. Beads are washed 2 times with 200 μΐ of Ethanol 80%. Elution of PCR product link to the beads is performed with 25 μΐ of H20. The purified solution of PCR product is place in a new tube while the plate is on the magnetic stand to separate PCR products and beads. Determination of library concentration:

To determine concentration of library for each sample, Kapa Biosystems qPCR Kit (KK4824, Cliniscience, Nanterre, France) is used. For each reaction, the mix is composed of 12 μΐ of Master mix, 0.4 μΐ of ROX dye, 3.6 μΐ of H20 and 4 μΐ of template (standards or library diluted at 1/10000). The samples were run on a ABI PRISM 7900HT (High ROX) will the following cycles: 95°C for 5 min, 35 cycles of 95°C for 30 sec and 65°C for 45°C with data collection followed by a dissociation curve. Standards are corresponding to an amplicon of 452 bp whereas the amplicon of NPP with barcode correspond to 115 bp. The ratio is applied to determine the concentration of each library.

Sample pooling and sequencing

Each sample are pooled in order to generate a pooled library at 4 nM. From this pooled library, 5 μΐ where mixed with 0.2N NaOH freshly prepared and incubated for 2 min. The solution was vortexed briefly and centrifuged at 280g for 1 minute and mixed with 990 μΐ of pre-chiller HT1 buffer (Illumina NextSeq Reagent v2 kit, Illumina, Paris, France). 15% PhiX (PhiX control v3, Illumina, Paris, France) at 20 pM was prepared. 260 μΐ of prepared denatured library at 20 pM was mix with 39 μΐ of 20 pM PhiX and 1001 μΐ of HT1 Buffer was loaded into Illumina NextSeq500 Mid Output V2 150 cycles kit and sequenced.

Sequence Analysis

Data reconstruction and analysis was performed using directly FASTQ files from the Illumina Sequencer and processed by the HTG Parser software.

Statistics

The data are normalized using the method recommended by the supplier. This normalization is based on total count of sequences after filtering out all values below 70 (threshold recommended by the supplier). After normalization, all miRNAs with 75th percentile of this threshold are considered as noise and have been deleted.

In order to determine miRNAs that discriminate Alzheimer disease patients from healthy control subjects, the following statistical methods are applied to the normalized data: two-tailed Welch t test and computation fold change were performed on normalized data. Individual ROC Curve was performed for each miRNA detected in the samples. The random Forest algorithm with the best miRNA in term of AUROC was performed to establish a predictive model for the diagnosis of Alzheimer disease.

The final list of miRNAs of interest was chosen with p value < 0,05 or fold change > 1.49 or < 0.75 in Log2 and/or individual AUC determined by the selection of all the miRNAs that are at least in one of the three lists of selected biomarkers (tables 1, 2 and 3).

Method for IncRNA Samples sequencing

Ribonucleic acid (RNA) extraction is performed starting from 1.5ml of serum, using Norgen Serum/plasma extraction and RNA Clean-Up and Concentration Micro-Elute Kits according to the manufacturer's instructions.

Sequencing libraries are prepared from the total amount of extracted RNA, using the Illumina TruSeq stranded total RNA library preparation kit combined with the human/mouse/rat RiboZero rRNA removal kit (Illumina Inc. San Diego, USA, C). All steps are performed with the low-throughput protocol and according to the manufacturer's instructions, with no fragmentation step. Briefly, cytoplasmic ribosomal RNA (rRNA) are hybridized to biotinylated target-specific oligos and removed using streptavidin coated magnetic beads. rRNA depleted RNA samples are then reverse transcribed into complementary deoxyribonucleic acid (cDNA). To ensure strand specificity, single stranded cDNA is first synthetized using Super-Script II reverse transcriptase (Invitrogen) and random primers in the presence of Actinomycin D, and then converted to double stranded cDNA with the second strand marking mix that incorporates dUTP in place of dTTP. Resulting blunt ended cDNA are purified using AMPure XP magnetic beads. After a 3'end adenylation step, Illumina's adapters ligation is performed. So, obtained singled indexed libraries are washed twice using AMPure XP beads to remove excess adapters and enriched by PCR (15 cycles). PCR products are purified with a final AMPure XP beads wash and sequencing ready libraries are eluted in 30μ1 of resuspension buffer. For quality control, Ιμΐ of each library is run on the Agilent Technologies 2100 Bioanalyzer using a DNA 1000 chip according to the manufacturer's recommendations. Absence of adapter dimers is checked and the average library size is determined by a region table. Libraries are quantified on Qubit 2.0 using Qubit dsDNA High Sensitivity assay kit (Invitrogen). Library size previously determined on the Bioanalyzer is used to calculate molar concentrations from mass concentrations.

All libraries are sequenced with the Illumina NextSeq500 (2x75bp). Bioinformatic analysis

RNA-seq data analysis is performed using Partek Flow (Partek Inc., St Louis, MO, USA build 6). The pre-alignment QA/QC module of Partek Flow is used to visualize the read quality of the FASTQ files. All reads are examined. The raw FASTQ files are trimmed at the 3' end in function of their quality score (Phred score). The parameters used are an end minimum quality level of 30 and a minimum trimmed read length of 50. Unaligned reads are mapped using the Homo sapiens hgl9 genome. This mapping is done using the software STAR version 2.5.3 (Dobin, A., Davis, C.A., Schlesinger, F., Drenkow, J., Zaleski, C, Jha, S., Batut, P., Chaisson, M., and Gingeras, T.R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinforma. Oxf. Engl. 29, 15-21). The default parameters are used. The post-alignment QC module of Partek Flow was used to visualize the average base quality score per position as well as the mapping quality per alignment. The mapped reads were quantified using the GTF file with the patented IncRNA annotation for quantification using the Partek Expectation/Maximization (E/M) algorithm (Xing, Y., Yu, T., Wu, Y.N., Roy, M., Kim, J., and Lee, C. (2006). An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs. Nucleic Acids Res. 34, 3150-3160). The default parameters are used. The transcripts where their medians are under the median read density in intergenic regions are discarded for the next steps of the analysis. The transcript counts were normalized by CPM (counts per million). Only transcripts showing high expression (CPM≥ 10) in at least half the samples of one group are considered. Statistical analysis and predictive modelling

To determine differentially expressed IncRNA, a statistical analysis is performed using Wilcoxon Mann-Whitney parametric test. A IncRNA with a p value≤ 0.05 is considered as differentially expressed. In order to build classification models for the 2 classifications, the Classification for MicroArrays (CMA) package of R (Slawski M, Daumer M, Boulesteix AL. CMA: a comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 2008, 9:439) with a leave-one-out cross-validation has been used. The algorithms used for this predictive modelling are (a) random forest, (b) linear discriminant analysis and (c) naive Bayes (Breiman L. Random forests. Machine Learning, 45(1): 5-32, 2001).

The rank of the RNA candidate in a model was calculated by the mean rank of 10 candidates selections (per CV-fold). Per model (and RNA selection method) the AUC was plotted as a function of the number of RNAs in the model. The optimal number of RNAs per model was determined graphically. ROC curves and confusion matrices were generated to assess the predictive performance of our models. The values of AUC, accuracy, sensitivity, specificity, positive and negative predictive values are reported.

Additional method for quantification of miRNA and IncRNA Total RNA extraction

Total RNA was extracted using Paxgene Blood RNA kit (Qiagen, France) and Serum/Plasma mini kit (Norgen Biotek, Canada) respectively, according to manufacturer's instructions.

RNA quality was examined on an Agilent 2100 Bioanalyzer with the RNA 6000 Nano Kits (Agilent, France). RNA quantity was measured on Qubit 3.0 fluorometer using RNA High sensitivity kit (Fisher Scientific, France). cDNA synthesis Total RNA was used for miRNA and IncRNA cDNA synthesis as follows. For miRNA, 100 ng of RNA was reverse transcribed in a final volume of 10 μΐ including 1 μΐ of lOx poly (A) polymerase buffer, 0.1 mM of ATP, 1 μΜ of universal miRNA RT-primer, 0.1 mM of each deoxynucleotide (dATP, dCTP, dGTP and dTTP), 100 units of MuLV reverse transcriptase (New England Biolabs, USA) and 1 unit of poly(A) polymerase (New England Biolabs, USA) was incubated at 42°C for 1 hour followed by enzyme inactivation at 95°C for 5 minutes. The sequence of the RT-primer was 5'-CAGGTCCAGTTTT TTTTTTTTTTTVN, where V is A, C and G and N is A, C, G and T. The primer was purchased from IDT (Integrated DNA Technologies, Belgium).

For IncRNAs, Reverse transcription was performed using high capacity cDNA reverse transcription kit (Fisher scientific, France) according to the manufacturer instructions. qPCR quantification

For IncRNA, preamplification reactions were prepared using Applied Biosystems preamplification master mix with 0.1X (ΙΟΟηΜ) of each of the primers pairs corresponding to the IncRNAs. 16 preamplification cycles were performed as preconized by the furnisher (50°C 2 minutes, 96°C 10 minutes, 40 cycles at 95°C for 15 seconds and 60°C for 1 minute). For miRNA amplification, primers were designed using miRprimer tool (PMID: 24472427). Quantitative PCR of biological samples was done in 10 μΐ total volume with 1 μΐ of cDNA or preamplification product diluted 1/20 in TE buffer (for miRNA and IncRNA respectively), 5 μΐ of 2x Soadvanced SYBR green PCR master mix (Biorad, USA) and 250 nM of each primer. Cycling conditions were 95°C for 5-10 min followed by 40 cycles of 95°C for 10-30 sec and 60°C 30-60 sec. A melting curve analysis (60°C to 99°C) was performed after the thermal profile to ensure specificity in the amplification. Relative expression level was determined against a standard curve realized on a 5 log scale using CFX maestro Software (Biorad,USA).

FIMAP quantification

IncRNA and miRNAs of interest were also quantified using the FIMAP/QMAP platform developed by Quantamatrix (South Korea). Briefly, IncRNAs and miRNAs were amplified by PCR using primers with the same sequences as for qPCR that were chemically modified. Forward primers were phosphorylated in 5' and Reverse primers biotinylated in 5'. Multiplexed PCR reactions were performed on Biorad T100 thermocycler in 20μ1 reactions containing 2μ1 of miRNA or IncRNA cDNA, 250 nM of each primers and 10 μΐ of 2X One Taq Hot Start 2X master Mix (New England Biolabs, USA) with the following conditions: 30 seconds at 94°C, 25 cycles of 30s at 94°C, 1 min at 60°C and 1 min at 68°C, followed by a final extension at 68°C for 5 minutes. PCR products were then digested with 25U of lambda exonuclease for 30 minutes at 37°C to eliminate the phosphorylated strand and keep only the biotinylated ones. So digested products were quantified on QMAP using coded silica microdisks coated with oligos complementary to the RNAs of interest (one code per target oligo). Briefly, biotinylated PCR products were incubated with the coated microdisks in a 96 well plate, and hybridized products revealed by addition of a fluorescent streptavidin conjugate, SAPE (Prozyme, Denmark) after washing steps. Plates were imaged on QMAP that takes 2 images per spot: one dark image to quantify the fluorescence and 1 white light image to read the microdisks codes. Each target relative expression is then calculated by QMAP software by assigning fluorescence signal to each target according to the associated microdisk code.

RESULTS Profiling of 2083 miRNAs based on the miRbase v20 was performed on plasma samples from patients suffering of mild impairment or moderate Alzheimer disease and cognitively intact healthy subjects. After normalization of the results generated with HTG EdgeSeq technology, averages of 406 miRNAs par sample were positively identified. From these 406 miRNAs identified, statistical analysis was performed in order to identify miRNAs with predictive value of Alzheimer disease.

Random Forest algorithm was used for the classification model. From this, several sets of 2 to 20 miRNAs with high predictive value were identified and provide an AUC ranged from 0.839 to 0.793 with an accuracy over 0.77, specificity over 0.77 and sensitivity superior to 0.74. Figure 2 shows a predictive model combining 3 miRNAs with an AUC of 0.839.

A list of miRNAs useful for diagnosis of Alzheimer disease was generated. This list was determined using multiple statistical analyses: individual AUC, fold change and t-test for p value (Tables 2, 3 and 4). 74 miRNAs were selected from plasma lithium heparin samples, from the data set with a p value under 0.05. They were further analyzed based on fold change > 1.49 or < 0.75 and individual AUC >0,6 or <0,4. See Tables 2, 3 and 4.

Profiling 127,802 transcripts based on LNCipedia v5.2 was performed on serum samples from patients suffering of Alzheimer disease and cognitively intact healthy subjects using total-RNAseq technology. Above 127,802 transcripts, 19867 IncRNAs were identified with expression level of over 10 CPM in at least half the samples of one group.

Out of 19867 IncRNAs, 1008 IncRNAs useful for diagnosis of Alzheimer disease was selected based on the statistical significance (p value <0.05, Wilcoxon test). Random Forest algorithm was used for the classification model. From this, several sets of 2-20 IncRNAs with high predictive value were identified. The sets of selected 2-20 IncRNAs provide an AUC ranged from 0.7 and up to 1 with an accuracy, sensitivity and specificity ranged from 0.7 to 1.

Figures 3 to 12 show examples of results on a) the level of expression of individual IncRNA (Figure 3), b) the statistical significance (p value) to differentiate the 2 populations, schematized as a Volcano plot (Figure 4), c) predictive modelling using the Random Forest algorithm enabling identification of several sets of IncRNA signatures for diagnosis of Alzheimer (Figures 5-10) and d) correlation of the IncRNA level with neuroimaging or CSF biomarkers (Figures 11, 12).