WO2012094366A1 - Circulating mirnas as biomarkers for coronary artery disease - Google Patents

Abstract

The invention relates to methods of identifying subjects at risk of coronary artery disease (CAD) based on MicroRNA (miRNA) marker expression measurements and to computer systems, kits, and software for their implementation.

Description

TITLE

[0001] Circulating miRNAs as Biomarkers for Coronary Artery Disease.

CROSS REFERENCE TO RELATED APPLICATION(S)

[0002] This application claims the benefit of U.S. Provisional Application

No. 61/430,346, filed January 6, 2011, which is hereby incorporated by reference in its entirety for all purposes.

BACKGROUND

Field of the invention

[0003] The invention relates to methods of identifying subjects at risk of coronary artery disease (CAD) based on MicroRNA (miRNA) marker expression data; and to computer systems, kits, and software for their implementation.

Description of Related Art

[0004] Chronic coronary artery disease (CAD), including chronic stable angina, affects 16.5 million patients in the United States and is diagnosed in approximately 500,000 new patients annually (1). Even more new patients are evaluated for chest pain or other symptoms that suggest CAD, but CAD is ultimately diagnosed in less than one-half of these (2- 4). The clinical evaluation of patients with suspected CAD varies and includes diagnostic tests with varying levels of accuracy, reproducibility, ease of use, and potential for patient morbidity (5). Many patients who present with symptoms suggesting CAD are tested with invasive diagnostic coronary angiography despite the widespread availability of noninvasive diagnostic methods, but are not diagnosed as having CAD (6). A method for diagnosing CAD coupling good diagnostic accuracy with low patient burden would thus be beneficial.

[0005] We have previously shown that levels of CAD correlate with the expression levels of a set of genes expressed in circulating peripheral white blood cells {See U.S. Ser. No.

12/816,232, herein incorporated by reference). Coronary artery disease has a strong inflammatory component, and it is likely that the changes in gene expression observed are in response to inflammatory processes occurring at the site of inflammation, e.g., the coronary lesion. Inflammatory signals (e.g., chemokines or other proteins) may be released from the lesion and interact with receptors on the circulating cells, resulting in changes in gene expression. Alternatively, circulating cells may directly interact with arterial cells at the lesion, resulting in gene expression changes.

[0006] Other types of biological molecules may be released from coronary lesions, either as part of the inflammatory process or through a partial rupture of the lesion. Additionally, other non-coronary tissues may release molecules in response to CAD or concurrent conditions or diseases. miRNAs are small (e.g., 21-22 nucleotide) RNAs involved in regulating gene expression in a variety of tissues. miRNAs have been shown to be present in the circulation and can act as diagnostic markers for a variety of diseases (7). Thus, it is possible that circulating miRNAs are also diagnostic for CAD; as such we investigated whether or not we could detect changes in RNA expression levels of circulating miRNAs in response to the presence of CAD.

SUMMARY

[0007] Disclosed herein is a method for identifying a subject at risk of coronary artery disease (CAD), comprising: obtaining a first dataset associated with a sample obtained from the subject, wherein the first dataset comprises quantitative expression data for a MicroRNA (miRNA) marker selected from Table 8; and analyzing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject.

[0008] In some embodiments, the first dataset comprises quantitative expression data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8.

[0009] In some embodiments, the analysis further comprises comparing the first dataset to a second dataset associated with a control sample, wherein the second dataset comprises quantitative expression data for a control miRNA marker, and wherein a statistically significant difference between expression of the miRNA marker and expression of the control miRNA marker indicates an increased risk of CAD in the subject. In some embodiments, the control sample is associated with a control subject or with a control population. In some embodiments, expression of the miRNA marker is significantly decreased compared to expression of the control miRNA marker. In some embodiments, expression of the miRNA marker is significantly increased compared to expression of the control miRNA marker. In some embodiments, the statistically significant difference is determined using linear regression analysis. In some embodiments, the control sample is associated with a control subject or a control population characterized by absence of stenosis in any major coronary artery.

[0010] In some embodiments, the expression level of the miRNA marker positively correlates with an increased risk of CAD in the subject. In some embodiments, the expression level of the miRNA marker negatively correlates with an increased risk of CAD in the subject.

[0011] In some embodiments, the subject is male. In some embodiments, the subject is female.

[0012] In some embodiments, the method is implemented on one or more computers.

[0013] In some embodiments, the first dataset is obtained stored on a storage memory. In some embodiments, obtaining the first dataset associated with the sample comprises obtaining the sample and processing the sample to experimentally determine the first dataset. In some embodiments, obtaining the first dataset associated with the sample comprises receiving the first dataset directly or indirectly from a third party that has processed the sample to experimentally determine the first dataset.

[0014] In some embodiments, the method includes rating CAD risk as low, medium, or high based on the analysis.

[0015] In some embodiments, the quantitative expression data is obtained from a nucleotide -based assay. In some embodiments, the quantitative expression data is obtained from an RT-PCR assay, a sequencing-based assay, or a microarray assay.

[0016] In some embodiments, the subject is a human subject.

[0017] In some embodiments, the method further includes assessing an mRNA marker in the subject; and combining the assessment with the analysis of the first dataset to identify risk of CAD in the subject. In some embodiments, the mRNA marker is selected from the group consisting of: AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and CD8A. In some embodiments, the method further includes assessing a SNP marker in the subject; and combining the assessment with the analysis of the first dataset to identify risk of CAD in the subject. In some embodiments, the method further includes assessing a clinical factor in the subject; and combining the assessment with the analysis of the first dataset to identify risk of CAD in the subject. In some embodiments, the clinical factor is selected from the group consisting of: age, gender, chest pain type, neutrophil count, ethnicity, disease duration, diastolic blood pressure, systolic blood pressure, a family history parameter, a medical history parameter, a medical symptom parameter, height, weight, a body-mass index, resting heart rate, and smoker/non-smoker status.

[0018] Also described herein is a method for determining CAD risk in a subject, comprising: obtaining a sample from the subject, wherein the sample comprises a miRNA marker selected from Table 8; contacting the sample with a reagent; generating a complex between the reagent and the miRNA marker; detecting the complex to obtain a first dataset associated with the sample, wherein the first dataset comprises quantitative expression data for the miRNA marker; and analyzing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject. In some embodiments, the first dataset comprises quantitative expression data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8.

[0019] Also described herein is a computer-implemented method for identifying a subject at risk of a CAD, comprising: storing, in a storage memory, a first dataset associated with a sample obtained from the subject, wherein the first dataset comprises quantitative expression data for a miRNA marker selected from Table 8; and analyzing, by a computer processor, the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject. In some embodiments, the first dataset comprises quantitative expression data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8. In some embodiments, the computer-implemented method can be used to implement a method for identifying a subject at risk of CAD described herein.

[0020] Also described herein is a system for quantifying CAD risk in a subject, the system comprising: a storage memory for storing a first dataset associated with a sample obtained from the subject, wherein the first dataset comprises quantitative expression data for a miRNA marker selected from Table 8; and a processor communicatively coupled to the storage memory for analyzing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject. In some embodiments, the first dataset comprises quantitative expression data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8. In some embodiments, the system can be used to implement a method for identifying a subject at risk of CAD described herein.

[0021] Also described herein is a computer-readable storage medium storing computer- executable program code, the program code comprising: program code for storing a first dataset associated with a sample obtained from a subject, wherein the first dataset comprises quantitative expression data for a miRNA marker selected from Table 8; and program code for analyzing the first dataset by comparing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject. In some embodiments, the computer-readable storage medium can be used to implement a method for identifying a subject at risk of CAD described herein.

[0022] Also described herein is a kit for use in quantifying CAD risk in a subject, comprising: a set of reagents comprising a plurality of reagents for determining from a sample obtained from the subject quantitative expression data for a miRNA marker selected from Table 8; and instructions for using the plurality of reagents to determine quantitative expression data from the sample and analyzing the first dataset by comparing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject. In some embodiments, the instructions further comprise instructions for conducting a nucleotide-based assay. In some embodiments, the quantitative expression data comprises data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8. In some embodiments, the kit can be used to implement a method for identifying a subject at risk of CAD described herein.

[0023] Also described herein is a kit for use in quantifying CAD risk in a subject, comprising: a set of reagents consisting essentially of a plurality of reagents for determining from a sample obtained from the subject quantitative expression data for a miRNA marker selected from Table 8; and instructions for using the plurality of reagents to determine quantitative expression data from the sample. In some embodiments, the instructions further comprise instructions for conducting a nucleotide-based assay. In some embodiments, the quantitative expression data comprises data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8. In some embodiments, the kit can be used to implement a method for identifying a subject at risk of CAD described herein.

DETAILED DESCRIPTION

[0024] In general, terms used in the claims and the specification are intended to be construed as having the plain meaning understood by a person of ordinary skill in the art. Certain terms are defined below to provide additional clarity. In case of conflict between the plain meaning and the provided definitions, the provided definitions are to be used.

[0025] The term "acute coronary syndrome" encompasses all forms of unstable coronary artery disease.

[0026] The term "coronary artery disease" or "CAD" encompasses all forms of atherosclerotic disease affecting the coronary arteries.

[0027] The term "Ct" refers to cycle threshold and is defined as the PCR cycle number where the fluorescent value is above a set threshold. Therefore, a low Ct value corresponds to a high level of expression, and a high Ct value corresponds to a low level of expression.

[0028] The term "Cp" refers to the crossing point and is defined as the intersection of the best fit of the log-linear portion of a standard's amplification curve in a real time PCR instrument such as, e.g., a LightCycler, and the noise band (set according to background fluorescence measurements).

[0029] The terms "marker" or "biomarker" encompass, without limitation, miRNAs, lipids, lipoproteins, proteins, cytokines, chemokines, growth factors, polypeptides, nucleic acids, RNA, DNA, genes, and oligonucleotides, together with their related complexes, metabolites, mutations, variants, polymorphisms, modifications, fragments, subunits, degradation products, elements, and other analytes or sample-derived measures. A marker can also include mutated proteins, mutated nucleic acids, variations in copy numbers, and/or transcript variants. In one embodiment, a marker is a miRNA marker.

[0030] The terms "highly correlated miRNA marker expression" or "highly correlated marker expression" refer to two or more marker expression values that have a sufficient degree of correlation to allow their interchangeable use in some embodiments of methods for identifying subjects at risk of CAD. For example, if miRNA marker x having expression value X is informative in identifying subjects at risk of CAD, highly correlated marker y having expression value Y can be substituted for miRNA marker x in a straightforward way readily apparent to those having ordinary skill in the art and the benefit of the instant disclosure. Highly correlated markers can be identified using methods known in the art, e.g., a Pearson correlation. Mathematical transformations known in the art can be used that effectively convert the expression value of marker y into the corresponding expression value for marker x. The terms "highly correlated marker" or "highly correlated substitute marker" refer to markers that can be substituted with the original marker of interest based on, e.g., the above criteria. A highly correlated substitute marker can be used, e.g., by substitution of the highly correlated substitute marker(s) for the original marker(s) of interest.

[0031] The term "mammal" encompasses both humans and non-humans and includes but is not limited to humans, non-human primates, canines, felines, murines, bovines, equines, and porcines. The term "human" generally refers to Homo sapiens.

[0032] The term "myocardial infarction" refers to an ischemic myocardial necrosis. This is usually the result of abrupt reduction in coronary blood flow to a segment of the myocardium, the muscular tissue of the heart. Myocardial infarction can be classified into ST-elevation and non-ST elevation MI (also referred to as unstable angina). Myocardial necrosis results in either classification. Myocardial infarction, of either ST-elevation or non- ST elevation classification, is an unstable form of atherosclerotic cardiovascular disease.

[0033] The term "sample" can include an RNA, a single cell or multiple cells or fragments of cells or an aliquot of body fluid, taken from a subject, by means including venipuncture, excretion, swabbing, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision, or intervention or other means known in the art.

[0034] The term "subject" encompasses a cell, tissue, or organism, human or non-human, whether in vivo, ex vivo, or in vitro, male or female.

[0035] The term "expression data" refers to a value that represents a direct, indirect, or comparative measurement of the level of expression of a nucleotide (e.g., RNA or DNA) or polypeptide. For example, "expression data" can refer to a value that represents a direct, indirect, or comparative measurement of the RNA expression level of a miRNA marker of interest.

[0036] The term "obtaining a dataset associated with a sample" or "obtaining a first dataset associated with a sample" encompasses obtaining a set of data determined from at least one sample. Obtaining a dataset encompasses obtaining a sample, and processing the sample to experimentally determine the data. The phrase also encompasses receiving a set of expression data directly or indirectly, e.g., from a third party that has processed the sample to experimentally determine the dataset. Additionally, the phrase encompasses mining data from at least one database or at least one publication or a combination of databases and publications. A dataset can be obtained by one of skill in the art via a variety of known ways including accessing a dataset stored on a storage memory.

[0037] The term "clinical factor" refers to a measure of a condition of a subject, e.g., disease activity or severity. "Clinical factor" encompasses all markers of a subject's health status, including non-sample markers, and/or other characteristics of a subject, such as, without limitation, age and gender. A clinical factor can be a score, a value, or a set of values that can be obtained from evaluation of a sample (or population of samples) from a subject or a subject under a determined condition. A clinical factor can also be predicted by markers and/or other parameters such as marker expression surrogates.

[0038] It must be noted that, as used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

Markers and Clinical Factors

[0039] In an embodiment, the invention includes identifying a subject at risk of coronary artery disease CAD, comprising obtaining a first dataset associated with a sample obtained from the subject, wherein the first dataset comprises quantitative expression data for a miRNA marker selected from Table 8. In some embodiments, the invention further includes analyzing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject.

[0040] In some embodiments, the analysis includes comparing the first dataset to a second dataset associated with a control sample, wherein the second dataset comprises quantitative expression data for a control miRNA marker, and wherein a statistically significant difference between expression of the miRNA marker and expression of the control miRNA marker indicates an increased risk of CAD in the subject.

[0041] In some embodiments, the analysis includes assessing an mRNA marker in the subject and combining the assessment with the analysis of the first dataset to identify risk of CAD in the subject. In some embodiments, an mRNA marker is AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRP1, AQP9, GLT1D1, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and/or CD8A. Additional mRNA markers are described in U.S. Ser. No. 12/816,232, herein incorporated by reference. In some embodiments the first dataset includes one or more mRNA markers. In an embodiment, an mRNA can be included within a dataset, e.g., the first dataset.

[0042] In some embodiments, the analysis includes assessing a SNP marker in the subject and combining the assessment with the analysis of the first dataset to identify risk of CAD in the subject. In some embodiments the first dataset includes one or more SNP markers. In an embodiment, a SNP can be included within a dataset, e.g., the first dataset.

[0043] The quantity of one or more markers of the invention can be indicated as a value. A value can be one or more numerical values resulting from evaluation of a sample under a condition. The values can be obtained, for example, by experimentally obtaining measures from a sample by an assay performed in a laboratory, or alternatively, obtaining a dataset from a service provider such as a laboratory, or from a database or a server on which the dataset has been stored, e.g., on a storage memory.

[0044] In an embodiment, the quantity of one or more markers can be one or more numerical values associated with RNA expression levels of miRNAs shown in the Tables below, e.g., resulting from evaluation of a sample under a condition. The miRNA

nomenclature used herein to refer to miRNAs is in accordance with guidelines provided by HGNC. Gene symbols and GenBank accession numbers based on these guidelines are given Table 2B. Sequence(s) for a given miRNA can be obtained from the NCBI GenBank website using the accession numbers referenced in Table 2B as of December 30, 2010.

[0045] In an embodiment, a condition can include one clinical factor or a plurality of clinical factors. In an embodiment, the invention can include assessing a clinical factor in a subject and combining the assessment with an analysis of the first dataset (see above) to identify risk of CAD in the subject. In an embodiment, a clinical factor can be included within a dataset, e.g., the first dataset. A dataset can include one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, or thirty or more overlapping or distinct clinical factor(s). A clinical factor can be, for example, the condition of a subject in the presence of a disease or in the absence of a disease. Alternatively, or in addition, a clinical factor can be the health status of a subject. Alternatively, or in addition, a clinical factor can be age, gender, chest pain type, neutrophil count, ethnicity, disease duration, diastolic blood pressure, systolic blood pressure, a family history parameter, a medical history parameter, a medical symptom parameter, height, weight, a body-mass index, resting heart rate, and smoker/non-smoker status. Clinical factors can include whether the subject has stable chest pain, whether the subject has typical angina, whether the subject has atypical angina, whether the subject has an anginal equivalent, whether the subject has been previously diagnosed with MI, whether the subject has had a revascularization procedure, whether the subject has diabetes, whether the subject has an inflammatory condition, whether the subject has an infectious condition, whether the subject is taking a steroid, whether the subject is taking an immunosuppressive agent, and/or whether the subject is taking a chemo therapeutic agent.

[0046] In an embodiment, a marker's associated value can be included in a dataset associated with a sample obtained from a subject. A dataset can include the marker expression value of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, or thirty or more marker(s).

[0047] In another embodiment, the invention includes obtaining a sample associated with a subject, where the sample includes one or more markers. The sample can be obtained by the subject or by a third party, e.g., a medical professional. Examples of medical

professionals include physicians, emergency medical technicians, nurses, first responders, psychologists, medical physics personnel, nurse practitioners, surgeons, dentists, and any other obvious medical professional as would be known to one skilled in the art. A sample can include RNA. A sample can also include one or more cells. The sample can be obtained from any bodily fluid, for example, amniotic fluid, aqueous humor, bile, lymph, breast milk, interstitial fluid, blood, blood plasma, cerumen (earwax), Cowper's fluid (pre-ejaculatory fluid), chyle, chyme, female ejaculate, menses, mucus, saliva, urine, vomit, tears, vaginal lubrication, sweat, serum, semen, sebum, pus, pleural fluid, cerebrospinal fluid, synovial fluid, intracellular fluid, and vitreous humour. In an example, the sample is obtained by a blood draw, where the medical professional draws blood from a subject, such as by a syringe. The bodily fluid can then be tested to determine the value of one or more markers using an assay, such as an assay described in the Examples section below. The value of the one or more markers can then be evaluated by the same party that performed the assay using the methods of the invention or sent to a third party for evaluation using the methods of the invention.

Assays

[0048] Examples of assays for one or more markers include sequencing assays, microarrays, polymerase chain reaction (PCR), RT-PCR, Southern blots, Northern blots, antibody-binding assays, enzyme-linked immunosorbent assays (ELISAs), flow cytometry, protein assays, Western blots, nephelometry, turbidimetry, chromatography, mass

spectrometry, immunoassays, including, by way of example, but not limitation, RIA, immunofluorescence, immunochemiluminescence, immunoelectrochemiluminescence, or competitive immunoassays, immunoprecipitation, and the assays described in the Examples section below. The information from the assay can be quantitative and sent to a computer system of the invention. The information can also be qualitative, such as observing patterns or fluorescence, which can be translated into a quantitative measure by a user or

automatically by a reader or computer system. In an embodiment, the subject can also provide information other than assay information to a computer system, such as race, height, weight, age, gender, eye color, hair color, family medical history and any other information that may be useful to a user, such as a clinical factor described herein.

Informative marker groups

[0049] In addition to the specific, exemplary markers identified in this application by name, accession number, or sequence, included within the scope of the invention are all variant sequences having at least 50, 60, 70, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, or 99% or greater identity to the exemplified marker sequences. The percentage of sequence identity may be determined using algorithms well known to those of ordinary skill in the art, including, e.g., BLASTn, and BLASTp, as described in Stephen F. Altschul et al, J. Mol. Biol. 215:403-410 (1990) and available at the National Center for Biotechnology Information website maintained by the National Institutes of Health. In accordance with an embodiment of the invention, all operable markers and methods for their use in identifying subjects at risk of CAD now known or later discovered to be highly correlated with the expression of an exemplary marker can be used in addition to or in lieu of that exemplary marker. For the purposes of the invention, such highly correlated markers are contemplated to be within the literal scope of the claimed inventions or alternatively encompassed as equivalents to the exemplary markers. Identification of markers having expression values that are highly correlated to those of the exemplary markers, and their use in identifying a subject at risk of CAD is well within the level of ordinary skill in the art.

Computer implementation

[0050] In one embodiment, a computer comprises at least one processor coupled to a chipset. Also coupled to the chipset are a memory, a storage device, a keyboard, a graphics adapter, a pointing device, and a network adapter. A display is coupled to the graphics adapter. In one embodiment, the functionality of the chipset is provided by a memory controller hub and an I/O controller hub. In another embodiment, the memory is coupled directly to the processor instead of the chipset.

[0051] The storage device is any device capable of holding data, like a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory holds instructions and data used by the processor. The pointing device may be a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard to input data into the computer system. The graphics adapter displays images and other information on the display. The network adapter couples the computer system to a local or wide area network.

[0052] As is known in the art, a computer can have different and/or other components than those described previously. In addition, the computer can lack certain components. Moreover, the storage device can be local and/or remote from the computer (such as embodied within a storage area network (SAN)).

[0053] As is known in the art, the computer is adapted to execute computer program modules for providing functionality described herein. As used herein, the term "module" refers to computer program logic utilized to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device, loaded into the memory, and executed by the processor.

[0054] The term percent "identity," in the context of two or more nucleic acid or polypeptide sequences, refer to two or more sequences or subsequences that have a specified percentage of nucleotides or amino acid residues that are the same, when compared and aligned for maximum correspondence, as measured using one of the sequence comparison algorithms described below (e.g., BLASTP and BLASTN or other algorithms available to persons of skill) or by visual inspection. Depending on the application, the percent "identity" can exist over a region of the sequence being compared, e.g., over a functional domain, or, alternatively, exist over the full length of the two sequences to be compared.

[0055] For sequence comparison, typically one sequence acts as a reference sequence to which test sequences are compared. When using a sequence comparison algorithm, test and reference sequences are input into a computer, subsequence coordinates are designated, if necessary, and sequence algorithm program parameters are designated. The sequence comparison algorithm then calculates the percent sequence identity for the test sequence(s) relative to the reference sequence, based on the designated program parameters.

[0056] Optimal alignment of sequences for comparison can be conducted, e.g., by the local homology algorithm of Smith & Waterman, Adv. Appl. Math. 2:482 (1981), by the homology alignment algorithm of Needleman & Wunsch, J. Mol. Biol. 48:443 (1970), by the search for similarity method of Pearson & Lipman, Proc. Nat'l. Acad. Sci. USA 85:2444 (1988), by computerized implementations of these algorithms (GAP, BESTFIT, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group, 575 Science Dr., Madison, Wis.), or by visual inspection (see generally Ausubel et al, infra).

[0057] One example of an algorithm that is suitable for determining percent sequence identity and sequence similarity is the BLAST algorithm, which is described in Altschul et al, J. Mol. Biol. 215:403-410 (1990). Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information.

[0058] Embodiments of the entities described herein can include other and/or different modules than the ones described here. In addition, the functionality attributed to the modules can be performed by other or different modules in other embodiments. Moreover, this description occasionally omits the term "module" for purposes of clarity and convenience. EXAMPLES

[0059] Below are examples of specific embodiments for carrying out the present invention. The examples are offered for illustrative purposes only, and are not intended to limit the scope of the present invention in any way. Efforts have been made to ensure accuracy with respect to numbers used (e.g., amounts, temperatures, etc.), but some experimental error and deviation should, of course, be allowed for.

[0060] The practice of the present invention will employ, unless otherwise indicated, conventional methods of protein chemistry, biochemistry, recombinant DNA techniques and pharmacology, within the skill of the art. Such techniques are explained fully in the literature. See, e.g., T.E. Creighton, Proteins: Structures and Molecular Properties (W.H. Freeman and Company, 1993); A.L. Lehninger, Biochemistry (Worth Publishers, Inc., current addition); Sambrook, et al, Molecular Cloning: A Laboratory Manual (2nd Edition, 1989); Methods In Enzymology (S. Colowick and N. Kaplan eds., Academic Press, Inc.);

Remington's Pharmaceutical Sciences, 18th Edition (Easton, Pennsylvania: Mack Publishing Company, 1990); Carey and Sundberg Advanced Organic Chemistry 3^rd Ed. (Plenum Press) Vols A and B(l 992).

Example 1: Study Population, Methods, and miRNA Expression Values

[0061] Subjects enrolled in the multicenter PREDICT trial {see ClinicalTrials.gov website) served as the starting population for this study.

[0062] For the purpose of the below analyses: 'Cases' are defined as subjects with stenosis in one or more major coronary arteries of >70%; and 'Controls' are subjects with no stenosis in any major coronary artery.

Methods

[0063] A pooled-sample approach was used initially to identify circulating miRNAs that are present in a set of PREDICT subjects. Four pools of 10 subjects were created:

[0064] Pool 1 : Female CAD Cases

[0065] Pool 2: Female CAD Controls

[0066] Pool 3 : Males CAD Cases

[0067] Pool 4: Male CAD Controls

[0068] Pools were balanced as closely for age as possible. Table 1 shows the age, sex, and case/control status of the subjects. [0069] 100 ul of serum from each sample was pooled for a final volume of 1000 ul per pool. Pooled serum was sent to Asuragen for miRNA purification. Asuragen subjected purified miRNA to RT-PCR analysis using Applied Biosystems miRNA TILDA card "A" (part number 4398965). The results of this experiment are shown in Table 2A. Non- detectable miRNA are denoted by a value of 40 and shading in Table 2A* (* note that miRNA with no detectable levels in any pool were excluded from Table 2A). Each miRNA was evaluated for differences between case and control expression values. For instance, hsa- miR-337 shows a 3.14 Ct decrease in pool 1 vs pool3, suggesting hsa-miR-337 is down- regulated in the presence of CAD. Table 3 gives the delta Ct values between pools 2 & 4, and pools 1 & 3. Table 4 summarizes the results from this analysis where the delta Ct was greater than one.

Example 2: Validation of TILDA Card and Statistical Analysis Using Linear Regression

[0070] To technically validate the results of the TILDA card, miRNA was purified from each of the individual 40 samples. The MagMAX™Viral RNA Isolation Kit (Ambion, Austin, TX; cat. no. AM 1939) was used for total plasma RNA (including miRNA) isolation following the manufacturer's instructions with some modifications. 400ul of plasma was thawed on ice and transferred to 96-deep well plate. 400ul of lysis/binding buffer and 40ul of proteinase K solution (20mg/ml) was added to each plasma sample. The plate was incubated at 50°C for 50 min. To allow for normalization of sample-to-sample variation during RNA isolation, synthetic C. elegans miRNAs cel-miR-39 (synthetic RNA oligonucleotides synthesized by Integrated DNA Technology) were added (1.2 fmol in a 6ul total volume). Each sample was then mixed with 1 ml of isoproponal and 15ul of beads solution. The plate was incubated at room temperature for 10 min. Bead binding and washing steps were performed based on the manufacturer's instructions. The RNA was eluted in 50ul of DEPC water. 2.5ul of RNA sample was used in cDNA reaction with TaqMan® MicroRNA Reverse Transcription Kit (Applied Biosystems; part number 4366596) and 2.5ul of the cDNA was used in pre-amp reaction (TaqMan® PreAmp Master Mix Kit; part number 4391128).

[0071] 47 miRNAs were chosen based on the magnitude of difference between case and control pools; these miRNA are shown in Table 5. Expression values for the 47 miRNA were determined using the 48x48 Expression Chip from Fludigim (part number BMK-M- 48.48); RT-PCR was performed on the Fluidigm BioMark. In addition, as a normalization control we also assessed the levels of the spiked in the C elegans miRNA, cel-miR-39. [0072] Table 6 shows the P values and directionality (T value) of the 47 assays, derived from linear regression, normalizing for age and sex. Linear regression was performed using MiniTab (version 15.1.30.0.). Both unadjusted Ct values and Ct values normalized to cel- miPv-39 were analyzed. 20 of the 47 miR As were significant (p < 0.05) when normalized {See Table 7 and Table 8), a larger number (36) when using unadjusted Ct values. Thus, 20 or more distinct miRNAs were identified that are significantly associated with CAD.

[0073] Of the 47 assays examined, 29 had shown a > 1 Ct decrease in expression in both male and female CAD subjects; 86% (25) showed significant decrease in expression in CAD subjects in the technical replication experiment.

Example 3: Biological Importance of miRNA Target mRNAs

[0074] Three publically available miRNA target databases (PicTar, Target Scan and miRrecord) were examined for potential mRNA targets of the miRNA. Potential targets were identified for 16 of the 20 significant miRNA (by normalization) in the technical replication experiment; results are shown in Table 7. The top 10 genes (based on their matrix score) were chosen from each database if there were more than 50 predicted targets.

[0075] While the invention has been particularly shown and described with reference to a preferred embodiment and various alternate embodiments, it will be understood by persons skilled in the relevant art that various changes in form and details can be made therein without departing from the spirit and scope of the invention.

[0076] All references, issued patents and patent applications cited within the body of the instant specification are hereby incorporated by reference in their entirety, for all purposes.

REFERENCES

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2. Martina B, Bucheli B, Stotz M, Battegay E, Gyr N. First Clinical Judgment by

Primary Care Physicians Distinguishes Well between Nonorganic and Organic Causes of Abdominal or Chest Pain. J Gen Intern Med. 1997;12:459-465.

3. Svavarsdottir AE, Jonasson MR, Gudmundsson GH, Fjeldsted K. Chest pain in family practice. Diagnosis and long-term outcome in a community setting. Can Fam

Physician. 1996;42:1122-8.

4. Klinkman MS, Stevens D, Gorenflo DW. Episodes of care for chest pain: a

preliminary report from MIRNET. Michigan Research Network. J Fam Pract.

1994;38(4):345-52.

5. Gibbons RJ, Abrams J, Chatterjee K, et al. ACC/AHA 2002 guideline update for the management of patients with chronic stable angina—summary article: a report of the American College of Cardiology/ American Heart Association Task Force on practice guidelines (Committee on the Management of Patients With Chronic Stable Angina). J Am Coll Cardiol. 2003;41(1): 159-68.

6. Patel MR, Peterson ED, Dai D, et al. Low diagnostic yield of elective coronary

angiography. N EnglJ Med. 2010;362(10):886-95.

7. Gupta SK, Bang C, Thum T. Circulating microRNAs as biomarkers and potential paracrine mediators of cardiovascular disease. Circ Cardiovasc Genet. 2010;3(5):484- 8.

TABLES

Table 2 A

F. Ctrl (po ol 2) F.case (pool 4) M. Ctrl (pool 1) M.case (pool 3) Ct Ct Ct Ct hsa-miR-337 32.04 32.31 30.44 33.58 hsa-miR-125b 28.61 31.95 32.92 32.34 hsa-miR-145 23.86 26.07 24.66 26.60 hsa-miR-221 22.17 23.94 23.03 24.66 hsa-miR-346 35.53 38.89 37.18 39.14 hsa-miR-484 18.89 20.00 19.02 21.12 hsa-miR-518f 40.00 40 00 40.00 34.03 hsa-miR-642 33.74 33.62 33.85 40 00 hsa-miR-147b 40.00 40.00 37.50 40.00 hsa-miR-424 33.70 32.65 32.48 34.26 hsa-miR-422a 33.65 32.36 33.54 40.00 hsa-miR-589 33.47 32.32 37.69 40.00 hsa-miR-215 32.60 31.29 35.14 37.27 hsa-miR-138 31.81 30.79 32.88 32.62 hsa-miR-501 37.07 35.13 40 00 40.00 hsa-miR-629 31.46 40.00 33.26 31.59 hsa-miR-376b 35.20 40.00 40.00 40.00 hsa-miR-223 14.19 15.86 15.81 16.66 hsa-miR-409 32.94 40.00 40.00 40.00 hsa-miR-98 35.73 40.00 40.00 40.00 hsa-miR-545 29.97 37.84 31.88 31.02 hsa-miR-551 b 33.03 40 00 40.00 40 00 hsa-miR-372 35.82 38.20 35.28 36.97 hsa-miR-431 33.92 40 00 34.94 40 00 hsa-miR-296 31.00 32.98 30.59 32.48 hsa-miR-671 28.48 29.89 28.43 31.62 hsa-miR-627 32.72 40.00 40.00 40.00 hsa-miR-548c 31.81 40.00 40.00 40.00 hsa-miR-518e 40 00 40.00 40.00 33.43 hsa-miR-505 30.36 40.00 35.95 34.91 hsa-miR-503 40 00 40.00 33.68 40.00 hsa-miR-486 31.29 35.15 40 00 40.00 hsa-miR-450a 31.97 40.00 40.00 40.00 hsa-miR-433 28.31 31.15 29.85 32.42 hsa-miR-382 28.75 34.29 33.74 35.52 hsa-miR-379 29.18 32.56 32.96 36.43 hsa-miR-370 27.48 30.68 28.66 33.24 hsa-miR-339 25.58 26.68 25.51 27.89 hsa-miR-339 29.41 35.52 29.93 36.86 hsa-miR-330 30.63 32.13 30.73 32.88 hsa-miR-329 32.80 37.1 1 40.00 40.00 hsa-miR-328 23.23 25.35 23.62 25.69 hsa-miR-326 31.90 38.70 34.93 35.33 hsa-miR-32 31.39 40.00 35.27 40.00 hsa-miR-24 18.49 20.04 18.94 20.97 nsa-miR-23a 30.39 40.00 31 .95 40.00 hsa-miR-22 28.24 37.65 37.13 37.35 Table 2 A

F. Ctrl (pool 2) F.case (pool 4) M. Ctrl ( pool 1) M.case (pool 3) Ct Ct CI t Ct hsa-miR-212 30.65 38.81 29.! 54 40.00 hsa-miR-21 1 31 .00 36.75 34. ί 32 40.00 hsa-miR-199b 32.66 40.00 34.' 16 34.79 hsa-miR-197 23.88 25.69 24. ( 36 26.83 hsa-miR-191 19.04 20.15 18.< 35 21 .48 hsa-miR-18b 28.61 32.91 31 . 1 1 35.48 hsa-miR-15a 31 .50 33.37 30.^ 13 34.26 hsa-miR-135a 30.31 40 00 33.< 39 40 00 hsa-miR-133a 25.89 28.07 27. ( 32 28.89 hsa-miR-132 24.84 27.36 27.: 27 30.01 hsa-miR-127 23.18 25.04 24.: .4 26.17 hsa-miR-125a 35.1 1 40 00 33. 15 40 00 hsa-let-7f 31 .72 40.00 40.( 30 40.00 hsa-miR-92a 20.65 21 .35 21 . ! 53 22.20 hsa-miR-320 20.43 21 .35 21 . ί 31 22.21 hsa-miR-150 21 .70 20.92 24.: .9 21 .61 hsa-miR-210 27.94 29.86 27. ί 38 32.55 hsa-miR-27a 23.15 24.85 25.! 53 25.35 nsa-miR-618 38.27 40.00 34.1 34 40.00 hsa-miR-107 33.33 39.94 40 ( 30 36.18 hsa-miR-224 26.60 27.99 28.: 38 31 .52 hsa-miR-758 30.77 32.29 32.: 24 36.88 hsa-miR-654 31 .16 40.00 31 .: 35 34.67 hsa-miR-133b 30.37 32.1 1 31 .: 34 40.00 hsa-miR-874 33.77 38.75 35.Ι 37 40.00 hsa-miR-493 30.40 32.94 33.: 24 40.00 hsa-miR-518d 33.72 40 00 37.( 38 40.00 hsa-miR-9 32.87 37.80 33.! 57 36.77 hsa-miR-361 27.12 29.45 27.! 58 30.07 hsa-miR-889 30.31 33.54 32.: 39 33.96 hsa-miR-134 27.38 29.1 1 27. ί 37 30.01 hsa-miR-181 c 32.91 34.55 33. < 35 36.07 hsa-miR-532 27.72 29.03 27.^ 15 29.88 hsa-miR-185 24.97 25.98 24.- 77 27.09 hsa-miR-491 28.47 29.54 28.( 38 30.27 hsa-miR-423 26.63 27.92 26.^" 79 28.62 hsa-miR-886 29.15 30.17 29.! 54 31 .61 hsa-miR-574 23.52 24.64 23. < 34 25.81 hsa-miR-203 40.00 32.20 40.( 30 33.45 hsa-miR-548a 40.00 32.72 40.( 30 32.39 hsa-miR-103 25.27 27.22 28.' 14 28.15 hsa-miR-454 24.83 25.68 27.' 12 27.02 hsa-miR-181 a 27.86 29.42 28.Ι 35 30.08 hsa-miR-519a 36.74 40 00 40.( 30 35.13 hsa-miR-518b 40.00 34.13 34.ί 38 40.00 hsa-miR-520g 40.00 40.00 38.! 51 40.00 hsa-miR-521 40.00 40.00 40.( 30 38.93 hsa-miR-517c 40.00 40.00 40.( 30 36.31 Table 2 A

F. Ctrl (pool 2) F.case (pool 4) M. Ctrl [pool 1) M.case (pool 3) Ct Ct C ;t ct hsa-miR-517a 40 00 40.00 40 00 34.29 hsa-miR-579 30.06 31.79 30 93 30.61 hsa-miR-122 25.32 25.79 27 42 25.62 hsa-miR-625 28.67 29.28 31 66 31.36 hsa-let-7a 27.91 33.03 40 .00 34.19 hsa-let-7b 23.32 24.02 26 .33 25.13 hsa-miR-214 36.85 31.60 32 1 1 32.96 hsa-miR-183 35.40 36.20 38 98 36.20 hsa-miR-130a 24.65 26.78 26 62 27.18 hsa-miR-29b 32.22 34.35 33 08 32.22 hsa-miR-19b 17.77 18.70 18 01 19.71 hsa-miR-616 37.60 40 00 40 .00 40 00 hsa-miR-374b 22.82 24.41 25 97 25.50 hsa-miR-374a 23.42 24.80 26 06 25.80 hsa-miR-143 27.04 29.23 31 21 28.37 hsa-miR-708 33.66 40.00 40 00 40.00 hsa-miR-340 24.95 26.60 28 00 27.14 hsa-miR-494 28.12 31.15 30 03 31.37 hsa-miR-495 25.45 28.10 27 45 28.81 hsa-miR-539 27.06 28.72 29 63 30.85 hsa-miR-487b 29.08 30.37 31 06 32.50 hsa-miR-376c 25.54 28.03 27 83 28.54 hsa-miR-154 37.32 40.00 40 .00 40.00 hsa-miR-655 29.92 31.95 32 77 32.36 hsa-miR-376a 25.86 27.33 28 12 28.16 hsa-miR-410 28.67 30.08 30 09 30.1 1 hsa-miR-41 1 29.29 30.52 32 61 31.85 hsa-miR-487a 33.35 40 00 40 .00 34.38 hsa-miR-744 24.78 26.59 25 87 27.13 hsa-miR-130b 25.48 28.29 27 20 28.61 hsa-miR-301 b 28.54 31.26 30 01 31.05 hsa-miR-502 31.30 33.51 32 99 34.30 hsa-miR-15b 24.01 26.05 25 66 27.05 hsa-miR-18a 24.76 26.84 26 39 27.55 hsa-miR-652 25.47 27.42 27 46 28.70 hsa-miR-425 25.52 27.00 26 30 27.78 hsa-miR-186 21.47 22.55 21 91 23.70 hsa-miR-148b 27.20 28.21 28 32 29.79 hsa-miR-369 30.60 40.00 34 .75 35.28 hsa-miR-450b 33.33 40.00 40 .00 40.00 hsa-miR-548d 33.93 40.00 40 .00 40.00 hsa-miR-489 34.08 40.00 40 .00 40.00 hsa-miR-331 34.63 40.00 40 .00 40.00 hsa-miR-219 34.63 40.00 40 .00 40.00 hsa-miR-219 35.18 40.00 40 .00 40.00 hsa-miR-342 33.83 38.13 40 .00 40.00 hsa-miR-99b 27.46 31.45 30 62 30.36 hsa-miR-10a 28.21 30.90 30 52 31.12 Table 2 A

F. Ctrl (pool 2) F.case (pool 4) M. Ctrl (pool 1) M.case (pool 3) ct Ct Ct Ct hsa-miR-654 36.73 40.00 40 00 40.00 hsa-miR-141 33.00 36.25 34.72 34.46 hsa-miR-338 30.75 33.19 32.27 32.79 hsa-miR-323 28.23 30.08 29.26 30.18 hsa-miR-598 27.03 28.58 28.76 29.74 hsa-miR-452 29.53 31.09 31.87 32.69 hsa-miR-194 29.34 30.94 29.97 30.70 hsa-miR-30c 21.34 22.89 23.16 23.89 hsa-miR-28 23.57 24.91 24.93 25.84 hsa-miR-146a 18.28 19.57 20.05 20.94 hsa-miR-29c 26.84 28.56 28.19 28.59 hsa-miR-485 26.74 28.53 28.29 28.60 hsa-miR-301a 25.96 27.66 28.00 28.38 hsa-miR-193a 29.01 30.29 29.97 30.76 hsa-miR-200a 35.03 37.08 40.00 40.00 hsa-miR-128 26.39 28.32 28.57 28.68 hsa-miR-335 24.94 27.16 27.24 27.03 hsa-miR-1 30.82 32.89 32.90 32.82 hsa-miR-27b 25.37 27.55 28.15 27.84 hsa-miR-34a 29.31 30.93 30.68 30.77 hsa-miR-93 21.82 23.24 23.52 23.79 hsa-miR-152 25.37 26.65 27.07 27.44 hsa-miR-139 27.40 28.76 29.25 29.54 hsa-miR-21 21.31 22.91 23.76 23.78 hsa-miR-30b 21.76 23.01 23.38 23.76 hsa-miR-146b 32.25 33.87 40.00 40 00 hsa-miR-20b 22.22 23.62 24.46 24.64 hsa-miR-125a 27.00 28.76 30.66 30.42 hsa-miR-17 18.41 19.63 20.62 20.82 hsa-miR-19a 21.98 23.13 23.95 24.19 hsa-miR-29a 24.37 25.53 25.64 25.85 hsa-miR-142 26.58 27.77 28.77 28.80 hsa-miR-20a 19.19 20.68 21.81 21.49 hsa-miR-324 27.30 28.57 28.98 28.87 hsa-miR-199a 22.46 23.87 25.09 24.82 hsa-miR-490 38.90 40.00 40 00 40.00 hsa-miR-26a 21.47 23.25 24.70 23.99 hsa-miR-146b 22.71 24.01 25.13 24.90 hsa-miR-28 26.12 27.28 28.36 28.23 hsa-miR-140 23.22 24.61 25.80 25.38 hsa-miR-26b 23.26 25.05 26.63 25.80 hsa-miR-324 26.74 28.05 29.24 28.65 hsa-miR-148a 26.33 27.33 27.95 27.66 hsa-miR-331 24.46 25.47 26.95 26.64 hsa-miR-101 26.92 28.02 29.07 28.60 hsa-miR-142 21.52 22.94 25.08 24.12 hsa-miR-449b 35.08 40.00 40.00 37.76 hsa-miR-218 35.95 40.00 33.93 32.16 Table 2 A

F. Ctrl (r. )ool 2) F.case (pool 4) M. Ctrl (pool 1) M.case (pool 3)

0 Ct Ct Ct hsa-π iiR-99a 30. ( 32 34.15 33.09 30.65 hsa-let-7g 23. < 35 26.19 27.82 26.52 hsa-miR-363 29.: 39 33.94 35.35 31 .30 hsa-miR-597 30.: 30 32.58 33.61 31 .77 hsa-let-7e 21 . < 37 24.33 26.52 24.55 hsa-miR-126 18.: 35 19.79 21 .91 20.77 hsa-miR-576 29. ( 39 31 .34 32.85 31 .49 hsa-miR-365 29. ί 50 30.62 31 .19 30.04 hsa-miR-548d 34.^" 77 40 00 40.00 34.46 hsa-miR-182 34. < 37 40 00 40.00 34.00 hsa-let-7d 24.: 36 26.12 27.93 24.95 hsa-miR-100 32.: 38 34.52 34.20 30.66 hsa-miR-200b 30.< 19 33.88 38.88 33.96 hsa-miR-51 1 31 . ( 31 34.15 40.00 33.70 hsa-miR-204 29. ( 30 30.57 40.00 31 .47 hsa-miR-202 39. 13 37.12 40.00 40.00 hsa-miR-124 40. ( 30 35.34 40.00 40.00 hsa-miR-193a 40. ( 30 34.77 40.00 40.00 hsa-miR-548c 40. ( 30 33.08 33.12 33.36 hsa-miR-362 40. ( 30 31 .57 32.99 32.62 hsa-miR-582 40. ( 30 24.14 40.00 40.00 hsa-miR-184 40. ( 30 33.40 33.53 40.00 hsa-miR-106b 23.< 14 24.38 23.77 25.08 hsa-miR-342 22.: 33 23.08 22.53 23.79 hsa-miR-501 40. ( 30 40 00 38.89 40 00 hsa-miR-16 18.! 56 19.39 21 .39 20.03 has-miR-155 26.^" 76 27.62 29.38 27.94 hsa-miR-486 25.^" 79 26.17 27.89 26.85 hsa-miR-451 21 . <^■ 15 21 .90 23.66 22.46 hsa-miR-195 24.: 27 24.77 26.54 24.96 hsa-miR-885 26.^" 73 27.68 29.55 27.32 hsa-miR-636 31 . 1 1 31 .54 36.87 35.02 hsa-miR-192 26. ( 30 26.83 28.95 27.19 hsa-let-7c 30. ( 35 30.01 33.15 31 .27

MammU6-

4395470- 24.^ 14 23.61 26.05 23.77 hsa-miR-136 40. ( 30 40 00 40.00 36.29

RNU48-4373383- 29. ί 35 29.19 32.47 28.48 hsa-miR-886 40. ( 30 40 00 40.00 35.28 hsa-miR-429 32. ! 32 32.86 40.00 34.97 hsa-miR-196b 30. ( 36 31 .20 40.00 32.88 hsa-miR-31 31 . 18 31 .78 40.00 32.14 hsa-miR-542 34.< 14 33.83 40.00 33.06 hsa-miR-190 40 ( 30 40.00 40.00 32.32 hsa-miR-345 25.( 38 26.47 26.30 26.97 hsa-miR-140 27.^" 75 28.16 27.75 28.61 hsa-miR-362 31 .^" 79 32.52 31 .83 32.33 hsa-miR-106a 18.( 36 19.62 20.55 20.69 Table 2 A

F. Ctrl (pool 2) F.case (pool 4) M. ctrl (pool 1) M.case (pool 3) Ct Ct Ct Ct hsa-miR-375 28.21 28.78 28.22 28.68 hsa-miR-95 30.94 31.92 40.00 40.00 hsa-miR-576 34.33 35.28 40.00 40.00 hsa-miR-199a 30.27 30.75 32.86 33.32 hsa-miR-222 20.15 20.97 21.86 21.89 hsa-miR-193b 28.37 28.33 28.57 29.43 hsa-miR-520a 39.21 40.00 40.00 40.00 hsa-miR-205 37.03 37.64 40.00 40.00 hsa-miR-25 23.39 24.08 24.88 24.69 hsa-miR-891a 33.51 33.93 40.00 40 00 hsa-miR-200c 28.31 29.04 31.45 31.1 1 hsa-miR-532 25.47 26.20 27.87 27.52 hsa-miR-139 24.16 24.86 26.35 25.99 hsa-miR-660 25.50 25.89 26.63 26.25 hsa-miR-590 24.36 25.08 26.56 25.84 hsa-miR-500 31.15 31.03 40 00 40.00 hsa-miR-628 28.77 29.18 32.78 31.78 hsa-miR-483 27.30 26.97 27.50 27.16 hsa-miR-570 34.43 33.55 40 00 40.00

Table 2B

Λιτιη I I) I I I ( ,() ( ,en SMnh.)l Accession Number hsa-miR-128 miR128-l NR 029672 hsa-miR-130a miR130a NR 029673 hsa-miR-130b miR130b NR 029845 hsa-miR-132 miR132 NR 029674 hsa-miR-133a miR133al NR 029675 hsa-miR-133b miR133b NR 029903 hsa-miR-134 miR134 NR 029698 hsa-miR-135a miR135al NR 029677 hsa-miR-136 miR136 NR 029699 hsa-miR-138 miR138-l NR 029700 hsa-miR-139 miR139 NR 029603 hsa-miR-139 miR139 NR 029603 hsa-miR-140 miR140 NR 029681 hsa-miR-140 miR140 NR 029681 hsa-miR-141 miR141 NR 029682 hsa-miR-142 miR142 NR 029683 hsa-miR-142 miR142 NR 029683 hsa-miR-143 miR143 NR 029684 hsa-miR-145 miR145 NR 029686 hsa-miR-146a miR146a NR 029701 hsa-miR-146b miR146b NR 030169 hsa-miR-146b miR146b NR 030169 hsa-miR-147b miR147b NR 030599 hsa-miR-148a miR148a NR 029597 hsa-miR-148b miR148b NR 029894 hsa-miR-150 miR150 NR 029703 hsa-miR-152 miR152 NR 029687 hsa-miR-154 miR154 NR 029704 hsa-miR-15a miR15a NR 029485 hsa-miR-15b miR15b NR 029663 hsa-miR-16 miR16-l NR 029486 hsa-miR-17 miR17 NR 029487 hsa-miR-181a miR181al NR 029626 hsa-miR-181c miR181c NR 029613 hsa-miR-182 miR182 NR 029614 hsa-miR-183 miR183 NR 029615 hsa-miR-184 miR184 NR 029705 hsa-miR-185 miR185 NR 029706 hsa-miR-186 miR186 NR 029707 hsa-miR-18a miR18a NR 029488 hsa-miR-18b miR18b NR 029949 hsa-miR-190 miR190 NR 029709 hsa-miR-191 miR191 NR 029690 hsa-miR-192 miR192 NR 029578 hsa-miR-193a miR193a NR 029710 Table 2B

Λιτιη I I) I I I ( ,() ( ,en SMnh.)l Accession Number hsa-miR-193a miR193a NR 029710 hsa-miR-193b miR193b NR 030177 hsa-miR-194 miR194-l NR 02971 1 hsa-miR-195 miR195 NR 029712 hsa-miR-196b miR196b NR 02991 1 hsa-miR-197 miR197 NR 029583 hsa-miR-199a miR199al NR 029586 hsa-miR-199a miR199al NR 029586 hsa-miR-199b miR199b NR 029619 hsa-miR-19a miR19a NR 029489 hsa-miR-19b miR19bl NR 029490 hsa-miR-200a miR200a NR 029834 hsa-miR-200b miR200b NR 029639 hsa-miR-200c miR200c NR 029779 hsa-miR-202 miR202 NR 030170 hsa-miR-203 miR203 NR 029620 hsa-miR-204 miR204 NR 029621 hsa-miR-205 miR205 NR 029622 hsa-miR-20a miR20a NR 029492 hsa-miR-20b miR20b NR 029950 hsa-miR-21 miR21 NR 029493 hsa-miR-210 miR210 NR 029623 hsa-miR-21 1 miR21 1 NR 029624 hsa-miR-212 miR212 NR 029625 hsa-miR-214 miR214 NR 036066 hsa-miR-215 miR215 NR 029628 hsa-miR-218 miR218-l NR 029631 hsa-miR-219 miR219-l NR 029633 hsa-miR-219 miR219-l NR 029633 hsa-miR-22 miR22 NR 029494 hsa-miR-221 miR221 NR 029635 hsa-miR-222 miR222 NR 029636 hsa-miR-223 miR223 NR 029637 hsa-miR-224 miR224 NR 029638 hsa-miR-23a miR23a NR 029495 hsa-miR-24 miR24-l NR 029496 hsa-miR-25 miR25 NR 029498 hsa-miR-26a miR26al NR 029499 hsa-miR-26b miR26b NR 029500 hsa-miR-27a miR27a NR 029501 hsa-miR-27b miR27b NR 029665 hsa-miR-28 miR28 NR 029502 hsa-miR-28 miR28 NR 029502 hsa-miR-296 miR296 NR 029844 hsa-miR-29a miR29a NR 029503 Table 2B

Λιτιη I I) I I I ( ,() ( .enc SMnbol Accession Number hsa-miR-29b miR29bl NR 02951 hsa-miR-29c miR29c NR 029832 hsa-miR-301a miR301a NR 029842 hsa-miR-301b miR301b NR 030622 hsa-miR-30b miR30b NR 029666 hsa-miR-30c miR30cl NR 029833 hsa-miR-31 miR31 NR 029505 hsa-miR-32 miR32 NR 029506 hsa-miR-320 miR320 NR 029714 hsa-miR-323 miR323 NR 029890 hsa-miR-324 miR324 NR 029896 hsa-miR-324 miR324 NR 029896 hsa-miR-326 miR326 NR 029891 hsa-miR-328 miR328 NR 029887 hsa-miR-329 miR329-l NR 029967 hsa-miR-330 miR330 NR 029886 hsa-miR-331 miR331 NR 029895 hsa-miR-331 miR331 NR 029895 hsa-miR-335 miR335 NR 029899 hsa-miR-337 miR337 NR 029889 hsa-miR-338 miR338 NR 036151 hsa-miR-339 miR339 NR 029898 hsa-miR-339 miR339 NR 029898 hsa-miR-340 miR340 NR 029885 hsa-miR-342 miR342 NR 029888 hsa-miR-342 miR342 NR 029888 hsa-miR-345 miR345 NR 029906 hsa-miR-346 miR346 NR 029907 hsa-miR-34a miR34a NR 029610 hsa-miR-361 miR361 NR 029848 hsa-miR-362 miR362 NR 029850 hsa-miR-362 miR362 NR 029850 hsa-miR-363 miR363 NR 029852 hsa-miR-365 miR365-l NR 029854 hsa-miR-369 miR369 NR 029862 hsa-miR-370 miR370 NR 029863 hsa-miR-372 miR372 NR 029865 hsa-miR-374a miR374a NR 030785 hsa-miR-374b miR374b NR 030620 hsa-miR-375 miR375 NR 029867 hsa-miR-376a miR376al NR 029868 hsa-miR-376b miR376b NR 030157 hsa-miR-376c miR376c NR 030157 hsa-miR-379 miR379 NR 029871 hsa-miR-382 miR382 NR 029874 Table 2B

Λιτιη I I) I I I ( ,() ( .enc SMnbol Accession Number hsa-miR-409 miR409 NR 029975 hsa-miR-410 miR410 NR 030156 hsa-miR-41 1 miR41 1 NR 030389 hsa-miR-422a miR422a NR 029944 hsa-miR-423 miR423 NR 029945 hsa-miR-424 miR424 NR 029946 hsa-miR-425 miR425 NR 029948 hsa-miR-429 miR429 NR 029957 hsa-miR-431 miR431 NR 029965 hsa-miR-433 miR433 NM 001 134888 hsa-miR-449b miR449b NR 030387 hsa-miR-450a miR450al NR 029962 hsa-miR-450b miR450b NR 030587 hsa-miR-451 miR451 NR 029970 hsa-miR-452 miR452 NR 029973 hsa-miR-454 miR454 NR 03041 1 hsa-miR-483 miR483 NR 030158 hsa-miR-484 miR484 NR 030159 hsa-miR-485 miR485 NR 030160 hsa-miR-486 miR486 NR 030161 hsa-miR-486 miR486 NR 030161 hsa-miR-487a miR487a NR 030162 hsa-miR-487b miR487b NR 030267 hsa-miR-489 miR489 NR 030164 hsa-miR-490 miR490 NR 030165 hsa-miR-491 miR491 NR 030166 hsa-miR-493 miR493 NR 030172 hsa-miR-494 miR494 NR 030174 hsa-miR-495 miR495 NR 030175 hsa-miR-500 miR500 NR 030224 hsa-miR-501 miR501 NR 030225 hsa-miR-501 miR501 NR 030225 hsa-miR-502 miR502 NR 030226 hsa-miR-503 miR503 NR 030228 hsa-miR-505 miR505 NR 030230 hsa-miR-51 1 miR51 1-l NR 030167 hsa-miR-517a miR517a NR 030201 hsa-miR-517c miR517c NR 030214 hsa-miR-518b miR518b NR 030196 hsa-miR-518d miR518d NR 03021 1 hsa-miR-518e miR518e NR 030209 hsa-miR-518f miR518f NR 030194 hsa-miR-519a miR519al NR 030218 hsa-miR-520a miR520a NR 030189 hsa-miR-520g miR520g NR 030206 Table 2B

Λιτιη I I) I I I ( ,() ( ,en SMnh.)l Accession Number hsa-miR-521 miR521-l NR 030216 hsa-miR-532 miR532 NR 030241 hsa-miR-532 miR532 NR 030241 hsa-miR-539 miR539 NR 030256 hsa-miR-542 miR542 NR 030399 hsa-miR-545 miR545 NR 030258 hsa-miR-548a miR548al NR 030312 hsa-miR-548c miR548c NR 030347 hsa-miR-548c miR548c NR 030347 hsa-miR-548d miR548dl NR 030382 hsa-miR-548d miR548dl NR 030382 hsa-miR-551b miR551b NR 030294 hsa-miR-570 miR570 NR 030296 hsa-miR-574 miR574 NR 030300 hsa-miR-576 miR576 NR 030302 hsa-miR-576 miR576 NR 030302 hsa-miR-579 miR579 NR 030305 hsa-miR-582 miR582 NR 030308 hsa-miR-589 miR589 NR 030318 hsa-miR-590 miR590 NR 030321 hsa-miR-597 miR597 NR 030327 hsa-miR-598 miR598 NR 030328 hsa-miR-616 miR616 NR 030346 hsa-miR-618 miR618 NR 030349 hsa-miR-625 miR625 NR 030355 hsa-miR-627 miR627 NR 030357 hsa-miR-628 miR628 NR 030358 hsa-miR-629 miR629 NR 030714 hsa-miR-636 miR636 NR 030366 hsa-miR-642 miR642 NR 030372 hsa-miR-652 miR652 NR 030381 hsa-miR-654 miR654 NR 030390 hsa-miR-654 miR654 NR 030390 hsa-miR-655 miR655 NR 030391 hsa-miR-660 miR660 NR 030397 hsa-miR-671 miR671 NR 030407 hsa-miR-708 miR708 NR 030598 hsa-miR-744 miR744 NR 030613 hsa-miR-758 miR758 NR 030406 hsa-miR-874 miR874 NR 030588 hsa-miR-885 miR885 NR 030614 hsa-miR-886 miR886 NR 030583 hsa-miR-886 miR886 NR 030583 hsa-miR-889 miR889 NR 030595 hsa-miR-891a miR891a NR 030581 Table 2B

Λιτιη I I) I I I ( ,() ( ,en SMnh.)l Accession Number hsa-miR-9 miR9-l NR 029691 hsa-miR-92a miR92al NR 029508 hsa-miR-93 miR93 NR 029510 hsa-miR-95 miR95 NR 02951 1 hsa-miR-98 miR98 NR 029513 hsa-miR-99a miR99a NR 029514 hsa-miR-99b miR99b NR 029843

MammU6-4395470 RNU6-1 NR 004394

RNU48-4373383 SNORD48 NR 002745

Table 3

miRNA female delta male delta hsa-miR-337 0.265366 3.139882 hsa-miR-125b 3.339802 -0.581573 hsa-miR-145 2.203802 1.939792 hsa-miR-221 1.772247 1.630571 hsa-miR-346 3.359291 1.963407 hsa-miR-484 1.10788 2.092586 hsa-miR-518f 0 -5.97325 hsa-miR-642 -0.126944 6.152294 hsa-miR-147b 0 2.500298 hsa-miR-424 -1.047746 1 .78804 hsa-miR-422a -1.286546 6.46137 hsa-miR-589 -1.15056 2.30962 hsa-miR-215 -1.312353 2.135714 hsa-miR-138 -1.021249 -0.267029 hsa-miR-501 -1.939014 0 hsa-miR-629 8.539604 -1.676797 hsa-miR-376b 4.79804 0 hsa-miR-223 1.663657 0.843446 hsa-miR-409 7.060555 0 hsa-miR-98 4.272022 0 hsa-miR-545 7.870607 -0.863256 hsa-miR-551 b 6.967823 0 hsa-miR-372 2.380085 1.684493 hsa-miR-431 6.083874 5.058533 hsa-miR-296 1.983398 1.896328 hsa-miR-671 1.406275 3.186279 hsa-miR-627 7.281715 0 hsa-miR-548c 8.193584 0 hsa-miR-518e 0 -6.565647 hsa-miR-505 9.641624 -1.035335 hsa-miR-503 0 6.31562 hsa-miR-486 3.860565 0 hsa-miR-450a 8.025917 0 hsa-miR-433 2.837091 2.568468 hsa-miR-382 5.535646 1.781376 hsa-miR-379 3.38 3.472919 Table 3

miRNA female delta male delta hsa-miR-370 3.201986 4.576571 hsa-miR-339 1.105196 2.38991 hsa-miR-339 6.1 1 1931 6.934125 hsa-miR-330 1.49689 2.148588 hsa-miR-329 4.306132 0 hsa-miR-328 2.121491 2.070654 hsa-miR-326 6.7953 0.39699 hsa-miR-32 8.609531 4.728542 hsa-miR-24 1.543722 2.030996 hsa-miR-23a 9.612034 8.04925 hsa-miR-22 9.405457 0.220635 hsa-miR-212 8.156353 10.464304 hsa-miR-21 1 5.748183 5.183365 hsa-miR-199b 7.33943 0.323516 hsa-miR-197 1.810129 2.766905 hsa-miR-191 1.1 1 1871 2.522405 hsa-miR-18b 4.29487 4.366865 hsa-miR-15a 1.870973 3.836552 hsa-miR-135a 9.686739 6.013405 hsa-miR-133a 2.176593 1.867258 hsa-miR-132 2.525568 2.735283 hsa-miR-127 1.864581 1.930978 hsa-miR-125a 4.8889 6.84581 hsa-let-7f 8.283436 0 hsa-miR-92a 0.694546 0.675783 hsa-miR-320 0.914227 0.405972 hsa-miR-150 -0.774653 -2.678729 hsa-miR-210 1.927103 4.667803 hsa-miR-27a 1.695993 -0.17527 hsa-miR-618 1.728905 5.956707 hsa-miR-107 6.608824 -3.815178 hsa-miR-224 1.389512 3.139908 hsa-miR-758 1.521 105 4.634002 hsa-miR-654 8.841219 3.320798 hsa-miR-133b 1.738642 8.656235 hsa-miR-874 4.982077 4.325256 hsa-miR-493 2.53284 6.76367 hsa-miR-518d 6.282616 2.32006 hsa-miR-9 4.92759 3.198795 hsa-miR-361 2.327515 2.487121 hsa-miR-889 3.225618 1.565609 hsa-miR-134 1.725749 2.140496 hsa-miR-181 c 1.640826 2.1 16935 hsa-miR-532 1.312257 2.43141 hsa-miR-185 1.007801 2.320103 hsa-miR-491 1.063431 2.189573 hsa-miR-423 1.297178 1.835305 hsa-miR-886 1.017279 2.065173 hsa-miR-574 1.1 19953 1 .87072 Table 3

miRNA female delta male delta hsa-miR-203 -7.803913 -6.546772 hsa-miR-548a -7.283882 -7.609364 hsa-miR-103 1.946012 -0.288632 hsa-miR-454 0.849964 -0.402193 hsa-miR-181a 1.557598 1 .432715 hsa-miR-519a 3.263123 -4.872787 hsa-miR-518b -5.867603 5.12159 hsa-miR-520g 0 1.485935 hsa-miR-521 0 -1.06527 hsa-miR-517c 0 -3.690712 hsa-miR-517a 0 -5.71 1647 hsa-miR-579 1.729291 -0.323908 hsa-miR-122 0.471 1 19 -1.798822 hsa-miR-625 0.607936 -0.301033 hsa-let-7a 5.1 1354 -5.809315 hsa-let-7b 0.700977 -1 .201887 hsa-miR-214 -5.246382 0.850945 hsa-miR-183 0.800594 -2.778894 hsa-miR-130a 2.128969 0.560791 hsa-miR-29b 2.12651 -0.854236 hsa-miR-19b 0.927152 1 .695036 hsa-miR-616 2.395485 0 hsa-miR-374b 1.592791 -0.463095 hsa-miR-374a 1.38056 -0.259928 hsa-miR-143 2.192092 -2.840192 hsa-miR-708 6.33548 0 hsa-miR-340 1.652601 -0.862356 hsa-miR-494 3.032563 1 .336012 hsa-miR-495 2.65249 1 .364533 hsa-miR-539 1.658257 1 .228863 hsa-miR-487b 1.282961 1 .440474 hsa-miR-376c 2.488883 0.712642 hsa-miR-154 2.678406 0 hsa-miR-655 2.029393 -0.4151 1 hsa-miR-376a 1.476548 0.038331 hsa-miR-410 1.404245 0.017922 hsa-miR-41 1 1.228199 -0.76228 hsa-miR-487a 6.652107 -5.620026 hsa-miR-744 1.814169 1 .256264 hsa-miR-130b 2.818981 1 .407075 hsa-miR-301 b 2.723705 1 .039043 hsa-miR-502 2.215819 1 .31 1692 hsa-miR-15b 2.040403 1 .386833 hsa-miR-18a 2.076241 1 .161 183 hsa-miR-652 1.945585 1 .238563 hsa-miR-425 1.48001 1 .482731 hsa-miR-186 1.075266 1 .795192 hsa-miR-148b 1.008922 1 .468439 hsa-miR-369 9.395731 0.525619 Table 3

miRNA female delta male delta hsa-miR-450b 6.672432 0 hsa-miR-548d 6.07042 0 hsa-miR-489 5.92487 0 hsa-miR-331 5.372574 0 hsa-miR-219 5.367306 0 hsa-miR-219 4.823006 0 hsa-miR-342 4.302181 0 hsa-miR-99b 3.992073 -0.269106 hsa-miR-10a 2.693689 0.604276 hsa-miR-654 3.271 168 0 hsa-miR-141 3.249107 -0.259907 hsa-miR-338 2.434234 0.523216 hsa-miR-323 1.851096 0.912873 hsa-miR-598 1.548191 0.978708 hsa-miR-452 1.5631 1 0.819933 hsa-miR-194 1.606649 0.732062 hsa-miR-30c 1.5421 16 0.728447 hsa-miR-28 1.337851 0.908291 hsa-miR-146a 1.296261 0.892735 hsa-miR-29c 1.724459 0.401601 hsa-miR-485 1.795581 0.312587 hsa-miR-301a 1.704037 0.382133 hsa-miR-193a 1.280949 0.790633 hsa-miR-200a 2.057251 0 hsa-miR-128 1.933741 0.1 10084 hsa-miR-335 2.224979 -0.212315 hsa-miR-1 2.070186 -0.083988 hsa-miR-27b 2.178095 -0.316592 hsa-miR-34a 1.621438 0.085363 hsa-miR-93 1.417673 0.26868 hsa-miR-152 1.282286 0.365816 hsa-miR-139 1.355492 0.281271 hsa-miR-21 1.605709 0.028795 hsa-miR-30b 1.249899 0.379046 hsa-miR-146b 1.61499 0 hsa-miR-20b 1.404061 0.174448 hsa-miR-125a 1.758339 -0.232262 hsa-miR-17 1.212774 0.202539 hsa-miR-19a 1.148202 0.242733 hsa-miR-29a 1.167807 0.21276 hsa-miR-142 1.191866 0.034382 hsa-miR-20a 1.483925 -0.320927 hsa-miR-324 1.270718 -0.1 10091 hsa-miR-199a 1.405233 -0.271656 hsa-miR-490 1.10309 0 hsa-miR-26a 1.780483 -0.706442 hsa-miR-146b 1.299128 -0.231564 hsa-miR-28 1.164787 -0.12831 1 hsa-miR-140 1.383122 -0.420754 Table 3

miRNA female delta male delta hsa-miR-26b 1.79388 -0.833593 hsa-miR-324 1.31 1722 -0.587302 hsa-miR-148a 1.006625 -0.294146 hsa-miR-331 1.01206 -0.301559 hsa-miR-101 1.098608 -0.47159 hsa-miR-142 1.423866 -0.961988 hsa-miR-449b 4.92179 -2.2391 13 hsa-miR-218 4.050156 -1.769668 hsa-miR-99a 3.531505 -2.437666 hsa-let-7g 2.239885 -1.301 18 hsa-miR-363 4.54836 -4.042355 hsa-miR-597 2.273536 -1.837347 hsa-let-7e 2.361954 -1 .976896 hsa-miR-126 1.439925 -1 .14336 hsa-miR-576 1.649406 -1.3681 1 1 hsa-miR-365 1.123142 -1 .15605 hsa-miR-548d 5.22735 -5.54259 hsa-miR-182 5.027103 -5.998936 hsa-let-7d 1.755186 -2.986798 hsa-miR-100 2.139499 -3.538757 hsa-miR-200b 3.389501 -4.916676 hsa-miR-51 1 2.535099 -6.301006 hsa-miR-204 1.562865 -8.534904 hsa-miR-202 -2.003601 0 hsa-miR-124 -4.65824 0 hsa-miR-193a -5.229626 0 hsa-miR-548c -6.91587 0.242134 hsa-miR-362 -8.427149 -0.367022 hsa-miR-582 -15.858229 0 hsa-miR-184 -6.604774 6.474003 hsa-miR-106b 0.936735 1 .302399 hsa-miR-342 0.75146 1 .255104 hsa-miR-501 0 1.1 12545 hsa-miR-16 0.830278 -1.365194 has-miR-155 0.861688 -1.444736 hsa-miR-486 0.377901 -1 .03861 hsa-miR-451 0.457214 -1.191987 hsa-miR-195 0.504707 -1.581642 hsa-miR-885 0.954605 -2.232054 hsa-miR-636 0.433733 -1.850723 hsa-miR-192 0.224407 -1.764717 hsa-let-7c -0.032646 -1.887318

MammU6-4395470- -0.83912 -2.28239875 hsa-miR-136 0 -3.70619

RNU48-4373383- -0.664734 -3.989494 hsa-miR-886 0 -4.7179 hsa-miR-429 0.041023 -5.03037 hsa-miR-196b 0.539068 -7.1 15746 hsa-miR-31 0.603983 -7.85959 Table 3

miRNA female delta male delta hsa-miR-542 -0.608593 -6.940697 hsa-miR-190 0 -7.677643 hsa-miR-345 0.790356 0.662722 hsa-miR-140 0.41019 0.855379 hsa-miR-362 0.73059 0.496395 hsa-miR-106a 0.953033 0.133487 hsa-miR-375 0.570946 0.468828 hsa-miR-95 0.978849 0 hsa-miR-576 0.948708 0 hsa-miR-199a 0.487221 0.458237 hsa-miR-222 0.813671 0.030497 hsa-miR-193b -0.035856 0.864878 hsa-miR-520a 0.789845 0 hsa-miR-205 0.607708 0 hsa-miR-25 0.687748 -0.18792 hsa-miR-891a 0.418802 0 hsa-miR-200c 0.73153 -0.341 163 hsa-miR-532 0.731775 -0.348646 hsa-miR-139 0.698475 -0.363134 hsa-miR-660 0.381395 -0.385712 hsa-miR-590 0.713827 -0.723677 hsa-miR-500 -0.125502 0 hsa-miR-628 0.41 1634 -0.999843 hsa-miR-483 -0.32571 -0.346844 hsa-miR-570 -0.879844 0

Table 6

T P

normalized no rmalized T Raw F > Raw miR-132 3.12 0.004 3.25 0.003 imiR-9 2.94 0.006 3.04 0.004 miR-450a 2.88 0.007 2.95 0.005 miR-433 2.67 0.01 1 3.22 0.003 miR-181 c 2.67 0.01 1 2.83 0.008 imiR-379 2.6 0.013 2.77 0.009 miR-197 2.53 0.016 2.98 0.005 miR-135a 2.49 0.017 2.9 0.006 miR-330 2.47 0.018 2.65 0.012 miR-486-3p 2.31 0.027 3.41 0.002 miR-22 2.26 0.03 2.78 0.008 miR-671 -3p 2.23 0.032 2.42 0.021 miR-382 2.19 0.035 2.55 0.015 miR-503 2.18 0.036 2.37 0.024 miR-191 2.16 0.038 2.62 0.013 miR-346 2.14 0.039 3.61 0.0I miR-15a 2.1 1 0.042 2.47 0.018 miR-548a 2.08 0.044 2.26 0.03 miR-582-3p 2.07 0.046 2.55 0.015 miR-24 2.06 0.047 2.61 0.013 imiR-127 2.02 0.051 2.48 0.018 miR-145 2 0.053 2.37 0.023 miR-518e 1.98 0.056 2.24 0.031 miR-484 1.92 0.062 2.72 0.01 miR-328 1.92 0.062 2.45 0.019 miR-337-5p 1.81 0.079 2.03 0.05 miR-339-5p 1.78 0.083 2.13 0.04 miR-505 1.78 0.084 2.12 0.041 miR-483-5p 1.76 0.087 2.28 0.028 miR-370 1.73 0.092 2.65 0.012 miR-125a-3p 1.69 0.099 2.18 0.036 miR-221 1.69 0.1 2.07 0.0' miR-23a 1.67 0.103 2.04 0.049 miR-21 1 1.56 0.127 2.42 0.021 miR-329 1.53 0.135 1.67 0.104 miR-32 1.51 0.141 2.24 0 031 miR-654-3p -1.5 0.142 0 1 has-miR-let7f 1.5 0.143 1.84 0.073 miR-326 1.48 0.146 2.67 0 0 1 1 miR-18b 1.44 0.158 1.74 0.091 miR-199b 1.41 0.167 1.56 0.127 miR-627 -1.35 0.185 0.28 0.783 miR-375 1.18 0.247 1.8 0.08 miR-212 0.69 0.494 1.96 0.058 miR-203 0.63 0.536 0.97 0.338 miR-133a 0.61 0.546 2.6 0.0 13 miR-134 -0.34 0.733 1.63 0.1 1 1

Target scan OXSR1 oxidative-stress responsive 1

Target scan AAK1 AP2 associated kinase 1

Claims

1. A method for identifying a subject at risk of coronary artery disease (CAD), comprising: obtaining a first dataset associated with a sample obtained from the subject, wherein the first dataset comprises quantitative expression data for a MicroRNA (miRNA) marker selected from Table 8; and

analyzing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject.

2. The method of claim 1, wherein the miRNA marker is miR-132.

3. The method of claim 1, wherein the analysis further comprises comparing the first dataset to a second dataset associated with a control sample, wherein the second dataset comprises quantitative expression data for a control miRNA marker, and wherein a statistically significant difference between expression of the miRNA marker and expression of the control miRNA marker indicates an increased risk of CAD in the subject.

4. The method of claim 3, wherein the control sample is associated with a control subject or with a control population.

5. The method of claim 3, wherein expression of the miRNA marker is significantly decreased compared to expression of the control miRNA marker.

6. The method of claim 3, wherein expression of the miRNA marker is significantly increased compared to expression of the control miRNA marker.

7. The method of claim 1 , wherein the expression level of the miRNA marker positively correlates with an increased risk of CAD in the subject.

8. The method of claim 1 , wherein the expression level of the miRNA marker negatively correlates with an increased risk of CAD in the subject.

9. The method of claim 3, wherein the control sample is associated with a control subject or a control population characterized by absence of stenosis in any major coronary artery.

10. The method of claim 1, wherein the subject is male.

11. The method of claim 1, wherein the subject is female.

12. The method of claim 1, wherein the method is implemented on one or more computers.

13. The method of claim 1, wherein the first dataset is obtained stored on a storage memory.

14. The method of claim 1, wherein obtaining the first dataset associated with the sample comprises obtaining the sample and processing the sample to experimentally determine the first dataset.

15. The method of claim 1, wherein obtaining the first dataset associated with the sample comprises receiving the first dataset directly or indirectly from a third party that has processed the sample to experimentally determine the first dataset.

16. The method of claim 3, wherein the statistically significant difference is determined using linear regression analysis.

17. The method of claim 1, further comprising rating CAD risk as low, medium, or high based on the analysis.

18. The method of claim 1, wherein the quantitative expression data is obtained from a nucleotide -based assay.

19. The method of claim 18, wherein the quantitative expression data is obtained from an RT-PCR assay, a sequencing-based assay, or a microarray assay.

20. The method of claim 1, wherein the subject is a human subject.

21. The method of claim 1, further comprising assessing an mRNA marker in the subject; and combining the assessment with the analysis of the first dataset to identify risk of CAD in the subject.

22. The method of claim 21, wherein the mRNA marker is selected from the group consisting of: AF161365, HNRPF, ACBD5, TFCP2, DDX18, AF289562, CD248, CD79B, CD19, SPIB, BLK, CD3D, LCK, TMC8, CCT2, S100A12, MMP9, CLEC4E, ALOX5AP, S100A8, NAMPT, RPL28, SSRPl, AQP9, GLTIDI, NCF4, NCF2, CASP5, H3F3B, IL18RAP, TXN, TNFAIP6, PLAUR, IL8RB, BCL2A1, TNFRSF10C, PTAFR, KCNE3, LAMP2, TLR4, TYROBP, SLAMF7, CX3CR1, KLRC4, and CD8A.

23. The method of claim 1, further comprising assessing a SNP marker in the subject; and combining the assessment with the analysis of the first dataset to identify risk of CAD in the subject.

24. The method of claim 1, further comprising assessing a clinical factor in the subject; and combining the assessment with the analysis of the first dataset to identify risk of CAD in the subject.

25. The method of claim 24, wherein the clinical factor is selected from the group consisting of: age, gender, chest pain type, neutrophil count, ethnicity, disease duration, diastolic blood pressure, systolic blood pressure, a family history parameter, a medical history parameter, a medical symptom parameter, height, weight, a body-mass index, resting heart rate, and smoker/non-smoker status.

26. A method for determining CAD risk in a subject, comprising: obtaining a sample from the subject, wherein the sample comprises a miRNA marker selected from Table 8;

contacting the sample with a reagent;

generating a complex between the reagent and the miRNA marker; detecting the complex to obtain a first dataset associated with the sample, wherein the first dataset comprises quantitative expression data for the miRNA marker; and analyzing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject.

27. The method of claim 26, wherein the first dataset comprises quantitative expression data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8.

28. A computer-implemented method for identifying a subject at risk of a CAD, comprising: storing, in a storage memory, a first dataset associated with a sample obtained from the subject, wherein the first dataset comprises quantitative expression data for a miR A marker selected from Table 8; and

analyzing, by a computer processor, the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject.

29. The method of claim 28, wherein the first dataset comprises quantitative expression data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8.

30. A system for quantifying CAD risk in a subject, the system comprising:

a storage memory for storing a first dataset associated with a sample obtained from the subject, wherein the first dataset comprises quantitative expression data for a miRNA marker selected from Table 8; and a processor communicatively coupled to the storage memory for analyzing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject.

31. The system of claim 30, wherein the first dataset comprises quantitative expression data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8.

32. A computer-readable storage medium storing computer-executable program code, the program code comprising: program code for storing a first dataset associated with a sample obtained from a subject, wherein the first dataset comprises quantitative expression data for a miRNA marker selected from Table 8; and program code for analyzing the first dataset by comparing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject.

33. A kit for use in quantifying CAD risk in a subject, comprising:

a set of reagents comprising a plurality of reagents for determining from a sample obtained from the subject quantitative expression data for a miRNA marker selected from Table 8; and instructions for using the plurality of reagents to determine quantitative expression data from the sample and analyzing the first dataset by comparing the first dataset to determine the expression level of the miRNA marker, wherein the expression level of the miRNA marker positively or negatively correlates with an increased risk of CAD in the subject.

34. The kit of claim 33, wherein the instructions further comprise instructions for conducting a nucleotide -based assay.

35. The kit of claim 33, wherein the quantitative expression data comprises data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8.

36. A kit for use in quantifying CAD risk in a subject, comprising: a set of reagents consisting essentially of a plurality of reagents for determining from a sample obtained from the subject quantitative expression data for a miRNA marker selected from Table 8; and

instructions for using the plurality of reagents to determine quantitative expression data from the sample.

37. The kit of claim 36, wherein the instructions further comprise instructions for conducting a nucleotide -based assay.

38. The kit of claim 36, wherein the quantitative expression data comprises data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8.

39. The method of claim 1, wherein the first dataset comprises quantitative expression data for at least two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty or more miRNA markers selected from Table 8.