Dataset Information

Detecting Differentially Variable MicroRNAs via Model-Based Clustering.

ABSTRACT: Identifying differentially variable (DV) genomic probes is becoming a new approach to detect novel genomic risk factors for complex human diseases. The F test is the standard equal-variance test in statistics. For high-throughput genomic data, the probe-wise F test has been successfully used to detect biologically relevant DNA methylation marks that have different variances between two groups of subjects (e.g., cases versus controls). In addition to DNA methylation, microRNA (miRNA) is another important mechanism of epigenetics. However, to the best of our knowledge, no studies have identified DV miRNAs. In this article, we proposed a novel model-based clustering method to improve the power of the probe-wise F test to detect DV miRNAs. We imposed special structures on covariance matrices for each cluster of miRNAs based on the prior information about the relationship between variances in cases and controls and about the independence among them. Simulation studies showed that the proposed method seems promising in detecting DV probes. Based on two real datasets about human hepatocellular carcinoma (HCC), we identified 7 DV-only miRNAs (hsa-miR-1826, hsa-miR-191, hsa-miR-194-star, hsa-miR-222, hsa-miR-502-3p, hsa-miR-93, and hsa-miR-99b) using the proposed method, one (hsa-miR-1826) of which has not yet been reported to be related to HCC in the literature.

SUBMITTER: Li X

PROVIDER: S-EPMC6079429 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Detecting Differentially Variable MicroRNAs via Model-Based Clustering.

Li Xuan X Fu Yuejiao Y Wang Xiaogang X DeMeo Dawn L DL Tantisira Kelan K Weiss Scott T ST Qiu Weiliang W

International journal of genomics 20180712

Identifying differentially variable (DV) genomic probes is becoming a new approach to detect novel genomic risk factors for complex human diseases. The <i>F</i> test is the standard equal-variance test in statistics. For high-throughput genomic data, the probe-wise <i>F</i> test has been successfully used to detect biologically relevant DNA methylation marks that have different variances between two groups of subjects (e.g., cases versus controls). In addition to DNA methylation, microRNA (miRNA ...[more]

PMID: 30116730

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Project description:BackgroundIndividuals with homozygous familial hypercholesterolemia (HoFH) have a severe clinical problem in their first decade of life, which is not usually present in heterozygous FH (HeFH) individuals. For this latter group of patients, FH diagnosis is mostly severely delayed with a significant increase in the risk of angina, myocardial infarction, peripheral artery disease, stroke, and cardiovascular and all-cause mortality.MethodsThis study used various bioinformatics tools to analyze microarray data and identify critical miRNAs and their target genes associated with FH and its severity. Differentially expressed serum miRNAs from direct hybridization microarray data in three groups of subjects: healthy, HeFH, and HoFH. The differential expressed miRNAs were determined according to a log of fold-change (LFC) <-0.5 or >0.5 and of p < 0.05. Then, we assessed their target genes in silico. Gene ontology (GO) enrichment was applied by Cytoscape. The protein-protein interaction and co-expression network were analyzed by the STRING and GeneMANIA plugins of Cytoscape, respectively.ResultsWe identified increased expression of circulating hsa-miR-604, hsa-miR-652-5p, and hsa-miR-4451 as well as reduced expression of hsa-miR-3140-3p, hsa-miR-550a-5p, and hsa-miR-363-3p in both group of FH vs. healthy subjects. Higher levels of hsa-miR-1183, hsa-miR-1185-1-3p, hsa-miR-122-5p, hsa-miR-19a-3p, hsa-miR-345-3p, and hsa-miR-34c-5p were detected in HeFH in respect to HoFH when compared to healthy subjects. Most upregulated miRNAs mainly affected gene related to cardiac myofibrillogenesis, cholesterol synthesis, RNA editing for apolipoprotein B, and associated with LDL-cholesterol levels. In contrast, down-regulated miRNAs mainly affected gene related to plasma biomarker for coronary artery disease, lipids metabolism, cell adhesion and migration, genetic predictors of type 2 diabetes and cholesterol metabolism. The essential genes were primarily enriched in GO regarding biological regulation, intracellular nucleic acid binding, and the KEGG pathway of TGF-β signaling.ConclusionsThe case-control nature of this study precluded the possibility of assessing the predictive role of the identified differentially expressed miRNAs for cardiovascular events. Therefore, the signature of miRNAs reflecting the pathogenesis of both HeFH and HoFH.

Dataset Information

Detecting Differentially Variable MicroRNAs via Model-Based Clustering.

Publications

Detecting Differentially Variable MicroRNAs via Model-Based Clustering.

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