Dataset Information

Gene set enrichment analysis: A genome-wide expression profile-based strategy for discovering functional microRNA-disease relationships.

ABSTRACT: Objective To explore stable and functional microRNA (miRNA)-disease relationships using a genome-wide expression profile pattern matching strategy. Methods We applied the ranked microarray pattern matching strategy Gene Set Enrichment Analysis to identify miRNA permutations with similar expression patterns to diseases. We also used quantitative reverse transcription PCR to validate the predicted expression levels of miRNAs in three diseases: inflammatory bowel disease (IBD), oesophageal cancer, and colorectal cancer. Results We found that hsa-miR-200?c was upregulated more than 40-fold in oesophageal cancer. The expression of miR-16 and miR-124 was not consistently upregulated in IBD or colorectal cancer. Conclusions Our results suggest that this expression profile matching strategy can be used to identify functional miRNA-disease relationships.

SUBMITTER: Ni Y

PROVIDER: S-EPMC5971487 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene set enrichment analysis: A genome-wide expression profile-based strategy for discovering functional microRNA-disease relationships.

Ni Yin Y Song Caiyun C Jin Shuqing S Chen Zhoufeng Z Ni Ming M Han Lu L Wu Jiansheng J Jin Yin Y

The Journal of international medical research 20170424 2

Objective To explore stable and functional microRNA (miRNA)-disease relationships using a genome-wide expression profile pattern matching strategy. Methods We applied the ranked microarray pattern matching strategy Gene Set Enrichment Analysis to identify miRNA permutations with similar expression patterns to diseases. We also used quantitative reverse transcription PCR to validate the predicted expression levels of miRNAs in three diseases: inflammatory bowel disease (IBD), oesophageal cancer, ...[more]

PMID: 28436302

Similar Datasets

Project description:Recent genome-wide association studies (GWAS) of chronic periodontitis (CP) offer rich data sources for the investigation of candidate genes, functional elements, and pathways. We used GWAS data of CP (n = 4,504) and periodontal pathogen colonization (n = 1,020) from a cohort of adult Americans of European descent participating in the Atherosclerosis Risk in Communities study and employed a MAGENTA approach (i.e., meta-analysis gene set enrichment of variant associations) to obtain gene-centric and gene set association results corrected for gene size, number of single-nucleotide polymorphisms, and local linkage disequilibrium characteristics based on the human genome build 18 (National Center for Biotechnology Information build 36). We used the Gene Ontology, Ingenuity, KEGG, Panther, Reactome, and Biocarta databases for gene set enrichment analyses. Six genes showed evidence of statistically significant association: 4 with severe CP (NIN, p = 1.6 × 10(-7); ABHD12B, p = 3.6 × 10(-7); WHAMM, p = 1.7 × 10(-6); AP3B2, p = 2.2 × 10(-6)) and 2 with high periodontal pathogen colonization (red complex-KCNK1, p = 3.4 × 10(-7); Porphyromonas gingivalis-DAB2IP, p = 1.0 × 10(-6)). Top-ranked genes for moderate CP were HGD (p = 1.4 × 10(-5)), ZNF675 (p = 1.5 × 10(-5)), TNFRSF10C (p = 2.0 × 10(-5)), and EMR1 (p = 2.0 × 10(-5)). Loci containing NIN, EMR1, KCNK1, and DAB2IP had showed suggestive evidence of association in the earlier single-nucleotide polymorphism-based analyses, whereas WHAMM and AP2B2 emerged as novel candidates. The top gene sets included severe CP ("endoplasmic reticulum membrane," "cytochrome P450," "microsome," and "oxidation reduction") and moderate CP ("regulation of gene expression," "zinc ion binding," "BMP signaling pathway," and "ruffle"). Gene-centric analyses offer a promising avenue for efficient interrogation of large-scale GWAS data. These results highlight genes in previously identified loci and new candidate genes and pathways possibly associated with CP, which will need to be validated via replication and mechanistic studies.

Project description:BACKGROUND: Determining the functions of uncharacterized proteins is one of the most pressing problems in the post-genomic era. Large scale protein-protein interaction assays, global mRNA expression analyses and systematic protein localization studies provide experimental information that can be used for this purpose. The data from such experiments contain many false positives and false negatives, but can be processed using computational methods to provide reliable information about protein-protein relationships and protein function. An outstanding and important goal is to predict detailed functional annotation for all uncharacterized proteins that is reliable enough to effectively guide experiments. RESULTS: We present AVID, a computational method that uses a multi-stage learning framework to integrate experimental results with sequence information, generating networks reflecting functional similarities among proteins. We illustrate use of the networks by making predictions of detailed Gene Ontology (GO) annotations in three categories: molecular function, biological process, and cellular component. Applied to the yeast Saccharomyces cerevisiae, AVID provides 37,451 pair-wise functional linkages between 4,191 proteins. These relationships are approximately 65-78% accurate, as assessed by cross-validation testing. Assignments of highly detailed functional descriptors to proteins, based on the networks, are estimated to be approximately 67% accurate for GO categories describing molecular function and cellular component and approximately 52% accurate for terms describing biological process. The predictions cover 1,490 proteins with no previous annotation in GO and also assign more detailed functions to many proteins annotated only with less descriptive terms. Predictions made by AVID are largely distinct from those made by other methods. Out of 37,451 predicted pair-wise relationships, the greatest number shared in common with another method is 3,413. CONCLUSION: AVID provides three networks reflecting functional associations among proteins. We use these networks to generate new, highly detailed functional predictions for roughly half of the yeast proteome that are reliable enough to drive targeted experimental investigations. The predictions suggest many specific, testable hypotheses. All of the data are available as downloadable files as well as through an interactive website at http://web.mit.edu/biology/keating/AVID. Thus, AVID will be a valuable resource for experimental biologists.

Dataset Information

Gene set enrichment analysis: A genome-wide expression profile-based strategy for discovering functional microRNA-disease relationships.

Publications

Gene set enrichment analysis: A genome-wide expression profile-based strategy for discovering functional microRNA-disease relationships.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets