Integrative Transcriptome-Wide Analyses Uncover Novel Risk-Associated MicroRNAs in Hormone-Dependent Cancers - Jayarathna, Dulari K.; Rentería, Miguel E.; Malik, Adil; Sauret, Emilie; Batra, Jyotsna; Gandhi, Neha S. (Frontiers in Genetics, 2021)
Background: Hormone-dependent cancers (HDC) are among the leading causes of death worldwide among both men and women. Some of the established risk factors of HDC include unhealthy lifestyles, environmental factors, and genetic influences. Numerous studies have been conducted to understand gene-cancer associations. Transcriptome-wide association studies (TWAS) integrate data from genome-wide association studies (GWAS) and gene expression (expression quantitative trait loci-eQTL) to yield meaningful information on biological pathways associated with complex traits/diseases. Recently, TWAS have enabled the identification of novel associations between HDC risk and protein-coding genes. Methods: In the present study, we performed a TWAS analysis using the summary data-based Mendelian randomization (SMR)- heterogeneity in dependent instruments (HEIDI) method to identify microRNAs, a group of non-coding RNAs (ncRNAs) associated with HDC risk. We obtained eQTL and GWAS summary statistics from the ncRNA-eQTL database and the National Human Genome Research Institute (NHGRI)-European Bioinformatics Institute (EBI) GWAS Catalog. Results: We identified thirteen TWAS-significant microRNAs at cis-regions (± 1Mb) associated with HDC risk (two, five, one, two and three microRNAs for prostate, breast, ovarian, colorectal, and endometrial cancers, respectively). Among them, eight novel microRNAs were recognized in HDC risk. Eight protein-coding genes targeted by TWAS-identified microRNAs, SIRT1, SOX4, RUNX2, FOXA1, ABL2, SUB1, HNRNPH1, and WAC are associated with HDC-functions and signaling pathways. Conclusion: Overall, identifying risk-associated microRNAs across a group of related cancers may help to understand cancer biology, and provide novel insights into cancer genetic mechanisms. This customized approach can be applied to identify significant microRNAs in any trait/disease of interest.