Abstract
BackgroundHormone-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.MethodsIn 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 (miRNAs), 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–European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog.ResultsWe identified 13 TWAS-significant miRNAs at cis regions (±1 Mb) associated with HDC risk (two, five, one, two, and three miRNAs for prostate, breast, ovarian, colorectal, and endometrial cancers, respectively). Among them, eight novel miRNAs were recognized in HDC risk. Eight protein-coding genes targeted by TWAS-identified miRNAs (SIRT1, SOX4, RUNX2, FOXA1, ABL2, SUB1, HNRNPH1, and WAC) are associated with HDC functions and signaling pathways.ConclusionOverall, identifying risk-associated miRNAs 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 miRNAs in any trait/disease of interest.
Highlights
According to the World Health Organization (WHO), cancers cause one in six deaths worldwide, representing a major public health issue (Ferlay et al, 2015)
The false discovery rate (FDR) method have improved the Transcriptome-wide association studies (TWAS) outcome compared to Bonferroni correction
Using summary data-based Mendelian randomization (SMR), we initially identified 38 miRNA–hormone-dependent cancers (HDC) risk associations, and 13 of them were selected by a subsequent heterogeneity in dependent instruments (HEIDI) test after removing linkage associations
Summary
According to the World Health Organization (WHO), cancers cause one in six deaths worldwide, representing a major public health issue (Ferlay et al, 2015) Hormones such as testosterone, estrogen, and progesterone play an important role in the risk to multiple common cancers, such as prostate, breast, ovarian, endometrial, and colorectal (La Vecchia and Franceschi, 1991; Riman et al, 1998; Emons et al, 2000; Risbridger et al, 2010). Genomewide association studies (GWAS) have been successful in uncovering thousands of associations between genetic variants and different cancer types (Sud et al, 2017) These associations are often challenging to interpret and translate into knowledge about cancer biology because GWAS studies alone cannot provide functional evidence of causal genes mediated by GWAS-recognized single-nucleotide polymorphisms (SNPs; Visscher et al, 2017). TWAS have enabled the identification of novel associations between HDC risk and protein-coding genes
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