A supervised machine learning approach identifies gene-regulating factor-mediated competing endogenous RNA networks in hormone-dependent cancers

Competing endogenous RNAs have become an emerging topic in cancer research due to their role in gene regulatory networks. To date, traditional competing endogenous RNA bioinformatic studies have investigated microRNAs as the only factor regulating gene expression. Growing evidence suggests that genomic (e.g., copy number alteration), transcriptomic (e.g., transcription factors), and epigenomic (e.g., DNA methylation) factors can influence competing endogenous RNA regulatory networks. Herein, we used the Least Absolute Shrinkage and Selection Operator regression, a machine learning approach, to integrate DNA methylation, copy number alteration, and transcription factors data with RNA expression to infer competing endogenous RNA networks in cancer risk. The gene-regulating factors-mediated competing endogenous RNA networks were identified in four hormone-dependent cancer types: prostate, breast, colorectal, and endometrial. The shared competing endogenous RNAs across hormone-dependent cancer types were further investigated using survival analysis, functional enrichment analysis, and protein-protein interaction network analysis. We found two (BUB1 and EXO1) and one (RRM2) survival-significant competing endogenous RNA(s) shared across breast-colorectal-endometrial and prostate-colorectal-endometrial combinations, respectively. Both BUB1 and BUB1B genes were identified as shared competing endogenous RNAs across more than two hormone-dependent cancers of interest. These genes play a critical role in cell division, spindle-assembly checkpoint signalling, and correct chromosome alignment. Furthermore, shared competing endogenous RNAs across multiple hormone-dependent cancers have been involved in essential cancer pathways such as cell cycle, p53 signalling, and chromosome segregation. Identifying competing endogenous RNAs' roles across multiple related cancers will improve our understanding of their shared disease biology. Moreover, it contributes to the knowledge of RNA-mediated cancer pathogenesis