Molecular mechanisms of sexual dimorphism in cancer through improved miRNA regulation-level network-based approach: MIRROR2

Abstract

A current open challenge in precision medicine is sex-specific medicine: the study of how sex-based biological differences influence people’s health. With recent advancements in high-throughput technologies, large-scale molecular data are being generated for individual cancer patients, however, extracting meaningful insights from these complex datasets remains a challenge. Network-based approaches, being inherently holistic, can lead to a better understanding of the molecular mechanisms underlying a disease. For this reason, this study focuses on the development of a network-based method to investigate sexual dimorphism in cancer using transcriptomic data. Previous studies have already shown how miRNAs are involved in differentiating patients by sex in different types of cancer; however, they focused only on evaluating changes in the expression level, without conducting a more comprehensive analysis of miRNA expression or investigating miRNAs’ targets. The aim of this study is therefore to carry out a multi-layer study involving both miRNAs and their target genes’ expression data. In particular, we developed a generalizable algorithm (MIRROR2), which can be used on cancer patients to help identify key regulatory mechanisms and molecules that act as differentiators between males and females. Here we implemented and tested MIRROR2 on three different cancers (colon adenocarcinoma, hepatocellular carcinoma, and low-grade gliomas) and assessed its performance by comparing it to state-of-the-art approaches. This revealed MIRROR2’s efficacy in identifying sex-specific key genes (and how to integrate them with clinical features), presenting it as a viable alternative to state-of-the-art methods which fail to capture these differences.