Department of Computer, Control and Management Engineering

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. Previous studies have 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. This study aims to develop a network-based method to uncover the molecular mechanisms underlying sexual dimorphism in cancer. More specifically, we developed a generalizable algorithm (MIRROR2) based on the analysis and integration of miRNAs and their target genes' expression data. Here we implemented MIRROR2, tested it on three different cancers datasets (colon adenocarcinoma, hepatocellular carcinoma, and low-grade gliomas), and assessed its performance by comparing it to state-of-the-art approaches. In all three datasets, MIRROR2 identified sex-specific genes which are involved in numerous co-expression changes between case and control samples and are part of a wider network of genes involved in sex-biased pathways. In conclusion, MIRROR2 showed its efficacy in identifying key genes and regulatory mechanisms that differentiate male and female cancer patients and that can be integrated with clinical features, presenting it as a valid alternative to state-of-the-art methods which fail to capture these differences.