Petti, M. | Search bioRxiv

bioRxiv2025

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

A current open challenge in precision medicine is sex-specific medicine: the study of how sex-based biological differences influence peoples 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 MIRROR2s 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.

bioinformatics↗

bioRxiv2021

Assessment of community efforts to advance computational prediction of protein-protein interactions

Comprehensive insights from the human protein-protein interaction (PPI) network, known as the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of new PPIs. Many such approaches have been proposed. However, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 24 representative network-based methods to predict PPIs across five different interactomes, including a synthetic interactome generated by the duplication-mutation-complementation model, and the interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. We selected the top-seven methods through a computational validation on the human interactome. We next experimentally validated their top-500 predicted PPIs (in total 3,276 predicted PPIs) using the yeast two-hybrid assay, finding 1,177 new human PPIs (involving 633 proteins). Our results indicate that task-tailored similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods. Through experimental validation, we confirmed that the top-ranking methods show promising performance externally. For example, from the top 500 PPIs predicted by an advanced similarity-base method [MPS(B&T)], 430 were successfully tested by Y2H with 376 testing positive, yielding a precision of 87.4%. These results establish advanced similarity-based methods as powerful tools for the prediction of human PPIs.

systems biology↗