GoiStrat - Gene-of-interest-based sample stratification for the evaluation of functional differences

Purpose : Understanding the impact of gene expression in pathological processes, such as carcinogenesis, is crucial for understanding the biology of cancer and advancing personalised medicine. Yet, current methods lack biologically-informed-omics approaches to stratify cancer patients effectively, limiting our ability to dissect the underlying molecular mechanisms. Results : To address this gap, we present a novel workflow for the stratification and combined analysis of RNA-Seq and DNA methylation samples. Leveraging MSigDB curated gene sets, and utilising graph machine learning and ensemble clustering, we identify specific pathways and functional genes. We applied our workflow to analyse nearly a thousand prostate cancer samples, focusing on the varying expression of the \textit{FOLH1} gene. We identified pathways such as the PI3K-AKT-mTOR gene sets as well as signatures linked to prostate tumour aggressiveness. Conclusion : Our comprehensive approach provides a novel tool to identify disease-relevant functions of genes of interest (GOI) in large datasets. This integrated approach offers a valuable framework for understanding the role of the expression variation of a GOI in complex diseases and for informing on targeted therapeutic strategies.