# Goistrat: gene-of-interest-based sample stratification for the evaluation of functional differences

## TL;DR

This paper introduces a new method to analyze cancer data by focusing on specific genes to better understand their role in disease and treatment.

## Contribution

A novel workflow using gene sets and machine learning to stratify cancer samples based on gene expression for functional analysis.

## Key findings

- The workflow outperformed existing methods in separating cancer samples for biological analysis.
- Application to prostate cancer data revealed pathways linked to tumor aggressiveness via FOLH1 gene expression.
- The approach identifies disease-relevant functions of genes in large datasets.

## Abstract

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.

To address this gap, we present a novel workflow for the stratification and further analysis of multi-omics samples with matched RNA-Seq data that relies on MSigDB curated gene sets, graph machine learning and ensemble clustering. We compared the performance of our workflow in the top 8 TCGA datasets and showed its clear superiority in separating samples for the study of biological differences. We also applied our workflow to analyse nearly a thousand prostate cancer samples, focusing on the varying expression of the FOLH1 gene, and identified specific pathways such as the PI3K-AKT-mTOR gene sets as well as signatures linked to prostate tumour aggressiveness.

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.