BMC bioinformatics · 2025
Goistrat: gene-of-interest-based sample stratification for the evaluation of functional differences.
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Pérez Malla Carlos Uziel, Kalla Jessica, Tiefenbacher Andreas, Wasinger Gabriel, Kluge Kilian, Egger Gerda, Sheibani-Tezerji Raheleh
Аннотация
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 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.
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.
RESULTS: 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.
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.