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Yet, current methods lack biologically-informed-omics approaches to stratify cancer patients effectively, limiting our ability to dissect the underlying molecular mechanisms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>FOLH1<\/jats:italic>\n                      gene, and identified specific pathways such as the PI3K-AKT-mTOR gene sets as well as signatures linked to prostate tumour aggressiveness.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-025-06109-0","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T05:30:40Z","timestamp":1743831040000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Goistrat: gene-of-interest-based sample stratification for the evaluation of functional differences"],"prefix":"10.1186","volume":"26","author":[{"given":"Carlos Uziel","family":"P\u00e9rez Malla","sequence":"first","affiliation":[]},{"given":"Jessica","family":"Kalla","sequence":"additional","affiliation":[]},{"given":"Andreas","family":"Tiefenbacher","sequence":"additional","affiliation":[]},{"given":"Gabriel","family":"Wasinger","sequence":"additional","affiliation":[]},{"given":"Kilian","family":"Kluge","sequence":"additional","affiliation":[]},{"given":"Gerda","family":"Egger","sequence":"additional","affiliation":[]},{"given":"Raheleh","family":"Sheibani-Tezerji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,5]]},"reference":[{"issue":"6797","key":"6109_CR1","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1038\/35021093","volume":"406","author":"CM Perou","year":"2000","unstructured":"Perou CM, S\u00f8rlie T, Eisen MB, Van De Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, et al. 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