Large scale gene set ranking for survival-related gene sets

Špendl, Martin (author), Kokošar, Jaka (author), Praznik, Ela (author), Ausec, Luka (author), Štajdohar, Miha (author), Zupan, Blaž (author)

Disease progression is closely linked to shifts in the expression levels of specific genes within molecular pathways. While gene set enrichment analysis is a widely employed method for identifying key disease markers, it has been underutilized in survival analysis. Here, we introduce a novel computational approach that adapts gene set enrichment analysis for survival analysis. The proposed approach considers a gene set, computes a single-sample gene set enrichment score, and, based on this score, splits the samples into cohorts. It then scores the gene sets by evaluating the differences in survival rates between the resulting cohorts. We aim to find gene sets that can lead to cohorts with significantly different survival probabilities. Utilizing gene expression data from The Cancer Genome Atlas and gene sets from the Molecular Signature Database, our results demonstrate that existing empirical research consistently supports the top gene sets our approach associates with survival prognosis. The proposed method broadens gene set enrichment analysis applications to include information on survival, bridging the gap between alterations in molecular pathways and their implications on survival.

• Language: English
• Keywords: gene set ranking, survival analysis, censored data, survival curve, gene expression, single-sample gene set enrichment scoring
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 10 str.
• Numbering: Vol. 167, art. 103149
• PID: 20.500.12556/RUL-169617
• UDC: 616-006:575
• ISSN on article: 1873-2860
• DOI: 10.1016/j.artmed.2025.103149
• COBISS.SI-ID: 238507011
• Publication date in RUL: 06.06.2025

Record is a part of a journal

• Title: Artificial intelligence in medicine
• Publisher: Elsevier
• ISSN: 1873-2860
• COBISS.SI-ID: 23192325

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: genetika, genomi, onkologija, razvrščanje genov, analiza preživetja, krivulja preživetja, markerji bolezni, dejavniki tveganja