Large scale gene set ranking for survival-related gene sets

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

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.

• Jezik: Angleški jezik
• Ključne besede: gene set ranking, survival analysis, censored data, survival curve, gene expression, single-sample gene set enrichment scoring
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 10 str.
• Številčenje: Vol. 167, art. 103149
• PID: 20.500.12556/RUL-169617
• UDK: 616-006:575
• ISSN pri članku: 1873-2860
• DOI: 10.1016/j.artmed.2025.103149
• COBISS.SI-ID: 238507011
• Datum objave v RUL: 06.06.2025

Gradivo je del revije

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

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: genetika, genomi, onkologija, razvrščanje genov, analiza preživetja, krivulja preživetja, markerji bolezni, dejavniki tveganja