Abstract
The onset and progression of a disease are often associated with changes in the expression of groups of genes from a particular molecular pathway. Gene set enrichment analysis has thus become a widely used tool in studying disease expression data; however, it has scarcely been utilized in the domain of survival analysis. Here we propose a computational approach to gene set enrichment analysis tailored to survival data. Our technique computes a single-sample gene set enrichment score for a particular gene set, separates the samples into an enriched and non-enriched cohort, and evaluates the separation according to the difference in survival of the cohorts. Using our method on the data from The Cancer Genome Atlas and Molecular Signatures Database Hallmark gene set collection, we successfully identified the gene sets whose enrichment is predictive of survival in particular cancer types. We show that the results of our method are supported by the empirical literature, where genes in the top-ranked gene sets are associated with survival prognosis. Our approach presents the potential of applying gene set enrichment to the domain of survival analysis, linking the disease-related changes in molecular pathways to survival prognosis.
Supported by the Slovenian Research Agency grants P2-0209 and L2-3170.
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Špendl, M., Kokošar, J.: biolab/aime-2023-paper: Version 1.0 (2023). https://doi.org/10.5281/zenodo.7572951
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Špendl, M., Kokošar, J., Praznik, E., Ausec, L., Zupan, B. (2023). Ranking of Survival-Related Gene Sets Through Integration of Single-Sample Gene Set Enrichment and Survival Analysis. In: Juarez, J.M., Marcos, M., Stiglic, G., Tucker, A. (eds) Artificial Intelligence in Medicine. AIME 2023. Lecture Notes in Computer Science(), vol 13897. Springer, Cham. https://doi.org/10.1007/978-3-031-34344-5_39
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