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Large scale gene set ranking for survival-related gene sets
Martin Špendl1, Jaka Kokošar1, Ela Praznik1
1Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana, 1000, Slovenia.
Artificial Intelligence in Medicine
|May 31, 2025
Summary
This study introduces a new computational method to analyze gene expression data for predicting patient survival. It adapts gene set enrichment analysis to identify molecular pathways linked to significant differences in survival rates.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Disease progression correlates with gene expression changes in molecular pathways.
- Gene set enrichment analysis (GSEA) is common for identifying disease markers but underutilized in survival analysis.
Purpose of the Study:
- To develop a novel computational approach adapting GSEA for survival analysis.
- To identify gene sets associated with significant differences in patient survival probabilities.
Main Methods:
- Computed single-sample gene set enrichment scores.
- Split samples into cohorts based on enrichment scores.
- Scored gene sets by evaluating survival differences between cohorts.
Main Results:
- The approach successfully identified gene sets linked to survival prognosis.
- Results are supported by existing empirical research.
- Demonstrated the utility of the method using The Cancer Genome Atlas data.
Conclusions:
- The novel method expands GSEA applications to survival analysis.
- Bridges the gap between molecular pathway alterations and survival outcomes.
- Offers a new tool for understanding disease prognosis.
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