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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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An R package for survival-based gene set enrichment analysis.

Xiaoxu Deng1, Jeffrey Thompson1,2

  • 1Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, United States of America.

Peerj
|July 15, 2025
PubMed
Summary
This summary is machine-generated.

Survival-based Gene Set Enrichment Analysis (SGSEA) identifies biological functions linked to disease survival. This new R package and Shiny app utilize hazard ratios to find mortality-associated pathways, aiding cancer research.

Keywords:
Functional enrichment analysisGene set enrichment analysis (GSEA)Kidney renal clear cell carcinoma (KIRC)Pathway analysisR packageShiny appSurvival analysisTranscriptomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Functional enrichment analysis typically assesses experimental differences, not direct links to health outcomes like survival.
  • Understanding transcriptomic variation's relationship with survival is crucial for disease research.

Purpose of the Study:

  • To introduce Survival-based Gene Set Enrichment Analysis (SGSEA) for identifying biological functions associated with disease survival.
  • To develop and present an R package and Shiny app for performing SGSEA.

Main Methods:

  • SGSEA adapts Gene Set Enrichment Analysis (GSEA) by using hazard ratios instead of log-fold change to rank genes.
  • The method was demonstrated using a study of kidney renal clear cell carcinoma (KIRC).

Main Results:

  • Pathways enriched with genes showing increased transcription linked to mortality (NES > 0, adjusted p-value < 0.15) were identified.
  • These enriched pathways were previously associated with KIRC survival, validating the SGSEA approach.

Conclusions:

  • SGSEA offers a valuable method for rapidly identifying disease-variant pathways impacting survival.
  • The developed R package and Shiny app provide accessible tools for researchers to supplement standard GSEA.