ArticlePeerJ2025
An R package for survival-based gene set enrichment analysis.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Mutational Landscape Analysis of BRCA1/2 and Identification of Extracellular-Vesicle-Related Biomarkers in Triple-Negative Breast Cancer.Biomedicines · 2026Article
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- Mendelian Randomization Analysis of Mitochondria-Related Genes and Screening of Prognostic Genes in Colorectal Cancer.Cancer medicine · 2025Article
- Lactate metabolism-related genes serve as potential biomarkers for predicting gastric cancer progression and immunotherapy.Discover oncology · 2025Article
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- Prognostic value of PLEKHA4 and its correlation with tumor-infiltrating immune cells in breast cancer: a comprehensive study based on bioinformatics and clinical analysis validation.Translational cancer research · 2024Article
- Single cell RNA-seq: a novel tool to unravel virus-host interplay.Virusdisease · 2024Review
- Analysis of risk factors affecting the prognosis of angiosarcoma patients: a retrospective study.American journal of cancer research · 2024Article
- Mitochondria-associated programmed cell death: elucidating prognostic biomarkers, immune checkpoints, and therapeutic avenues in multiple myeloma.Frontiers in immunology · 2024Article
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Abstract
Functional enrichment analysis is usually used to assess the effects of experimental differences. However, researchers sometimes want to understand the relationship between transcriptomic variation and health outcomes like survival. Therefore, we suggest the use of Survival-based Gene Set Enrichment Analysis (SGSEA) to help determine biological functions associated with a disease's survival. Despite the availability of this method to researchers, there are no standard tools or software to perform this analysis. We developed an R package and Shiny app called SGSEA and presented a study of kidney renal clear cell carcinoma (KIRC) to demonstrate the approach. In Gene Set Enrichment Analysis (GSEA), the log-fold change in expression between treatments is used to rank genes, to determine if a biological function has a non-random distribution of altered gene expression. SGSEA is a variation of GSEA using the hazard ratio instead of a log fold change. Our study shows that pathways enriched with genes whose increased transcription is associated with mortality (NES > 0, adjusted
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