Deng Xiaoxu, Thompson Jeffrey
Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, United States of America.
University of Kansas Cancer Center, Kansas City, KS, United States of America.
PeerJ. 2025 Jul 11;13:e19489. doi: 10.7717/peerj.19489. eCollection 2025.
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 -value < 0.15) have previously been linked to KIRC survival, helping to demonstrate the value of this approach. This method allows rapid identification of disease variant pathways and provides supplementary information to standard GSEA, all within a single R package at https://github.com/ShellsheDeng/SGSEA or via the convenient app at https://biostats-shinyr.kumc.edu/SGSEA/.
功能富集分析通常用于评估实验差异的影响。然而，研究人员有时想要了解转录组变异与生存等健康结果之间的关系。因此，我们建议使用基于生存的基因集富集分析（SGSEA）来帮助确定与疾病生存相关的生物学功能。尽管研究人员可以使用这种方法，但目前尚无执行此分析的标准工具或软件。我们开发了一个名为SGSEA的R包和Shiny应用程序，并展示了一项关于肾透明细胞癌（KIRC）的研究以证明该方法。在基因集富集分析（GSEA）中，处理之间表达的对数倍变化用于对基因进行排名，以确定生物学功能是否具有基因表达改变的非随机分布。SGSEA是GSEA的一种变体，它使用风险比而不是对数倍变化。我们的研究表明，富含转录增加与死亡率相关的基因的通路（NES>0，调整后p值<0.15）先前已与KIRC生存相关，这有助于证明该方法的价值。这种方法允许快速识别疾病变异通路，并为标准GSEA提供补充信息，所有这些都可以在https://github.com/ShellsheDeng/SGSEA上的单个R包中或通过https://biostats-shinyr.kumc.edu/SGSEA/上的便捷应用程序获得。
