Deng Xiaoxu, Thompson Jeffrey A
University of Kansas Medical Center.
Res Sq. 2023 Sep 26:rs.3.rs-3367968. doi: 10.21203/rs.3.rs-3367968/v1.
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. We developed an R package and corresponding Shiny App called SGSEA for this analysis 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 p-value < 0.15) have previously been linked to KIRC survival, helping to demonstrate the value of this approach. This approach allows researchers to quickly identify disease variant pathways for further research and provides supplementary information to standard GSEA, all within a single R package or through using the convenient app.
功能富集分析通常用于评估实验差异的影响。然而，研究人员有时希望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。我们为此分析开发了一个名为SGSEA的R包和相应的Shiny应用程序，并展示了一项关于肾透明细胞癌（KIRC）的研究以说明该方法。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改变的基因表达的非随机分布。SGSEA是GSEA的一种变体，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生存相关联，这有助于证明该方法的价值。这种方法使研究人员能够快速识别疾病变异通路以进行进一步研究，并在单个R包内或通过使用便捷的应用程序为标准GSEA提供补充信息。
