Wang Po-Wen, Su Yi-Hsun, Chou Po-Hao, Huang Ming-Yueh, Chen Ting-Wen
Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 30068, Taiwan.
Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, 30068, Taiwan.
BMC Genomics. 2022 May 4;22(Suppl 5):918. doi: 10.1186/s12864-022-08581-x.
Pan-cancer studies have disclosed many commonalities and differences in mutations, copy number variations, and gene expression alterations among cancers. Some of these features are significantly associated with clinical outcomes, and many prognosis-predictive biomarkers or biosignatures have been proposed for specific cancer types. Here, we systematically explored the biological functions and the distribution of survival-related genes (SRGs) across cancers.
We carried out two different statistical survival models on the mRNA expression profiles in 33 cancer types from TCGA. We identified SRGs in each cancer type based on the Cox proportional hazards model and the log-rank test. We found a large difference in the number of SRGs among different cancer types, and most of the identified SRGs were specific to a particular cancer type. While these SRGs were unique to each cancer type, they were found mostly enriched in cancer hallmark pathways, e.g., cell proliferation, cell differentiation, DNA metabolism, and RNA metabolism. We also analyzed the association between cancer driver genes and SRGs and did not find significant over-representation amongst most cancers.
In summary, our work identified all the SRGs for 33 cancer types from TCGA. In addition, the pan-cancer analysis revealed the similarities and the differences in the biological functions of SRGs across cancers. Given the potential of SRGs in clinical utility, our results can serve as a resource for basic research and biotech applications.
泛癌研究揭示了不同癌症在突变、拷贝数变异和基因表达改变方面的许多共性与差异。其中一些特征与临床结局显著相关，并且针对特定癌症类型已经提出了许多预后预测生物标志物或生物特征。在此，我们系统地探索了生存相关基因（SRGs）在各种癌症中的生物学功能及分布情况。
我们对来自TCGA的33种癌症类型的mRNA表达谱进行了两种不同的统计生存模型分析。我们基于Cox比例风险模型和对数秩检验在每种癌症类型中鉴定出SRGs。我们发现不同癌症类型中SRGs的数量存在很大差异，并且大多数鉴定出的SRGs是特定于某一特定癌症类型的。虽然这些SRGs在每种癌症类型中都是独特的，但它们大多富集于癌症标志性通路，例如细胞增殖、细胞分化、DNA代谢和RNA代谢。我们还分析了癌症驱动基因与SRGs之间的关联，发现在大多数癌症中没有显著的过度代表性。
总之，我们的工作鉴定出了来自TCGA的33种癌症类型的所有SRGs。此外，泛癌分析揭示了SRGs在不同癌症中的生物学功能方面存在的异同。鉴于SRGs在临床应用中的潜力，我们的结果可为基础研究和生物技术应用提供资源。
