Lv Zhengtong, Qi Lin, Hu Xiheng, Mo Miao, Jiang Huichuan, Li Yuan
Department of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Graduate School of Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
Front Oncol. 2021 Mar 17;11:633950. doi: 10.3389/fonc.2021.633950. eCollection 2021.
Accumulating evidences indicate significant alterations in the aerobic glycolysis in clear cell renal cell carcinoma (ccRCC). We aim to develop and validate a glycolysis-related genes signature for predicting the clinical outcomes of patients with ccRCC.
mRNA expression profiling of ccRCC was obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. The protein expression levels of the core genes were obtained from the Human Protein Atlas database. We used four external independent data sets to verify the predictive power of the model for prognosis, tyrosine kinase inhibitor (TKI) therapy, and immunotherapy responses, respectively. Finally, we explored the potential mechanism of this signature through gene set enrichment analysis (GSEA).
Through the GSEA, glycolysis-related gene sets were significantly different between ccRCC tissues and normal tissues. Next, we identified and constructed a seven-mRNA signature (GALM, TGFA, RBCK1, CD44, HK3, KIF20A, and IDUA), which was significantly correlated with worse survival outcome and was an independent prognostic indicator for ccRCC patients. Furthermore, the expression levels of hub genes were validated based on the Human Protein Atlas databases. More importantly, the model can predict patients' response to TKI therapy and immunotherapy. These findings were successfully validated in the external independent ccRCC cohorts. The mechanism exploration showed that the model may influence the prognosis by influencing tumor proliferation, base mismatch repair system and immune status of patients.
Our study has built up a robust glycolysis-based molecular signature that predicts the prognosis and TKI therapy and immunotherapy responses of patients with ccRCC with high accuracy, which might provide important guidance for clinical assessment. Also, clinical investigations in large ccRCC cohorts are greatly needed to validate our findings.
越来越多的证据表明，透明细胞肾细胞癌（ccRCC）的有氧糖酵解存在显著改变。我们旨在开发并验证一种与糖酵解相关的基因特征，以预测ccRCC患者的临床结局。
从癌症基因组图谱数据库获取ccRCC的mRNA表达谱。进行单变量Cox回归分析和套索Cox回归模型，以识别并构建预后基因特征。核心基因的蛋白质表达水平从人类蛋白质图谱数据库获取。我们分别使用四个外部独立数据集来验证该模型对预后、酪氨酸激酶抑制剂（TKI）治疗和免疫治疗反应的预测能力。最后，通过基因集富集分析（GSEA）探索该特征的潜在机制。
通过GSEA发现，ccRCC组织和正常组织之间与糖酵解相关的基因集存在显著差异。接下来，我们识别并构建了一个七基因特征（GALM、TGFA、RBCK1、CD44、HK3、KIF20A和IDUA），其与较差的生存结局显著相关，是ccRCC患者的独立预后指标。此外，基于人类蛋白质图谱数据库验证了枢纽基因的表达水平。更重要的是，该模型可以预测患者对TKI治疗和免疫治疗的反应。这些发现已在外部独立的ccRCC队列中成功验证。机制探索表明，该模型可能通过影响肿瘤增殖、碱基错配修复系统和患者的免疫状态来影响预后。
我们的研究建立了一个强大的基于糖酵解的分子特征，能够高精度地预测ccRCC患者的预后、TKI治疗和免疫治疗反应，这可能为临床评估提供重要指导。此外，迫切需要在大型ccRCC队列中进行临床研究以验证我们的发现。
