Abstract
BackgroundAccumulating 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.MethodsmRNA 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).ResultsThrough 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.ConclusionsOur 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.
Highlights
Renal cell carcinoma (RCC) is one of the top ten cancers in the world, with about 65,000 new cases occurring each year in the United States [1]
The most common and aggressive subtype is clear cell RCC, which accounts for about 80% of all RCC [2]. ccRCC is usually asymptomatic in the early stages, with metastases occurring in about 25–30% of patients at the time of diagnosis [3]
We firstly performed Gene set enrichment analysis (GSEA) to explore whether five glycolysisrelated gene sets were significantly different between ccRCC and normal samples
Summary
Renal cell carcinoma (RCC) is one of the top ten cancers in the world, with about 65,000 new cases occurring each year in the United States [1]. Because of the tumor heterogeneity, patients with the same degree of progression can show different prognosis and treatment responses [4]. It is necessary to find effective biomarkers to assess prognosis and identify potential patients at high risk for ccRCC. Cancer cells have a high degree of glycolysis. Studies have shown that tumor glycolysis is a promising target for the treatment of cancer [7]. 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
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