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. 2020 Jun 21;11(17):4996-5006.
doi: 10.7150/jca.45296. eCollection 2020.

Identification of a Glucose Metabolism-related Signature for prediction of Clinical Prognosis in Clear Cell Renal Cell Carcinoma

Sheng Wang  1   2 , Ling Zhang  1   2 , Zhihong Yu  2 , Kequn Chai  2 , Jiabin Chen  2
Affiliations

Identification of a Glucose Metabolism-related Signature for prediction of Clinical Prognosis in Clear Cell Renal Cell Carcinoma

Sheng Wang et al. J Cancer. .

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent and invasive histological subtypes among all renal cell carcinomas (RCC). Cancer cell metabolism, particularly glucose metabolism, has been reported as a hallmark of cancer. However, the characteristics of glucose metabolism-related gene sets in ccRCC have not been systematically profiled. Methods: In this study, we downloaded a gene expression profile and glucose metabolism-related gene set from TCGA (The Cancer Genome Altas) and MSigDB, respectively, to analyze the characteristics of glucose metabolism-related gene sets in ccRCC. We used a multivariable Cox regression analysis to develop a risk signature, which divided patients into low- and high- risk groups. In addition, a nomogram that combined the risk signature and clinical characteristics was created for predicting the 3- and 5-year overall survival (OS) of ccRCC. The accuracy of the nomogram prediction was evaluated using the area under the receiver operating characteristic curve (AUC) and a calibration plot. Results: A total of 231 glucose metabolism-related genes were found, and 68 differentially expressed genes (DEGs) were identified. After screening by univariate regression analysis, LASSO regression analysis and multivariable Cox regression analysis, six glucose metabolism-related DEGs (FBP1, GYG2, KAT2A, LGALS1, PFKP, and RGN) were selected to develop a risk signature. There were significant differences in the clinical features (Fuhrman nuclear grade and TNM stage) between the high- and low-risk groups. The multivariable Cox regression indicated that the risk score was independent of the prognostic factors (training set: HR=3.393, 95% CI [2.025, 5.685], p<0.001; validation set: HR=1.933, 95% CI [1.130, 3.308], p=0.016). The AUCs of the nomograms for the 3-year OS in the training and validation sets were 0.808 and 0.819, respectively, and 0.777 and 0.796, respectively, for the 5- year OS. Conclusion: We demonstrated a novel glucose metabolism-related risk signature for predicting the prognosis of ccRCC. However, additional in vitro and in vivo research is required to validate our findings.

Keywords: clear cell renal cell carcinoma; glucose metabolism; signature.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Stratification of ccRCC based on the glucose metabolism-related gene set. A, Consensus clustering matrix of 515 ccRCC samples for k = 2. B, Consensus clustering CDF for k = 2 to k = 10. C, Principal component analysis (PCA) of cluster 1 and cluster 2 based on whole gene expression data. D, survival analysis of ccRCC patients in cluster 1 and cluster 2.
Figure 2
Figure 2
Identification of the six glucose metabolism-related genes signature. A, Volcano plot of DEGs. Red dots represent 32 upregulated genes and green dots represent 36 downregulated genes. B, “Leave- one-out-cross-validation” for parameter selection in LASSO regression. C, The forest map of multivariate Cox regression analysis. D, The prognostic nomogram for the prediction of 3- and 5-year overall survival in ccRCC.
Figure 3
Figure 3
Evaluation of the six glucose metabolism-related genes signature. A, the area under the receiver operating characteristic (ROC) curve (AUC) for the 3-year overall survival of ccRCC patients in the training set. B, the AUC for the 5-year overall survival of ccRCC patients in the training set. C the AUC for 3-year overall survival of the ccRCC patients in the validation set. D, the AUC for 5-year overall survival of the ccRCC patients in the validation set. E, Calibration curve of the nomogram model for the 3- year overall survival in the training set. F, Calibration curve of the nomogram model for the 5- year overall survival in the training set.
Figure 4
Figure 4
Correlation between risk score and clinical characteristics. A, Heat map of the association of risk scores and clinicopathological features in the training set. B, Heat map of the association of risk scores and clinicopathological features in the validation set. *P<0.05, ** P<0.01, ***P<0.001.
Figure 5
Figure 5
The six-gene signature is an independent prognostic factor of survival. A, Kaplan-Meier survival curves for low- and high- risk groups in the training set. B, Kaplan-Meier survival curves for low- and high- risk groups in the validation set. C, the result of univariable Cox regression analysis in the training set. D, the result of univariable Cox regression analysis in the validation set. E, the result of multivariable Cox regression analysis in the training set. F, the result of multivariable Cox regression analysis in the validation set.
Figure 6
Figure 6
Functional enrichment analysis and PPI network. A, the pathways enriched for 217 genes highly related with risk score. B, GO enrichment analysis. C, Protein-protein interaction (PPI) network.
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