基于TCGA数据库鉴定与透明细胞肾细胞癌患者预后相关的新型糖酵解相关基因特征

Identification of Novel Glycolysis-Related Gene Signatures Associated With Prognosis of Patients With Clear Cell Renal Cell Carcinoma Based on TCGA.

作者信息

Wu Chengjiang, Cai Xiaojie, Yan Jie, Deng Anyu, Cao Yun, Zhu Xueming

机构信息

Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, Suzhou, China.

Department of Radiology, Affiliated Changshu Hospital of Soochow University, First People's Hospital of Changshu City, Suzhou, China.

出版信息

Front Genet. 2020 Dec 18;11:589663. doi: 10.3389/fgene.2020.589663. eCollection 2020.

OBJECTIVE

The purpose of the present study was to detect novel glycolysis-related gene signatures of prognostic values for patients with clear cell renal cell carcinoma (ccRCC).

METHODS

Glycolysis-related gene sets were acquired from the Molecular Signatures Database (V7.0). Gene Set Enrichment Analysis (GSEA) software (4.0.3) was applied to analyze glycolysis-related gene sets. The Perl programming language (5.32.0) was used to extract glycolysis-related genes and clinical information of patients with ccRCC. The receiver operating characteristic curve (ROC) and Kaplan-Meier curve were drawn by the R programming language (3.6.3).

RESULTS

The four glycolysis-related genes (B3GAT3, CENPA, AGL, and ALDH3A2) associated with prognosis were identified using Cox proportional regression analysis. A risk score staging system was established to predict the outcomes of patients with ccRCC. The patients with ccRCC were classified into the low-risk group and high-risk group.

CONCLUSIONS

We have successfully constructed a risk staging model for ccRCC. The model has a better performance in predicting the prognosis of patients, which may have positive reference value for the treatment and curative effect evaluation of ccRCC.