Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data

作者：Banlai Ruan, Xianzhen Feng, Xueyi Chen, Zhiwei Dong, Qi Wang, Kai Xu, Jinping Tian, Liu Jie, Ziyin Chen, Wenzhen Shi, Man Wang, Qian Lü, Qianshan Ding · 发表于：Disease Markers · 年份：2020 · DOI：10.1155/2020/8824717 · 被引用次数：16 · 研究领域：Ferroptosis and cancer prognosis、Renal cell carcinoma treatment、Bladder and Urothelial Cancer Treatments

BACKGROUND: With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC). METHODS: Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis. RESULTS: < 0.01). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC. CONCLUSION: The gene expression model consisting of CDKL2, LRFN1, STAT2, and ...