Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma - Ning, Xiang-hui; Li, Ning-yang; Qi, Yuan-yuan; Li, Song-chao; Jia, Zhan-kui; Yang, Jin-jian (Frontiers in Oncology, 2022)
Purpose: The present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. Methods: Patients' data were downloaded from TCGA and ICGC database, hypoxia related genes were accessed from Molecular Signatures Database. The differentially expressed genes were evaluated and then the differential expressions hypoxia genes were screened. The TCGA cohort was randomly divided into discovery TCGA cohort and validation TCGA cohort. The discovery TCGA cohort was using for constructing the hypoxia genes risk model through Lasso regression, univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were used to assess the reliability and sensitivity of our model. Then, we established a nomogram to predict the probably one-, three-, and five-year overall survival rate. Lastly, the Tumor Immune Dysfunction and Exclusion (TIDE) score of patients was calculated. Results: We established a six hypoxia-related genes prognostic model of KIRC patients in TCGA database and validated in ICGC database. The patients with high riskscore present poorer prognosis than those with low riskscore in the three TCGA cohorts and ICGC cohort. ROC curves shown that our six-genes model with a robust predictive capability in these four cohorts. In addition, we constructed a nomogram for KIRC patients in TCGA database. Finally, the high risk-group had a high TIDE score than the patients with low riskscore. Conclusions: We established a six hypoxia-related genes risk model for independent prediction of the prognosis of KIRC patients was established and constructed a robust nomogram. The different riskscore might be a biomarker for immunotherapy strategy.