An Effective Hypoxia-Related Long Non-Coding RNAs Assessment Model for Prognosis of Clear Cell Renal Carcinoma.

Abstract

Hypoxia is a significant clinical feature and regulates various tumor processes in clear cell renal carcinoma (ccRCC). Increasing evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with the survival outcomes of ccRCC patients and regulates hypoxia-induced tumor processes. Thus, this study aimed to develop a hypoxia-related lncRNA (HRL) prognostic model for predicting the survival outcomes in ccRCC. LncRNAs in ccRCC samples were extracted from The Cancer Genome Atlas database. Hypoxia-related genes were downloaded from the Molecular Signatures Database. A co-expression analysis between differentially expressed lncRNAs and hypoxia-related genes in ccRCC samples was performed to identify HRLs. Univariate and multivariate Cox regression analyses were performed to select nine optimal lncRNAs for developing the HRL model. The prognostic model showed good performance in predicting prognosis among patients with ccRCC, and the validation sets reached consistent results. The model was also found to be related to the clinicopathologic parameters of tumor grade and tumor stage and to tumor immune infiltration. In conclusion, our findings indicate that the hypoxia-lncRNA assessment model may be useful for prognostication in ccRCC cases. Furthermore, the nine HRLs included in the model might be useful targets for investigating the tumorigenesis of ccRCC and designing individualized treatment strategies.