A prognostic model of clear cell renal cell carcinoma based on telomere-related lncRNAs

Abstract Background Telomeres have been demonstrated to be critical in the development of multiple tumors. However, the association of telomere-related lncRNAs with clear cell renal cell carcinoma (ccRCC) and their prognostic roles in ccRCC patients remain unknown. METHODS Expression matrix and clinicopathological data of ccRCC patients were extracted from The Cancer Genome Altas and UCSC Xena browser. The differentially expressed genes were identified and intersected with the telomere-related genes downloaded from the Telnet database. Telomere-related lncRNAs were screened by the univariate Cox regression analysis. Each patient's risk score was calculated to establish a nomogram based on eight telomere-related lncRNAs screened by the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis. The correlation between telomere-related lncRNAs and immune cells was assessed by the CIBEERSORT algorithm. The immune and stromal infiltrations were quantified by the ESTIMATE algorithm. Gene set enrichment analysis (GSEA) was performed to explore the selected lncRNA functions. Result We screened eight telomere-related lncRNAs and established a risk score model for predicting survival in ccRCC patients. A nomogram was developed to predict the survival outcomes of postoperative patients by integrating several clinical factors, and a well-predictive effect was observed. The correlation between selected lncRNAs and immune function was explored by the CIBEERSORT and ESTIMATE algorithms. Besides, GSEA showed that telomere-related lncRNAs could affect ccRCC prognosis through multiple pathways.