Telomere-based Risk Model for Prognosis Prediction in Clear Cell Renal Cell Carcinoma

Telomeres have become extensively studied in renal cell carcinoma (RCC), and this study aims to identify relevant diagnostic biomarkers in the predominant RCC subtype, clear cell RCC (ccRCC).

This study retrieved telomere-related genes from the TelNet database and integrated data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) to calculate telomere enrichment scores using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were then applied to identify candidate genes, which were further refined through protein-protein interaction (PPI) network construction and two machine learning methods: least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE). The associations between the identified feature genes and immune cell infiltration were subsequently evaluated using CIBERSORT and ESTIMATE. Furthermore, single-cell analysis was employed to determine the highly expressed genes in different cell clusters. Finally, using a ccRCC cell line, quantitative real-time PCR, wound healing, and Transwell assays were performed to validate the expression and potential biological functions of the selected key genes.

A higher telomere score was observed in ccRCC. The common genes from the DEGs and the gene modules were mainly enriched in cell division- and senescence-related pathways. Moreover, six genes (ASPM, CENPF, CEP55, MELK, BUB1, and EXO1) were identified as feature genes with satisfactory diagnostic efficacy and high expression in ccRCC; they were positively correlated with most immune cells and highly expressed in T cells. Notably, CEP55 knockdown suppressed the migration and invasion of ccRCC cells.

Our present study, based on the data from the public databases, unraveled 6 genes with diagnostic efficacy in ccRCC, which may aid the development of a relevant future diagnostic method in ccRCC.

This study identified six telomere-related genes with high expression and strong diagnostic value in ccRCC, highlighting their association with immune infiltration and potential as diagnostic and therapeutic targets.