Construction of a novel prognostic model based on lncRNAs-related to DNA damage repair for predicting the prognosis of clear cell renal cell carcinoma

• Published in: Annals of medicine (Helsinki) Vol. 57; no. 1; p. 2480755
• Main Authors: Chen, Peng, Li, Jian, Tian, Renli
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 01.12.2025; Taylor & Francis Group
• Subjects: Aged; Biomarkers, Tumor - genetics; Carcinoma, Renal Cell - drug therapy; Carcinoma, Renal Cell - genetics; Carcinoma, Renal Cell - mortality; Carcinoma, Renal Cell - pathology; Clear cell renal cell carcinoma; DNA Damage - genetics; DNA damage repair; DNA Repair - genetics; Female; Gene Expression Regulation, Neoplastic; Humans; Kidney Neoplasms - drug therapy; Kidney Neoplasms - genetics; Kidney Neoplasms - mortality; Kidney Neoplasms - pathology; lncRNAs; Male; Middle Aged; Oncology; Prognosis; Proportional Hazards Models; risk score; RNA, Long Noncoding - genetics; survival; lncRNAs; DNA damage repair; Clear cell renal cell carcinoma; risk score; survival
• ISSN: 0785-3890; 1365-2060; 1365-2060
• DOI: 10.1080/07853890.2025.2480755

CcRCC has the characteristics of high aggression, high metastasis, high mortality, wide tumour heterogeneity and variable clinical course. The purpose of this study was to explore the potential value of lncRNAs-related to DNA damage repair (DDR) in predicting the prognosis of ccRCC by construction and verification a novel prognostic model. RNA-seq data and clinical data of ccRCC were downloaded from public databases. Subsequently, Pearson correlation analysis and differential expression analysis were performed to identify DElncRNAs-related to DDR. Then, through univariate Cox analysis and LASSO analysis, the DElncRNAs-related to DDR associated with prognosis were screened for the construction of novel risk score prognostic model. In addition, functional annotation, tumour mutation burden, immune correlation and drug sensitivity analyses were performed based on risk score to assess the characteristics of patients in different risk score groups. Based on univariate Cox analysis and LASSO analysis, four best DElncRNAs-related to DDR were selected. Subsequently, a novel risk score prognostic model based on these four DElncRNAs was constructed through LASSO. Multivariate Cox analysis showed that risk score and age were independent prognostic factors for ccRCC (  < 0.05). Functional enrichment analysis showed that DDR-related biological processes were mainly enriched in the high risk group. The highly mutated genes in the high and low risk groups were the same (VHL, PBRM1 and TTN), but they also had their own unique mutated genes. Pearson correlation analysis showed that the risk score was significantly (  < 0.05) positively correlated with the infiltration degree of CD8 T cells evaluated by six algorithms. In addition, it was found that the high and low risk groups had different sensitivities to the drugs Etoposide, Imatinib, Sorafenib, Bosutinib and Sunitinib. A novel prognostic model was constructed based on four DElncRNAs-related to DDR. The model has satisfactory accuracy in predicting survival of ccRCC patients.