Identification of a cancer driver gene-associated lncRNA signature for prognostic prediction and immune response evaluation in clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) predominates among kidney cancer cases and is influenced by mutations in cancer driver genes (CDGs). However, significant obstacles persist in the early diagnosis and treatment of ccRCC. While various genetic models offer new hopes for improving ccRCC management, the relationship between CDG-related long non-coding RNAs (CDG-RlncRNAs) and ccRCC remains poorly understood. Therefore, this study aims to construct prognostic molecular features based on CDG-RlncRNAs to predict the prognosis of ccRCC patients, and aims to provide a new strategy to enhance clinical management of ccRCC patients. Methods: This study employed Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses to comprehensively investigate the association between lncRNAs and CDGs in ccRCC. Leveraging The Cancer Genome Atlas (TCGA) dataset, we identified 97 prognostically significant CDG-RlncRNAs and developed a robust prognostic model based on these CDG-RlncRNAs. The performance of the model was rigorously validated using the TCGA dataset for training and the International Cancer Genome Consortium (ICGC) dataset for validation. Functional enrichment analysis elucidated the biological relevance of CDG-RlncRNA features in the model, particularly in tumor immunity. Experimental validation further confirmed the functional role of representative CDG-RlncRNA SNHG3 in ccRCC progression. Results: Our analysis revealed that 97 CDG-RlncRNAs are significantly associated with ccRCC prognosis, enabling patient stratification into different risk groups. Development of a prognostic model incorporating key lncRNAs such as HOXA11-AS, AP002807.1, APCDD1L-DT, AC124067.2, and SNHG3 demonstrated robust predictive accuracy in both training and validation datasets. Importantly, risk stratification based on the model revealed distinct immune-related gene expression patterns. Notably, SNHG3 emerged as a key regulator of the ccRCC cell cycle, highlighting its potential as a therapeutic target. Conclusions: Our study established a concise CDG-RlncRNA signature and underscored the pivotal role of SNHG3 in ccRCC progression. It emphasizes the clinical relevance of CDG-RlncRNAs in prognostic prediction and targeted therapy, offering potential avenues for personalized intervention in ccRCC.