Machine learning-driven prognostic analysis of cuproptosis and disulfidptosis-related lncRNAs in clear cell renal cell carcinoma: a step towards precision oncology

• Published in: European journal of medical research Vol. 29; no. 1; pp. 176 - 17
• Main Authors: Chen, Ronghui, Wu, Jun, Che, Yinwei, Jiao, Yuzhuo, Sun, Huashan, Zhao, Yinuo, Chen, Pingping, Meng, Lingxin, Zhao, Tao
• Format: Journal Article
• Language: English
• Published: London BioMed Central 16.03.2024; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Biomedicine; Cancer; Cancer therapies; Carcinoma, Renal cell; Cell death; Clear cell renal cell carcinoma; Copper; Cuproptosis; Data mining; Development and progression; Disulfidptosis; Drug therapy; Ferroptosis; Genes; Genetic aspects; Genomes; Immunotherapy; Infectious Diseases; Internal Medicine; Kidney cancer; Kinases; Long non-coding RNA; Machine learning; Machine learning algorithm; Medical prognosis; Medicine; Medicine & Public Health; Metabolism; Mutation; Nomograms; Oncology; Patients; Precision medicine; Prognosis; Prognostic risk model; Proteins; RNA; Surgery; Survival analysis; China; Immune inhibitors; Disulfidptosis; Cuproptosis; Machine learning algorithm; Prognostic risk model; Clear cell renal cell carcinoma; Targeted drugs; Long non-coding RNA
• ISSN: 2047-783X; 0949-2321; 2047-783X
• DOI: 10.1186/s40001-024-01763-1

Cuproptosis and disulfidptosis, recently discovered mechanisms of cell death, have demonstrated that differential expression of key genes and long non-coding RNAs (lncRNAs) profoundly influences tumor development and affects their drug sensitivity. Clear cell renal cell carcinoma (ccRCC), the most common subtype of kidney cancer, presently lacks research utilizing cuproptosis and disulfidptosis-related lncRNAs (CDRLRs) as prognostic markers. In this study, we analyzed RNA-seq data, clinical information, and mutation data from The Cancer Genome Atlas (TCGA) on ccRCC and cross-referenced it with known cuproptosis and disulfidptosis-related genes (CDRGs). Using the LASSO machine learning algorithm, we identified four CDRLRs—ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT—that are strongly associated with prognosis and used them to construct a prognostic risk model. To verify the model's reliability and validate these four CDRLRs as significant prognostic factors, we performed dataset grouping validation, followed by RT-qPCR and external database validation for differential expression and prognosis of CDRLRs in ccRCC. Gene function and pathway analysis were conducted using Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) for high- and low-risk groups. Additionally, we have analyzed the tumor mutation burden (TMB) and the immune microenvironment (TME), employing the oncoPredict and Immunophenoscore (IPS) algorithms to assess the sensitivity of diverse risk categories to targeted therapeutics and immunosuppressants. Our predominant objective is to refine prognostic predictions for patients with ccRCC and inform treatment decisions by conducting an exhaustive study on cuproptosis and disulfidptosis.