Single-cell transcriptomics combined with Mendelian randomization reveals the pivotal role of WDR83OS in clear cell...

This study aimed to comprehensively investigate the molecular mechanisms of clear cell renal cell carcinoma (ccRCC), identify key cellular subpopulations and genes, develop an effective diagnostic model, and screen potential targeted therapies for ccRCC. We analyzed single-cell transcriptomic sequencing data to identify the major cellular subpopulations in ccRCC. High-dimensional weighted gene co-expression network analysis and multiple machine learning algorithms were used to identify key genes and develop a diagnostic model. Two-sample Mendelian randomization analysis was performed to assess causality. Molecular docking was used to identify a candidate therapeutic agent. Data processing was conducted using R and Python. The proportion of endothelial cells was significantly higher in ccRCC (P < .001). High-dimensional weighted gene co-expression network analysis showed that the pink module was closely associated with endothelial cells. Univariate logistic regression and Least Absolute Shrinkage and Selection Operator identified 11 key genes: WDR83OS, TMA7, PFDN5, DSTN, PHPT1, HMGN3, TMSB10, RPL27A, RPL23A, RPL15, and RPL27. Based on these genes, a diagnostic model for ccRCC was developed using multiple machine learning algorithms and achieved an area under the receiver operating characteristic curve of 0.960. In addition, 2-sample Mendelian randomization analysis supported a causal association between WDR83OS and ccRCC (inverse-variance weighted: odds ratio = 1.160, P = .033). Molecular docking indicated that oxyphenbutazone had a high binding affinity for WDR83OS, with a binding energy of -7.124 kcal/mol. By integrating multiple bioinformatic approaches, this study identified key cellular populations and genes in ccRCC, developed a reliable diagnostic model, and highlighted WDR83OS as a potentially important therapeutic target.