Identification of shared diagnostic biomarkers and molecular pathways between chronic kidney disease and renal cell carcinoma using transcriptomics and machine learning

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Background: Chronic kidney disease (CKD) is a prevalent condition associated with an increased risk of renal cell carcinoma (RCC). However, the molecular mechanisms underlying the link between CKD and RCC remain largely unexplored. This study aimed to identify key biomarkers and molecular pathways associated with CKD and RCC. Methods: ) and causal relationship between CKD and RCC using genome-wide association studies (GWAS) data. Differentially expressed genes (DEGs) and gene modules associated with CKD and RCC were identified from the transcriptomic data of 257 samples. The shared genes were further analyzed, and machine learning algorithms were applied to identify key hub genes. Receiver operating characteristic (ROC) curves were used to evaluate the discovery datasets and assess the correlations among key hub genes, immune cell abundance, and RCC clinical stage. Additionally, single-gene gene set enrichment analysis (GSEA) was performed to explore the potential mechanisms. To assess diagnostic utility, logistic regression (LR), random forest (RF), and support vector machine (SVM) models based on the three hub genes were developed with independent CKD and RCC training datasets and validated externally. Results: , served as coexistence genes in both diseases. These key hub genes exhibited high area under the curve (AUC) values (≥0.80) in independent CKD and RCC datasets. The predictive models further demonstrated strong diagnostic performance, with AUC values of 0.9689 (CKD, LR) and 0.9568 (RCC, RF) in external validation datasets. Conclusions: This study suggests that three key hub genes underlie the comorbidity mechanism between CKD and RCC and may serve as potential therapeutic targets.

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