Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma - Li, Yi; Zeng, Rui; Huang, Yuhua; Zhuo, Yumin; Huang, Jun (Frontiers in Oncology, 2025)
PurposeType 1 diabetes mellitus (T1DM), as an autoimmune disease, can increase susceptibility to clear cell renal cell carcinoma (ccRCC) due to its proinflammatory effects. ccRCC is characterized by its subtle onset and unfavorable prognosis. Thus, the aim of this study was to highlight prevention and early detection opportunities in high-risk populations by identifying common biomarkers for T1DM and ccRCC.MethodsBased on multiple publicly available datasets, WGCNA was applied to identify gene modules closely associated with T1DM, which were then integrated with prognostic DEGs in ccRCC. Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.ResultsOverall, three hub genes (KIF21A, PIGH, and RPS6KA2) were identified as shared biomarkers for TIDM and ccRCC. Analysis of clinical samples and multiple datasets revealed that KIF21A and PIGH were significantly downregulated and that PIG was upregulated in the disease group. KIF21A and PIGH are mainly expressed in NK and T cells, PRS6KA2 is mainly expressed in endothelial and epithelial cells, and the MIF signaling pathway may be related to hub genes.ConclusionOur results demonstrated the pivotal roles of hub genes in T1DM and ccRCC. These genes hold promise as novel biomarkers, offering potential avenues for preventive strategies and the development of new precision treatment modalities.