Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.

ABSTRACT:

Purpose

Type 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.

Methods

Based 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 pattern

SUBMITTER: Li Y 

PROVIDER: S-EPMC11911197 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature