Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma
Yi Li, Rui Zeng, Yuhua Huang, Yumin Zhuo, Jun Huang

TL;DR
This study uses machine learning and single-cell sequencing to find shared biomarkers between type 1 diabetes and kidney cancer, aiming to improve early detection and treatment.
Contribution
The study identifies three shared hub genes between T1DM and ccRCC using machine learning and single-cell data, offering new biomarkers for early detection and treatment.
Findings
Three hub genes (KIF21A, PIGH, RPS6KA2) were identified as shared between T1DM and ccRCC.
KIF21A and PIGH were downregulated, while PIGH was upregulated in disease groups.
The MIF signaling pathway may be related to these hub genes.
Abstract
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. 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 patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential…
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Taxonomy
TopicsFerroptosis and cancer prognosis · Renal cell carcinoma treatment · GDF15 and Related Biomarkers
