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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Metabolism
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1543806
This article is part of the Research Topic Obesity, Diabetes, and Their Impact on Cancer View all 5 articles

Integrating Machine Learning and Single-Cell Sequencing to Identify Shared Biomarkers in Type 1 Diabetes Mellitus and Clear Cell Renal Cell Carcinoma

Provisionally accepted
Yi Li Yi Li 1Rui Zeng Rui Zeng 2Yuhua Huang Yuhua Huang 3Yumin Zhuo Yumin Zhuo 3Jun Huang Jun Huang 4*
  • 1 Department of Ultrasound, Guangdong Second Provincial General Hospital, Guangzhou, China, Guangzhou, China
  • 2 Department of pathology, School of Medicine, South China University of Technology, Guangzhou, China
  • 3 Department of Urology, The First Affiliated Hospital of Jinan University, Guangzhou, China
  • 4 Department of Ultrasound, The First Affiliated Hospital of Jinan University, Guangzhou, China, Guangzhou, China

The final, formatted version of the article will be published soon.

    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 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.Results: Overall, 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. Conclusion: Our 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.

    Keywords: type 1 diabetes mellitus, Clear cell renal cell carcinoma, key genes, machine learning, Single cell sequencing

    Received: 11 Dec 2024; Accepted: 07 Feb 2025.

    Copyright: © 2025 Li, Zeng, Huang, Zhuo and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jun Huang, Department of Ultrasound, The First Affiliated Hospital of Jinan University, Guangzhou, China, Guangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.