Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma
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 a...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1543806/full |
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| author | Yi Li Rui Zeng Rui Zeng Yuhua Huang Yumin Zhuo Jun Huang |
| author_facet | Yi Li Rui Zeng Rui Zeng Yuhua Huang Yumin Zhuo Jun Huang |
| author_sort | Yi Li |
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| description | 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. |
| format | Article |
| id | doaj-art-51e60524b50644fdb87f8ef85881aaaa |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-51e60524b50644fdb87f8ef85881aaaa2025-08-20T02:18:20ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-03-011510.3389/fonc.2025.15438061543806Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinomaYi Li0Rui Zeng1Rui Zeng2Yuhua Huang3Yumin Zhuo4Jun Huang5Department of Ultrasound, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, Guangzhou, ChinaDepartment of Pathology, School of Medicine, South China University of Technology, Guangzhou, ChinaDepartment of Pathology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Guangzhou, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Jinan University, Guangzhou, ChinaDepartment of Urology, The First Affiliated Hospital of Jinan University, Guangzhou, ChinaDepartment of Ultrasound, The First Affiliated Hospital of Jinan University, Guangzhou, ChinaPurposeType 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.https://www.frontiersin.org/articles/10.3389/fonc.2025.1543806/fulltype 1 diabetes mellitusclear cell renal cell carcinomakey genesmachine learningsingle cell sequencing |
| spellingShingle | Yi Li Rui Zeng Rui Zeng Yuhua Huang Yumin Zhuo Jun Huang Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma Frontiers in Oncology type 1 diabetes mellitus clear cell renal cell carcinoma key genes machine learning single cell sequencing |
| title | Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma |
| title_full | Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma |
| title_fullStr | Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma |
| title_full_unstemmed | Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma |
| title_short | Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma |
| title_sort | integrating machine learning and single cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma |
| topic | type 1 diabetes mellitus clear cell renal cell carcinoma key genes machine learning single cell sequencing |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1543806/full |
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