Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision
Abstract Introduction Diabetic nephropathy (DN) is a common diabetes-related complication with unclear underlying pathological mechanisms. Although recent studies have linked glycolysis to various pathological states, its role in DN remains largely underexplored. Methods In this study, the expressio...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | Biology Direct |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13062-025-00601-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585978224574464 |
|---|---|
| author | Chenglong Fan Guanglin Yang Cheng Li Jiwen Cheng Shaohua Chen Hua Mi |
| author_facet | Chenglong Fan Guanglin Yang Cheng Li Jiwen Cheng Shaohua Chen Hua Mi |
| author_sort | Chenglong Fan |
| collection | DOAJ |
| description | Abstract Introduction Diabetic nephropathy (DN) is a common diabetes-related complication with unclear underlying pathological mechanisms. Although recent studies have linked glycolysis to various pathological states, its role in DN remains largely underexplored. Methods In this study, the expression patterns of glycolysis-related genes (GRGs) were first analyzed using the GSE30122, GSE30528, and GSE96804 datasets, followed by an evaluation of the immune landscape in DN. An unsupervised consensus clustering of DN samples from the same dataset was conducted based on differentially expressed GRGs. The hub genes associated with DN and glycolysis-related clusters were identified via weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Finally, the expression patterns of these hub genes were validated using single-cell sequencing data and quantitative real-time polymerase chain reaction (qRT-PCR). Results Eleven GRGs showed abnormal expression in DN samples, leading to the identification of two distinct glycolysis clusters, each with its own immune profile and functional pathways. The analysis of the GSE142153 dataset showed that these clusters had specific immune characteristics. Furthermore, the Extreme Gradient Boosting (XGB) model was the most effective in diagnosing DN. The five most significant variables, including GATM, PCBD1, F11, HRSP12, and G6PC, were identified as hub genes for further investigation. Single-cell sequencing data showed that the hub genes were predominantly expressed in proximal tubular epithelial cells. In vitro experiments confirmed the expression pattern in NC. Conclusion Our study provides valuable insights into the molecular mechanisms underlying DN, highlighting the involvement of GRGs and immune cell infiltration. |
| format | Article |
| id | doaj-art-35ae1367ed91482e8dec72c99a76afd5 |
| institution | Kabale University |
| issn | 1745-6150 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Biology Direct |
| spelling | doaj-art-35ae1367ed91482e8dec72c99a76afd52025-01-26T12:19:29ZengBMCBiology Direct1745-61502025-01-0120111610.1186/s13062-025-00601-6Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precisionChenglong Fan0Guanglin Yang1Cheng Li2Jiwen Cheng3Shaohua Chen4Hua Mi5Department of Urology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Urology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Urology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Urology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Urology, Guangxi Medical University Cancer HospitalDepartment of Urology, The First Affiliated Hospital of Guangxi Medical UniversityAbstract Introduction Diabetic nephropathy (DN) is a common diabetes-related complication with unclear underlying pathological mechanisms. Although recent studies have linked glycolysis to various pathological states, its role in DN remains largely underexplored. Methods In this study, the expression patterns of glycolysis-related genes (GRGs) were first analyzed using the GSE30122, GSE30528, and GSE96804 datasets, followed by an evaluation of the immune landscape in DN. An unsupervised consensus clustering of DN samples from the same dataset was conducted based on differentially expressed GRGs. The hub genes associated with DN and glycolysis-related clusters were identified via weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Finally, the expression patterns of these hub genes were validated using single-cell sequencing data and quantitative real-time polymerase chain reaction (qRT-PCR). Results Eleven GRGs showed abnormal expression in DN samples, leading to the identification of two distinct glycolysis clusters, each with its own immune profile and functional pathways. The analysis of the GSE142153 dataset showed that these clusters had specific immune characteristics. Furthermore, the Extreme Gradient Boosting (XGB) model was the most effective in diagnosing DN. The five most significant variables, including GATM, PCBD1, F11, HRSP12, and G6PC, were identified as hub genes for further investigation. Single-cell sequencing data showed that the hub genes were predominantly expressed in proximal tubular epithelial cells. In vitro experiments confirmed the expression pattern in NC. Conclusion Our study provides valuable insights into the molecular mechanisms underlying DN, highlighting the involvement of GRGs and immune cell infiltration.https://doi.org/10.1186/s13062-025-00601-6Diabetic nephropathyGlycolysisGlycolysis-related genesMachine learning algorithmHub genes |
| spellingShingle | Chenglong Fan Guanglin Yang Cheng Li Jiwen Cheng Shaohua Chen Hua Mi Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision Biology Direct Diabetic nephropathy Glycolysis Glycolysis-related genes Machine learning algorithm Hub genes |
| title | Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision |
| title_full | Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision |
| title_fullStr | Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision |
| title_full_unstemmed | Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision |
| title_short | Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision |
| title_sort | uncovering glycolysis driven molecular subtypes in diabetic nephropathy a wgcna and machine learning approach for diagnostic precision |
| topic | Diabetic nephropathy Glycolysis Glycolysis-related genes Machine learning algorithm Hub genes |
| url | https://doi.org/10.1186/s13062-025-00601-6 |
| work_keys_str_mv | AT chenglongfan uncoveringglycolysisdrivenmolecularsubtypesindiabeticnephropathyawgcnaandmachinelearningapproachfordiagnosticprecision AT guanglinyang uncoveringglycolysisdrivenmolecularsubtypesindiabeticnephropathyawgcnaandmachinelearningapproachfordiagnosticprecision AT chengli uncoveringglycolysisdrivenmolecularsubtypesindiabeticnephropathyawgcnaandmachinelearningapproachfordiagnosticprecision AT jiwencheng uncoveringglycolysisdrivenmolecularsubtypesindiabeticnephropathyawgcnaandmachinelearningapproachfordiagnosticprecision AT shaohuachen uncoveringglycolysisdrivenmolecularsubtypesindiabeticnephropathyawgcnaandmachinelearningapproachfordiagnosticprecision AT huami uncoveringglycolysisdrivenmolecularsubtypesindiabeticnephropathyawgcnaandmachinelearningapproachfordiagnosticprecision |