Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning.
Diabetic kidney disease (DKD) is a major cause of end-stage renal disease globally, with podocytes being implicated in its pathogenesis. However, the underlying mechanisms of podocyte involvement remain unclear. The aim of the present study was to identify podocyte molecular markers associated with...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0328352 |
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| author | Hailin Li Quhuan Li Zuyan Fan Yue Shen Jiao Li Fengxia Zhang |
| author_facet | Hailin Li Quhuan Li Zuyan Fan Yue Shen Jiao Li Fengxia Zhang |
| author_sort | Hailin Li |
| collection | DOAJ |
| description | Diabetic kidney disease (DKD) is a major cause of end-stage renal disease globally, with podocytes being implicated in its pathogenesis. However, the underlying mechanisms of podocyte involvement remain unclear. The aim of the present study was to identify podocyte molecular markers associated with DKD using single-cell RNA sequencing (scRNA-seq) data from patients with early DKD. Through enrichment analysis, subcluster clustering, and ligand-receptor (LR) interaction analysis, we elucidated the role of podocytes in early DKD progression. Podocyte heterogeneity and functional differences in DKD were observed. Multiple machine-learning algorithms were used to screen and construct diagnostic models to identify hub differentially expressed podocyte marker genes (DE-podos), revealing ARHGEF26 as a significantly downregulated marker in DKD. Validation using external datasets, reverse transcription quantitative real-time PCR (RT-qPCR) and Western blot confirmed it as a potential diagnostic biomarker. Our findings elucidate podocyte function in DKD and provide viable therapeutic targets, potentially improving diagnostic accuracy and treatment outcomes. |
| format | Article |
| id | doaj-art-c879101c76c24d8a98649ced6f931f06 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-c879101c76c24d8a98649ced6f931f062025-08-20T03:13:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032835210.1371/journal.pone.0328352Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning.Hailin LiQuhuan LiZuyan FanYue ShenJiao LiFengxia ZhangDiabetic kidney disease (DKD) is a major cause of end-stage renal disease globally, with podocytes being implicated in its pathogenesis. However, the underlying mechanisms of podocyte involvement remain unclear. The aim of the present study was to identify podocyte molecular markers associated with DKD using single-cell RNA sequencing (scRNA-seq) data from patients with early DKD. Through enrichment analysis, subcluster clustering, and ligand-receptor (LR) interaction analysis, we elucidated the role of podocytes in early DKD progression. Podocyte heterogeneity and functional differences in DKD were observed. Multiple machine-learning algorithms were used to screen and construct diagnostic models to identify hub differentially expressed podocyte marker genes (DE-podos), revealing ARHGEF26 as a significantly downregulated marker in DKD. Validation using external datasets, reverse transcription quantitative real-time PCR (RT-qPCR) and Western blot confirmed it as a potential diagnostic biomarker. Our findings elucidate podocyte function in DKD and provide viable therapeutic targets, potentially improving diagnostic accuracy and treatment outcomes.https://doi.org/10.1371/journal.pone.0328352 |
| spellingShingle | Hailin Li Quhuan Li Zuyan Fan Yue Shen Jiao Li Fengxia Zhang Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning. PLoS ONE |
| title | Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning. |
| title_full | Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning. |
| title_fullStr | Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning. |
| title_full_unstemmed | Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning. |
| title_short | Identification of podocyte molecular markers in diabetic kidney disease via single-cell RNA sequencing and machine learning. |
| title_sort | identification of podocyte molecular markers in diabetic kidney disease via single cell rna sequencing and machine learning |
| url | https://doi.org/10.1371/journal.pone.0328352 |
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