Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndrome
The objective of this study was to identify key secretory protein-encoding differentially expressed genes (SP-DEGs) in adipose tissue in female metabolic syndrome, thus detecting potential targets in treatment. We examined gene expression profiles in 8 women with metabolic syndrome and 7 healthy, no...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Adipocyte |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21623945.2024.2446243 |
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| author | Jiandong Zhou Yunshan Guo Xuan Liu Weijie Yuan |
| author_facet | Jiandong Zhou Yunshan Guo Xuan Liu Weijie Yuan |
| author_sort | Jiandong Zhou |
| collection | DOAJ |
| description | The objective of this study was to identify key secretory protein-encoding differentially expressed genes (SP-DEGs) in adipose tissue in female metabolic syndrome, thus detecting potential targets in treatment. We examined gene expression profiles in 8 women with metabolic syndrome and 7 healthy, normal body weight women. A total of 143 SP-DEGs were screened, including 83 upregulated genes and 60 downregulated genes. GO analyses of these SP-DEGs included proteolysis, angiogenesis, positive regulation of endothelial cell proliferation, immune response, protein processing, positive regulation of neuroblast proliferation, cell adhesion and ER to Golgi vesicle-mediated transport. KEGG pathway analysis of the SP-DEGs were involved in the TGF-beta signalling pathway, cytokine‒cytokine receptor interactions, the hippo signalling pathway, Malaria. Two modules were identified from the PPI network, namely, Module 1 (DNMT1, KDM1A, NCoR1, and E2F1) and Module 2 (IL-7 R, IL-12A, and CSF3). The gene DNMT1 was shared between the network modules and the WGCNA brown module. According to the single-gene GSEA results, DNMT1 was significantly positively correlated with histidine metabolism and phenylalanine metabolism. This study identified 7 key SP-DEGs in adipose tissue. DNMT1 was selected as the central gene in the development of metabolic syndrome and might be a potential therapeutic target. |
| format | Article |
| id | doaj-art-b1a70de16a2d49d495c5566328f4b48c |
| institution | Kabale University |
| issn | 2162-3945 2162-397X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Adipocyte |
| spelling | doaj-art-b1a70de16a2d49d495c5566328f4b48c2025-01-17T06:45:35ZengTaylor & Francis GroupAdipocyte2162-39452162-397X2025-12-0114110.1080/21623945.2024.2446243Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndromeJiandong Zhou0Yunshan Guo1Xuan Liu2Weijie Yuan3Department of Nephrology, Shanghai General Hospital of Nanjing Medical University, Shanghai, ChinaDepartment of Nephrology, Shanghai General Hospital of Nanjing Medical University, Shanghai, ChinaDepartment of Nephrology, Shanghai General Hospital of Nanjing Medical University, Shanghai, ChinaDepartment of Nephrology, Shanghai General Hospital of Nanjing Medical University, Shanghai, ChinaThe objective of this study was to identify key secretory protein-encoding differentially expressed genes (SP-DEGs) in adipose tissue in female metabolic syndrome, thus detecting potential targets in treatment. We examined gene expression profiles in 8 women with metabolic syndrome and 7 healthy, normal body weight women. A total of 143 SP-DEGs were screened, including 83 upregulated genes and 60 downregulated genes. GO analyses of these SP-DEGs included proteolysis, angiogenesis, positive regulation of endothelial cell proliferation, immune response, protein processing, positive regulation of neuroblast proliferation, cell adhesion and ER to Golgi vesicle-mediated transport. KEGG pathway analysis of the SP-DEGs were involved in the TGF-beta signalling pathway, cytokine‒cytokine receptor interactions, the hippo signalling pathway, Malaria. Two modules were identified from the PPI network, namely, Module 1 (DNMT1, KDM1A, NCoR1, and E2F1) and Module 2 (IL-7 R, IL-12A, and CSF3). The gene DNMT1 was shared between the network modules and the WGCNA brown module. According to the single-gene GSEA results, DNMT1 was significantly positively correlated with histidine metabolism and phenylalanine metabolism. This study identified 7 key SP-DEGs in adipose tissue. DNMT1 was selected as the central gene in the development of metabolic syndrome and might be a potential therapeutic target.https://www.tandfonline.com/doi/10.1080/21623945.2024.2446243Secretory proteinsepigeneticsadipose tissuemetabolic syndromebioinformatics |
| spellingShingle | Jiandong Zhou Yunshan Guo Xuan Liu Weijie Yuan Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndrome Adipocyte Secretory proteins epigenetics adipose tissue metabolic syndrome bioinformatics |
| title | Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndrome |
| title_full | Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndrome |
| title_fullStr | Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndrome |
| title_full_unstemmed | Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndrome |
| title_short | Bioinformatics analysis identifies key secretory protein-encoding differentially expressed genes in adipose tissue of metabolic syndrome |
| title_sort | bioinformatics analysis identifies key secretory protein encoding differentially expressed genes in adipose tissue of metabolic syndrome |
| topic | Secretory proteins epigenetics adipose tissue metabolic syndrome bioinformatics |
| url | https://www.tandfonline.com/doi/10.1080/21623945.2024.2446243 |
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