Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation
BackgroundType 2 diabetes (T2DM) combined with fatty liver is a subtype of metabolic fatty liver disease (MAFLD), and the relationship between T2DM and MAFLD is close and mutually influential. However, the connection and mechanisms between the two are still unclear. Therefore, we aimed to identify p...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1512503/full |
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| author | Guiling Wu Guiling Wu Sihui Wu Sihui Wu Tian Xiong Tian Xiong Tian Xiong You Yao You Yao Yu Qiu Yu Qiu Yu Qiu Liheng Meng Cuihong Chen Xi Yang Xi Yang Xi Yang Xinghuan Liang Yingfen Qin |
| author_facet | Guiling Wu Guiling Wu Sihui Wu Sihui Wu Tian Xiong Tian Xiong Tian Xiong You Yao You Yao Yu Qiu Yu Qiu Yu Qiu Liheng Meng Cuihong Chen Xi Yang Xi Yang Xi Yang Xinghuan Liang Yingfen Qin |
| author_sort | Guiling Wu |
| collection | DOAJ |
| description | BackgroundType 2 diabetes (T2DM) combined with fatty liver is a subtype of metabolic fatty liver disease (MAFLD), and the relationship between T2DM and MAFLD is close and mutually influential. However, the connection and mechanisms between the two are still unclear. Therefore, we aimed to identify potential biomarkers for diagnosing both conditions.MethodsWe performed differential expression analysis and weighted gene correlation network analysis (WGCNA) on publicly available data on the two diseases in the Gene Expression Omnibus database to find genes related to both conditions. We utilised protein–protein interactions (PPIs), Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes to identify T2DM-associated MAFLD genes and potential mechanisms. Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. Finally, we collected whole blood from patients with T2DM-related MAFLD, MAFLD patients and healthy individuals, and used high-fat, high-glucose combined with high-fat cell models to verify the expression of hub genes.ResultsDifferential expression analysis and WGCNA identified 354 genes in the MAFLD dataset. The differential expression analysis of the T2DM-peripheral blood mononuclear cells/liver dataset screened 91 T2DM-associated secreted proteins. PPI analysis revealed two important modules of T2DM-related pathogenic genes in MAFLD, which contained 49 nodes, suggesting their involvement in cell interaction, inflammation, and other processes. TNFSF10, SERPINB2, and TNFRSF1A were the only coexisting genes shared between MAFLD key genes and T2DM-related secreted proteins, enabling the construction of highly accurate diagnostic models for both disorders. Additionally, high-fat, high-glucose combined with high-fat cell models were successfully produced. The expression patterns of TNFRSF1A and SERPINB2 were verified in patient blood and our cellular model. Immune dysregulation was observed in MAFLD, with TNFRSF1A and SERPINB2 strongly linked to immune regulation.ConclusionThe sensitivity and accuracy in diagnosing and predicting T2DM-associated MAFLD can be greatly improved using SERPINB2 and TNFRSF1A. These genes may significantly influence the development of T2DM-associated MAFLD, offering new diagnostic options for patients with T2DM combined with MAFLD. |
| format | Article |
| id | doaj-art-8123ff0545e848708959f4193294a503 |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-8123ff0545e848708959f4193294a5032025-01-28T05:10:36ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-01-011610.3389/fendo.2025.15125031512503Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validationGuiling Wu0Guiling Wu1Sihui Wu2Sihui Wu3Tian Xiong4Tian Xiong5Tian Xiong6You Yao7You Yao8Yu Qiu9Yu Qiu10Yu Qiu11Liheng Meng12Cuihong Chen13Xi Yang14Xi Yang15Xi Yang16Xinghuan Liang17Yingfen Qin18Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Geriatric Endocrinology and Metabolism, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, Guangxi, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Geriatric Endocrinology and Metabolism, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, Guangxi, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Key Laboratory of Precision Medicine in Cardio-Cerebrovascular Diseases Control and Prevention, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Geriatric Endocrinology and Metabolism, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi, ChinaGuangxi Clinical Research Center for Cardio-Cerebrovascular Diseases, Nanning, Guangxi, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaDepartment of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, ChinaBackgroundType 2 diabetes (T2DM) combined with fatty liver is a subtype of metabolic fatty liver disease (MAFLD), and the relationship between T2DM and MAFLD is close and mutually influential. However, the connection and mechanisms between the two are still unclear. Therefore, we aimed to identify potential biomarkers for diagnosing both conditions.MethodsWe performed differential expression analysis and weighted gene correlation network analysis (WGCNA) on publicly available data on the two diseases in the Gene Expression Omnibus database to find genes related to both conditions. We utilised protein–protein interactions (PPIs), Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes to identify T2DM-associated MAFLD genes and potential mechanisms. Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. Finally, we collected whole blood from patients with T2DM-related MAFLD, MAFLD patients and healthy individuals, and used high-fat, high-glucose combined with high-fat cell models to verify the expression of hub genes.ResultsDifferential expression analysis and WGCNA identified 354 genes in the MAFLD dataset. The differential expression analysis of the T2DM-peripheral blood mononuclear cells/liver dataset screened 91 T2DM-associated secreted proteins. PPI analysis revealed two important modules of T2DM-related pathogenic genes in MAFLD, which contained 49 nodes, suggesting their involvement in cell interaction, inflammation, and other processes. TNFSF10, SERPINB2, and TNFRSF1A were the only coexisting genes shared between MAFLD key genes and T2DM-related secreted proteins, enabling the construction of highly accurate diagnostic models for both disorders. Additionally, high-fat, high-glucose combined with high-fat cell models were successfully produced. The expression patterns of TNFRSF1A and SERPINB2 were verified in patient blood and our cellular model. Immune dysregulation was observed in MAFLD, with TNFRSF1A and SERPINB2 strongly linked to immune regulation.ConclusionThe sensitivity and accuracy in diagnosing and predicting T2DM-associated MAFLD can be greatly improved using SERPINB2 and TNFRSF1A. These genes may significantly influence the development of T2DM-associated MAFLD, offering new diagnostic options for patients with T2DM combined with MAFLD.https://www.frontiersin.org/articles/10.3389/fendo.2025.1512503/fullsecreted proteinmetabolic associated fatty liver diseasetype 2 diabetes mellitusTNFRSF1ASERPINB2 |
| spellingShingle | Guiling Wu Guiling Wu Sihui Wu Sihui Wu Tian Xiong Tian Xiong Tian Xiong You Yao You Yao Yu Qiu Yu Qiu Yu Qiu Liheng Meng Cuihong Chen Xi Yang Xi Yang Xi Yang Xinghuan Liang Yingfen Qin Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation Frontiers in Endocrinology secreted protein metabolic associated fatty liver disease type 2 diabetes mellitus TNFRSF1A SERPINB2 |
| title | Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation |
| title_full | Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation |
| title_fullStr | Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation |
| title_full_unstemmed | Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation |
| title_short | Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation |
| title_sort | identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation |
| topic | secreted protein metabolic associated fatty liver disease type 2 diabetes mellitus TNFRSF1A SERPINB2 |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1512503/full |
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