Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls
Background<p>Type 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 i...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , , , , , |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1852023221125120000 |
|---|---|
| author | Guiling Wu (6031019) |
| author2 | Sihui Wu (20544915) Tian Xiong (5563013) You Yao (20625767) Yu Qiu (143428) Liheng Meng (11872526) Cuihong Chen (10193723) Xi Yang (112884) Xinghuan Liang (461264) Yingfen Qin (461263) |
| author2_role | author author author author author author author author author |
| author_facet | Guiling Wu (6031019) Sihui Wu (20544915) Tian Xiong (5563013) You Yao (20625767) Yu Qiu (143428) Liheng Meng (11872526) Cuihong Chen (10193723) Xi Yang (112884) Xinghuan Liang (461264) Yingfen Qin (461263) |
| author_role | author |
| dc.creator.none.fl_str_mv | Guiling Wu (6031019) Sihui Wu (20544915) Tian Xiong (5563013) You Yao (20625767) Yu Qiu (143428) Liheng Meng (11872526) Cuihong Chen (10193723) Xi Yang (112884) Xinghuan Liang (461264) Yingfen Qin (461263) |
| dc.date.none.fl_str_mv | 2025-01-28T05:10:56Z |
| dc.identifier.none.fl_str_mv | 10.3389/fendo.2025.1512503.s003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Table_5_Identification_of_biomarkers_for_the_diagnosis_of_type_2_diabetes_mellitus_with_metabolic_associated_fatty_liver_disease_by_bioinformatics_analysis_and_experimental_validation_xls/28293428 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Metabolism secreted protein metabolic associated fatty liver disease type 2 diabetes mellitus TNFRSF1A SERPINB2 |
| dc.title.none.fl_str_mv | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background<p>Type 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.</p>Methods<p>We 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.</p>Results<p>Differential 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.</p>Conclusion<p>The 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.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_a4c6e547f134bdf80e609bdbfda294de |
| identifier_str_mv | 10.3389/fendo.2025.1512503.s003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28293428 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xlsGuiling Wu (6031019)Sihui Wu (20544915)Tian Xiong (5563013)You Yao (20625767)Yu Qiu (143428)Liheng Meng (11872526)Cuihong Chen (10193723)Xi Yang (112884)Xinghuan Liang (461264)Yingfen Qin (461263)Cell Metabolismsecreted proteinmetabolic associated fatty liver diseasetype 2 diabetes mellitusTNFRSF1ASERPINB2Background<p>Type 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.</p>Methods<p>We 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.</p>Results<p>Differential 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.</p>Conclusion<p>The 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.</p>2025-01-28T05:10:56ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fendo.2025.1512503.s003https://figshare.com/articles/dataset/Table_5_Identification_of_biomarkers_for_the_diagnosis_of_type_2_diabetes_mellitus_with_metabolic_associated_fatty_liver_disease_by_bioinformatics_analysis_and_experimental_validation_xls/28293428CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/282934282025-01-28T05:10:56Z |
| spellingShingle | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls Guiling Wu (6031019) Cell Metabolism secreted protein metabolic associated fatty liver disease type 2 diabetes mellitus TNFRSF1A SERPINB2 |
| status_str | publishedVersion |
| title | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls |
| title_full | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls |
| title_fullStr | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls |
| title_full_unstemmed | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls |
| title_short | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls |
| title_sort | Table 5_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation.xls |
| topic | Cell Metabolism secreted protein metabolic associated fatty liver disease type 2 diabetes mellitus TNFRSF1A SERPINB2 |