Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis
BackgroundType 2 diabetes mellitus (T2DM) is a chronic metabolic disease that accounts for > 90% of all diabetes cases. Acute pancreatitis (AP) can be triggered by various factors and is a potentially life-threatening condition. Although T2DM has been shown to have a close relationship with A...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2024.1405726/full |
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| author | Lei Zhong Xi Yang Yuxuan Shang Yao Yang Junchen Li Shuo Liu Yunshu Zhang Jifeng Liu Xingchi Jiang |
| author_facet | Lei Zhong Xi Yang Yuxuan Shang Yao Yang Junchen Li Shuo Liu Yunshu Zhang Jifeng Liu Xingchi Jiang |
| author_sort | Lei Zhong |
| collection | DOAJ |
| description | BackgroundType 2 diabetes mellitus (T2DM) is a chronic metabolic disease that accounts for > 90% of all diabetes cases. Acute pancreatitis (AP) can be triggered by various factors and is a potentially life-threatening condition. Although T2DM has been shown to have a close relationship with AP, the common mechanisms underlying the two conditions remain unclear.MethodsWe identified common differentially expressed genes (DEGs) in T2DM and AP and used functional enrichment analysis and Mendelian randomization to understand the underlying mechanisms. Subsequently, we used several machine learning algorithms to identify candidate biomarkers and construct a diagnostic nomogram for T2DM and AP. The diagnostic performance of the model was evaluated using ROC, calibration, and DCA curves. Furthermore, we investigated the potential roles of core genes in T2DM and AP using GSEA, xCell, and single-cell atlas and by constructing a ceRNA network. Finally, we identified potential small-molecule compounds with therapeutic effects on T2DM and AP using the CMap database and molecular docking.ResultsA total of 26 DEGs, with 14 upregulated and 12 downregulated genes, were common between T2DM and AP. According to functional and DisGeNET enrichment analysis, these DEGs were mainly enriched in immune effector processes, blood vessel development, dyslipidemia, and hyperlipidemia. Mendelian randomization analyses further suggested that lipids may be a potential link between AP and T2DM. Machine learning algorithms revealed ARHGEF9 and SLPI as common genes associated with the two diseases. ROC, calibration, and DCA curves showed that the two-gene model had good diagnostic efficacy. Additionally, the two genes were found to be closely associated with immune cell infiltration. Finally, imatinib was identified as a potential compound for the treatment of T2DM and AP.ConclusionThis study suggests that abnormal lipid metabolism is a potential crosstalk mechanism between T2DM and AP. In addition, we established a two-gene model for the clinical diagnosis of T2DM and AP and identified imatinib as a potential therapeutic agent for both diseases. |
| format | Article |
| id | doaj-art-527bbd3232e54ca0a17dc2742e7fd10f |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-527bbd3232e54ca0a17dc2742e7fd10f2024-11-20T04:35:04ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922024-11-011510.3389/fendo.2024.14057261405726Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysisLei Zhong0Xi Yang1Yuxuan Shang2Yao Yang3Junchen Li4Shuo Liu5Yunshu Zhang6Jifeng Liu7Xingchi Jiang8Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of Plastic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of Plastic Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of Endocrinology and Metabolic Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaDepartment of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, ChinaBackgroundType 2 diabetes mellitus (T2DM) is a chronic metabolic disease that accounts for > 90% of all diabetes cases. Acute pancreatitis (AP) can be triggered by various factors and is a potentially life-threatening condition. Although T2DM has been shown to have a close relationship with AP, the common mechanisms underlying the two conditions remain unclear.MethodsWe identified common differentially expressed genes (DEGs) in T2DM and AP and used functional enrichment analysis and Mendelian randomization to understand the underlying mechanisms. Subsequently, we used several machine learning algorithms to identify candidate biomarkers and construct a diagnostic nomogram for T2DM and AP. The diagnostic performance of the model was evaluated using ROC, calibration, and DCA curves. Furthermore, we investigated the potential roles of core genes in T2DM and AP using GSEA, xCell, and single-cell atlas and by constructing a ceRNA network. Finally, we identified potential small-molecule compounds with therapeutic effects on T2DM and AP using the CMap database and molecular docking.ResultsA total of 26 DEGs, with 14 upregulated and 12 downregulated genes, were common between T2DM and AP. According to functional and DisGeNET enrichment analysis, these DEGs were mainly enriched in immune effector processes, blood vessel development, dyslipidemia, and hyperlipidemia. Mendelian randomization analyses further suggested that lipids may be a potential link between AP and T2DM. Machine learning algorithms revealed ARHGEF9 and SLPI as common genes associated with the two diseases. ROC, calibration, and DCA curves showed that the two-gene model had good diagnostic efficacy. Additionally, the two genes were found to be closely associated with immune cell infiltration. Finally, imatinib was identified as a potential compound for the treatment of T2DM and AP.ConclusionThis study suggests that abnormal lipid metabolism is a potential crosstalk mechanism between T2DM and AP. In addition, we established a two-gene model for the clinical diagnosis of T2DM and AP and identified imatinib as a potential therapeutic agent for both diseases.https://www.frontiersin.org/articles/10.3389/fendo.2024.1405726/fullacute pancreatitistype 2 diabetes mellitusmolecular dockingmachine learningbiomarker |
| spellingShingle | Lei Zhong Xi Yang Yuxuan Shang Yao Yang Junchen Li Shuo Liu Yunshu Zhang Jifeng Liu Xingchi Jiang Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis Frontiers in Endocrinology acute pancreatitis type 2 diabetes mellitus molecular docking machine learning biomarker |
| title | Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis |
| title_full | Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis |
| title_fullStr | Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis |
| title_full_unstemmed | Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis |
| title_short | Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis |
| title_sort | exploring the pathogenesis biomarkers and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis |
| topic | acute pancreatitis type 2 diabetes mellitus molecular docking machine learning biomarker |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2024.1405726/full |
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