Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis

• Published in: Frontiers in endocrinology (Lausanne) 2024-11, Vol.15, p.1405726
• Main Authors: Zhong, Lei, Yang, Xi, Shang, Yuxuan, Yang, Yao, Li, Junchen, Liu, Shuo, Zhang, Yunshu, Liu, Jifeng, Jiang, Xingchi
• Format: Article
• Language: English
• Subjects: Acute Disease; acute pancreatitis; biomarker; Biomarkers - analysis; Biomarkers - metabolism; Computational Biology - methods; Diabetes Mellitus, Type 2 - drug therapy; Diabetes Mellitus, Type 2 - genetics; Diabetes Mellitus, Type 2 - metabolism; Endocrinology; Gene Expression Profiling; Humans; machine learning; Mendelian Randomization Analysis; molecular docking; Molecular Docking Simulation; Pancreatitis - drug therapy; Pancreatitis - genetics; Pancreatitis - metabolism; type 2 diabetes mellitus
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2024.1405726

Type 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. We 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. A 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. This 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.