Single-cell Transcriptomics, Web-based Systems Pharmacology and Integrated Transcriptomics Network Analysis Identified Diagnostic Targets and Drug Candidates for Type 2 Diabetes.

Aim: To discover new therapeutic targets for Type 2 diabetes (T2D) and develop a new diagnostic model.

Background: T2D is a chronic disease that can be controlled by oral hypoglycemic drugs, however, it cannot be fully cured. The continued increase in the prevalence of T2D and the limitations of existing treatments urgently call for the development of new drugs to be able to effectively control the progression of the disease.

Objective: We aimed to discover new therapeutic targets for T2D and to develop a new diagnostic model.

Method: Single-cell transcriptome, web-based systematic pharmacology, and transcriptology were applied to identify T2D diagnostic targets and drug candidates and to analyze the underlying molecular mechanisms.

Results: By single-cell clustering analysis, we identified seven subsets between the normal islet β-cell samples and T2D islet β-cell samples. A total of 27 key genes in the intersection of insulin- related genes and diabetes-related genes were selected by protein-protein interaction (PPI) analysis and MolecularComplexDetection (MCODE) analysis. Notably, ESR1, MME, and CCR5 had the area under curves (AUC) values as high as 67.95%, 66.67%, and 66.03% for the diagnosis of T2D, respectively. Since the expression of MME in T2D samples was significantly higher than in normal samples, we screened 155 drug candidates against MME in T2D. Finally, the molecular docking revealed a strong binding strength between MME and DB05490, which was one of the most effective candidate drugs for treating T2D.

Conclusion: Our study screens for diagnostic signatures and potential therapeutic agents for T2D, which provides valuable insights into the development of T2D biomarkers and their drug discovery.