The duplexity of insulin: The integrated bioinformatics analysis and machine learning identified key genes for type 2 diabetes

Background

Insulin therapy is still the most important treatment for T2DM, but the discussion about whether insulin brings more benefits or harms to T2DM patients has not stopped. Therefore, we used high-throughput RNA sequencing to investigate the role of insulin in T2DM and its molecular changes.

Method

We collected peripheral blood samples from 16 patients with T2DM, and performed RNA-seq on peripheral blood mononuclear cells. Bioinformatics analysis and machine learning were uesd to identify the key differential genes and transcription factor networks. In addition, we performed the flow cytometry and staining to observe ROS level and endothelial-monocyte adhesion in PBMCs of both groups.

Results

A total of 529 differential genes were identified by bioinformatics analysis. 8 genes were identified as key genes, among which IL-6 had high importance in the random forest model. In transcription factor analysis, IL-6, RETN, CTSG and ELANE have abundant transcriptional regulatory relationships. Flow cytometry showed that ROS production, phagocytosis, leukocyte adhesion in insulin treatment group were lower than that in non-insulin treatment group.

Conclusion

Insulin therapy is bidirectional, it can cause islet B cell damage and vascular complications, but also can reduce the level of inflammation and oxidative stress.