Integrative bioinformatics analysis to decipher common pathogenic processes in type 2 diabetes mellitus and pancreatic cancer

Type 2 Diabetes Mellitus (T2DM) and pancreatic cancer (PC) are complex diseases with shared molecular mechanisms that are not fully understood. This study used bioinformatics analysis to uncover shared transcriptional patterns and central genes between T2DM and PC, identifying 388 dysregulated genes. Functional annotation and pathway analysis highlighted involvement in immune responses and pathways such as Type II diabetes mellitus and the Rap1 signalling. Ten hub genes, including FN1, FCGR3A, CXCL10, CD86, CCL5, CRP, ITGB2, FCER1G, FCGR2B, and CD48, were identified as potential biomarkers. Machine learning classifiers, particularly Random Forest demonstrated the highest accuracy in classifying samples based on the expression of these hub genes. FN1, a key gene involved in cell adhesion was further investigated using molecular docking and molecular dynamics simulations analysis. This study provides insights into the common molecular mechanisms underlying T2DM and PC, with FN1 as a potential  therapeutic target. These findings could potentially be used in the future to develop personalized treatments aimed at preventing the occurrence of both T2DM and PC.