Exploring potential pathways and biomarkers of pancreatic cancer associated with lynch syndrome and type 2 diabetes: An integrated bioinformatics analysis

Abstract

Pancreatic cancer (PC) is a devastating malignancy with intricate genetic underpinnings and a complex etiology. Emerging evidence suggests the presence of lynch syndrome (LS) and type 2 diabetes (T2D) associated susceptibility to PC. This study presents integrated computational and systems biology approaches to identify the genetic risk factors underlying the association between PC, LS, and T2D. Patient data for these three diseases have been collected from NCBI and differentially expressed genes (DEGs) identified by the GREIN web platform. Furthermore, protein-protein interaction (PPI), gene ontology (GO), and signaling pathway networks were analyzed through STRING and DAVID databases, respectively. Autodock Vina has been used for prospective analysis of ligand-protein interaction. About 60 unique common DEGs were identified by statistical analysis. In addition to the utilization of five distinct algorithms within the Cytoscape framework, we have reported three potential target candidates: TNF, CXCL1, and TNFSF10. In particular, the immune and inflammatory response, the chemokine-mediated signaling pathway, rheumatoid arthritis, and IL-17 signaling pathways emerged as prominently enriched pathways. Furthermore, the interaction of 162 phytochemicals from Nigella sativa was assessed with the identified hub proteins. Among these, thujopsene emerged as a notable ligand candidate, demonstrating the most favorable binding energy against the TNF (−9.6 kca/mol TNFSF10 (−8.5 kcal/mol), and CXCL1 (−9.1 kcal/mol) proteins. Besides, pharmacokinetics, toxicity, and drug-likeness properties of the thujopsene ligand showed an acceptable range for selection of a drug candidate. Collectively, these findings shed light on the intricate interplay of genes, pathways, and potential therapeutic compounds, providing a basis for further exploration and validation in the context of relevant diseases.