MicroRNA profiling in pancreatic cancer and chronic pancreatitis: Novel insights and pathway analysis

Abstract

Pancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDAC), poses significant prognostic challenges, with a 5-year survival rate of around 10 %. Early detection remains elusive due to nonspecific symptoms and the disease's aggressive nature. Chronic pancreatitis (CP) significantly increases the risk of PDAC, with studies indicating a 14-fold increase. This paper investigates the role of microRNAs (miRNAs) in the molecular pathogenesis of CP and PDAC, employing a comprehensive analysis of microarray datasets to identify differentially expressed miRNAs (DEMs) among these conditions and healthy controls. Utilizing bioinformatics tools such as Weighted Gene Co-Expression Network Analysis and functional enrichment analysis, we highlighted key signaling pathways, including PI3K/AKT/mTOR and TNF-α via NF-κB, crucial to both diseases. We identified critical genes—AKT1, TP53, EGFR, MYC, and CTNNB1—that play significant roles in PDAC and CP. Furthermore, we developed a Support Vector Machine model to classify PDAC, CP, and healthy individuals, achieving an accuracy of 88.3 %. Our findings pinpoint several DEMs with considerable diagnostic potential, particularly hsa-miR-130b and hsa-miR-148a for PDAC, hsa-miR-192 and hsa-miR-150 for CP, and hsa-miR-222 for differentiating PDAC from CP. These results underscore the promise of miRNAs as biomarkers and therapeutic targets, warranting further validation to improve diagnostic approaches and patient outcomes.