Integrative Bioinformatics Analysis Reveals Key Hub Genes and Pathways Driving Pancreatic Adenocarcinoma Progression

Abstract

Background
Pancreatic adenocarcinoma (PAAD) is one of malignant tumors with the highest fatality rate worldwide. This disease has several particularly intractable characteristics: most cases have progressed to an advanced stage when diagnosed, the condition progresses rapidly, and there are still very few effective clinical treatment options available at present. To identify new diagnostic markers and therapeutic targets, it is necessary to first comprehensively clarify the molecular mechanism underlying this cancer; otherwise, no new direction for diagnosis and treatment can be found.
Methods
Genes related to pancreatic cancer were recovered from GeneCards as well as CTD databases, moreover overlapping targets were subjected to KEGG pathway enrichment and protein–protein interaction (PPI) analysis using STRING as well as Cytoscape. Hub genes were recognized through CytoHubba & MCODE plug-ins. Their expression patterns were evaluated using UALCAN, GEPIA, OncoDB, and MuTarget, while TIMER and TISIDB were utilized to assess ICI and immunoregulatory correlations. MEXPRESS was applied to examine promoter methylation, and DGIdb was utilized to determine drug–gene interactions.
Results
The study ultimately confirmed six hub genes: AKT1, TP53, EGFR, KRAS, MTOR, and STAT3, which are core factors regulating the development of pancreatic adenocarcinoma. Compared with normal tissue samples, these genes all showed significant abnormal expression in all tumor samples, and these abnormal levels of expression were also associated with tumor stage, grade, and the mutation status of the patient's own TP53 gene.
Subsequent survival analysis showed that the higher the expression levels of the four genes EGFR, KRAS, MTOR, and STAT3, the worse the patient's prognosis. Analysis of methylation and mutation status also proved that these genes are simultaneously controlled by the both genetic as well as epigenetic mechanisms, while the results of drug interaction analysis suggested that they can all be used as potential therapeutic targets for subsequent targeted drug development.
Conclusion
This comprehensive bioinformatics study identified six core hub genes that have diagnostic, prognostic, and therapeutic value for pancreatic adenocarcinoma. These results provide important support for understanding the molecular mechanism behind the progression of pancreatic adenocarcinoma, and also point out promising candidate directions for the subsequent development of precise diagnosis and treatment plans.