Integrative Bioinformatic Identification and Molecular Docking of Quercetin and Sulforaphane-Associated Prognostic Targets in Pancreatic Adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) remains a highly lethal malignancy with limited therapeutic options, motivating the search for robust prognostic markers and tractable therapeutic targets. In this study, we applied an integrative bioinformatic pipeline combining cross-cohort differential expression analysis, high-confidence protein-protein interaction network reconstruction, and topological hub-gene prioritization. Hub candidates were then intersected with curated target repertoires of multi-target chemicals (notably quercetin and sulforaphane [SFN]) to nominate pharmacologically accessible "elite" targets. Downstream in silico validation included comparative mRNA and protein expression profiling, correlations with immune infiltration metrics, survival prognostic assessments, and molecular docking to evaluate ligand-target complementarity. This multilayered approach consistently highlighted extracellular matrix remodeling, integrin-mediated adhesion, and pericellular proteolysis as central processes in PAAD biology and identified COL1A1, ITGA2, and PLAU as top-priority targets that combine high network centrality with overlap to phytochemical target spaces. These genes demonstrated tumor-enriched expression, adverse survival associations, and distinct immune-microenvironment correlations, suggesting a potential involvement in pro-tumorigenic remodeling processes. Molecular docking analyses suggested computationally feasible ligand-target binding hypotheses, with quercetin exhibiting comparatively stronger predicted affinities than SFN across all targets.