Integrative Bioinformatic Identification and Molecular Docking of Quercetin and Sulforaphane‐Associated Prognostic Targets in Pancreatic Adenocarcinoma
Abstract
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…
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