Integrative Bioinformatic Identification and Molecular Docking of Quercetin and Sulforaphane-Associated Prognostic Targets in Pancreatic Adenocarcinoma
Chemistry and Biodiversity, vol.23, no.3, 2026 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 23 Issue: 3
- Publication Date: 2026
- Doi Number: 10.1002/cbdv.202503423
- Journal Name: Chemistry and Biodiversity
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, EMBASE, MEDLINE
- Keywords: bioinformatics, differentially expressed genes (DEGs), docking, pancreatic adenocarcinoma
- Ataturk University Affiliated: Yes
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 exhibiting comparatively stronger predicted affinities than SFN across all targets.