Identifying significant genes as prognostic biomarkers in pancreatic cancer through integrated transcriptomic and in silico pharmacology approaches

Pancreatic cancer remains one of the most lethal malignancies globally, with a 5-year survival rate below 10%. The identification of robust prognostic biomarkers and druggable targets is critical for improving therapeutic outcomes. This study employed an integrative bioinformatics approach to discover key molecular drivers and therapeutic candidates. Gene expression profiling was conducted using the GSE16515 dataset (36 tumors vs. 16 normal samples), identifying 546 differentially expressed genes (DEGs) (|LogFC| > 2, p < 0.05). Network topology and enrichment analyses revealed CDK1 as a significant hub gene. Despite showing downregulation in the microarray dataset, CDK1 was significantly upregulated in independent TCGA RNA-seq data, and its high expression strongly correlated with reduced overall survival (HR = 2.0, p = 0.0006) and disease-free survival (HR = 1.9, p = 0.0041), supporting its role as a prognostic biomarker. Given its central role in cell cycle regulation, CDK1 was prioritized as a therapeutic target. Virtual screening of 1862 natural compounds against CDK1 identified Benzoquinazolinone 12 (CID: 46196414) as the top-ranking compound with a binding affinity of -11.0 kcal/mol. In silico ADME profiling indicated high gastrointestinal absorption and drug-likeness, while toxicity prediction suggested a low toxicity profile (LD₅₀ > 1000 mg/kg, Class 5). Molecular dynamics simulations demonstrated stable ligand–protein interactions (RMSF = 0.980 Å). This integrative computational study nominates CDK1 as a prognostic biomarker and therapeutic target in pancreatic cancer and proposes natural compounds, particularly Benzoquinazolinone 12, as promising lead candidates that warrant further experimental validation.