Current Computer-Aided Drug DesignCurrent Computer-Aided Drug DesignCurrent Computer-Aided Drug Design1573-40991875-6697Bentham Science64444510.2174/1573409920666230914100826ChemistryResearch ArticleIdentification of Prognostic Markers and Potential Therapeutic Targets using Gene Expression Profiling and Simulation Studies in Pancreatic CancerSinghSamvednainfo@benthamscience.netKaushikAmaninfo@benthamscience.netGuptaHimanshiinfo@benthamscience.netJhinjhariaDivyainfo@benthamscience.netSahiShaktiinfo@benthamscience.netSchool of Biotechnology, Gautam Buddha UniversityWuxi School of Medicine, Jiangnan University0106202420695597307012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://rjeid.com/1573-4099/article/view/644445

Background:Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage

Objective:The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer.

Methods:Gene expression profiles from the GEO database were analyzed to identify reliable prognostic markers and potential drug targets. The disease's molecular mechanism and biological pathways were studied by investigating gene ontologies, KEGG pathways, and survival analysis to understand the strong prognostic power of key DEGs. FDA-approved anti-cancer drugs were screened through cell line databases, and docking studies were performed to identify drugs with high affinity for ARNTL2 and PIK3C2A. Molecular dynamic simulations of drug targets ARNTL2 and PIK3C2A in their native state and complex with nilotinib were carried out for 100 ns to validate their therapeutic potential in PDAC.

Results:Differentially expressed genes that are crucial regulators, including SUN1, PSMG3, PIK3C2A, SCRN1, and TRIAP1, were identified. Nilotinib as a candidate drug was screened using sensitivity analysis on CCLE and GDSC pancreatic cancer cell lines. Molecular dynamics simulations revealed the underlying mechanism of the binding of nilotinib with ARNTL2 and PIK3C2A and the dynamic perturbations. It validated nilotinib as a promising drug for pancreatic cancer.

Conclusion:This study accounts for prognostic markers, drug targets, and repurposed anti-cancer drugs to highlight their usefulness for translational research on developing novel therapies. Our results revealed potential and prospective clinical applications in drug targets ARNTL2, EGFR, and PI3KC2A for pancreatic cancer therapy.

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