مقالات پذیرفته شده در نهمین کنگره بین المللی زیست پزشکی
Microarray-Based Differential Gene Expression and Network Pharmacology Analysis Reveal Therapeutic Targets of Parthenolide in Pancreatic Ductal Adenocarcinoma
Microarray-Based Differential Gene Expression and Network Pharmacology Analysis Reveal Therapeutic Targets of Parthenolide in Pancreatic Ductal Adenocarcinoma
Elina Khanehzar,1Fatemeh Shams,2Amir Sajad Jafari,3,*
1. Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran / Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran 2. Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran / Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran 3. Department of Basic Sciences, School of Veterinary Medicine, Shiraz University, Shiraz, Iran / Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
Introduction: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, characterized by rapid progression, early metastasis, dense desmoplastic stroma, and profound resistance to chemotherapy. Despite decades of research, therapeutic advances have been limited, and patient survival remains extremely poor. There is a critical need to identify novel therapeutic agents and elucidate their molecular mechanisms in PDAC.
Parthenolide, a sesquiterpene lactone derived from Tanacetum parthenium (feverfew), has demonstrated strong anticancer activity in several malignancies by inhibiting NF-κB signaling, inducing oxidative stress, and promoting apoptosis. However, its role in PDAC has not been systematically investigated.
This study aimed to explore the molecular mechanisms of parthenolide in PDAC by integrating microarray-based differential gene expression (DEG) analysis with network pharmacology, providing a systems-level view of its potential therapeutic targets and pathways.
Methods: Gene expression data were obtained from the curated GEO dataset GDS4336 (reference series GSE28735), which includes 90 samples representing 45 matched pairs of PDAC tumor and adjacent non-tumor tissues analyzed on the Affymetrix Human Gene 1.0 ST Array (GPL6244 platform). This dataset is widely cited and provides high-quality transcriptomic data for PDAC research.
Raw expression data were processed and analyzed using GEO2R, applying adjusted p-value <0.05 and |logFC| ≥ 0.5 as thresholds to identify differentially expressed genes (DEGs) between tumor and normal tissues.
Predicted parthenolide-associated targets were retrieved from SwissTargetPrediction. The overlap between parthenolide targets and PDAC DEGs was determined using Venny 2.0. These overlapping genes were considered potential therapeutic targets.
A protein–protein interaction (PPI) network was built using STRING, and topological properties (node degree, clustering coefficient) were used to identify key nodes. Functional enrichment analysis of overlapping genes was performed using Gene Ontology (GO) to classify Biological Processes (BP), Molecular Functions (MF), and Cellular Components (CC), and Kyoto Encyclopedia of Genes and Genomes (KEGG) to identify key signaling pathways associated with these targets.
Results: Microarray analysis identified 1836 DEGs in PDAC, while SwissTargetPrediction returned 101 predicted parthenolide targets. Venn analysis revealed 13 overlapping genes, representing the intersection between PDAC-specific dysregulation and parthenolide’s potential target space.
The PPI network comprised 13 nodes and 14 edges, with an average node degree of 2.15 and a clustering coefficient of 0.631, indicating significant interconnectivity. Key nodes included CTSB, CTSK, CTSV, BCL2L1, and SLC2A1, all known to be involved in tumor invasion, apoptosis regulation, metabolism, and drug resistance.
GO enrichment analysis showed these overlapping targets were involved in collagen catabolic processes, extracellular matrix disassembly, and cellular responses to chemical stimuli (Biological Process); collagen binding, cysteine-type endopeptidase activity, and serine-type endopeptidase activity (Molecular Function); and endolysosome and lysosomal lumen (Cellular Component).
KEGG pathway analysis highlighted apoptosis, lysosome, bile secretion, and HTLV-1 infection as the top enriched pathways, suggesting parthenolide may induce apoptosis, modulate lysosomal protease activity, and influence ECM remodeling in PDAC cells.
Conclusion: This study integrated microarray-based DEG analysis and network pharmacology to elucidate potential molecular targets and pathways of parthenolide in PDAC. The findings reveal a central lysosome–ECM–apoptosis axis, driven by proteases (CTSB, CTSK, CTSV) and anti-apoptotic regulators (BCL2L1, SLC2A1), as a potential mechanism through which parthenolide may act. These results provide novel systems-level insight into parthenolide’s therapeutic potential and support its further experimental validation as a promising candidate for the treatment of pancreatic ductal adenocarcinoma.