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A Network Biology Approach to Investigating Multiple Drug Resistance (MDR)
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S.M.E. Kamrani *   , F. Hadizadeh |
| Department of Medicinal Chemistry, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran |
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Abstract: (8 Views) |
Cancer is one of the leading causes of death worldwide in the 21st century. Several therapeutic strategies, including chemotherapy, radiotherapy, and immunotherapy, have been developed for cancer treatment. However, one of the major causes of treatment failure is the development of multiple drug resistance (MDR). Several mechanisms have been implicated in MDR, including alterations in metabolic pathways, overexpression of ATP-binding cassette (ABC) transporters, and other molecular alterations.
In this study, we analyzed microarray datasets comparing drug-resistant cancer cells with drug-sensitive counterparts to identify differentially expressed genes (DEGs). Protein–protein interaction (PPI) networks were subsequently constructed and analyzed using the STRING database. Based on topological network parameters, ten hub genes were identified, and their associated biological pathways were investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, Venn diagram analysis was performed to identify common upregulated and downregulated genes across the analyzed datasets that may represent potential therapeutic targets.
Our analysis revealed that ABCB1, a key transporter involved in MDR, interacts with several genes, including FN1, MMP2, KDR, and members of the SLC gene family. Furthermore, P3H2, RPH3AL, NCF1, and NCF2 were consistently identified as commonly upregulated genes across the three analyzed datasets. These findings provide insights into the molecular mechanisms underlying MDR and may contribute to the identification of potential biomarkers and therapeutic targets for overcoming drug resistance in cancer. |
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| Keywords: Multiple drug resistance (MDR), Network biology, Protein–protein interaction (PPI), Differentially expressed genes (DEGs), Hub genes, ABCB1, Bioinformatics |
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Full-Text [PDF 2279 kb]
(4 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2026/06/27 | Accepted: 2026/10/2 | Published: 2026/10/2
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