Gene network modeling and analysis in identifying genes causing resistance to paclitaxel in ovarian cancer
Gene network modeling and analysis in identifying genes causing resistance to paclitaxel in ovarian cancer
Mohammad Reza Dabbagh,1,*Mojtaba Aghaei,2Seyed Sobhan Bahreiny,3Mohammad Mehdi abolhasani,4Ehsan Sarbazjoda,5Mohammad Sharif Sharifani,6
1. Department of Biology, Faculty of Science, Shahid Chamran University of Ahvaz, Ahvaz, Iran. 2. Thalassemia & Hemoglobinopathy Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 3. Medical Basic Sciences Research Institute, Physiology Research Center, Department of Physiology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. 4. Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. 5. Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. 6. Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Introduction: Introduction: Resistance to chemotherapeutic agents is one of the most important factors in the failure of cancer treatment. Ovarian cancer, one of the most common female cancers, is also affected by this problem. Treatment of this cancer is usually with the drug cisplatin, whereupon resistance to chemotherapy occurs. In this study, the molecular mechanisms and genes involved in the development of resistance to cisplatin during treatment of ovarian cancer will be investigated by gene network analysis and bioinformatics analysis.
Methods: Material and methods: In the present study, we analyzed a microarray dataset GSE50831 from the Gene Expression Omnibus (GEO) database and used Transcriptome Analysis Console v4.0 software to search OC cells for differentially expressed genes (DEGs). For functional annotation of DEGs, we used Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) using the STRING database. Protein–protein interaction networks (PPI) were also created using the STRING database, and Cytoscape software was used for visualization.
Results: Results: A total of 261 DEGs were identified in six samples from the GEO microarray dataset, of which 199 genes were upregulated and 62 genes were downregulated. GO Analysis of the subnetworks showed that the biological process of DEGs mainly focuses on the regulation of DNA endoreduplication. Among the major molecular functions is the binding of the DNA replication origin. Cellular components include the BRCA1-B complex. KEGG pathway analysis of the subnetwork mainly focused on the p53 pathway, Fanconi anemia pathway and homologous recombination.
In addition, 10 genes BRCA1, EXO1, CDC45, MCM10, CDKN1A, TOP2A, BRCA2, FBXO5, WDHD1 and FANCI were identified as hub genes. KEGG pathway analysis of the hub genes revealed that they are mainly involved in Fanconi anemia signaling, homologous recombination, and platinum drug resistance.
Conclusion: Conclusion: The results of this study demonstrate the importance of using bioinformatics analysis in identifying the molecular mechanisms of paclitaxel resistance in ovarian cancer. The genes and pathway obtained from this analysis likely play a role in paclitaxel resistance and can be considered as potential biomarkers for detecting paclitaxel resistance in ovarian cancer. However, further studies and experiments are needed to confirm the role of these genes and their signaling pathways in the development of drug resistance to paclitaxel in ovarian cancer.