Gene Network Analysis for Identifying Hub Genes and Biological Pathways Associated with Breast Cancer Progression Using Bioinformatics Analysis

Introduction:The molecular process of tumorigenesis in breast cancer is still not fully comprehended.Identifying related genes in breast cancer progression will clarify its basic molecular mechanisms.Therefore, the present study aimed to identify key differentially expressed genes associated with breast cancer prognosis and further investigate their potential link to pathogenicity.Materials and Methods: The GSE45827, GSE65194, and GSE42568 datasets from Gene Expression Omnibus were downloaded and analyzed in the NCBI database to identify differentially expressed genes.The KEGG gene ontology and pathway were also studied.The STITCH website revealed the proteinprotein interaction network of differentially expressed genes, which visualized using Cytoscape and further analyzed using the MCODE plugin.The expression levels of Hub genes were analyzed using UALCAN.Kaplan-Meier plotter was used to assess the association between the expression levels of identified genes and patient survival, and then a gene-drug interaction network was created using Comparative Toxicogenomics Database to explore potential drugs that could target the identified genes.Results: A total of 599 differentially expressed genes were identified, and four Hub genes were selected for further analysis through a high degree of connectivity and the GTEx database.The results showed that NDE1, RAD21, ZWILCH, and ZWINT may be key genes in cell cycle signaling, P53, PI3K-Akt, and AMPK breast cancer progression pathways. Conclusion:Given the critical role of these four hub genes in the fundamental and well-known pathways of breast cancer, these genes can be considered as valuable potential prognostic biomarkers and novel therapeutic targets.A better understanding of these genes and pathways can provide insights into breast cancer progression.Therefore, further research is required in this regard.