The breast cancer biomarkers associated with the development of the disease; an in-silico-based study

Abstract

Introduction: Breast cancer (BC) is among the top causes of mortality among women worldwide. Identifying genes by differential expression associated with the development of the disease helps us to better understanding the molecular mechanisms of BC. Objectives: Our study used in-silico analysis to identify hub genes could trigger the development of BC. Materials and Methods: We identified GSE38959 and GSE45827 for differentially expressed genes (DEGs) in the Gene Expression Omnibus (GEO) database, with an adjusted P<0.05. In both sets, logFC ≥ 2 and logFC ≤ -2 were observed in the DEGs that express themselves within cases and normal BC samples. A comparison was then performed, detecting two common datasets of DEGs using the GEO2R tool. Pathways were elucidated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology databases. Thereafter, protein-protein interactions (PPIs) were analyzed using Cytoscape and Gephi. Finally, a GEPIA analysis was conducted to validate the target genes. Results: Using the GEO, 322 common DEGs were identified and 65 hub genes as PPIs. The DEGs were enriched in functions associated with cell division, chromosomes, centromeric regions, microtubule binding, and the cell cycle based on the gene ontology (GO) and KEGG pathways analysis. The expression of 6 genes, CDK1, CCNB1, TOP2A, CXCL12, IGF1, and KIT, represented statistically significant values when the normal and tumor samples were compared via GEPIA analysis. Conclusion: This study introduced six genes (CDK1, CCNB1, TOP2A, CXCL12, IGF1, and KIT) with high expression significantly, which could act as a biomarker for BC development (P<0.05 for all genes). Further comprehensive experimental in vivo studies are needed to describe their role in BC.