Identification and verification of promising diagnostic genes in bisphenol A-associated breast cancer development via in silico analysis

Abstract

Lifestyle patterns, exposure to toxic chemicals or environmental pollutants are the strongest risk factors for the chances of developing breast cancer, the leading and lethal form of cancer in women. Bisphenol A (BPA), found in various consumer products, is known to deregulate multiple cellular signaling, but its effect on cancer initiation and development in breast tissue has not yet been fully elucidated. Therefore, the identification of hub drivers is necessary to understand the molecular mechanisms underlying BPA-related malignancy and may help determine novel diagnosis and treatment strategies. This study aimed to contribute to the understanding of the molecular mechanism of action of BPA on breast cancer formation using a bioinformatics analysis approach. A microarray dataset appropriate for study purposes was downloaded from the Gene Expression Omnibus (GEO) repository. After the screening of the differentially expressed genes (DEGs), enrichment analysis was performed. A protein-protein interaction (PPI) network was constructed and analyzed to identify the hub genes. mRNA expression and the prognostic impacts of the identified hub genes were verified using GEPIA2 and KM-plotter tools. Finally, correlations between hub genes and immune infiltration levels were validated. According to PPI network results, CCNA2 and CCNB1 were identified as critical hub genes. Validation analyses clearly indicated that the identified genes are extremely critical in the development and progression of BPA-associated breast cancer. Findings from this study revealed that CCNA2 and CCNB1, two cell cycle signaling-related hub genes that are overexpressed as a consequence of BPA exposure, are strongly associated with poor prognosis in patients with breast cancer.