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

Abstract

Lifestyle patterns and exposure to toxic chemicals or environmental pollutants are the strongest risk factors for the chances of developing breast cancer, the leading and most lethal form of cancer in women. Bisphenol A (BPA), found in various consumer products, is known to deregulate multiple cellular signaling pathways, 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 work aims at elucidating the molecular actors and mechanisms of action involved in BPA-induced breast cancer development using a bioinformatics analysis approach. A microarray dataset suitable for the study purposes was obtained from the publicly available Gene Expression Omnibus (GEO) repository, followed by DEG (differentially expressed genes) extraction, enrichment, and protein-protein interaction analyses to identify the hub genes. Expressional patterns, prognostic potentials, and immune infiltration levels of identified targets were tested and validated in silico using GEPIA2 and KM-plotter tools. 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 BPA-associated breast cancer processes. 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 breast cancer.