Exploring the role of estrogen and progestins in breast cancer: A genomic approach to diagnosis.

Abstract

Breast cancer (BC) is the most common cancer among women and a major cause of death from cancer. The role of estrogen and progestins, including synthetic hormones like R5020, in the development of BC has been highlighted in numerous studies. In our study, we employed machine learning and advanced bioinformatics to identify genes that could serve as diagnostic markers for BC. We thoroughly analyzed the transcriptomic data of two BC cell lines, T47D and UDC4, and performed differential gene expression analysis. We also conducted functional enrichment analysis to understand the biological functions influenced by these genes. Our study identified several diagnostic genes strongly associated with BC, including MIR6728, ENO1-IT1, ENO1-AS1, RNU6-304P, HMGN2P17, RP3-477M7.5, RP3-477M7.6, and CA6. The genes MIR6728, ENO1-IT1, ENO1-AS1, and HMGN2P17 are involved in cancer control, glycolysis, and DNA-related processes, while CA6 is associated with apoptosis and cancer development. These genes could potentially serve as predictors for BC, paving the way for more precise diagnostic methods and personalized treatment plans. This research enhances our understanding of BC and offers promising avenues for improving patient care in the future.