Time-series expression patterns of EDAR and XEDAR as predictive markers in breast cancer: A New Paradigm for Personalized Treatment

Time-series analysis of gene expression patterns has become a powerful tool in cancer monitoring. A novel predictive model based on the temporal expression patterns of the EDAR and XEDAR genes in breast cancer is proposed here. This approach is based on recent discoveries that show distinct temporal regulation of these genes during normal development and their critical role in mammary gland morphogenesis. By tracking dynamic changes in EDAR and XEDAR expression, this model aims to detect resistance to treatment and disease progression earlier than conventional monitoring methods. Implementing this system could potentially reduce mortality in the overall population of breast cancer, up to 46,000-69,000 lives per year worldwide. This conceptual framework offers a new paradigm for personalized cancer monitoring, integrating developmental biology principles with clinical oncology.