Genes Associated with Apoptosis in an Experimental Breast Cancer Model

Breast cancer remains a leading cause of global mortality. According to international cancer data, significant progress has been made in treating breast cancer; however, metastasis and drug resistance continue to be the primary causes of mortality for many patients. This study investigated the modulation of apoptosis-related genes in response to ionizing radiation and estrogen exposure based on a human breast epithelial cell model (MCF-10F and its transformed variants: Estrogen, Alpha3, Alpha5, Tumor2) previously established, where cells were treated with high linear energy transfer alpha particles, with or without 17β-estradiol. Gene expression profiling was performed using an Affymetrix U133A microarray, and bioinformatic analyses assessed differential expression, estrogen receptor status, and correlations with overall survival. Distinct gene expression patterns emerged across cell lines and tumor subtypes. <i>TP53</i> expression correlated positively with <i>TP63</i>, <i>BIK</i>, <i>CFLAR</i>, <i>BIRC3</i>, and <i>BCLAF1</i>. <i>TP63</i>, <i>PERP</i>, <i>CFLAR</i>, <i>BCLAF1</i>, <i>GULP1</i>, and <i>BIRC3</i> were elevated in normal tissue, whereas <i>BIK</i>, <i>PHLDA2</i>, and <i>BBC3</i> were upregulated in tumors. ER-positive tumors exhibited higher <i>TP63</i>, <i>BIK</i>, <i>BCLAF1</i>, and <i>BBC3</i> expression, while ER-negative tumors showed increased <i>PERP</i>, <i>CFLAR</i>, <i>BIRC3</i>, and <i>PHLDA2</i>. Notably, elevated <i>BCLAF1</i> expression was associated with poorer survival in Luminal A patients, and high <i>PHLDA2</i> expression correlated with reduced survival in Luminal B cases. These findings indicate that resistance to apoptosis is a fundamental mechanism in breast cancer progression and therapeutic evasion. Breast tumors selectively alter the expression of key genes to promote growth, evade apoptosis, and develop therapeutic resistance. The differential expression and correlations of these apoptosis-related genes highlight their potential as molecular targets for future personalized cancer therapies and as valuable biomarkers for prognostic stratification and predicting therapeutic response.