Elucidating the molecular basis of ATP-induced cell death in breast cancer: Construction of a robust prognostic model

Abstract

BACKGROUND Breast cancer is a multifaceted and formidable disease with profound public health implications. Cell demise mechanisms play a pivotal role in breast cancer pathogenesis, with ATP-triggered cell death attracting mounting interest for its unique specificity and potential therapeutic pertinence. AIM To investigate the impact of ATP-induced cell death (AICD) on breast cancer, enhancing our understanding of its mechanism. METHODS The foundational genes orchestrating AICD mechanisms were extracted from the literature, underpinning the establishment of a prognostic model. Simultaneously, a microRNA (miRNA) prognostic model was constructed that mirrored the gene-based prognostic model. Distinctions between high- and low-risk cohorts within mRNA and miRNA characteristic models were scrutinized, with the aim of delineating common influence mechanisms, substantiated through enrichment analysis and immune infiltration assessment. RESULTS The mRNA prognostic model in this study encompassed four specific mRNAs: P2X purinoceptor 4, pannexin 1, caspase 7, and cyclin 2. The miRNA prognostic model integrated four pivotal miRNAs: hsa-miR-615-3p, hsa-miR-519b-3p, hsa-miR-342-3p, and hsa-miR-324-3p. B cells, CD4+ T cells, CD8+ T cells, endothelial cells, and macrophages exhibited inverse correlations with risk scores across all breast cancer subtypes. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis revealed that genes differentially expressed in response to mRNA risk scores significantly enriched 25 signaling pathways, while miRNA risk scores significantly enriched 29 signaling pathways, with 16 pathways being jointly enriched. CONCLUSION Of paramount significance, distinct mRNA and miRNA signature models were devised tailored to AICD, both potentially autonomous prognostic factors. This study's elucidation of the molecular underpinnings of AICD in breast cancer enhances the arsenal of potential therapeutic tools, offering an unparalleled window for innovative interventions. Essentially, this paper reveals the hitherto enigmatic link between AICD and breast cancer, potentially leading to revolutionary progress in personalized oncology.