Boolean Modeling and AI Validation of the ATM/miR-34a–miR-200 Axis Regulating Survival–Apoptosis switch in Breast Cancer

The Ataxia telangiectasia mutated (ATM) protein plays a crucial role in impeding cancer progression and is traditionally recognized for its involvement in the DNA damage response (DDR). Recent studies reveal that ATM induces the expression of Snail1 and ZEB1, key drivers of epithelial-mesenchymal transition (EMT) and drug resistance. MicroRNA-34a (miR-34a) and microRNA-200 (miR-200), both p53-inducible miRNAs, directly target Snail1 and ZEB1, thereby inhibiting EMT and drug resistance. However, the dynamic regulatory interplay within the ATM signaling pathway remains unclear. To address this, we constructed a dynamic Boolean model informed by literature-based cancer studies and validated it using independent experimental evidence from PubMed. Subsequently, model predictions were evaluated using PubTator3, an AI-driven literature mining platform, to verify predictive coherence with recent experimental discoveries. Gain- and loss-of-function perturbation analyses further revealed dual regulatory roles of ATM and p53-inducible miRNAs in promoting or inhibiting DDR and apoptosis pathways. Notably, we identified a double-negative feedback loop between miR-34a and ZEB1, acting as a bistable switch that governs EMT, stemness, drug resistance, and apoptosis. Collectively, this study demonstrates how Boolean network modeling combined with AI-based literature assessment can elucidate emergent regulatory behaviors in cancer systems. The ATM/miR-34a–miR-200 axis uncovered here provides mechanistic and therapeutic insights into DDR modulation and cell-fate dynamics in breast cancer.