Deep Learning-Based Prediction and Optimization of Histone Methylation-Oxidative Stress Networks in Doxorubicin-Induced Cardiomyopathy

Doxorubicin-induced cardiomyopathy (DIC) is a major dose-limiting toxicity in cancer chemotherapy. Its pathological mechanism involves epigenetic modification-mediated imbalance in redox homeostasis. This study constructed an intelligent computational framework that integrates graph neural networks (GNN) and deep reinforcement learning (DRL). The framework systematically elucidates the histone methylation-driven oxidative stress regulatory network and its role in DIC pathogenesis. We integrated multi-omics data to build heterogeneous biological networks. We used graph attention networks to extract key epigenetic-transcriptional-metabolic regulatory patterns. We combined temporal convolutional networks to achieve dynamic prediction of myocardial injury. We utilized deep Q-networks to optimize individualized treatment strategies. In a clinical cohort of 327 patients, the model achieved 92.3% accuracy in predicting myocardial injury (AUC=0.957). This represents a 15.7 percentage point improvement over traditional methods. The adaptive treatment regimen generated by reinforcement learning reduced cardiac toxicity events by 48.1%. Meanwhile, tumor response rates remained comparable. The study revealed that abnormal H3K27me3 enrichment in the promoter regions of key antioxidant genes such as SOD2 is a core epigenetic event in DIC pathogenesis. This work provides interpretable molecular targets and computational intelligence tools for precision cardioprotective strategies.