Machine learning unveils key Redox signatures for enhanced breast Cancer therapy

Breast cancer remains a leading cause of mortality among women worldwide, necessitating innovative prognostic models to enhance treatment strategies.

Our study retrospectively enrolled 9,439 breast cancer patients from 12 independent datasets and single-cell data from 12 patients (64,308 cells). Moverover, 30 in-house clinical cohort were collected for validation. We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. To affirm the efficacy of our developed model, immunohistochemistry assays were performed. Additionally, we investigated various potential immunotherapeutic and chemotherapeutic interventions.

This study introduces an Artificial Intelligence-aided Redox Signature (AIARS) as a novel prognostic tool, leveraging machine learning to identify critical redox-related gene signatures in breast cancer. Our results demonstrate that AIARS significantly outperforms existing prognostic models in predicting breast cancer outcomes, offering a robust tool for personalized treatment planning. Validation through immunohistochemistry assays on samples from 30 patients corroborated our results, underscoring the model's applicability on a wider scale. Furthermore, the analysis revealed that patients with low AIARS expression levels are more responsive to immunotherapy. Conversely, those exhibiting high AIARS were found to be more susceptible to certain chemotherapeutic agents, including vincristine.

Our study underscores the importance of redox biology in breast cancer prognosis and introduces a powerful machine learning-based tool, the AIARS, for personalized treatment strategies. By providing a more nuanced understanding of the redox landscape in breast cancer, the AIARS paves the way for the development of redox-targeted therapies, promising to enhance patient outcomes significantly. Future work will focus on clinical validation and exploring the mechanistic roles of identified genes in cancer biology.