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.

Breast cancer, a highly heterogeneous and complex disease, remains the leading cause of cancer-related death among women worldwide. Despite advances in treatment modalities, effective prognostic models and therapeutic strategies are still urgently needed. We retrospectively analyzed 15 independent breast cancer cohorts to explore the role of RNA modifications in the prognosis of patients with breast cancer. By integrating nine types of RNA modifications, we developed a comprehensive machine learning-based RNA modification signature (CMRS). Furthermore, single-cell RNA sequencing data were analyzed to understand the biological mechanisms underlying CMRS. In addition, immune infiltration levels were evaluated via six different algorithms, and immune checkpoint inhibitor responsiveness was predicted. Moreover, the response of high-CMIS patients to chemotherapy was predicted via multiple datasets. Finally, immunohistochemistry was performed on tissue samples from breast cancer patients to validate protein expression levels. Our analysis revealed five key RNA modification-related genes (ENO1, ARAF, WT1, GADD45A, and BIRC3) associated with breast cancer prognosis. The CMRS model demonstrated high predictive accuracy across multiple cohorts and was significantly correlated with patient survival outcomes. Multiomics analysis revealed that high CMRS was associated with increased tumor mutational burden and distinct mutational signatures, particularly in pathways related to TP53, MYC, and cell proliferation. Single-cell analysis highlighted the involvement of epithelial cells and MYC signaling in high CMRS activity. Cell‒cell communication analysis revealed reduced interaction strength in hig CMRS patients, indicating poor prognosis. Furthermore, low CMRS patients presented increased immune cell infiltration and improved responsiveness to immune checkpoint inhibitors, whereas high CMRS patients were identified as potential candidates for treatment with panobinostat and vincristine. Our study elucidates the significant role of RNA modifications in breast cancer prognosis and treatment. The CMRS model serves as a sensitive biomarker for predicting patient survival and treatment responsiveness, offering a new avenue for personalized therapy in patients with breast cancer.

Background: Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine Learning-Derived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and personalized treatment strategies for breast cancer patients.Methods: The study employed ten machine learning algorithms to construct 108 algorithm combinations for the MDLS model. The robustness of the model was evaluated using multi-omics and single-cell data across 14 breast cancer cohorts, involving 9,723 patients. Genetic mutation, copy number alterations, and single-cell RNA sequencing were analyzed to understand the molecular mechanisms and predictive capabilities of the MDLS model. Immunotherapy targets were predicted by evaluating immune cell infiltration and immune checkpoint expression. Chemotherapy targets were identified through correlation analysis and drug responsiveness prediction.Results: The MDLS model demonstrated superior prognostic power, with a mean C-index of 0.649, outperforming 69 published signatures across ten cohorts. High-MDLS patients exhibited higher tumor mutation burden and distinct genomic alterations, including significant gene amplifications and deletions. Single-cell analysis revealed higher MDLS activity in tumor-aneuploid cells and identified key regulatory factors involved in MDLS progression. Cell-cell communication analysis indicated stronger interactions in high-MDLS groups, and immunotherapy response evaluation showed better outcomes for low-MDLS patients.Conclusion: The MDLS model offers a robust and precise tool for predicting breast cancer prognosis and tailoring personalized treatment strategies. Its integration of multi-omics and machine learning highlights its potential clinical applications, particularly in improving the effectiveness of immunotherapy and identifying therapeutic targets for high-MDLS patients.

DNA repair is a vital mechanism in cells that protects against DNA damage caused by internal and external factors. It involves a network of signaling pathways that monitor and transmit damage signals, activating various cellular activities to repair DNA damage and maintain genomic integrity. Dysfunctions in this repair pathway are strongly associated with the development and progression of cancer. However, they also present an opportunity for targeted therapy in breast cancer. Extensive research has focused on developing inhibitors that play a crucial role in the signaling pathway of DNA repair, particularly due to the remarkable success of PARP1 inhibitors (PARPis) in treating breast cancer patients with BRCA1/2 mutations. In this review, we summarize the current research progress and clinical implementation of BRCA and BRCAness in targeted treatments for the DNA repair pathway. Additionally, we present advancements in diverse inhibitors of DNA repair, both as individual and combined approaches, for treating breast cancer. We also discuss the clinical application of DNA repair-targeted therapy for breast cancer, including the rationale, indications, and summarized clinical data for patients with different breast cancer subtypes. We assess their influence on cancer progression, survival rates, and major adverse reactions. Last, we anticipate forthcoming advancements in targeted therapy for cancer treatment and emphasize prospective areas of development.

Breast cancer remains the leading cause of cancer-related mortality among women worldwide. Poly(ADP-ribose) polymerase (PARP) inhibitors have emerged as a critical therapeutic option, particularly for patients with triple-negative breast cancer and other HER2-negative metastatic breast cancer harboring BRCA mutations. Despite their clinical success, the emergence of primary and acquired resistance to PARP inhibitors poses a significant challenge, limiting their long-term effectiveness. Here we provide a comprehensive overview of the mechanisms underlying the action of PARP inhibitors, as well as their clinical development and application. In addition, we discuss the factors driving resistance and potential strategies to overcome it in the context of PARP inhibitors.

Triple-positive breast cancer (TPBC), a unique subtype of luminal breast cancer, is characterized by concurrent positivity for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Owing to the crosstalk between the ER and HER2 signaling pathways, the standard of care and drug resistance of this particular subtype are difficult challenges. Recent research and clinical trials have indicated a shift in the treatment paradigm for TPBC from single-target therapies to multi-pathway, multitarget strategies aiming to comprehensively modulate intricate signaling networks, thereby overcoming resistance and enhancing therapeutic outcomes. Among multiple strategies, triple-drug therapy has emerged as a promising treatment modality, demonstrating potential efficacy in patients with TPBC. Moving forward, there is a critical need to perform in-depth analyses of specific mechanisms of cancer pathogenesis and metastasis, decipher the complex interactions between different genes or proteins, and identify concrete molecular targets, thus paving the way for the development of tailored therapeutic strategies to combat TPBC effectively.