Integrating machine learning algorithms to systematically assess reactive oxygen species levels to aid prognosis and novel treatments for triple -negative breast cancer patients

Breast cancer has become one of the top health concerns for women, and triple-negative breast cancer (TNBC) leads to treatment resistance and poor prognosis due to its high degree of heterogeneity and malignancy. Reactive oxygen species (ROS) have been found to play a dual role in tumors, and modulating ROS levels may provide new insights into prognosis and tumor treatment.This study attempted to establish a robust and valid ROS signature (ROSig) to aid in assessing ROS levels. The driver ROS prognostic indicators were searched based on univariate Cox regression. A well-established pipeline integrating 9 machine learning algorithms was used to generate the ROSig. Subsequently, the heterogeneity of different ROSig levels was resolved in terms of cellular communication crosstalk, biological pathways, immune microenvironment, genomic variation, and response to chemotherapy and immunotherapy. In addition, the effect of the core ROS regulator HSF1 on TNBC cell proliferation was detected by cell counting kit-8 and transwell assays.A total of 24 prognostic ROS indicators were detected. A combination of the Coxboost+ Survival Support Vector Machine (survival-SVM) algorithm was chosen to generate ROSig. ROSig proved to be the superior risk predictor for TNBC. Cellular assays show that knockdown of HSF1 can reduce the proliferation and invasion of TNBC cells. The individual risk stratification based on ROSig showed good predictive accuracy. High ROSig was identified to be associated with higher cell replication activity, stronger tumor heterogeneity, and an immunosuppressive microenvironment. In contrast, low ROSig indicated a more abundant cellular matrix and more active immune signaling. Low ROSig has a higher tumor mutation load and copy number load. Finally, we found that low ROSig patients were more sensitive to doxorubicin and immunotherapy.In this study, we developed a robust and effective ROSig model that can be used as a reliable indicator for prognosis and treatment decisions in TNBC patients. This ROSig also allows a simple assessment of TNBC heterogeneity in terms of biological function, immune microenvironment, and genomic variation.