Identification of an oxidative stress-related prognostic signature, tumor immune microenvironment, and development of subtypes for breast cancer patients receiving neoadjuvant chemotherapy.

The formation of tumors and the reaction to anticancer treatments are significantly influenced by the regulation of oxidative stress. Nevertheless, it is still unknown how useful oxidative stress-related genes are for forecasting how well a treatment will work and the prognosis of breast cancer. OSRG features were built using least absolute shrinkage and selection operator regression algorithm. The prognostic performance was evaluated via Kaplan-Meier analysis, receiver operating characteristic curves, and multivariate Cox regression in trainingand validation cohorts. Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) was used to detect the expression level of oxidative stress-related genes in the features. Furthermore, we investigated the correlation between OSRG features and molecular subtypes, immunological landscape, chemotherapeutic response, and clinicopathological features. A robust 7-gene risk model was established and validated. The high-risk group's relapse-free survival was shorter, and the receiver operating characteristic curve's area under the curve values at 1, 3, and 5 years were satisfactory, according to internal and external validation. Furthermore the 2 groups differed significantly in their sensitivity to chemotherapeutic drugs and immunological microenvironment. Based on OSRG expression, patients were stratified into 4 molecular clusters with divergent relapse-free survival and immune activity. OSRG signatures and molecular clusters serve as potent predictors of the immune microenvironment, therapeutic response, and survival prognosis, potentially providing new perspectives into the management and tailored therapy of breast cancer patients.