Ferroptosis-related genes mediate tumor microenvironment and prognosis in triple-negative breast cancer via integrated RNA-seq analysis DOI Creative Commons
Xuantong Gong, Lishuang Gu, Di Yang

и другие.

eLife, Год журнала: 2025, Номер 13

Опубликована: Июнь 2, 2025

Triple-negative breast cancer (TNBC), an aggressive malignancy with limited tools to predict recurrence and drug sensitivity, exhibits ferroptotic heterogeneity across subtypes. However, the tumor microenvironment (TME) mediated by ferroptosis-related genes remains poorly characterized. This study integrates single-cell bulk RNA sequencing data from Gene Expression Omnibus elucidate ferroptosis-driven TME features in TNBC, employing machine learning develop prognostic therapeutic response prediction models. At level, T cells were classified into three subpopulations macrophages two subpopulations, their infiltration degrees significantly correlated clinical outcomes. A risk score model constructed based on these findings demonstrated robust predictive performance, validated external cohorts 3-, 4-, 5-year area under receiver operating characteristic curves of 0.65, 0.67, 0.71, respectively. Notably, high-risk patients exhibited enhanced sensitivity 27 agents. By delineating ferroptosis-associated immune heterogeneity, this work provides a stratification tool enhance precision decision-making while identifying offer actionable targets for TNBC medicine.

Язык: Английский

Integrated Analysis of Single-Cell and Bulk RNA-Seq Data reveals that Ferroptosis-Related Genes Mediated the Tumor Microenvironment predicts Prognosis, and guides Drug Selection in Triple-Negative Breast Cancer DOI Open Access
Xuantong Gong, Lishuang Gu, Di Yang

и другие.

Опубликована: Май 12, 2025

Abstract Background Triple-negative breast cancer (TNBC) is aggressive, lacking methods to predict recurrence and drug sensitivity. Ferroptotic heterogeneity varies in TNBC subtypes. However, the tumor microenvironment (TME) mediated by ferroptosis genes unclear. Our study aims integrate single-cell bulk RNA sequencing (RNA-seq) data reveal ferroptosis-mediated TME TNBC, predicting prognosis guiding treatment. Methods The RNA-seq of were sourced from Gene Expression Omnibus (GEO) database. Using these data, a machine learning algorithm was employed analyze characteristics ferroptosis-related TNBC. Prediction models for survival treatment response established then validated an independent set. Results At individual cell level, T cells categorized into three distinct subpopulations, local macrophages two subpopulations. infiltration degree different subpopulations closely associated with outcomes. Based on this, risk score model we developed effectively predicted recurrence-free patients, independently pooled 3-, 4-, 5-year Area Under Curves (AUCs) 0.65, 0.67, 0.71, respectively. Additionally, found that patients high-risk group may be more responsive 27 drugs. Conclusions We have uncovered immune clusters ferroptosis. A constructed identify which can assist physicians disease monitoring precision therapy. identified hold significant potential as therapeutic targets patients. Funding This project funded National Natural Science Foundation China (81974268, 82472000, 82304151), Talent Incentive Program Cancer Hospital Chinese, Academy Medical Sciences (801032247), Cooperation Fund CHCAMS (CFA202202023), open Beijing Key Laboratory Tumor Invasion Metastasis Mechanism, Capital University(2023ZLKF03). Impact Statement Integrating elucidates role offering prognostic personalized insights.

Язык: Английский

Процитировано

0

Ferroptosis-related genes mediate tumor microenvironment and prognosis in triple-negative breast cancer via integrated RNA-seq analysis DOI Creative Commons
Xuantong Gong, Lishuang Gu, Di Yang

и другие.

eLife, Год журнала: 2025, Номер 13

Опубликована: Июнь 2, 2025

Triple-negative breast cancer (TNBC), an aggressive malignancy with limited tools to predict recurrence and drug sensitivity, exhibits ferroptotic heterogeneity across subtypes. However, the tumor microenvironment (TME) mediated by ferroptosis-related genes remains poorly characterized. This study integrates single-cell bulk RNA sequencing data from Gene Expression Omnibus elucidate ferroptosis-driven TME features in TNBC, employing machine learning develop prognostic therapeutic response prediction models. At level, T cells were classified into three subpopulations macrophages two subpopulations, their infiltration degrees significantly correlated clinical outcomes. A risk score model constructed based on these findings demonstrated robust predictive performance, validated external cohorts 3-, 4-, 5-year area under receiver operating characteristic curves of 0.65, 0.67, 0.71, respectively. Notably, high-risk patients exhibited enhanced sensitivity 27 agents. By delineating ferroptosis-associated immune heterogeneity, this work provides a stratification tool enhance precision decision-making while identifying offer actionable targets for TNBC medicine.

Язык: Английский

Процитировано

0