AUTHOR=Ye Yinghui , Luo Yulou , Guo Tong , Zhang Chenguang , Sun Yutian , Xu Anping , Ji Ling , Ou Jianghua , Wu Shang Ying 

TITLE=Leveraging senescence-oxidative stress co-relation to predict prognosis and drug sensitivity in breast invasive carcinoma

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1179050

DOI=10.3389/fendo.2023.1179050

ISSN=1664-2392

ABSTRACT=Female breast cancer has risen to be the most common malignancy worldwide, causing huge disease burden for both patients and society. Both senescence and oxidative stress attach importance to cancer development and progression. However, the prognostic roles of senescence and oxidative stress remain obscure in breast cancer. In this present study, we attempted to establish a predictive model based on senescence-oxdative stress co-relation genes (SOSCRGs) and evaluate its clinical utility in multiple dimensions. Two breast cancer subtypes were determined based on SOSCRGs and subtype 1 showed active immune landscape. A SOSCRGs-based predictive model was subsequently developed and the risk score was clarified as independent prognostic predictors in breast cancer. A novel nomogram was constructed and exhibited favourable predictive capability. We ascertained that the infiltration levels of immune cells and expressions of immune checkpoints were significantly influenced by the risk score. The two risk groups were identified to characterize by distinct functional strengthens. Sugar metabolism and glycolysis were significantly upregulated in the high risk group. Low risk group was deciphered to harbor PIK3CA mutation-driven tumorigenesis, while TP53 mutation was dominant in the high risk group. Analysis further revealed the significantly positive correlation between risk score and TMB. We also implied that patients in the low risk group may sensitively respond to several drug agents. Moreover, the expression pattern of seven SOSCRGs was dissected in the tumor microenvironment of breast cancer by single-cell analysis. Additionally, the expression levels of the seven SOSCRGs in five different breast cancer cell lines were quantified by qPCR respectively. Multidimensional evaluations verified the clinical utility of the SOSCRGs-based predictive model to predict prognosis, aid clinical decision and risk stratification for patients with breast cancer.