女性乳腺癌已成为全球最常见的恶性肿瘤，给患者和社会带来巨大的疾病负担。衰老和氧化应激在癌症发展和进展中都非常重要。然而，衰老和氧化应激在乳腺癌中的预后作用仍不清楚。本研究试图建立基于衰老-氧化应激相关基因（SOSCRGs）的预测模型，并在多个维度上评估其临床效用。通过相关分析确定了SOSCRGs。癌症转录组数据和临床信息从TCGA和GSE96058中获取。采用支持向量机算法对基于SOSCRGs的乳腺侵袭性癌（BRCA）患者进行亚型分类。利用LASSO回归分析建立基于SOSCRGs的预测模型。组织了预测模型的疗效评估、亚组分析、临床关联、免疫浸润、功能强度、突变特征和药物敏感性分析。单细胞分析用于解析肿瘤微环境中关键SOSCRGs的表达模式。此外，通过荧光定量PCR检测了五种不同乳腺癌细胞系中关键SOSCRGs的表达水平。共鉴定了246个SOSCRGs。基于SOSCRGs确定了两种乳腺癌亚型，其中亚型1显示出活跃的免疫格局。随后发展了基于SOSCRGs的预测模型，并将风险评分明确定义为乳腺癌的独立预后预示因子。构建了一种新的预测图，并展现了良好的预测能力。我们进一步确定风险评分明显影响免疫细胞浸润水平和免疫检查点的表达水平。两个风险组具有不同的功能强度特征。高风险组中糖代谢和糖酵解显著上调。低风险组中检测到PIK3CA突变驱动的肿瘤发生，而高风险组中TP53突变占优势。分析进一步证实了风险评分与TMB之间显著正相关。低风险组患者还可能对几种药物敏感。单细胞分析显示ERRFI1、ETS1、NDRG1和ZMAT3在肿瘤微环境中表达。此外，通过荧光定量PCR分别定量比较了五种不同乳腺癌细胞系中这七个关键SOSCRGs的表达水平。多维评估验证了基于SOSCRGs的预测模型在预测乳腺癌预后、辅助临床决策和风险分层方面的临床效用。版权所有 © 2023 Ye, Luo, Guo, Zhang, Sun, Xu, Ji, Ou and Wu.

Female breast cancer has risen to be the most common malignancy worldwide, causing a 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-oxidative stress co-relation genes (SOSCRGs) and evaluate its clinical utility in multiple dimensions.SOSCRGs were identified via correlation analysis. Transcriptome data and clinical information of patients with breast invasive carcinoma (BRCA) were accessed from The Cancer Genome Atlas (TCGA) and GSE96058. SVM algorithm was employed to process subtype classification of patients with BRCA based on SOSCRGs. LASSO regression analysis was utilized to establish the predictive model based on SOSCRGs. Analyses of the predictive model with regards to efficacy evaluation, subgroup analysis, clinical association, immune infiltration, functional strength, mutation feature, and drug sensitivity were organized. Single-cell analysis was applied to decipher the expression pattern of key SOSCRGs in the tumor microenvironment. Additionally, qPCR was conducted to check the expression levels of key SOSCRGs in five different breast cancer cell lines.A total of 246 SOSCRGs were identified. Two breast cancer subtypes were determined based on SOSCRGs and subtype 1 showed an 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 favorable predictive capability. We further 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 characterized by distinct functional strengths. Sugar metabolism and glycolysis were significantly upregulated in the high risk group. The low risk group was deciphered to harbor PIK3CA mutation-driven tumorigenesis, while TP53 mutation was dominant in the high risk group. The analysis further revealed a significantly positive correlation between risk score and TMB. Patients in the low risk group may also sensitively respond to several drug agents. Single-cell analysis dissected that ERRFI1, ETS1, NDRG1, and ZMAT3 were expressed in the tumor microenvironment. Moreover, the expression levels of the seven SOSCRGs in five different breast cancer cell lines were quantified and compared 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.Copyright © 2023 Ye, Luo, Guo, Zhang, Sun, Xu, Ji, Ou and Wu.