Qiao Enqi, Ye Jiayi, Huang Kaiming
Breast Surgical Department, Shaoxing Maternity & Child Health Hospital, 305 Dongjie Steet, Yuecheng District, Shaoxing, Zhejiang 312006, China.
Department of Urological Surgery, Suichang County People's Hospital, 143 North Street, Miaogao Street, Suichang County, Lishui City, Zhejiang 323300, China.
Hum Mol Genet. 2024 Dec 4. doi: 10.1093/hmg/ddae170.
Endoplasmic Reticulum Stress (ER stress) was an important event in the development of breast cancer. We aimed to predict prognosis based on ER stress related key genes.
Data of the RNA-seq and clinical information of breast cancer cases were downloaded from the TCGA database. A total of 4 genes related with ER stress was identified by the univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression analysis. The predictive ability of the ER stress model was evaluated by utilizing Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves. Moreover, we verified 4 genes expression and its relationship with clinical breast cancer cases in real-world.
4 genes including RNF186, BCAP31, SERPINA1, TAPBP were identified as a prognostic risk score model. Based on that, we found patients of breast cancer had a better survival with low-risk score. And also, ER stress model showed a good diagnostic efficacy with AUC curve. The risk score was significantly associated with patients' age, T stage and clinical stage. A nomogram was constructed to estimate individual survival. Further GO and KEGG analysis showed our model was related with immune infiltration. Patients of breast cancer with high-risk scores were usually accompanied with poor immune infiltration. It was predicted that high risk group was more sensitive to Vinorelbine, Docetaxel and Cisplatin. At last, we verified the expression of four signature genes using qRT-PCR and immunohistochemistry.
Our ER stress model performed a valuable prediction on breast cancer patients.
内质网应激（ER应激）是乳腺癌发展过程中的一个重要事件。我们旨在基于内质网应激相关关键基因预测预后。
从TCGA数据库下载乳腺癌病例的RNA测序数据和临床信息。通过单变量Cox回归和最小绝对收缩与选择算子（LASSO）惩罚的Cox比例风险回归分析，共鉴定出4个与内质网应激相关的基因。利用Kaplan-Meier曲线和时间依赖性受试者工作特征（ROC）曲线评估内质网应激模型的预测能力。此外，我们在现实世界中验证了这4个基因的表达及其与临床乳腺癌病例的关系。
包括RNF186、BCAP31、SERPINA1、TAPBP在内的4个基因被确定为一个预后风险评分模型。基于此，我们发现低风险评分的乳腺癌患者生存情况更好。而且，内质网应激模型的AUC曲线显示出良好的诊断效能。风险评分与患者年龄、T分期和临床分期显著相关。构建了列线图以估计个体生存情况。进一步的基因本体（GO）和京都基因与基因组百科全书（KEGG）分析表明我们的模型与免疫浸润有关。高风险评分的乳腺癌患者通常伴有较差的免疫浸润。预测高风险组对长春瑞滨、多西他赛和顺铂更敏感。最后，我们使用qRT-PCR和免疫组织化学验证了4个特征基因的表达。
我们的内质网应激模型对乳腺癌患者具有有价值的预测作用。
