Fan Hao, Dong Mingjie, Ren Chaomin, Shao Pengfei, Gao Yu, Wang Yushan, Feng Yi
Department of Orthopedics, Second Hospital of Shanxi Medical University, Taiyuan, China.
Shanxi Medical University, Taiyuan, China.
Heliyon. 2024 Mar 16;10(6):e28279. doi: 10.1016/j.heliyon.2024.e28279. eCollection 2024 Mar 30.
Breast cancer (BC) is the primary cause of cancer mortality. Herein, we aimed to establish and verify a prognostic model consisting of endoplasmic reticulum stress and apoptosis related genes (ERAGs) to predict patient survival.
The Cancer Genome Atlas (TCGA) database was used to download gene expression and clinical data to identify the differentially expressed genes (DEGs). Using univariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression analysis, the prognostic ERAGs were screened. The predictive performance was evaluated using Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Furthermore, a nomogram model incorporating clinical parameters and risk scores was constructed and subsequently evaluated using ROC and KM analysis. The correlation analysis, mutation analysis, functional enrichment analysis, and immune infiltration analysis were employed to investigate the specific mechanism of ERAGs. We also used Quantitative Real-Time PCR (RT-qPCR) to verify the differential expression of DE-ERAGs between the breast cancer cell line and mammary epithelial cell line.
We constructed a prognostic signature comprising 16 ERAGs. ROC, KM analysis and the nomogram model demonstrated high effectiveness in accurately predicting the overall survival (OS) of BRCA patients. The results of these analysis could provide reference for further mechanism exploration.
We developed and assessed a novel molecular predictive model for breast cancer that focuses on endoplasmic reticulum stress and apoptosis in this study. It is a valuable complement to the existing prognostic prediction models for breast cancer.
乳腺癌（BC）是癌症死亡的主要原因。在此，我们旨在建立并验证一个由内质网应激和凋亡相关基因（ERAGs）组成的预后模型，以预测患者的生存情况。
使用癌症基因组图谱（TCGA）数据库下载基因表达和临床数据，以识别差异表达基因（DEGs）。通过单变量Cox回归分析和最小绝对收缩和选择算子（LASSO）惩罚的Cox比例风险回归分析，筛选出预后ERAGs。使用Kaplan-Meier（KM）生存分析和受试者工作特征（ROC）曲线分析评估预测性能。此外，构建了一个包含临床参数和风险评分的列线图模型，并随后使用ROC和KM分析进行评估。采用相关性分析、突变分析、功能富集分析和免疫浸润分析来研究ERAGs的具体机制。我们还使用定量实时聚合酶链反应（RT-qPCR）来验证乳腺癌细胞系和乳腺上皮细胞系之间DE-ERAGs的差异表达。
我们构建了一个包含16个ERAGs的预后特征。ROC、KM分析和列线图模型在准确预测BRCA患者的总生存期（OS）方面显示出高效性。这些分析结果可为进一步的机制探索提供参考。
在本研究中，我们开发并评估了一种针对乳腺癌的新型分子预测模型，该模型侧重于内质网应激和凋亡。它是对现有乳腺癌预后预测模型的有价值补充。
