Department of General Surgery, Cancer Center, Division of Breast Surgery, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China.
Department of General Surgery, The 7th Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
PeerJ. 2022 Feb 9;10:e12878. doi: 10.7717/peerj.12878. eCollection 2022.
Triple-negative breast cancer (TNBC) is a highly aggressive type of cancer with few available treatment methods. The aim of the current study was to provide a prognostic autophagy-related gene (ARG) model to predict the outcomes for TNBC patients using bioinformatic analysis.
mRNA expression data and its clinical information for TNBC samples obtained from The Cancer Genome Atlas (TCGA) and Metabric databases were extracted for bioinformatic analysis. Differentially expressed autophagy genes were identified using the Wilcoxon rank sum test in R software. ARGs were downloaded from the Human Autophagy Database. The Kaplan-Meier plotter was employed to determine the prognostic significance of the ARGs. The sample splitting method and Cox regression analysis were employed to establish the risk model and to demonstrate the association between the ARGs and the survival duration. The corresponding ARG-transcription factor interaction network was visualized using the Cytoscape software.
A signature-based risk score model was established for eight genes (, , , , , , , and ) using the TCGA data and the model was validated with the GSE38959 and Metabric datasets, respectively. Patients with high risk scores had worse survival outcomes than those with low risk scores. Of note, amplification of and reduction of were confirmed to be significantly correlated with the clinical stage of TNBC.
An eight-gene autophagic signature model was developed in this study to predict the survival risk for TNBC. The genes identified in the study may favor the design of target agents for autophagy control in advanced TNBC.
三阴性乳腺癌（TNBC）是一种侵袭性很强的癌症，治疗方法有限。本研究旨在通过生物信息学分析，提供一个预测 TNBC 患者预后的自噬相关基因（ARG）模型。
从癌症基因组图谱（TCGA）和 Metabric 数据库中提取 TNBC 样本的 mRNA 表达数据及其临床信息，用于生物信息学分析。使用 R 软件中的 Wilcoxon 秩和检验鉴定差异表达的自噬基因。从人类自噬数据库下载 ARGs。使用 Kaplan-Meier 绘图器确定 ARGs 的预后意义。采用样本拆分方法和 Cox 回归分析建立风险模型，并证明 ARGs 与生存时间之间的关联。使用 Cytoscape 软件可视化相应的 ARG-转录因子相互作用网络。
使用 TCGA 数据建立了基于 8 个基因（,,,,,,, 和 ）的风险评分模型，并分别使用 GSE38959 和 Metabric 数据集进行验证。高风险评分患者的生存结局较低风险评分患者差。值得注意的是，和的扩增和 的减少被证实与 TNBC 的临床分期显著相关。
本研究建立了一个基于 8 个自噬基因的signature 模型，用于预测 TNBC 的生存风险。该研究中鉴定的基因可能有利于设计针对晚期 TNBC 自噬控制的靶向药物。
