出身背景
三阴性乳腺癌症（TNBC）是一种侵袭性很强的癌症，几乎没有可用的治疗方法。本研究的目的是提供一个预后自噬相关基因（ARG）模型，通过生物信息学分析预测TNBC患者的预后。
方法
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秩和检验来鉴定差异表达的自噬基因。ARG是从人类自噬数据库下载的。Kaplan–Meier绘图仪用于确定ARGs的预后意义。采用样本分割法和Cox回归分析建立风险模型，并证明ARGs与生存期之间的相关性。使用Cytoscape软件对相应的ARG转录因子相互作用网络进行可视化。
后果
使用TCGA数据为8个基因（ITGA3、HSPA8、CTSD、ATG12、CLN3、ATG7、MAP1LC3C和WIPI1）建立了基于特征码的风险评分模型，并分别用GSE38959和Metabolic数据集验证了该模型。风险评分高的患者的生存结果比风险评分低的患者差。值得注意的是，ATG12的扩增和WIPI的减少被证实与TNBC的临床分期显著相关。
结论
本研究开发了一个八基因自噬特征模型来预测TNBC的生存风险。研究中确定的基因可能有利于设计晚期TNBC自噬控制的靶向药物。
Background
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.

Methods
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.

Results
A signature-based risk score model was established for eight genes (ITGA3, HSPA8, CTSD, ATG12, CLN3, ATG7, MAP1LC3C, and WIPI1) 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 ATG12 and reduction of WIPI were confirmed to be significantly correlated with the clinical stage of TNBC.

Conclusion
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.

https://peerj.com/articles/12878/