Chen Chongxiang, Chen Siliang, Hu Xiaochun, Wang Jiaojiao, Wen Tianmeng, Fu Juan, Li Huan
Department of Intensive Care Unit, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Guangzhou Institute of Respiratory Diseases, State Key Laboratory of Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
Transl Cancer Res. 2020 Mar;9(3):1947-1959. doi: 10.21037/tcr.2020.02.07.
Several studies show that autophagy plays an important part in the biological processes of lung adenocarcinoma. Therefore, this work aimed to establish one scoring system on the basis of the expression profiles of differentially expressed autophagy-related genes (DEARGs) in patients with lung adenocarcinoma.
The Cancer Genome Atlas (TCGA) was applied to retrieve lung adenocarcinoma data. The overall survival (OS)-associated DEARGs were selected for the DEARG scoring scale. Moreover, the online database Kaplan-Meier Plotter (www.Kmplot.com) was employed to verify the accuracy of our results.
The expression patterns of DEARG were detected in lung adenocarcinoma as well as normal lung tissues. A gene set related to autophagy was identified, along with 9 genes that showed marked significance in predicting the lung adenocarcinoma prognosis. According to the cox regression results, DEARGs (including ITGB4, BIRC5, ERO1A, and NLRC4) were applied to calculate the DEARGs risk score. Patients with lower DEARGs risk scores were associated with better OS. Moreover, based on analysis with the receiver operating characteristic (ROC) curve, DEARGs accurately distinguished the healthy tissues from lung adenocarcinoma tissues [area under the curve (AUC) value of >0.6].
A scoring system is constructed based on the primary DEARGs, which accurately predicts the outcomes of lung adenocarcinoma.
多项研究表明自噬在肺腺癌的生物学过程中发挥重要作用。因此，本研究旨在基于肺腺癌患者差异表达的自噬相关基因（DEARG）表达谱建立一种评分系统。
应用癌症基因组图谱（TCGA）检索肺腺癌数据。选择与总生存期（OS）相关的DEARG用于构建DEARG评分量表。此外，利用在线数据库Kaplan-Meier Plotter（www.Kmplot.com）验证我们结果的准确性。
在肺腺癌组织以及正常肺组织中检测到DEARG的表达模式。鉴定出一组与自噬相关的基因，以及9个在预测肺腺癌预后方面具有显著意义的基因。根据cox回归结果，应用DEARG（包括整合素β4、杆状病毒IAP重复序列5、内质网氧化还原酶1α和NOD样受体家族C成员4）计算DEARG风险评分。DEARG风险评分较低的患者总生存期较好。此外，基于受试者工作特征（ROC）曲线分析，DEARG能够准确区分健康组织和肺腺癌组织[曲线下面积（AUC）值>0.6]。
基于主要的DEARG构建了一种评分系统，该系统能够准确预测肺腺癌的预后。
