AUTHOR=Cui Heyang , Weng Yongjia , Ding Ning , Cheng Chen , Wang Longlong , Zhou Yong , Zhang Ling , Cui Yongping , Zhang Weimin 

TITLE=Autophagy-Related Three-Gene Prognostic Signature for Predicting Survival in Esophageal Squamous Cell Carcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.650891

DOI=10.3389/fonc.2021.650891

ISSN=2234-943X

ABSTRACT=Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China and it’s prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on expression pattern of ARGs. We used two ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort and TCGA ESCC set (94 samples) as the validation cohort. In the discovery cohort, we identified 34 differentially expressed genes out of 222 ARGs and divided ESCC patients into 3 groups that showed significant differences in prognosis. Then we analysed the prognosis of 34 differentially expressed ARGs. Three genes (PARP1, ITGA6, FADD) were ultimately obtained through random forest feature selection and were constructed as a ARG-related prognostic model. This model was further validated in the TCGA ESCC set. Cox regression analysis confirmed that the 3-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohort by OS (P = 5.1615E-8 and P = 0.051759, respectively). We also constructed a clinical nomogram with a concordance index of 0.73 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARGs signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARGs-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.