AUTHOR=Zhou Zheng , Mo Shaobo , Dai Weixing , Ying Zhen , Zhang Long , Xiang Wenqiang , Han Lingyu , Wang Zhimin , Li Qingguo , Wang Renjie , Cai Guoxiang
TITLE=Development and Validation of an Autophagy Score Signature for the Prediction of Post-operative Survival in Colorectal Cancer
JOURNAL=Frontiers in Oncology
VOLUME=9
YEAR=2019
URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2019.00878
DOI=10.3389/fonc.2019.00878
ISSN=2234-943X
ABSTRACT=Background: Survival rates for Colorectal cancer (CRC) patients who experienced early relapse have usually been relatively low. Our study aims at developing an autophagy signature that could help to detect early relapse cases in CRC.
Methods: Propensity score matching analysis was carried out between patients from the early relapse group and the long-term survival group from GSE39582. For both groups, respectively, global autophagy expression changes were then analyzed to identify the differentially expressed prognostic autophagy related genes by conducting Linear Models for Microarray data method analysis. Then, the multi-gene signature was validated in TCGA and Fudan University Shanghai Cancer Center (FUSCC) cohorts. Time-dependent ROC were used to test the efficiency of this signature feature in predicting the prognosis of CRC patients.
Results: 5 autophagy genes were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into low- or high-risk group. Time-dependent ROC analyses proved its prognostic accuracy, with AUC 0.841 and 0.803 at 1 and 3 years, respectively. Then, we validated its prognostic value in two external validation series (GSE17538 and GSE33113) and proved that the result is indeed significant irrespective of datasets in two external independent validation cohorts (TCGA and FUSCC cohorts). A nomogram was constructed to guide individualized treatment of patients with CRC.
Conclusions: The identification of robust autophagy-related features can effectively classify CRC patients into groups with low and high risk of early relapse. This signature may be used to help select high-risk CRC patients who require more aggressive treatment interventions.