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.
Highlights
As a worldwide malignant tumor, colorectal cancer (CRC) causes 1,632 deaths per day in the United States in 2016, representing ∼35% of the CRC patients [1]
Stage IV CRC patients from GSE39582 set were divided into early relapse group and long-term survival group
Using Cox proportional hazards regression modeling, we derived a 5-autophagy-related genes signature to calculate the risk score for each patient based on the expression levels of the 5 genes weighted by their regression coefficients: Risk score = (0.81995105 ∗ expression level of CAPN10) + (−0.03919814 ∗ expression level of Deathassociated protein kinase 2 (DAPK2)) + (0.82646924 ∗ expression level of DNAJB9) + (1.06760178 ∗ expression level of GNAI3) + (0.21783501 ∗ expression level of PPP1R15A)
Summary
As a worldwide malignant tumor, colorectal cancer (CRC) causes 1,632 deaths per day in the United States in 2016, representing ∼35% of the CRC patients [1]. In 2017, the incidence of CRC in China was about 37.6/100,000, ranking third in all malignant tumors, with about 19.1/100,000 mortality rate at the fourth place [2]. The post-operative survival of patients with CRC in different stages varies greatly. There are still no effective methods for the quantitative prognosis of post-operative patients. In the present clinical work, clinicians judge the prognosis of CRC patients with disease stage and pathological features. Vague judging criteria undoubtedly aggravates patients’ concerns. It blocks the efficient operation of clinical work to a certain extent. Survival rates for Colorectal cancer (CRC) patients who experienced early relapse have usually been relatively low.
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