Prognostic and predictive value of an autophagy-related signature for early relapse in stages I–III colon cancer. (19th February 2019) - Mo, Shaobo; Dai, Weixing; Xiang, Wenqiang; Li, Yaqi; Feng, Yang; Zhang, Long; Li, Qingguo; Cai, Guoxiang
Abstract: We postulated that expression differences of autophagy-related genes are instrumental in stratifying the risk of early relapse after surgery and evaluating the prognosis of patients with stages I–III colon cancer. Therefore, propensity score matching analysis was performed between patients in early relapse group and long-term survival group from GSE39582 test series and internal validation series. Using Cox regression model, a nine-autophagy-related signature (CAPN2, ATG16L2, TP63, SIRT1, RPS6KB1, PEX3, ATG5, UVRAG, NAF1) was established to classify patients into those at high risk of early relapse (high-risk group), and those at low risk of early relapse (low-risk group). Relapse-free survival (RFS) was significantly different between the two groups in test [hazard ratio (HR): 2.019, 95% confidence interval (CI): 1.362–2.992, P < 0.001], internal validation (HR: 2.464, 95% CI: 1.196–5.079, P < 0.001) and another two external validation series (GSE14333—HR: 2.250, 95% CI: 1.227–4.126, P = 0.007; GSE33113—HR: 5.552, 95% CI: 2.098–14.693, P < 0.001). Then, based on RFS, we developed a nomogram, integrating the nine-autophagy-related classifier and four clinicopathological risk factors to evaluate prognosis of stages I–III colon cancer patients. Time-dependent receiver operating curve at 2 years showed that the integrated signature (area under curve = 0.758) had better prognostic accuracy than American Joint Committee on Cancer TNM stage (area under curve = 0.620). In conclusion, we identified and built a nine-autophagy-related signature, a credible approach to early relapse prediction in stages I–III colon cancer patients, which can assist physicians in devising more efficient therapeutic strategies. Abstract : Here, we identified and validated a nine-autophagy-related signature, a credible approach to early relapse prediction in stages I-III colon cancer patients. Additionally, we constructed a nomogram based on RFS to assist physicians in devising more efficient therapeutic strategies.