Development and validation of a prognostic signature for preoperative prediction of overall survival in gastric cancer patients.

Abstract

BackgroundAs a serious challenge for public health, the prognosis of gastric cancer patients is still poor. The current study aimed to develop and validate a prognostic signature to predict the overall survival of gastric cancer patients.Patients and methodsThe dataset in the present study was obtained from The Cancer Genome Atlas database. The present study finally included 343 gastric cancer patients with information on long non-coding RNA (lncRNA) expression and overall survival.ResultsA prognostic model named Eleven-lncRNA signature was constructed according to the expression values of eleven prognostic lncRNA predictors identified by univariate and multivariate Cox regression model. According to time-dependent receiver operating characteristic curves, the Harrell’s concordance indexes of Eleven-lncRNA signature were 0.764 (95% CI 0.720–0.808), 0.776 (95% CI 0.732–0.820), and 0.807 (95% CI 0.763–0.851) for 1-year overall survival, 3-year overall survival, and 5-year overall survival respectively in the model group. In the validation group, the Harrell’s concordance indexes of Eleven-lncRNA signature were 0.748 (95% CI 0.704–0.792), 0.794 (95% CI 0.750–0.838), and 0.798 (95% CI 0.754–0.842) for 1-year overall survival, 3-year overall survival, and 5-year overall survival respectively. The gastric cancer patients (n=343) in the model group could be stratified into low-risk group (n=171) and high-risk group (n=172) according to the median of Eleven-lncRNA signature score. Kaplan–Meier survival curves showed that the mortality rate in the high-risk group was significantly poorer than that in the low-risk group (P<0.001).ConclusionThe present study constructed and validated a prognostic model named Eleven-lncRNA signature for preoperative individual mortality risk prediction in gastric cancer patients. This Eleven-lncRNA signature can predict the individual mortality risk of gastric cancer patients and is helpful in improving clinical decision making regarding individualized treatment.