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

BACKGROUND: As 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 METHODS: The 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. RESULTS: A 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). CONCLUSION: The 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.