Integrated analysis reveals potential long non-coding RNA biomarkers and their potential biological functions for disease free survival in gastric cancer patients

BackgroundIncreasing evidences supported the association between long non-coding RNA (lncRNA) and disease free survival in gastric cancer (GC) patients. The purpose of the current study was to construct and verify a noninvasive preoperative predictive tool for disease free survival in GC patients.MethodsThere were 265 and 300 GC patients in model dataset and validation dataset respectively. The associations between the lncRNA biomarkers and disease free survival were evaluated by univariate and multivariate Cox regression.ResultsThirteen lncRNA biomarkers (GAS5-AS1, AL109615.3, KDM7A-DT, AP000866.2, KCNJ2-AS1, LINC00656, LINC01777, AC046185.3, TTTY14, LINC01526, LINC02523, LINC00592, and C5orf66) were identified as prognostic biomarkers with disease free survival. These thirteen lncRNA biomarkers were combined to construct a prognostic signature for disease free survival. The C-indexes of the current predictive signature in model cohort were 0.849 (95% CI 0.803-0.895), 0.859 (95% CI 0.813-0.905) and 0.888 (95% CI 0.842-0.934) for 1-year, 3-year and 5-year disease free survival respectively. Based on thirteen-lncRNA prognostic signature, patients in model cohort could be stratified into high risk group and low risk group with significant different disease free survival rate (hazard ratio [HR]=7.355, 95% confidence interval [CI] 4.378-12.356). Good reproducibility of thirteen-lncRNA prognostic signature was confirmed in an external validation cohort (GSE62254) with HR 3.919 and 95% CI 2.817-5.453. Further analysis demonstrated that the prognostic significance of thirteen-lncRNA prognostic signature was independent of other clinical characteristics.ConclusionsIn conclusion, a simple noninvasive prognostic signature was established for preoperative prediction of disease free survival in GC patients. This prognostic signature might predict the individual mortality risk of disease free survival without pathological information and facilitate individual treatment decision-making.