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

BACKGROUND: Increasing 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. METHODS: There 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. RESULTS: Thirteen 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. CONCLUSIONS: In 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.

Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model.

BACKGROUND: The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm. METHODS: The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People's Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models. RESULTS: Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients. CONCLUSIONS: The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.

Clinicopathologic and prognostic significance of human epidermal growth factor receptor in patients with gastric cancer: An updated meta-analysis.

PURPOSE: The aim of this update meta-analysis was to clarify the clinicopathologic and prognostic significance of human epidermal growth factor receptor(EGFR) expression in gastric cancer patients. EXPERIMENTAL DESIGN: Several electronic databases were searched from January 1970 to May 2016. The odds ratio (OR) was calculated to assess the association between EGFR expression and pathological parameters. The hazard ratio (HR) and 95% CI were calculated to explore the relationship between EGFR expression and overall survival. RESULTS: Finally 7229 patients with gastric cancer from 25 eligible studies were included in the present meta analysis. High EGFR expression was found to be significantly related with tumor differentiation (OR=1.96, 95%CI: 1.14-3.34, Z=2.43, P=0.015), lymph node metastasis (OR=2.20, 95% CI: 1.63-2.96, Z=5.17, P=0.001), and tumor stage (OR=2.13, 95% CI: 1.35-3.36, Z=3.25, P=0.001). However, high EGFR expression was not significantly associated with invasion depth (OR=2.09, 95% CI: 0.4-11.05, Z=0.87, P=0.385). The pooled HR suggested that high EGFR expression was significantly correlated with overall survival (HR=1.19, 95% CI 1.04-1.37, Z=2.44, P=0.015). CONCLUSIONS: The present meta-analysis demonstrated that high EGFR expression significantly predicts poor prognosis, suggesting that high EGFR expression may serve as a predictive biomarker for poor prognosis in patients with gastric cancer.

Clinicopathologic significance and prognostic value of Ki-67 expression in patients with gastric cancer: a meta-analysis.

BACKGROUND: The prognostic value and clinicopathologic significance of Ki-67 expression in gastric cancer patients was controversial. This meta-analysis was performed to clarify the prognostic value and clinicopathologic significance of Ki-67 expression in gastric cancer patients. MATERIALS AND METHODS: Several electronic databases were searched for eligible studies. The pooled odds ratio (OR), hazard ratios (HR) and 95% confidence interval(CI) were calculated to explore the prognostic value and clinicopathologic significance of Ki-67 expression for disease free survival and overall survival. RESULTS: Totally 5600 gastric cancer patients from 29 studies were included in this study. High Ki-67 expression was significantly related with Lauren's classification (OR = 1.70; P = 0.001; 95%CI: 1.40-2.06) and tumor size(OR = 1.54; P = 0.006; 95%CI: 1.14-2.09). However, high Ki-67 expression was not significantly associated with lymph node metastasis (OR = 1.37; P = 0.138; 95% CI: 0.90-2.08) , tumor stage (OR = 1.31; P = 0.296; 95% CI: 0.79-2.16) and tumor differentiation (OR = 1.03; P = 0.839; 95% CI: 0.78-1.35). The pooled HRs were 1.87(P = 0.001; 95% CI 1.30-2.69) for disease free survival and 1.23(P = 0.005; 95% CI 1.06-1.42) for overall survival. CONCLUSIONS: High Ki-67 expression may serve as a predictive biomarker for poor prognosis in gastric cancer patients. Stratification by Ki-67 expression may be a consideration for selection of therapeutic regimen and integrated managements.

Prognostic Significance of High VEGF-C Expression for Patients with Breast Cancer: An Update Meta Analysis.

BACKGROUND: The prognostic significance of vascular endothelial growth factor C (VEGF-C) expression in breast cancer (BC) patients remains controversial. Therefore, this meta-analysis was performed to determine the prognostic significance of VEGF-C expression in BC patients. MATERIALS AND METHODS: Several electronic databases were searched from January 1991 to August 2016. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to evaluate the prognostic significance of VEGF-C expression for disease free survival (DFS) and overall survival (OS). RESULTS: The present meta analysis totally included 21 eligible studies and 2828 patients with BC. The combined HRs were 1.87(95% CI 1.25-2.79, P = 0.001) for DFS and 1.96(95% CI 1.15-3.31, P = 0.001) for OS. The pooled HRs of non-Asian subgroup were 2.04(95%CI 1.36-3.05, P = 0.001) for DFS and 2.61(95%CI 1.51-4.52, P = 0.001) for OS, which were significantly higher than that of Asian subgroup. The funnel plot for publication bias was symmetrical. The further Egger's test and Begg's test did not detect significant publication bias (all P>0.05). CONCLUSIONS: The present meta analysis strongly supported the prognostic role of VEGF-C expression for DFS and OS in BC patients, especially for patients in non-Asian countries. Furthermore, stratification by VEGF-C expression may help to optimize the treatments and the integrated managements for BC patients.