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.

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.

Increased expression of Ki-67 is a poor prognostic marker for colorectal cancer patients: a meta analysis.

BACKGROUND: The prognostic value of Ki-67 expression in colorectal cancer patients was controversial. Therefore, this meta analysis was conducted to ascertain the prognostic value of Ki-67 expression in colorectal cancer patients. METHODS: The electronic databases, including EMBASE, PubMed, Cochrane Library and Web of Knowledge database, were searched from January 1970 to July 2017. The pooled hazard ratios and 95% confidence intervals were calculated to evaluate the prognostic value of Ki-67 expression for colorectal cancer patients. RESULTS: Totally 34 eligible studies and 6180 colorectal cancer patients were included in the present meta analysis. The pooled hazard ratios were 1.54(95% CI 1.17-2.02, P = 0.005) for overall survival and 1.43(1.12-1.83, P = 0.008) for disease free survival in univariate analysis. After adjustment of other prognostic factors, the pooled HR was 1.50(95% CI 1.02-2.22, P = 0.03) for overall survival in multivariate analysis. CONCLUSION: The present meta analysis demonstrated that high Ki-67 expression is significantly correlated with poor overall survival and disease free survival, indicating that high Ki-67 expression may serve as a valuable predictive method for poor prognosis of colorectal cancer patients.

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.

Dietary mustard seeds (Sinapis alba Linn) suppress 1,2-dimethylhydrazine-induced immuno-imbalance and colonic carcinogenesis in rats.

In a Wistar rat model, prolonged supplementation of mustard seed (MS) to the diet significantly ameliorates the induction of colorectal carcinomas by 1,2-dimethylhydrazine (DMH). The expression of the splenocyte major histocompatibility complex class I (MHCI) was found significantly enhanced, whereas that of the major histocompatibility complex class II (MHCII) was significantly decreased. Compared to that of control animals, the proportion of spleenic B- and dendritic cells (DC) was amplified in the MS group. The expressions of MHCI, as well as that of MHCII, were increased in DC cells; whereas in B cells, MHCI expression was augmented but that of MHCII moderately decreased. The percentages of CD8+CD28+ and CD4+CD28+ cells were increased in the MS group, while the CD4+CD25+Foxp3+ subset was depressed. Plasma analysis showed that DMH-exposure induced amplified amounts of interleukin (IL)-4, IL-5, IL-10, and transforming growth factor-beta, whereas MS feeding counteracted this effect but enhanced IL-2, IL12p70, IL21, TNF-alpha, and interferon-gamma. In the SW480 colon adenocarcinoma cell-line, the cytotoxicity of spleenic T-cells from MS-fed animals was significantly increased. In the DMH-exposed rats, the expression of perforin in the spleenic T-cells was dramatically decreased, whereas MS abolished this depression. In summary, dietary MS suppresses DMH-induced immuno-imbalance as well as colon carcinogenesis in rats.

Mustard seeds (Sinapis Alba Linn) attenuate azoxymethane-induced colon carcinogenesis.

Mustard seeds (MS), which are consumed in considerable amounts by the Japanese people that, interestingly, have the longest life expectancy in the world, are known to contain a number of yet not fully defined but quite powerful anti-oxidants. A suspension of extracted MS was found to suppress oxidized-LDL-induced macrophage respiratory burst in vitro, to prevent growth, and to induce apoptotic death of SW480 cells (a human colon cancer cell line), while no such effects were found for normal 3T3 cells. A diet enriched with MS decreased plasma levels of the lipid peroxidation product malonaldehyde in mice exposed to the colon cancer-inducer azoxymethane (AOM). Such a diet also dose-dependently enhanced the activity of several anti-oxidant enzymes, such as superoxide dismutase (SOD), catalase, and GSH-peroxidase and, moreover, reduced AOM-mediated formation of colon adenomas by about 50%. Further studies are required to detail and explore the beneficial effects of MS and their rich content of powerful anti-oxidants.