Zinc finger protein 671 has a cancer-inhibiting function in colorectal carcinoma via the deactivation of Notch signaling.

Zinc finger protein 671 (ZNF671) has been described as a vital cancer inhibitor in multiple neoplasms, yet the functional roles of ZNF671 in colorectal carcinoma (CRC) remain unresolved. This project examined the possible link between ZNF671 and CRC. Lower levels of ZNF671 were observed in CRC tissue compared with noncancerous tissue, which were related to a worse survival rate in CRC patients. High methylation levels at the ZNF671 gene promoter region were shown in CRC tissue, which were inversely correlated with ZNF671 expression. Treatment with demethylation agents restored ZNF671 levels in CRC cell lines. Up-regulation of ZNF671 resulted in suppressive effects on the proliferative ability and metastatic potency of CRC cells. Moreover, the up-regulation of ZNF671 reinforced the chemosensitivity of CRC cells. A mechanism study determined ZNF671 to be a vital mediator of Notch signaling. The up-regulation of ZNF671 decreased the expression of Notch1 and lowered the levels of NICD, HES1, and HEY1. The overexpression of NICD1 diminished ZNF671-mediated antitumor effects. ZNF671 depletion reinforced Notch signaling, and Notch suppression reversed ZNF671-depletion-elicited protumor effects. Moreover, the overexpression of ZNF671 weakened the tumorigenicity of CRC cells in a xenograft model in vivo. In summary, ZNF671 exerts a cancer-inhibiting function in CRC via the deactivation of Notch signaling. Low ZNF671 levels caused by gene promoter hypermethylation contribute to the malignant transformation of CRC. This work underlines the interest of ZNF671 as a target candidate for exploiting novel anti-CRC therapies.

Letter to editor 'Non-invasive model for predicting high-risk esophageal varices based on liver and spleen stiffness'.

This letter to the editor relates to the study entitled "Non-invasive model for predicting high-risk esophageal varices based on liver and spleen stiffness". Acute bleeding caused by esophageal varices is a life-threatening complication in patients with liver cirrhosis. Due to the discomfort, contraindications, and associated complications of upper gastrointestinal endoscopy screening, it is crucial to identify an imaging-based non-invasive model for predicting high-risk esophageal varices in patients with cirrhosis.

Non-invasive model for predicting high-risk esophageal varices based on liver and spleen stiffness.

BACKGROUND: Acute bleeding due to esophageal varices (EVs) is a life-threatening complication in patients with cirrhosis. The diagnosis of EVs is mainly through upper gastrointestinal endoscopy, but the discomfort, contraindications and complications of gastrointestinal endoscopic screening reduce patient compliance. According to the bleeding risk of EVs, the Baveno VI consensus divides varices into high bleeding risk EVs (HEVs) and low bleeding risk EVs (LEVs). We sought to identify a non-invasive prediction model based on spleen stiffness measurement (SSM) and liver stiffness measurement (LSM) as an alternative to EVs screening. AIM: To develop a safe, simple and non-invasive model to predict HEVs in patients with viral cirrhosis and identify patients who can be exempted from upper gastrointestinal endoscopy. METHODS: Data from 200 patients with viral cirrhosis were included in this study, with 140 patients as the modelling group and 60 patients as the external validation group, and the EVs types of patients were determined by upper gastrointestinal endoscopy and the Baveno VI consensus. Those patients were divided into the HEVs group (66 patients) and the LEVs group (74 patients). The effect of each parameter on HEVs was analyzed by univariate and multivariate analyses, and a non-invasive prediction model was established. Finally, the discrimination ability, calibration ability and clinical efficacy of the new model were verified in the modelling group and the external validation group. RESULTS: Univariate and multivariate analyses showed that SSM and LSM were associated with the occurrence of HEVs in patients with viral cirrhosis. On this basis, logistic regression analysis was used to construct a prediction model: Ln [P/(1-P)] = -8.184 -0.228 × SSM + 0.642 × LSM. The area under the curve of the new model was 0.965. When the cut-off value was 0.27, the sensitivity, specificity, positive predictive value and negative predictive value of the model for predicting HEVs were 100.00%, 82.43%, 83.52%, and 100%, respectively. Compared with the four prediction models of liver stiffness-spleen diameter to platelet ratio score, variceal risk index, aspartate aminotransferase to alanine aminotransferase ratio, and Baveno VI, the established model can better predict HEVs in patients with viral cirrhosis. CONCLUSION: Based on the SSM and LSM measured by transient elastography, we established a non-invasive prediction model for HEVs. The new model is reliable in predicting HEVs and can be used as an alternative to routine upper gastrointestinal endoscopy screening, which is helpful for clinical decision making.

Non-invasive model for predicting esophageal varices based on liver and spleen volume.

BACKGROUND: Upper endoscopy is the gold standard for predicting esophageal varices in China. Guidelines and consensus suggest that patients with liver cirrhosis should undergo periodic upper endoscopy, most patients undergo their first upper endoscopy when esophageal variceal bleeds. Therefore, it is important to develop a non-invasive model to early diagnose esophageal varices. AIM: To develop a non-invasive predictive model for esophageal varices based on liver and spleen volume in viral cirrhosis patients. METHODS: We conducted a cross-sectional study based on viral cirrhosis crowd in the Second Affiliated Hospital of Xi'an Jiaotong University. By collecting the basic information and clinical data of the participants, we derived the independent risk factors and established the prediction model of esophageal varices. The established model was compared with other models. Area under the receiver operating characteristic curve, calibration plot and decision curve analysis were used to test the discriminating ability, calibration ability and clinical practicability in both the internal and external validation. RESULTS: The portal vein diameter, the liver and spleen volume, and volume change rate were the independent risk factors of esophageal varices. We successfully used the factors to establish the predictive model [area under the curve (AUC) 0.87, 95%CI: 0.80-0.95], which showed better predictive value than other models. The model showed good discriminating ability, calibration ability and the clinical practicability in both modelling group and external validation group. CONCLUSION: The developed non-invasive predictive model can be used as an effective tool for predicting esophageal varices in viral cirrhosis patients.

Prediction model using readily available clinical data for colorectal cancer in a chinese population.

BACKGROUND: In China, health screening has become common, although colonoscopy is not always available or acceptable. We sought to develop a prediction model of colorectal cancer (CRC) for health screening population based on readily available clinical data to reduce labor and economic costs. METHODS: We conducted a cross-sectional study based on a health screening population in Karamay Central Hospital. By collecting clinical data and basic information from participants, we identified independent risk factors and established a prediction model of CRC. Internal and external validation, calibration plot, and decision curve analysis were employed to test discriminating ability, calibration ability, and clinical practicability. RESULTS: Independent risk factors of CRC, which were readily available in primary public health institutions, included high-density lipoprotein cholesterol, male sex, total cholesterol, advanced age, and hemoglobin. These factors were successfully incorporated into the prediction model (AUC 0.740, 95% CI 0.713-0.767). The model demonstrated a high degree of discrimination and calibration, in addition to a high degree of clinical practicability in high-risk people. CONCLUSIONS: The prediction model exhibits good discrimination and calibration and is pragmatic for CRC screening in rural areas and primary public health institutions.

Appropriate body mass index cutoffs for type 2 diabetes in Xinjiang population: defining the influence of liver aminotransferase.

BACKGROUND/PURPOSE: Recent study suggested that type 2 diabetes (T2DM) attributed to body mass index (BMI) could be influenced by liver aminotransferase. We aim to ascertain the cut-off point of BMI associated with T2DM and the influence of both elevated aminotransferase (AST) and alanine aminotransferase (ALT). MATERIALS AND METHODS: In our retrospective cohort study, T2DM was diagnosed when FBS ≥ 7.0 mmol/L, BMI of participants with baseline fasting (FBS) < 7.0 mmol/L was divided by percentiles and by aminotransferanse (ALT and AST ≥ 20 U/L, ALT or AST < 20 U/L). Hazard ratios and the turning point of BMI of high T2DM risk was estimated in totality and different aminotransferanse groups. RESULTS: During an average follow-up time of 3.71 years of 33346 participants, 1486 developed T2DM, and the average baseline BMI of participants who developed T2DM was 26.22 kg/m2. Cumulative incidence of T2DM was more than 5% when ALT and AST ≥ 20U/L, age over 44, male sex or BMI over 25.39 kg/m2; The risk of T2DM incidence increased as the BMI grow. The turning point of BMI at high risk of T2DM was 25.0 kg/m2 in totality, 25.1 kg/m2 when ALT or AST < 20 U/L and 26.1 kg/m2 when ALT and AST ≥ 20U/L. CONCLUSIONS: BMI of 25.0 kg/m2 was the cutoff point for T2DM development, and there is greater association between BMI and T2DM when ALT or AST < 20 U/L.

Non-invasive prediction model for high-risk esophageal varices in the Chinese population.

BACKGROUND: There are two types of esophageal varices (EVs): high-risk EVs (HEVs) and low-risk EVs, and HEVs pose a greater threat to patient life than low-risk EVs. The diagnosis of EVs is mainly conducted by gastroscopy, which can cause discomfort to patients, or by non-invasive prediction models. A number of non-invasive models for predicting EVs have been reported; however, those that are based on the formula for calculation of liver and spleen volume in HEVs have not been reported. AIM: To establish a non-invasive prediction model based on the formula for liver and spleen volume for predicting HEVs in patients with viral cirrhosis. METHODS: Data from 86 EV patients with viral cirrhosis were collected. Actual liver and spleen volumes of the patients were determined by computed tomography, and their calculated liver and spleen volumes were calculated by standard formulas. Other imaging and biochemical data were determined. The impact of each parameter on HEVs was analyzed by univariate and multivariate analyses, the data from which were employed to establish a non-invasive prediction model. Then the established prediction model was compared with other previous prediction models. Finally, the discriminating ability, calibration ability, and clinical efficacy of the new model was verified in both the modeling group and the external validation group. RESULTS: Data from univariate and multivariate analyses indicated that the liver-spleen volume ratio, spleen volume change rate, and aspartate aminotransferase were correlated with HEVs. These indexes were successfully used to establish the non-invasive prediction model. The comparison of the models showed that the established model could better predict HEVs compared with previous models. The discriminating ability, calibration ability, and clinical efficacy of the new model were affirmed. CONCLUSION: The non-invasive prediction model for predicting HEVs in patients with viral cirrhosis was successfully established. The new model is reliable for predicting HEVs and has clinical applicability.

Association of proton pump inhibitors with risk of hepatic encephalopathy in advanced liver disease: A meta-analysis.

BACKGROUND: Several studies have explored the association between the use of proton pump inhibitors (PPIs) and the risk of developing hepatic encephalopathy (HE) in patients with advanced liver disease. However, the evidence-based conclusions are controversial. We hypothesized that using PPIs may increase the risk of HE in patients with advanced liver disease. If confirmed, clinicians must strictly adhere to the indications for PPI treatment in this population. AIM: To evaluate the pooled risk of HE in patients with advanced liver disease who use PPIs. METHODS: Three electronic databases (PubMed, EMBASE, and the Cochrane Library) were searched from the date of database inception through January 8, 2019 to identify comparative studies evaluating the association between PPI use and the risk of HE. Data from the included studies were extracted. The random-effects model was used for pooling risk estimates and the corresponding 95% confidence intervals (CIs). Subgroup and sensitivity analyses were also performed. RESULTS: In total, 4342 patients from five case-control studies and 188053 patients from four cohort studies were included in this analysis. In patients with advanced liver disease, PPI use was associated with an elevated risk of developing HE, with significant heterogeneity. The pooled odds ratio for case-control studies was 2.58 (95%CI: 1.68-3.94, I 2 = 72%). The pooled RR for cohort studies was 1.67 (95%CI: 1.30-2.14, I 2 = 67%). The results of the subgroup analyses suggested that the heterogeneity may be the result of differences in the study designs and the definitions of PPI use. The sensitivity and subgroup analyses did not alter our findings. CONCLUSION: In patients with advanced liver disease, PPI use is associated with an elevated risk of HE. Future large prospective studies are needed to confirm this association.

Prognostic value of risk scoring systems for cirrhotic patients with variceal bleeding.

BACKGROUND: Acute variceal bleeding is one of the deadliest complications of cirrhosis, with a high risk of in-hospital rebleeding and mortality. Some risk scoring systems to predict clinical outcomes in patients with upper gastrointestinal bleeding have been developed. However, for cirrhotic patients with variceal bleeding, data regarding the predictive value of these prognostic scores in predicting in-hospital outcomes are limited and controversial. AIM: To validate and compare the overall performance of selected prognostic scoring systems for predicting in-hospital outcomes in cirrhotic patients with variceal bleeding. METHODS: From March 2017 to June 2019, cirrhotic patients with acute variceal bleeding were retrospectively enrolled at the Second Affiliated Hospital of Xi'an Jiaotong University. The clinical Rockall score (CRS), AIMS65 score (AIMS65), Glasgow-Blatchford score (GBS), modified GBS (mGBS), Canada-United Kingdom-Australia score (CANUKA), Child-Turcotte-Pugh score (CTP), model for end-stage liver disease (MELD) and MELD-Na were calculated. The overall performance of these prognostic scoring systems was evaluated. RESULTS: A total of 330 cirrhotic patients with variceal bleeding were enrolled; the rates of in-hospital rebleeding and mortality were 20.3% and 10.6%, respectively. For in-hospital rebleeding, the discriminative ability of the CTP and CRS were clinically acceptable, with area under the receiver operating characteristic curves (AUROCs) of 0.717 (0.648-0.787) and 0.716 (0.638-0.793), respectively. The other tested scoring systems had poor discriminative ability (AUROCs < 0.7). For in-hospital mortality, the CRS, CTP, AIMS65, MELD-Na and MELD showed excellent discriminative ability (AUROCs > 0.8). The AUROCs of the mGBS, CANUKA and GBS were relatively small, but clinically acceptable (AUROCs > 0.7). Furthermore, the calibration of all scoring systems was good for either in-hospital rebleeding or death. CONCLUSION: For cirrhotic patients with variceal bleeding, in-hospital rebleeding and mortality rates remain high. The CTP and CRS can be used clinically to predict in-hospital rebleeding. The performances of the CRS, CTP, AIMS65, MELD-Na and MELD are excellent at predicting in-hospital mortality.