Association of α-fetoprotein levels with liver stiffness measurement in outpatients with chronic hepatitis B

A novel chemiluminescence sensor for alpha-fetoprotein detection based on an aptamer-luminol modified magnetic graphene oxide and copper-based MOF composite

Herein, an aptamer-luminol modified magnetic graphene oxide and copper-based MOF composite was prepared and used to build a novel target-triggered "turn on" chemiluminescence (CL) sensor for alpha-fetoprotein (AFP) detection. Magnetic graphene oxide (MGO) was functionalized with the complementary sequence of the AFP aptamer (cDNA), and then MGO-cDNA was linked to aptamer modified luminol (Apt-luminol) through the complementary base pairing effect. The functionalized magnetic graphene oxide (MGO-cDNA/Apt-luminol) was prepared as a specific magnetic separation and signal switch material. ZnONPs-Au@CuMOFs shows excellent catalytic performance and was used as a catalyst for the luminol-H2O2 reaction. AFP will specifically recognize and bind to Apt on MGO-cDNA/Apt-luminol when AFP is present, which causes luminol release and triggers the CL reaction. The released luminol encounters ZnONPs-Au@CuMOFs and produces strong CL intensity. Therefore, a novel target-triggered "turn on" CL method with high selectivity and sensitivity for detecting AFP has been established. The linear range and detection limit were 1.0 × 10-4-50 ng mL-1 and 4.2 × 10-5 ng mL-1, respectively. The sensor also exhibited good selectivity, reproducibility and stability, and was finally used for AFP detection in serum samples.

Value of XGBoost machine learning model for diagnosis of hepatitis B cirrhosis

BACKGROUND

The progression of chronic hepatitis B into cirrhosis is slow and easily ignored, and the construction of a noninvasive diagnostic model for cirrhosis based on routine clinical indicators has become a hot research topic. However, there is still a lack of machine learning models regarding the early diagnosis of cirrhosis.

AIM

To investigate the performance of the extreme gradient boosting (XGBoost) machine model in the diagnosis of hepatitis B cirrhosis.

METHODS

A retrospective analysis was performed on 1087 patients with chronic hepatitis B virus infection (CHBV) diagnosed for the first time at the Department of Infection, The First/Second Affiliated Hospital of Anhui Medical University from 2010 to 2018. The patients were divided into training and validation sets in a 3:1 ratio according to the randomization principle. Clinical data of all study participants were collected and prediction models were constructed using XGBoost machine learning model. Meanwhile, the aspartate aminotransferase/platelet ratio index (APRI) and fibrosis-4 index (FIB-4) scores were calculated and compared with the XGBoost machine learning model. Area under the curve (AUC) was used to assess the model discrimination, and calibration curve (CA) and decision curve analysis (DCA) were used to assess the model calibration and benefit.

RESULTS

A total of 1087 CHBV patients were included, including 817 in the training set and 270 in the validation set. There was no statistical difference between the training and validation sets for all predictor variables (P > 0.05). Cirrhosis occurred in 103 patients in the training set, and APRI and FIB-4 scores were significantly higher in cirrhotic patients than in non-cirrhotic patients (P < 0.05). The relative importance of platelets was the highest among all predictors. The AUCs of the model in the training and validation sets were 0.95 and 0.86 (P < 0.05), respectively, and the Kappa values were 0.78 and 0.74, which suggested that the model was reproducible. CA curve analysis indicated that the model predicted a high degree of agreement with the true situation fit. DCA of the training and validation sets implied that the developed model could result in a high degree of benefit for patients. XGBoost machine learning model was significantly more efficient for the diagnosis of cirrhosis than APRI and FIB-4 scores.

CONCLUSION

The XGBoost machine learning model constructed in this study based on common clinical information of CHBV patients has an excellent performance for the diagnosis of cirrhosis and deserves further clinical promotion.

Serum Alpha-Fetoprotein as a Predictor of Liver Fibrosis in HBeAg-Positive Chronic Hepatitis B Patients

Background and Objectives: Non-invasive methods for evaluating liver fibrosis have been a crucial focus of clinical research. The aim of the current study is to assess the accuracy of serum alpha-fetoprotein (AFP) in determining the stage of liver fibrosis in patients with chronic hepatitis B (CHB) who are positive for HBeAg. Materials and Methods: The current study included a total of 276 HBeAg-positive CHB patients who underwent liver biopsy. The levels of serum AFP were measured in these patients using electrochemiluminescence immunoassays. The correlations between serum AFP levels and other laboratory parameters were analyzed using Spearman's correlation analysis. Binary logistic regression analysis was performed to determine the independent associations between serum AFP levels and liver fibrosis. The diagnostic performance of serum AFP and other non-invasive markers was evaluated using receiver operating characteristic (ROC) curves. Results: A total of 59 (21.4%) patients were found to have elevated levels of serum AFP (>7 ng/mL). These patients displayed a significantly higher proportion of both advanced fibrosis and cirrhosis compared to those with normal serum AFP levels (0-7 ng/mL). The level of serum AFP was positively associated with levels of serum globulin (GLB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (TBIL), as well as the AST-to-platelet ratio (APRI), fibrosis-4 (FIB-4), and Scheuer's classification, and negatively correlated with platelet (PLT) counts. Furthermore, serum AFP was found to be independently associated with significant fibrosis, advanced fibrosis, and cirrhosis. The results of the ROC analysis showed that serum AFP was an effective predictor of significant fibrosis, advanced fibrosis, and cirrhosis, with an area under the receiver operating characteristic curve (AUROC) of 0.773 (95% CI: 0.721-0.821), 0.889 (95% CI: 0.847-0.923), and 0.925 (95% CI: 0.887-0.953), respectively. These values are higher than those of the APRI and FIB-4. Conclusions: Serum AFP could serve as a valuable supplemental biomarker for determining the severity of liver fibrosis in HBeAg-positive patients with chronic hepatitis B.

Construction of a Lasso regression-based prediction model for development of cirrhosis in chronic hepatitis B

BACKGROUND

Chronic hepatitis B (CHB) is a global epidemic, and its progression to cirrhosis is often overlooked by patients. Noninvasive diagnostic models for cirrhosis, which are developed using common clinical indicators, can provide reference value for the early diagnosis and treatment of cirrhosis in CHB.

AIM

To construct a prediction model for cirrhosis based on common clinical indicators.

METHODS

Patients with CHB who underwent liver biopsy at the Department of Infectious Diseases, The First or Second Affiliated Hospital of Anhui Medical University from 2010 to 2018 were selected, and their laboratory test indicators were collected and compared between patients with and without cirrhosis. Lasso regression model was used to screen the variables with predictive value for cirrhosis, and multivariate logistic regression analysis was performed to establish a prediction model for cirrhosis. The area under the curve (AUC) was calculated to assess the discrimination performance of the model. Decision curve analysis (DCA) was performed to assess the benefit of the model, and calibration curve-based analysis (CA) was performed to assess the calibration of the model.

RESULTS

A total of 1087 CHB cases were included, of which 135 had cirrhosis. All indicators were statistically different between the two groups except for hepatitis B virus (HBV) DNA, alanine transaminase (ALT) (P < 0.05). Lasso regression analysi identified the predictive variables as age, alpha-fetoprotein (AFP), albumin (ALB), globulin (GLB), glutamyl transpeptidase (GGT), and platelet count (PLT). A prediction model for cirrhosis was developed by multifactorial logistic regression analysis: Logit P = 1.26 + 0.02 × age + 0.001 × AFP - 0.10 × ALB + 0.07 × GLB + 0.004 × GGT - 0.02 × PLT. The AUC of the model for predicting cirrhosis was 0.83 (95% confidence interval: 0.79-0.87). DCA suggested that the use of the developed prediction model resulted in an increased net benefit for the patients, and CA suggested that the predictive effect of the prediction model was in accordance with the actual outcome.

CONCLUSION

The present study has developed a prediction model for cirrhosis based on age, AFP, ALB, GLB, GGT, and PLT in patients with CHB, and it is useful for the early diagnosis of cirrhosis in CHB.

RUNX1 regulates SMAD1 by transcriptionally activating the expression of USP9X, regulating the activation of hepatic stellate cells and liver fibrosis

Liver fibrosis (LF) is a common pathological process with high morbidity and mortality. Runt-related transcription factor 1 (RUNX1) is a transcription factor that could cause nephropathy and renal fibrosis, but its role in LF is unclear. Therefore, this study aimed to investigate the role RUNX1 in LF. Briefly, hepatic fibrosis was detected by Sirius Red staining. Transcript levels were quantified by qPCR, and proteins were assessed by western blotting or immunofluorescence. Cell viability and cell migration were measured by CCK8 assays and wound healing assays, respectively. The binding of RUNX1 and ubiquitin-specific protease 9X (USP9X) promoter was validated by ChIP assays and luciferase report assays, while the binding of USP9X and SMAD1 was confirmed by co-immunoprecipitation (Co-IP). Our studies found that the expression of RUNX1 was upregulated in LF mice, and RUNX1 knockdown alleviated CCl4-induced LF. RUNX1 silencing reduced the viability and migration of HSCs. Besides, RUNX1, as a transcription factor, bound to the promoter of USP9X and regulated the expression of USP9X. USP9X is a deubiquitination enzyme and was found to be up-regulated in LF mice. USP9X silencing reduced the viability and migration of HSCs, thereby inhibiting LF. Further studies showed that USP9X could stabilize downstream Smad1 expression. Furthermore, we also found that RUNX1 regulated the expression of SMAD1 by transcriptionally activating the expression of USP9X, thereby regulating the activation of hepatic stellate cells and liver fibrosis.