Purpose The aim of this study was to identify and validate novel biomarkers for distinguishing among hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC), liver fibrosis/liver cirrhosis (LF/LC) and chronic hepatitis B (CHB). Patients and Methods Transcriptomic sequencing was conducted on the liver tissues of 5 patients with HCC, 5 patients with LF/LC, 5 patients with CHB, and 4 healthy controls. The expression levels of selected mRNAs and proteins were assessed by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC) staining, and were verified in validation set (n=200) and testing set (n=400) via enzyme-linked immunosorbent assay (ELISA). Results A total of 9 hub mRNAs were identified by short time-series expression miner and weighted gene co-expression network analysis. Of note, the results of qRT-PCR and IHC staining demonstrated that SHC adaptor protein 1 (SHC1), SLAM family member 8 (SLAMF8), and interleukin-32 (IL-32) exhibited gradually increasing trends in the four groups. Subsequent ELISA tests on the validation cohort indicated that the plasma levels of SHC1, SLAMF8 and IL-32 also gradually increased. Furthermore, a diagnostic model APFSSI (age, PLT, ferritin, SHC1, SLAMF8 and IL-32) was established to distinguish among CHB, LF/LC and HCC. The performance of APFSSI model for discriminating CHB from healthy subjects (AUC=0.966) was much greater compared to SHC1 (AUC=0.900), SLAMF8 (AUC=0.744) and IL-32 (AUC=0.821). When distinguishing LF/LC from CHB, APFSSI was the most outstanding diagnostic parameter (AUC=0.924), which was superior to SHC1, SLAMF8 and IL-32 (AUC=0.812, 0.684 and 0.741, respectively). Likewise, APFSSI model with the greatest AUC value displayed an excellent performance for differentiating between HCC and LF/LC than other variables (SHC1, SLAMF8 and IL-32) via ROC analysis. Finally, the results in the test set were consistent with those in the validation set. Conclusion SHC1, SLAMF8 and IL-32 can differentiate among patients with HCC, LF/LC, CHB and healthy controls. More importantly, the APFSSI model greatly improves the diagnostic accuracy of HBV-associated liver diseases.
The aim of this study was to identify potential plasma biomarkers for hepatitis B virus (HBV)‐related liver diseases. High‐throughput transcriptome sequencing analysis was performed on five patients with chronic hepatitis B (CHB), five patients with HBV‐associated liver fibrosis/liver cirrhosis (LF/LC), and four healthy participants. By short time‐series expression miner and functional analysis, aquaporin 1 (AQP1), dystroglycan 1 (DAG1), and hemoglobin subunit beta (HBB) were identified as potential biomarkers. Immunohistochemical analysis revealed that the expression levels of AQP1, DAG1, and HBB were upregulated in the three groups. Subsequent enzyme‐linked immunosorbent assay tests on the training cohort (n = 150) indicated that the plasma levels of AQP1 and DAG1 were highest in LF/LC patients, followed by those in CHB patients, and the lowest in healthy controls. APAD model, a diagnostic panel incorporating age, platelet, AQP1, and DAG1 levels, exhibited the strongest stratification ability to distinguish LF/LC patients from CHB patients, and to differentiate CHB patients from healthy controls. Furthermore, the diagnostic accuracies of the biomarkers and APAD model were verified in an independent cohort consisting of 230 participants. In conclusion, both AQP1 and DAG1 have good diagnostic values and APAD model greatly enhances the diagnostic accuracy for HBV‐related hepatic diseases.
Aim: To explore the predictive value of plasma YAP1 for esophageal varices (EV) and high-risk EV (HRV) in patients with liver cirrhosis. Materials & methods: A total of 208 patients with liver cirrhosis were enrolled and categorized into four groups. Correlation analysis, logistic regression analysis and receiver operating characteristic curve analysis were performed to evaluate the diagnostic performance of plasma YAP1 for EV and HRV. Results: Plasma YAP1 levels were significantly elevated with the occurrence and progression of EV in cirrhotic patients. The multivariate logistic regression analysis revealed that plasma YAP1 is an independent predictor for EV and HRV. For predicting EV and HRV, the YAP1 cut-off values of 5.43 and 6.98 ng/ml yielded the area under the receiver operating characteristic curves of 0.944 and 0.955, respectively. Conclusion: Plasma YAP1 is a potential novel noninvasive biomarker for predicting EV and HRV in patients with liver cirrhosis.
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