Research progress of protein induced by vitamin K absence or antagonist II in liver transplantation for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common pathologic type of primary liver cancer. Liver transplantation (LT) is a radical strategy for treating patients with early-stage HCC, which may lead to a better prognosis compared to hepatectomy and ablation. However, survival of patients who develop HCC recurrence after LT is short, and early recurrence is the most common cause of death. Thus, efficient biomarkers are also needed in LT to guide precision therapy to improve patient prognosis and 5-year survival. Protein induced by vitamin K absence or antagonist II (PIVKA-II) is an abnormal prothrombin that cannot activate coagulation, and it is significantly increased in patients with HCC, obstructive jaundice, and those taking vitamin K antagonists. Over the past decades, substantial progress has been made in the study of PIVKA-II in diagnosing, surveilling, and treating HCC, but its role in LT still needs to be elaborated. In this review, we focused on the role of PIVKA-II as a biomarker in LT for HCC, especially its relationship with clinicopathologic features, early recurrence, long-term survival, and donor-recipient selection.

Radiomic features at Contrast-enhanced CT Predict Virus-driven Liver Fibrosis: A Multi-institutional Study.

BACKGROUND: Liver fibrosis is a major cause of morbidity and mortality among in chronic hepatitis patients. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability. METHODS: In this retrospective study, we developed three radiomics models using contrast-enhanced CT scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feature selection random forest (RFS-RF) and the least absolute shrinkage and selection operator (LASSO) logistic regression were used for feature selection and dimensionality reduction. Performance was assessed by area under the curve, C-index, calibration plots and decision curve analysis. External validation was performed on 114 patients from two institutions. RESULTS: Twenty-five radiomic features were significantly associated with fibrosis stage, with 80% of the top 10 features originating from portal venous phase spleen images. The radiomics models showed good performance in the validation cohort (C-indices: 0.723-0.808) and excellent calibration. Decision curve analysis indicated clinical benefits, with machine learning-based radiomics models (RFR-score and SVMR-score) providing more significant advantages. CONCLUSION: Radiomic features offer significant benefits over existing serum indices for staging virus-driven liver fibrosis, underscoring the value of radiomics in enhancing diagnostic accuracy. Specifically, radiomics analysis of the spleen presents additional noninvasive options for assessing fibrosis, highlighting its potential in improving patient management and outcomes.

PSAC-6mA: 6mA site identifier using self-attention capsule network based on sequence-positioning.

DNA N6-methyladenine (6mA) modifications play a pivotal role in the regulation of growth, development, and diseases in organisms. As a significant epigenetic marker, 6mA modifications extensively participate in the intricate regulatory networks of the genome. Hence, gaining a profound understanding of how 6mA is intricately involved in these biological processes is imperative for deciphering the gene regulatory networks within organisms. In this study, we propose PSAC-6mA (Position-self-attention Capsule-6mA), a sequence-location-based self-attention capsule network. The positional layer in the model enables positional relationship extraction and independent parameter setting for each base position, avoiding parameter sharing inherent in convolutional approaches. Simultaneously, the self-attention capsule network enhances dimensionality, capturing correlation information between capsules and achieving exceptional results in feature extraction across multiple spatial dimensions within the model. Experimental results demonstrate the superior performance of PSAC-6mA in recognizing 6mA motifs across various species.

Recent Advance of S100B Proteins in Spontaneous Intracerebral Hemorrhage and Aneurysmal Subarachnoid Hemorrhage.

Human health is seriously endangered by spontaneous intracerebral hemorrhage (ICH) and aneurysmal subarachnoid hemorrhage (aSAH). Because the majority of ICH and aSAH survivors experience disability, increased risk of stroke recurrence, cognitive decline, and systemic vascular disease, ICH and aSAH assume special importance in neurological disease. Early detection and prediction of neurological function and understanding of etiology and correction are the basis of successful treatment. ICH and aSAH cause complex inflammatory cascades in the brain. In order to establish precise staging and prognosis, as well as provide a basis for treatment selection and monitoring, it is imperative to determine appropriate biological markers according to pathological and physiological mechanisms. In this review, we focus on the research progress of S100B, an endogenous danger signaling molecule, as a potential biomarker for ICH and aSAH, assisting in the development of further basic research and clinical translational studies.

Circular RNA in cholangiocarcinoma: A systematic review and bibliometric analysis.

BACKGROUND: Cholangiocarcinoma (CCA) is a common primary liver malignancy with a poor prognosis. Many studies have demonstrated the involvement of circular RNAs (circRNAs) in tumorigenesis and progression. METHODS: Four online databases (PubMed, Web of Science, Embase, and Scopus) were searched on May 04, 2023, for original papers regarding CCA and circRNAs. Bibliometric analysis of included studies was performed on R Studio and GraphPad Prism. RESULTS: Thirty studies were included in the systematic review and bibliometric analysis. The systematic review showed that circRNAs were involved in CCA proliferation, invasion, metastasis, chemotherapy resistance, and other biological processes and were related to the prognosis of patients and many clinicopathological features. Exosomal circRNAs provide a new idea for the early diagnosis of CCA. The bibliometric analysis showed a significant upward trend in the number of studies on CCA and circRNAs. The 30 included papers had 201 authors and were published in 22 English journals. The first paper was published in 2018, and the second paper was the most cited (148 citations). CONCLUSION: This systematic review and bibliometric analysis demonstrates that circRNAs in CCA have not been studied enough. CircRNAs play an important role in the occurrence and progression of CCA. They may become new targets for the diagnosis, treatment, and prognostic monitoring of CCA.

A Novel Nomogram for the Preoperative Prediction of Edmondson-Steiner Grade III-IV in Hepatocellular Carcinoma Patients.

Background: Edmondson-Steiner (E-S) grade is a pathological indicator of the degree of hepatocellular carcinoma (HCC) differentiation, and E-S grade III-IV is a poor prognostic factor for HCC patients. Predicting poorly differentiated HCC has essential significance for clinical decision-making. Although some studies have developed predictive models based on magnetic resonance imaging (MRI) and radiomics, radiomic features that require specific software for analysis are impractical for clinical work. This study aims to develop a novel and user-friendly nomogram model to predict E-S grade III-IV. Patients and Methods: Medical data on patients meeting the inclusion criteria were obtained from the Nanjing Drum Tower Hospital HCC database (January 2020 to December 2022). Univariate analysis was used to screen for risk factors associated with E-S grade III-IV. A novel nomogram was established based on the subsequent multivariate logistic regression analysis. The performance of the established model was evaluated through diagnostic ability, calibration, and clinical benefits. Results: Overall, 240 HCC patients were included in this study. Among them, 103 were highly differentiated (E-S grade I-II) HCC and 137 were poorly differentiated (E-S grade III-IV) HCC. A nomogram model that integrated alpha-fetoprotein (AFP), des-γ-carboxy prothrombin (DCP), hepatitis B virus surface antigen (HBsAg), hepatitis C virus antibodies (HCVAb), aspartate aminotransferase to lymphocyte ratio index (ALRI), and macrovascular invasion was established. The novel model had a good diagnostic performance with an area under the curve (AUC) value of 0.763. Meanwhile, the model had a diagnostic accuracy of 72.5%, a sensitivity of 78.1%, and a specificity of 65.1%. The calibration curve showed good calibration of the nomogram model (mean absolute error = 0.043), and the decision curve analysis (DCA) demonstrated that the clinical benefit was provided. Conclusion: Our developed nomogram model could successfully predict E-S grade III-IV in HCC patients, which may be helpful in clinical decision-making.

A decision tree model to predict liver cirrhosis in hepatocellular carcinoma patients: a retrospective study.

Background: The severity of liver cirrhosis in hepatocellular carcinoma (HCC) patients is essential for determining the scope of surgical resection. It also affects the long-term efficacy of systemic anti-tumor therapy and transcatheter arterial chemoembolization (TACE). Non-invasive tools, including aspartate aminotransferase to platelet ratio index (APRI), fibrosis-4 (FIB-4), and γ-glutamyl transferase to platelet ratio (GPR), are less accurate in predicting cirrhosis in HCC patients. We aimed to build a novel decision tree model to improve diagnostic accuracy of liver cirrhosis. Patients and Methods: The Mann-Whitney U test, χ2 test, and multivariate logistic regression analysis were used to identify independent cirrhosis predictors. A decision tree model was developed using machine learning algorithms in a training cohort of 141 HCC patients. Internal validation was conducted in 99 HCC patients. The diagnostic accuracy and calibration of the established model were evaluated using receiver operating characteristic (ROC) and calibration curves, respectively. Results: Sex and platelet count were identified as independent cirrhosis predictors. A decision tree model integrating imaging-reported cirrhosis, APRI, FIB-4, and GPR was established. The novel model had an excellent diagnostic performance in the training and validation cohorts, with area under the curve (AUC) values of 0.853 and 0.817, respectively. Calibration curves and the Hosmer-Lemeshow test showed good calibration of the novel model. The decision curve analysis (DCA) indicated that the decision tree model could provide a larger net benefit to predict liver cirrhosis. Conclusion: Our developed decision tree model could successfully predict liver cirrhosis in HCC patients, which may be helpful in clinical decision-making.

Liquid Biopsy in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most deadly neoplasms with a poor prognosis. Due to the significant tumor heterogeneity of HCC, alpha-fetoprotein (AFP) or liver biopsy has not yet met the clinical needs in terms of early diagnosis or determining prognosis. In recent years, liquid biopsy techniques that analyze tumor by-products released into the circulation have shown great potential. Its ability to monitor tumors in real time and respond to their global characteristics is expected to improve the management of HCC patients clinically. This review discusses some of the findings of a liquid biopsy in terms of diagnosis and prognosis of HCC.

Conversion therapy for massive hepatocellular carcinoma: A case report and literature review.

Key Clinical Message: For potentially resectable HCC, a more aggressive conversion therapy strategy (high-intensity combined with multiple treatment modalities) can be used. Abstract: Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide. The best treatment for HCC is radical surgical resection, but 70%-80% of patients are ineligible for surgery. Although conversion therapy is an established treatment strategy for various solid tumors, there is no uniform protocol for treating HCC. In this case, we present a 69-year-old male patient diagnosed with massive HCC with Barcelona clinical liver cancer (BCLC) stage B. Because of the insufficient volume of the future liver remnant, we believed radical surgical resection was temporarily impossible. Therefore, the patient received conversion therapy, including four cycles of transcatheter arterial embolization (TAE) and hepatic arterial infusion chemotherapy (HAIC-Folfox), lenvatinib (8 mg orally once a day), and tislelizumab (an anti-PD-1 antibody, 200 mg intravenously once every 3 weeks). Fortunately, the patient achieved a good treatment response (smaller lesions and improved liver function) and underwent radical surgery finally. There was no clinical evidence of recurrence at 6 months of follow-up. For potentially resectable HCC, this case reveals that a more aggressive conversion therapy strategy (high-intensity combined with multiple treatment modalities) can be used.

Co-delivery of EGCG and melittin with self-assembled fluoro-nanoparticles for enhanced cancer therapy.

PURPOSE: Melittin (MPI) is a potential anticancer peptide due to its abilities of antitumor and immunomodulatory functions. Epigallocatechin-3-Ogallate (EGCG), a major extract of green tea, has shown great affinity for various types of biological molecules, especially for peptide/protein drugs. The aim of this study is to prepare a fluoro- nanoparticle (NP) formed by self-assembly of fluorinated EGCG (FEGCG) and MPI, and evaluate the effect of fluorine modification on MPI delivery and their synergistic antitumor effect. METHODS: Characterization of FEGCG@MPI NPs was determined by dynamic light scattering (DLS) and transmission electron microscope (TEM). Biology functions of FEGCG@MPI NPs were detected by hemolysis effect, cytotoxicity, apoptosis, cellular uptake with confocal microscopy and flow cytometry. The protein expression levels of Bcl-2/Bax, IRF, STATT-1, P-STAT-1, and PD-L1 were determined via western blotting. A transwell assay and wound healing assay were used to detect the cell migration and invasion. The antitumor efficacy of FEGCG@MPI NPs was demonstrated in a subcutaneous tumor model. RESULTS: Fluoro-nanoparticles could be formed by self-assembly of FEGCG and MPI, and fluorine modification on EGCG could ameliorate the side effect and delivery of MPI. The promoted therapeutics of FEGCG@MPI NPs could be achieved by regulating PD-L1 and apoptosis signaling, which might involve pathways of IRF, STAT-1/pSTAT-1, PD-L1, Bcl-2, and Bax in vitro. Moreover, FEGCG@MPI NPs could significantly inhibit the growth of tumor in vivo. CONCLUSIONS: FEGCG@MPI NPs may offer a potential platform and promising strategy in cancer therapy.

Contrast-enhanced CT findings-based model to predict MVI in patients with hepatocellular carcinoma.

BACKGROUND: Microvascular invasion (MVI) is important in early recurrence and leads to poor overall survival (OS) in hepatocellular carcinoma (HCC). A number of studies have reported independent risk factors for MVI. In this retrospective study, we designed to develop a preoperative model for predicting the presence of MVI in HCC patients to help surgeons in their surgical decision-making and improve patient management. PATIENTS AND METHODS: We developed a predictive model based on a nomogram in a training cohort of 225 HCC patients. We analyzed patients' clinical information, laboratory examinations, and imaging features from contrast-enhanced CT. Mann-Whitney U test and multiple logistic regression analysis were used to confirm independent risk factors and develop the predictive model. Internal and external validation was performed on 75 and 77 HCC patients, respectively. Moreover, the diagnostic performance of our model was evaluated using receiver operating characteristic (ROC) curves. RESULTS: In the training cohort, maximum tumor diameter (> 50 mm), tumor margin, direct bilirubin (> 2.7 µmol/L), and AFP (> 360.7 ng/mL) were confirmed as independent risk factors for MVI. In the internal and external validation cohort, the developed nomogram model demonstrated good diagnostic ability for MVI with an area under the curve (AUC) of 0.723 and 0.829, respectively. CONCLUSION: Based on routine clinical examinations, which may be helpful for clinical decision-making, we have developed a nomogram model that can successfully assess the risk of MVI in HCC patients preoperatively. When predicting HCC patients with a high risk of MVI, the surgeons may perform an anatomical or wide-margin hepatectomy on the patient.