Prediction Of Hepatocellular Carcinoma Recurrence Research Articles

Role of hepatitis B core-related antigen in predicting the occurrence and recurrence of hepatocellular carcinoma in patients with chronic hepatitis B: A systemic review and meta-analysis.

The purpose of the current study was to investigate the predictive value of hepatitis B core-related antigen (HBcrAg) on the occurrence and recurrence of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB). We searched PubMed, Embase, Scopus, and Web of Science from database inception to April 6, 2023. Pooled hazard ratio (HR) or odds ratio (OR) with 95% confidence interval (CI) was calculated for the occurrence and recurrence of HCC. Of the 464 articles considered, 18 articles recruiting 10320 patients were included. The pooled results showed that high serum HBcrAg level was an independent risk factor for the occurrence of HCC in CHB patients (adjusted HR=3.12, 95% CI: 2.40-4.06, P<0.001, I2=43.2%, P=0.043; OR=5.65, 95% CI: 3.44-5.82, P<0.001, I2=0.00%, P=0.42). Further subgroup analysis demonstrated that the predictive ability of HBcrAg for the occurrence of HCC is not influenced by the hepatitis B e antigen (HBeAg) status or the use of nucleoside/nucleotide analogs (NAs). In addition, our meta-analysis also suggests that HBcrAg is a predictor of HCC recurrence (adjusted HR=1.71, 95% CI: 1.26-2.32, P<0.001, I2=7.89%, P=0.031). For patients with CHB, serum HBcrAg may be a potential predictive factor for the occurrence of HCC, regardless of HBeAg status or NA treatment. It may also serve as a novel prognostic biomarker for the recurrence of HCC. More studies are needed to confirm our conclusions.

Impact of the diagnosis of metabolic dysfunction-associated fatty liver disease and non-alcoholic fatty liver disease in patients undergoing liver transplantation for hepatocellular carcinoma.

Whether the diagnosis of non-alcoholic fatty liver disease or metabolic dysfunction-associated fatty disease has a different impact on liver transplant recipients with hepatocellular carcinoma is not yet clear. Data from a two-center retrospective cohort study were collected to compare and investigate the differences between non-alcoholic fatty liver disease and metabolic dysfunction-associated fatty liver disease in clinicopathologic parameters and prognosis among liver transplant recipients with hepatocellular carcinoma. A total of 268 liver transplant recipients with hepatocellular carcinoma were included. The prevalence among pre- and post-transplant metabolic dysfunction-associated fatty liver disease was 10.82% and 30.22%, while for non-alcoholic fatty liver disease, it was 7.09% and 26.87%, respectively. The clinicopathological parameters were similar between the two pre-transplant groups. In contrast, the post-transplant group with metabolic dysfunction-associated fatty liver disease exhibited a higher prevalence of diabetes mellitus and a greater body mass index. However, the other parameters were similar between the two post-transplant groups (p > 0.05). Factors such as the largest tumor size > 4cm, microvascular invasion, lack of tumor capsule, post-transplant metabolic dysfunction-associated fatty liver disease, and decreased post-transplant lymphocyte percentage were related to an increased risk of recurrence. In patients undergone liver transplantation for hepatocellular carcinoma, the diagnosis of metabolic dysfunction-associated fatty disease is more strongly associated with metabolic abnormalities than the diagnosis of non-alcoholic fatty liver disease and is an independent predictor of hepatocellular carcinoma recurrence.

Abstract 7665: Detection of γ-OHPdG as a hepatocellular carcinoma recurrence biomarker by CTCs and LC-MS/MS

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer death globally. Recurrence risks following curative resection remain high, ~50% after three years and 75-100% over five years. A biomarker for HCC recurrence is urgently needed to help lead treatment strategies. We previously showed that hepatic γ-hydroxy-1, N2-propano-2′-deoxyguanosine (γ-OHPdG), a DNA adduct derived from lipid peroxidation, increased during hepatocarcinogenesis. Furthermore, in a retrospective study, higher γ-OHPdG levels in post-surgical liver tumor tissues were associated with poorer survival (p&amp;lt;0.0001) and shorter recurrence-free survival (p=0.007), suggesting that it may be a prognostic biomarker for HCC (Hepatology 2018). In this study, we aimed to test γ-OHPdG detected in livers after curative resection as a recurrence biomarker in a prospective study and, in addition, we developed a new non-invasive method for γ-OHPdG in circulating tumor cells (CTCs). This multicenter study seeks to validate γ-OHPdG as a biomarker for HCC recurrence. Methods: A total of 28 HCC patients undergoing hepatectomy at MedStar/Georgetown University Hospital, Johns Hopkins University Hospital, and the University of Maryland Medical Center were enrolled. γ-OHPdG was detected in DNA isolated from hepatic tumors and non-tumors by a previous liquid chromatograph-tandem mass spectrometry (LC-MS/MS) method and in HCC CTCs isolated from blood samples. CTCs were isolated using hepatocyte-specific rabbit anti-asialoglycoprotein-1 (ASGPR-1), followed by staining with mouse anti-γ-OHPdG antibodies. Clinical outcomes were correlated with γ-OHPdG in liver tissues and CTCs. Results: In our current cohort of 28 patients (mean age: 64.7 years; 82% male), five patients experienced HCC recurrence, with an estimated progression-free survival (PFS) rate of 85% over 8 months. γ-OHPdG levels (adducts/109 nucleotides) detected by LC-MS/MS varied widely in tumor tissues (5-56, mean: 11.8) and non-tumor tissue (7-71, mean: 20.6). Preliminary analysis showed shorter PFS associated with γ-OHPdG in non-tumors (cutoff: 16, p=0.0271). γ-OHPdG (arbitrary units x107) in CTCs also varied widely in the nucleus (0.95-18.84, mean: 2.85) and in the whole cell (0.16- 52.18, mean: 9.72). Total CTC γ-OHPdG intensity in both the nucleus (p=0.006) and in the whole cell (p=0.005) was associated with its levels in non-tumor tissue. Furthermore, a shorter PFS was associated with CTC count (cutoff: 270 cells, p=0.01), and total CTC whole cell γ-OHPdG intensity (cutoff: 21.17 × 107 arbitrary units, p=0.002). Conclusion: Preliminary results suggest that γ-OHPdG, measured in tissues by LC-MS/MS and in blood by CTC, predicts HCC recurrence. Once evaluated in all enrolled subjects, multiple variant analyses will be carried out to include stage, AFP, grade of differentiation, vascular invasion on surgical samples, and status of viral hepatitis infection. Citation Format: Mark Kuo, Merve Ozge Aktop, Elie Ghabi, Mahmoud A. Ouf, Margaret Carder, Vinona Muralidaran, Jhalen Ascue, Marketa Hlouskova, Pengfei Wu, Guang Cheng, Stephen S. Hecht, Hong-Bin Fang, Nader Hanna, Jin He, Alexander H. Kroemer, Monika Aggarwal, Aiwu R. He, Fung-Lung Chung. Detection of γ-OHPdG as a hepatocellular carcinoma recurrence biomarker by CTCs and LC-MS/MS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7665.

Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.

Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis. We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI. We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.

Alpha-fetoprotein level to total tumor volume as a predictor of hepatocellular carcinoma recurrence after resection. a retrospective cohort study

Alpha-fetoprotein level to total tumor volume as a predictor of hepatocellular carcinoma recurrence after resection. a retrospective cohort study

Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery

Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.

Imaging-Derived Biomarkers Integrated with Clinical and Laboratory Values Predict Recurrence of Hepatocellular Carcinoma After Liver Transplantation.

To investigate the prognostic value of computed tomography (CT) derived imaging biomarkers in hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) and develop a predictive nomogram model. This retrospective study included 178 patients with histopathologically confirmed HCC who underwent liver transplantation between 2007 and 2021 at the two academic liver centers. We evaluated dedicated imaging features from baseline multiphase contrast-enhanced CT supplemented by several clinical findings and laboratory parameters. Time-to-recurrence was estimated by Kaplan-Meier analysis. Univariable Cox proportional hazard regression and multivariable Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to assess independent prognostic factors for recurrence. A nomogram model was then built based on the independent factors selected through LASSO regression, to predict the probabilities of HCC recurrence at one, three, and five years. The rate of HCC recurrence after LT was 17.4% (31 of 178). The LASSO analysis revealed six independent predictors associated with an elevated risk of tumor recurrence. These predictors included the presence of peritumoral enhancement, the presence of over three tumor lesions, the largest tumor diameter greater than 3 cm, serum alpha-fetoprotein (AFP) levels exceeding 400 ng/mL, and the presence of a tumor capsule. Conversely, a history of bridging therapies was found to be correlated with a reduced risk of HCC recurrence. In addition, Kaplan-Meier curves showed patients with irregular margin, satellite nodules, or small lesions displayed shorter time-to-recurrence. Our nomogram demonstrated good performance, yielding a C-index of 0.835 and AUC values of 0.86, 0.88, and 0.85 for the predictions of 1-year, 3-year, and 5-year TTR, respectively. Imaging parameters derived from baseline contrast-enhanced CT showing malignant behavior and aggressive growth patterns, along with serum AFP and history of bridging therapies, show potential as biomarkers for predicting HCC recurrence after transplantation.

NK cell activity as a predictor of hepatocellular carcinoma recurrence and outcome in liver transplant patients

NK cell activity as a predictor of hepatocellular carcinoma recurrence and outcome in liver transplant patients

AFP-L3 and DCP strongly predict early hepatocellular carcinoma recurrence after liver transplantation

Alpha-fetoprotein (AFP) predicts hepatocellular carcinoma (HCC) recurrence after liver transplant (LT) but remains an imperfect biomarker. The role of DCP (des-gamma-carboxyprothrombin) and AFP-L3 (AFP bound to Lens culinaris agglutinin) in predicting HCC recurrence remains incompletely characterized. AFP-L3 and DCP could identify patients at high risk of post-transplant HCC recurrence and serve as liver transplant exclusion criteria to defer transplant until patients receive additional risk-reducing pre-transplant locoregional therapy. This prospective cohort study included consecutive patients with HCC who underwent LT (within or down-staged to Milan criteria) between 2017 and 2022. Pre-transplant AFP, AFP-L3, and DCP measurements were obtained. The primary endpoint was the ability of biomarkers to predict HCC recurrence-free survival. This cohort included 285 patients with a median age of 67 (IQR 63-71). At LT, median biomarker values were AFP 5.0ng/ml (IQR 3.0-12.1), AFP-L3 6.7% (0.5-13.2), and DCP 1.0ng/ml (0.3-2.8). Most (94.7%) patients received pre-LT locoregional therapy. After a median post-LT follow-up of 3.1 years, HCC recurrence was observed in 18 (6.3%) patients. AFP-L3 and DCP outperformed AFP with C-statistics of 0.81 and 0.86 respectively, compared with 0.74 for AFP. A dual-biomarker combination of AFP-L3 ≥15% and DCP ≥7.5 predicted 61.1% of HCC recurrences, whereas HCC only recurred in 7 of 265 (2.6%) patients not meeting this threshold. The Kaplan-Meier recurrence-free survival rate at 3 years post-LT was 43.7% for patients with dual-positive biomarkers compared to 97.0% for all others (p <0.001). Dual-positivity for AFP-L3 ≥15% and DCP ≥7.5 strongly predicted post-LT HCC recurrence. This model could refine LT selection criteria and identify high-risk patients who require additional locoregional therapy prior to LT. Alpha-fetoprotein (AFP) is used to predict hepatocellular carcinoma (HCC) recurrence after liver transplant, but it remains an imperfect biomarker. In this prospective study, the biomarkers DCP (des-gamma-carboxyprothrombin) and AFP-L3 (AFP bound to Lens culinaris agglutinin) strongly predicted early HCC recurrence and outperformed AFP. A dual-biomarker combination of AFP-L3 ≥15% and DCP ≥7.5 predicted the majority of recurrences and could be used to further refine liver transplant eligibility criteria.

EpCAM, Ki67, and ESM1 Predict Hepatocellular Carcinoma Recurrence After Liver Transplantation.

Liver transplantation (LT) is a good therapeutic decision, cures hepatocellular carcinoma (HCC) and promotes survival of cases with unrespectable HCC based on the Milan criteria. HCC still recur after LT. Identifying high risk tissue markers that predict recurrence becomes important for LT decision-making. Little is known regarding use of tissue expression of epithelial cell adhesion molecule (EpCAM) to predict HCC recurrence. This study investigates the role of EpCAM, Ki67, and endothelial-cell-specific molecule-1 (ESM1) as immunohistochemical markers to predict HCC recurrence after LT. It included 52 explanted HCC tissues from Egyptian patients who had undergone LT for HCC according to Milan criteria. Immunohistochemical staining was done on paraffin-embedded formalin-fixed tissue sections. HCC recurrence occurred in 13.5% cases. Positive EpCAM expression in HCC, was significantly associated with HCC recurrence, ( P =0.011), achieving 71.43% sensitivity, 84.44% specificity and 78.8% accuracy in predicting recurrence. High Ki67 percentage was significantly associated with HCC recurrence, ( P =0.005), achieving 57.14% sensitivity, 86.67% specificity and 82.69% accuracy in predicting HCC recurrence. ESM1 showed significant association with HCC recurrence ( P =0.041), with 71.43% sensitivity, 71.11% specificity and 71.15% accuracy in predicting HCC recurrence. EpCAM score and Ki67 percentage showed positive correlation. In conclusion, it is suggested that large tumor size (≥3cm), advanced pathologic staging and Ki67 could be stratified as high risk predictors of HCC recurrence after LT. Although higher classes of Child-Turcotte-Pugh classification, high serum alpha-fetoprotein, microvascular invasion, positive EpCAM and ESM1 are stratified as lower risk predictors of HCC recurrence after LT.

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC.

Longitudinal surveillance of three biomarkers to predict recurrence of hepatocellular carcinoma after radical resection.

Radical resection is a curative treatment for patients with hepatocellular carcinoma (HCC), but the incidence of recurrence remains high. We aimed to explore the performance of predicting HCC recurrence by longitudinal surveillance of the protein induced by vitamin K absence (PIVKA-II), alpha- fetoprotein (AFP), and lectin-reactive AFP (AFP-L3) during postoperative follow-up. Patients who underwent radical resection for HCC at the Ningbo Medical Centre Lihuili Hospital between January 2015 and December 2020 were included. All enrolled patients regularly monitor PIVKA-II, AFP, AFP-L3 every 3 months during postoperative follow-up. The surveillance performance of PIVKA-II, AFP, AFP-L3 during follow-up for the prediction of HCC recurrence was compared in patients. The generalized estimation equation (GEE) was used to analyze the trends of the tumor biomarkers and interactions with time. Area under the receiver operator characteristic (AUROC) curves, the optimal cut-off value, the sensitivity and specificity were calculated to evaluate the performance of the three biomarkers. The recurrence-free survival (RFS) and overall survival (OS) of patients with any of the elevated biomarkers was analyzed by Kaplan-Meier curves and the log-rank test. Multivariate logistic regression models were used to analyze potential risk factors for recurrence. The GEE analysis indicated that PIVKA-II, AFP, AFP-L3 in the recurrence patients were higher than the no recurrence patients during follow-up, PIVKA-II and AFP showed increasing trends from 6 months before recurrence. In predicting recurrence, the AUROCs for PIVKA-II, AFP, AFP-L3 and their combination were 0.885, 0.754, 0.781 and 0.885 respectively, the optimal cut-off value for PIVKA-II, AFP, AFP-L3 was 29.5 mAU/ml, 10.7 ng/L, 1.5% respectively. The sensitivity in predicting recurrence for PIVKA-II, AFP, AFP-L3 and combination were 75.0, 54.7, 57.8 and 79.7% respectively. The RFS and the OS of patients with any of the biomarkers elevated during the follow-up was significantly shorter than that without elevated biomarkers ( P < 0.001). Multivariate analysis showed that any of the biomarkers elevated was the independent risk factor of recurrence. Longitudinal surveillance of PIVKA-II, AFP and AFP-L3 can effectively predict recurrence of HCC after operation.

Tumor-derived proliferative CTCs and CTC clusters predict aggressiveness and early recurrence in hepatocellular carcinoma patients.

Circulating tumor cells (CTCs), an indispensable liquid biopsy classifier, can provide extra information for the diagnosis and prognosis of hepatocellular carcinoma (HCC). We systematically analyzed the peripheral blood of preoperative HCC patients (n = 270) for CTC number, Ki67 index reflecting the proliferative CTC percentage (PCP), and CTC clusters correlated with the characteristics of malignant HCC tumors. Driver gene mutations were found with high levels of consistency between CTCs and primary tumors (n = 73). CTC number and PCP were associated with tumor size, microvascular invasion (MVI), presence or absence of multiple tumors, and thrombosis significantly. CTC number and PCP robustly separated patients with and without relapse, with a sensitivity of 88.89%/81.48% and a specificity of 72.73%/94.81% in the training set (n = 104) and corresponding values of 80.00%/86.67% and 78.38%/89.19% in the validation set (n = 52), showing a better performance than that associated with the alpha-fetoprotein (AFP) level. CTC number, PCP, CTC clusters, and MVI were independent significant risk factors for HCC recurrence (P = 0.0375, P < 0.0001, P = 0.0017, and P = 0.0157). A nomogram model based on these risk factors showed a considerable prediction ability for HCC recurrence (area under the curve = 0.947). PCP (training: log-rank P < 0.0001; hazard ratio [HR] 30.13, 95% confidence interval [CI] = 11.12-81.62; validation: log-rank P < 0.0001; HR 25.73, 95% CI = 5.28-106.60) and CTC clusters (training: log-rank P < 0.0001; HR 17.34, 95% CI = 7.46-40.30; validation: log-rank P < 0.0001; HR 9.92, 95% CI = 2.55-38.58) were more significantly correlated with worse recurrence-free survival than CTC number (training: log-rank P < 0.0001; HR 14.93, 95% CI = 4.48-49.78; validation: log-rank P = 0.0007; HR 9.03, 95% CI = 2.53-32.24). PCP and CTC clusters may predict HCC recurrence and improve the performance of the serum biomarker AFP.

TOP-068 - Deep learning-based model in pre-operative computed tomography for prediction of hepatocellular carcinoma recurrence after curative surgery

TOP-068 - Deep learning-based model in pre-operative computed tomography for prediction of hepatocellular carcinoma recurrence after curative surgery

FRI-322 - Collagen proportionate area (CPA) measurement of liver parenchyma and hepatocellular carcinoma (HCC) can predict HCC recurrence after liver resection

FRI-322 - Collagen proportionate area (CPA) measurement of liver parenchyma and hepatocellular carcinoma (HCC) can predict HCC recurrence after liver resection

Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning

Tumor recurrence affects up to 70% of early-stage hepatocellular carcinoma (HCC) patients, depending on treatment option. Deep learning algorithms allow in-depth exploration of imaging data to discover imaging features that may be predictive of recurrence. This study explored the use of convolutional neural networks (CNN) to predict HCC recurrence in patients with early-stage HCC from pre-treatment magnetic resonance (MR) images. This retrospective study included 120 patients with early-stage HCC. Pre-treatment MR images were fed into a machine learning pipeline (VGG16 and XGBoost) to predict recurrence within six different time frames (range 1–6 years). Model performance was evaluated with the area under the receiver operating characteristic curves (AUC–ROC). After prediction, the model’s clinical relevance was evaluated using Kaplan–Meier analysis with recurrence-free survival (RFS) as the endpoint. Of 120 patients, 44 had disease recurrence after therapy. Six different models performed with AUC values between 0.71 to 0.85. In Kaplan–Meier analysis, five of six models obtained statistical significance when predicting RFS (log-rank p < 0.05). Our proof-of-concept study indicates that deep learning algorithms can be utilized to predict early-stage HCC recurrence. Successful identification of high-risk recurrence candidates may help optimize follow-up imaging and improve long-term outcomes post-treatment.

Pre-operative gamma-glutamyl transferase levels predict outcomes in hepatitis B-related hepatocellular carcinoma after curative resection

Surgical resection is a curative therapy for early-stage hepatocellular carcinoma (HCC); however, HCC recurrence is not uncommon. Identifying outcome predictors helps to manage the disease. Gamma-glutamyl transferase (GGT) may predict the development of HCC, but its role to predict the outcomes after surgical resection of HCC was unclear. This study aimed to investigate pre-operative GGT levels for outcome prediction in patients with hepatitis B virus (HBV)-related HCC. We conducted a retrospective cohort study to include patients with HBV-related HCC receiving surgical resection. Clinical information, HCC characteristics and usage of antiviral therapy were collected. A time-dependent Cox proportional hazard regression analysis were used to predict HCC recurrence and survival. A total of 699 consecutive patients with HBV-related HCC who received surgical resection with curative intent between 2004 and 2013 were included. After a median of 4.4 years, 266 (38%) patients had HCC recurrence. Pre-operative GGT positively correlated with cirrhosis, tumor burden and significantly increased in patients to develop HCC recurrence. Multivariable analysis demonstrated that pre-operative GGT ≥38 U/L increased 57% risk (hazard ratio [HR]: 1.57, 95% confidence interval [CI]: 1.20-2.06) of recurrent HCC after adjustment for confounding factors. Specifically, pre-operative GGT ≥38 U/L predicted early (<2 years) HCC recurrence (HR: 1.94, 95% CI: 1.30-2.89). Moreover, pre-operative GGT ≥38 U/L predicted all-cause mortality (HR: 1.73, 95% CI: 1.06-2.84) after surgery. Pre-operative GGT levels ≥38 U/L independently predict high risks of HCC recurrence and all-cause mortality in HBV-related HCC patients receiving surgical resection.

Increased plasma levels of monocyte chemoattractant protein-1 in patients with hepatitis B virus pre-S2 gene deletion mutation predict a higher risk of hepatocellular carcinoma recurrence after curative surgical resection.

Despite resection surgery as a curative therapy for hepatocellular carcinoma (HCC), the high rate of postoperative HCC recurrence remains a big challenge for patient survival. Chronic hepatitis B virus (HBV) infection is the most important risk factor for HCC. Deletion mutation in the HBV pre-S2 gene leads to expression of an essential viral oncoprotein called pre-S2 mutant and represents an independent prognostic biomarker for HCC recurrence after curative surgical resection. Additionally, cytokines are multifunctional secreted proteins and implicated in all stages of HBV-related HCC tumorigenesis. This study aimed to identify the cytokines whose plasma levels were associated with pre-S2 gene deletion mutation and HCC recurrence and evaluate their potential to be combined with pre-S2 gene deletion mutation in predicting HCC recurrence. Among a panel of 27 cytokines examined, plasma levels of monocyte chemoattractant protein-1 (MCP-1) were significantly upregulated in patients with pre-S2 gene deletion mutation or HCC recurrence. MCP-1 was validated as an independent prognostic biomarker for HCC recurrence. Moreover, patients with both the presence of pre-S2 gene deletion mutation and high levels of MCP-1 displayed a higher risk of HCC recurrence than patients with either one or none of these two biomarkers. The combination of pre-S2 gene deletion mutation and MCP-1 levels exhibited a better prognostic performance for HCC recurrence than each biomarker alone. This study discovered that MCP-1 levels had a significance to be as a combination biomarker with pre-S2 gene deletion mutation providing an improved performance in predicting HCC recurrence after curative surgical resection.

A new model based on gamma-glutamyl transpeptidase to lymphocyte ratio and systemic immune-inflammation index can effectively predict the recurrence of hepatocellular carcinoma after liver transplantation.

Liver transplantation (LT) is one of the most effective treatment modalities for hepatocellular carcinoma (HCC), but patients with HCC recurrence after LT always have poor prognosis. This study aimed to evaluate the predictive value of the gamma-glutamyl transpeptidase-to-lymphocyte ratio (GLR) and systemic immune-inflammation index (SII) in terms of HCC recurrence after LT, based on which we developed a more effective predictive model. The clinical data of 325 HCC patients who had undergone LT were collected and analyzed retrospectively. The patients were randomly divided into a development cohort (n = 215) and a validation cohort (n = 110). Cox regression analysis was used to screen the independent risk factors affecting postoperative recurrence in the development cohort, and a predictive model was established based on the results of the multivariate analysis. The predictive values of GLR, SII and the model were evaluated by receiver operating characteristic (ROC) curve analysis, which determined the cut-off value for indicating patients' risk levels. The Kaplan-Meier survival analysis and the competing-risk regression analysis were used to evaluate the predictive performance of the model, and the effectiveness of the model was verified further in the validation cohort. The recurrence-free survival of HCC patients after LT with high GLR and SII was significantly worse than that of patients with low GLR and SII (P<0.001). Multivariate Cox regression analysis identified GLR (HR:3.405; 95%CI:1.954-5.936; P<0.001), SII (HR: 2.285; 95%CI: 1.304-4.003; P=0.004), tumor number (HR:2.368; 95%CI:1.305-4.298; P=0.005), maximum tumor diameter (HR:1.906; 95%CI:1.121-3.242; P=0.017), alpha-fetoprotein level (HR:2.492; 95%CI:1.418-4.380; P=0.002) as independent risk factors for HCC recurrence after LT. The predictive model based on these risk factors had a good predictive performance in both the development and validation cohorts (area under the ROC curve=0.800, 0.791, respectively), and the performance of the new model was significantly better than that of single GLR and SII calculations (P<0.001). Survival analysis and competing-risk regression analysis showed that the predictive model could distinguish patients with varying levels of recurrence risk in both the development and validation cohorts. The GLR and SII are effective indicators for evaluating HCC recurrence after LT. The predictive model based on these indicators can accurately predict HCC recurrence after LT and is expected to guide preoperative patient selection and postoperative follow-up.

Surgical treatment of hepatocellular carcinoma after Fontan operation: three case reports and review of the literature.

We herein describe three patients with Fontan-associated liver disease who developed hepatocellular carcinoma (HCC). The first patient was a 28-year-old woman who had undergone the Fontan operation (FO) at the age of 4years. She was diagnosed with HCC (cT4aN0M0, Stage IVA, UICC 8th edition), for which she underwent extended posterior right sectionectomy and partial hepatectomy of S2. She developed recurrence of peritoneal dissemination after 12months, and she was alive 18months after surgery. The second patient was a 43-year-old man who had undergone the FO at the age of 3years. He was diagnosed with HCC (cT2N0M0, Stage II), for which he underwent laparoscopic-assisted partial hepatectomy of S3. He remained free from recurrent HCC for 17months. The third patient was a 21-year-old woman who had undergone the FO at the age of 3years. She was diagnosed with HCC (cT3N0M0, Stage III), for which she underwent laparoscopic-assisted partial hepatectomy of S2 and S4. She remained free from recurrent HCC for 30months. We reviewed 18 surgical cases of HCC arising from Fontan-associated liver disease, including our 3 cases, and found that a high preoperative alpha-fetoprotein concentration might be a predictor of HCC recurrence.