Prediction Of Hepatocellular Carcinoma Recurrence Research Articles

Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study

BackgroundThere is a growing need for new improved classifiers of prognosis in hepatocellular carcinoma (HCC) patients to stratify them effectively.MethodsA deep learning model was developed on a total of 1118 patients from 4 independent cohorts. A nucleus map set (n = 120) was used to train U-net to capture the nuclear architecture. The training set (n = 552) included HCC patients that had been treated by resection. The liver transplantation (LT) set (n = 144) contained patients with HCC that had been treated by LT. The train set and its nuclear architectural information extracted by U-net were used to train the MobileNet V2-based classifier (MobileNetV2_HCC_class). The classifier was then independently tested on the LT set and externally validated on the TCGA set (n = 302). The primary outcome was recurrence free survival (RFS).ResultsThe MobileNetV2_HCC_class was a strong predictor of RFS in both LT set and TCGA set. The classifier provided a hazard ratio of 3.44 (95% CI 2.01–5.87, p < 0.001) for high risk versus low risk in the LT set, and 2.55 (95% CI 1.64–3.99, p < 0.001) when known prognostic factors, remarkable in univariable analyses on the same cohort, were adjusted. The MobileNetV2_HCC_class maintained a relatively higher discriminatory power [time-dependent accuracy and area under curve (AUC)] than other factors after LT or resection in the independent validation set (LT and TCGA set). Net reclassification improvement (NRI) analysis indicated MobileNetV2_HCC_class exhibited better net benefits for the Stage_AJCC beyond other independent factors. A pathological review demonstrated that tumoral areas with the highest recurrence predictability featured the following features: the presence of stroma, a high degree of cytological atypia, nuclear hyperchromasia, and a lack of immune cell infiltration.ConclusionA prognostic classifier for clinical purposes had been proposed based on the use of deep learning on histological slides from HCC patients. This classifier assists in refining the prognostic prediction of HCC patients and identifies patients who have been benefited from more intensive management.

Novel Gene Signatures as Prognostic Biomarkers for Predicting the Recurrence of Hepatocellular Carcinoma.

Simple SummaryA high percentage of patients who undergo surgical resection for hepatocellular carcinoma (HCC) experience recurrence. Therefore, identification of accurate molecular markers for predicting recurrence of HCC is important. We analyzed recurrence and non-recurrence HCC tissues using two public omics datasets comprising microarray and RNA-sequencing and found novel gene signatures associated with recurrent HCC. These molecules might be used to not only predict for recurrence of HCC but also act as potential prognostic indicators for patients with HCC.Hepatocellular carcinoma (HCC) has a high rate of cancer recurrence (up to 70%) in patients who undergo surgical resection. We investigated prognostic gene signatures for predicting HCC recurrence using in silico gene expression analysis. Recurrence-associated gene candidates were chosen by a comparative analysis of gene expression profiles from two independent whole-transcriptome datasets in patients with HCC who underwent surgical resection. Five promising candidate genes, CETN2, HMGA1, MPZL1, RACGAP1, and SNRPB were identified, and the expression of these genes was evaluated using quantitative reverse transcription PCR in the validation set (n = 57). The genes CETN2, HMGA1, RACGAP1, and SNRPB, but not MPZL1, were upregulated in patients with recurrent HCC. In addition, the combination of HMGA1 and MPZL1 demonstrated the best area under the curve (0.807, 95% confidence interval [CI] = 0.681–0.899) for predicting HCC recurrence. In terms of clinicopathological correlation, CETN2, MPZL1, RACGAP1, and SNRPB were upregulated in patients with microvascular invasion, and the expression of MPZL1 and SNRPB was increased in proportion to the Edmonson tumor differentiation grade. Additionally, overexpression of CETN2, HMGA1, and RACGAP1 correlated with poor overall survival (OS) and disease-free survival (DFS) in the validation set. Finally, Cox regression analysis showed that the expression of serum alpha-fetoprotein and RACGAP1 significantly affected OS, whereas platelet count, microvascular invasion, and HMGA1 expression significantly affected DFS. In conclusion, HMGA1 and RACGAP1 may be potential prognostic biomarkers for predicting the recurrence of HCC after surgical resection.

R3-AFP score is a new composite tool to refine prediction of hepatocellular carcinoma recurrence after liver transplantation

Background & AimsPatients with hepatocellular carcinoma (HCC) are selected for liver transplantation (LT) based on pre-LT imaging ± alpha-foetoprotein (AFP) level, but discrepancies between pre-LT tumour assessment and explant are frequent. Our aim was to design an explant-based recurrence risk reassessment score to refine prediction of recurrence after LT and provide a framework to guide post-LT management.MethodsAdult patients who underwent transplantation between 2000 and 2018 for HCC in 47 centres were included. A prediction model for recurrence was developed using competing-risk regression analysis in a European training cohort (TC; n = 1,359) and tested in a Latin American validation cohort (VC; n=1,085).ResultsIn the TC, 76.4% of patients with HCC met the Milan criteria, and 89.9% had an AFP score of ≤2 points. The recurrence risk reassessment (R3)-AFP model was designed based on variables independently associated with recurrence in the TC (with associated weights): ≥4 nodules (sub-distribution of hazard ratio [SHR] = 1.88, 1 point), size of largest nodule (3–6 cm: SHR = 1.83, 1 point; >6 cm: SHR = 5.82, 5 points), presence of microvascular invasion (MVI; SHR = 2.69, 2 points), nuclear grade >II (SHR = 1.20, 1 point), and last pre-LT AFP value (101–1,000 ng/ml: SHR = 1.57, 1 point; >1,000 ng/ml: SHR = 2.83, 2 points). Wolber’s c-index was 0.76 (95% CI 0.72–0.80), significantly superior to an R3 model without AFP (0.75; 95% CI 0.72–0.79; p = 0.01). Four 5-year recurrence risk categories were identified: very low (score = 0; 5.5%), low (1–2 points; 15.1%), high (3–6 points; 39.1%), and very high (>6 points; 73.9%). The R3-AFP score performed well in the VC (Wolber’s c-index of 0.78; 95% CI 0.73–0.83).ConclusionsThe R3 score including the last pre-LT AFP value (R3-AFP score) provides a user-friendly, standardised framework to design post-LT surveillance strategies, protocols, or adjuvant therapy trials for HCC not limited to the Milan criteria.Clinical Trials RegistrationNCT03775863.Lay summaryConsidering discrepancies between pre-LT tumour assessment and explant are frequent, reassessing the risk of recurrence after LT is critical to further refine the management of patients with HCC. In a large and international cohort of patients who underwent transplantation for HCC, we designed and validated the R3-AFP model based on variables independently associated with recurrence post-LT (number of nodules, size of largest nodule, presence of MVI, nuclear grade, and last pre-LT AFP value). The R3-AFP model including last available pre-LT AFP value outperformed the original R3 model only based on explant features. The final R3-AFP scoring system provides a robust framework to design post-LT surveillance strategies, protocols, or adjuvant therapy trials, irrespective of criteria used to select patients with HCC for LT.

A nomogram for predicting the risk of postoperative recurrence of hepatitis B virus-related hepatocellular carcinoma in patients with high preoperative serum glutamyl transpeptidase.

Recurrence is a major risk factor affecting the postoperative survival of patients with hepatocellular carcinoma (HCC), especially those with high preoperative serum γ-glutamyl transpeptidase (GGT) levels. This study had the aim of developing a personalized predictive tool to accurately determine the risk of postoperative recurrence of hepatitis B-virus (HBV)-related HCC in patients with high preoperative serum GGT levels. Patients who underwent curative liver resection of HBV-related HCC and had high preoperative GGT levels were consecutively enrolled between 2008 and 2011. Prognostic indicators for recurrence were determined using Cox regression analysis. A nomogram was then developed and assessed by integrating the independent risk factors into the model. A total of 603 eligible patients were included. The final nomogram for predicting HCC recurrence in patients with high preoperative GGT levels consisted of five independent prognostic factors: α-fetoprotein (AFP), HBV-DNA, satellite nodules, microvascular invasion, and tumor grade. The C-index of the nomogram for predicting recurrence was 0.759, and validation showed high accuracy and discriminatory. The predictive nomogram developed and validated in this study performs well in predicting postoperative recurrence of HBV-related HCC in patients with high preoperative GGT levels. It can provide personalized assessments to inform the development of surveillance strategies and allows patients with a high risk of recurrence to be selected for further adjuvant treatment.

External Validation of the RETREAT Score for Prediction of Hepatocellular Carcinoma Recurrence after Liver Transplantation.

Simple SummaryLiver transplantation (LT) is a potentially curative treatment option for hepatocellular carcinoma (HCC), but is frequently complicated by HCC recurrence. In order to estimate the recurrence risk a novel risk score was developed in the United States: the Risk Estimation of Tumor Recurrence After Transplant (RETREAT). External validation of this novel risk score, in a different patient population with other LT selection criteria, is needed. In this study we demonstrate that the RETREAT score is able to predict the risk of HCC recurrence after liver transplantation in a European population. These findings may be used to inform patients of recurrence risk and as a basis for studies on surveillance strategies.Background. We aimed to externally validate the performance of the RETREAT score in a European population. Methods. This single center retrospective cohort study enrolled all consecutive patients with HCC who underwent LT between 1989 and 2019. The performance of RETREAT was assessed in the overall population and after stratification between being within or beyond the Milan criteria based on the explant pathology report. Recurrence probabilities were estimated by using the Kaplan–Meier method and compared by log-rank test. Results. We studied 203 patients; 42 patients were beyond the Milan criteria based on explant pathology. The median follow-up was 26.8 months (IQR 7.2–60.7). Overall cumulative HCC recurrence rates were 10.6%, 21.3%, and 23.0% at 2, 5, and 10 years, with the majority of recurrences extrahepatic and at multiple sites. Higher RETREAT scores were associated with higher recurrence rates, with a 10-year recurrence rate of 60.5% in patients with RETREAT ≥ 3 (n = 65), compared to 6.2% in those with RETREAT ≤2 (n = 138; p < 0.001). HCC recurrence rates were even lower in patients within the Milan criteria who also had a low RETREAT score (n = 122; 2.7% at 10 years). Conclusion. Low RETREAT scores identify patients at low risk of HCC recurrence after LT in patients within the Milan criteria based on explant pathology.

A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection.

Background and AimsPatients with hepatocellular carcinoma (HCC) surgically resected are at risk of recurrence; however, the risk factors of recurrence remain poorly understood. This study intended to establish a novel machine learning model based on clinical data for predicting early recurrence of HCC after resection.MethodsA total of 220 HCC patients who underwent resection were enrolled. Classification machine learning models were developed to predict HCC recurrence. The standard deviation, recall, and precision of the model were used to assess the model’s accuracy and identify efficiency of the model.ResultsRecurrent HCC developed in 89 (40.45%) patients at a median time of 14 months from primary resection. In principal component analysis, tumor size, tumor grade differentiation, portal vein tumor thrombus, alpha-fetoprotein, protein induced by vitamin K absence or antagonist-II (PIVKA-II), aspartate aminotransferase, platelet count, white blood cell count, and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model. After comparing different machine learning methods, including logistic regression, decision tree, naïve Bayes, deep neural networks, and k-nearest neighbor (K-NN), we choose the K-NN model as the optimal prediction model. The accuracy, recall, precision of the K-NN model were 70.6%, 51.9%, 70.1%, respectively. The standard deviation was 0.020.ConclusionsThe K-NN classification algorithm model performed better than the other classification models. Estimation of the recurrence rate of early HCC can help to allocate treatment, eventually achieving safe oncological outcomes.

Machine Learning-Based Personalized Prediction of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation.

Radiofrequency ablation (RFA) is a widely accepted, minimally invasive treatment for hepatocellular carcinoma (HCC). This study aimed to develop a machine learning (ML) model to predict the risk of HCC recurrence after RFA treatment for individual patients. We included a total of 1778 patients with treatment-naïve HCC who underwent RFA. The cumulative probability of overall recurrence after the initial RFA treatment was 78.9% and 88.0% at 5 and 10 years, respectively. We developed a conventional Cox proportional hazard model and 6 ML models-including the deep learning-based DeepSurv model. Model performance was evaluated using Harrel's c-index and was validated externally using the split-sample method. The gradient boosting decision tree (GBDT) model achieved the best performance with a c-index of 0.67 from external validation, and it showed a high discriminative ability in stratifying the external validation sample into 2, 3, and 4 different risk groups (P < .001 among all risk groups). The c-index of DeepSurv was 0.64. In order of significance, the tumor number, serum albumin level, and des-gamma-carboxyprothrombin level were the most important variables for the prediction of HCC recurrence in the GBDT model. Also, the current GBDT model enabled the output of a personalized cumulative recurrence prediction curve for each patient. We developed a novel ML model for the personalized risk prediction of HCC recurrence after RFA treatment. The current model may lead to the personalization of effective follow-up strategies after RFA treatment according to the risk stratification of HCC recurrence.

The Role of Human U Three Protein 14a as a Predictor for Hepatocellular Carcinoma Recurrence After Microwave Ablation.

Human U three protein 14a (hUTP14a) is a nucleolar protein which promotes carcinogenesis by causing degradation of the tumor suppressor protein, p53. This study aimed to investigate hUTP14a expression in hepatocellular carcinoma (HCC) and its value as a predictor for HCC recurrence after treatment with microwave ablation (MWA). The hUTP14a expression was evaluated using immunohistochemistry on ultrasound-guided fine needle aspiration biopsy material from the tumor and the surrounding cirrhotic nontumor tissues. The relation between hUTP14a expression and clinic-pathologic variables was analyzed. Nuclear hUTP14a showed significant high expression in HCC tumor tissue compared with corresponding nontumor tissue (P<0.001). Tumoral hUTP14a expression was significantly higher in patients who experienced recurrence than those who were recurrence-free after MWA (P<0.001). We concluded that, hUTP14a has an oncogenic potential, as it is highly expressed in HCC tissues compared with surrounding nontumor cirrhotic tissues. Moreover, nuclear hUTP14a could be used as a promising prognostic biomarker for prediction of HCC recurrence after treatment with MWA.

Pre-Transplant Alpha-Fetoprotein > 25.5 and Its Dynamic on Waitlist Are Predictors of HCC Recurrence after Liver Transplantation for Patients Meeting Milan Criteria.

Simple SummaryIn the setting of liver transplantation, prediction of hepatocellular carcinoma (HCC) recurrence persists as a fundamental issue, and few tools are available during the waitlist. Biological features such as alpha-fetoprotein values are strong predictors, but it is necessary to understand how we can use them. In this study, an AFP cut-off value was individuated and also its dynamic increase on the waitlist was defined as a predictor.Background and Aim: Hepatocellular carcinoma (HCC) recurrence rates after liver transplantation (LT) range between 8 and 20%. Alpha-fetoprotein (AFP) levels at transplant can predict HCC recurrence, however a defined cut-off value is needed to better stratify patients. The aim of this study was to evaluate the rate of HCC recurrence at our centre and to identify predictors, focusing on AFP. Methods: We retrospectively analysed 236 consecutive patients that were waitlisted for HCC who all met the Milan criteria from January 2001 to December 2017 at our liver transplant centre. A total of twenty-nine patients dropped out while they were waitlisted, and 207 patients were included in the final analysis. All survival analyses included the competing-risk model. Results: The mean age was 56.8 ± 6.8 years. A total of 14% were female (n = 29/207). The median MELD (model for end-stage liver disease) at LT was 12 (9–16). The median time on the waitlist was 92 (41–170) days. The HCC recurrence rate was 16.4% (n = 34/208). The mean time to recurrence was 3.3 ± 2.8 years. The median AFP levels at transplant were higher in patients with HCC recurrence (p < 0.001). At multivariate analysis, the AFP value at transplant that was greater than 25.5 ng/mL (AUC 0.69) was a strong predictor of HCC recurrence after LT [sHR 3.3 (1.6–6.81); p = 0.001]. The HCC cumulative incidence function (CIF) of recurrence at 10 years from LT was significantly higher in patients with AFP > 25.5 ng/mL [34.3% vs. 11.5% (p = 0.001)]. Moreover, an increase in AFP > 20.8%, was significantly associated with HCC recurrence (p = 0.034). Conclusions: In conclusion, in our retrospective study, the AFP level at transplant > 25.5 ng/mL and its increase greater than 20.8% on the waitlist were strong predictors of HCC recurrence after LT in a cohort of patients that were waitlisted within the Milan criteria. However further studies are needed to validate these data.

Combined proteomic/transcriptomic signature of recurrence post-liver transplantation for hepatocellular carcinoma beyond Milan

Background and aimsLiver transplantation (LT) can be offered to patients with Hepatocellular carcinoma (HCC) beyond Milan criteria. However, there are currently limited molecular markers on HCC explant histology to predict recurrence, which arises in up to 20% of LT recipients. The goal of our study was to derive a combined proteomic/transcriptomic signature on HCC explant predictive of recurrence post-transplant using unbiased, high-throughput approaches.MethodsPatients who received a LT for HCC beyond Milan criteria in the context of hepatitis B cirrhosis were identified. Tumor explants from patients with post-transplant HCC recurrence (N = 7) versus those without recurrence (N = 4) were analyzed by mass spectrometry and gene expression array. Univariate analysis was used to generate a combined proteomic/transcriptomic signature linked to recurrence. Significantly predictive genes and proteins were verified and internally validated by immunoblotting and immunohistochemistry.ResultsSeventy-nine proteins and 636 genes were significantly differentially expressed in HCC tumors with subsequent recurrence (p < 0.05). Univariate survival analysis identified Aldehyde Dehydrogenase 1 Family Member A1 (ALDH1A1) gene (HR = 0.084, 95%CI 0.01–0.68, p = 0.0152), ALDH1A1 protein (HR = 0.039, 95%CI 0.16–0.91, p = 0.03), Galectin 3 Binding Protein (LGALS3BP) gene (HR = 7.14, 95%CI 1.20–432.96, p = 0.03), LGALS3BP protein (HR = 2.6, 95%CI 1.1–6.1, p = 0.036), Galectin 3 (LGALS3) gene (HR = 2.89, 95%CI 1.01–8.3, p = 0.049) and LGALS3 protein (HR = 2.6, 95%CI 1.2–5.5, p = 0.015) as key dysregulated analytes in recurrent HCC. In concordance with our proteome findings, HCC recurrence was linked to decreased ALDH1A1 and increased LGALS3 protein expression by Western Blot. LGALS3BP protein expression was validated in 29 independent HCC samples.ConclusionsSignificantly increased LGALS3 and LGALS3BP gene and protein expression on explant were associated with post-transplant recurrence, whereas increased ALDH1A1 was associated with absence of recurrence in patients transplanted for HCC beyond Milan criteria. This combined proteomic/transcriptomic signature could help in predicting HCC recurrence risk and guide post-transplant surveillance.

Can Liver Ultrasound Elastography Predict the Risk of Hepatocellular Carcinoma Recurrence After Radiofrequency Ablation? A Systematic Review and Meta-Analysis.

The role of liver stiffness (LS) on ultrasound elastography in the prediction of hepatocellular carcinoma (HCC) recurrence after treatment with radiofrequency ablation (RFA) is still unclear. Our aim was to perform a systematic review and meta-analysis to assess whether LS can predict the recurrence of HCC after RFA. Medline via PubMed, Embase, Scopus, and Cochrane Library databases, and abstracts of international conference proceedings were searched up to June 30, 2020. Cohort studies were included if they assessed the association between LS values measured by ultrasound elastography before RFA and HCC recurrence. 9 studies including 1373 patients with HCC treated by RFA, 643 of whom developed HCC recurrence, were identified. The mean value of LS before RFA was significantly higher in patients who developed HCC recurrence than in those who did not (weighted mean difference=11.98 kPa, 95%CI: 7.60-16.35, I2=63.8%). There was a significant positive association between LS value and HCC recurrence both at univariate (unadjusted HR=1.03, 95%CI: 1.00-1.07, I2=72.7%) and multivariate analysis (adjusted HR=1.03, 95%CI: 1.02-1.04, I2=0). Patients with LS value ≥13-14 kPa or >1.5 m/s have a higher risk of both HCC recurrence (unadjusted HR=2.18, 95%CI: 1.46-3.25, I2=49.7%; adjusted HR=2.41, 95%CI: 1.53-3.79, I2=0) and overall mortality (adjusted HR=4.38; 95%CI: 2.33-8.25, I2=0) in comparison with those with LS below these cutoffs. Liver ultrasound elastography appears to be a reliable tool to predict HCC recurrence and overall survival after RFA. This technique may be useful for the management of patients with HCC treated by RFA.

Mac-2-Binding Protein Glycosylation Isomer as a Novel Predictor of Hepatocellular Carcinoma Recurrence in Patients with Hepatitis C Virus Eradication

Hepatitis C virus (HCV)-related hepatocellular carcinoma (HCC) can recur even after achievement of a sustained virologic response (SVR). Mac-2-binding protein glycosylation isomer (M2BPGi) is a newly identified biomarker correlated with liver fibrosis. This study aimed to clarify outcomes for patients with an SVR and to assess the prognostic value of M2BPGi. This single-center retrospective study analyzed patients who underwent surgical resection for primary HCV-related HCC between 2008 and 2018. The study enrolled 81 patients whose M2BPGi could be evaluated after an SVR. The relationship between liver fibrosis-related factors and scores (including M2BPGi) and HCC recurrence, was evaluated. Of the 81 patients, 57 (70.4%) with HCV-related HCC obtained an SVR, whereas 24 patients (29.6%) did not. The patients with an SVR had a significantly more favorable recurrence-free survival (RFS) than the patients with no SVR (P < 0.0001, log-rank). Among the SVR groups, M2BPGi predicted a shorter RFS after hepatic resection with a higher degree of accuracy than other markers and scores in the SVR group. The high-M2BPGi group had worse liver function, RFS, and overall survival (OS) (P = 0.0014 and 0.0006,log-rank, respectively). In the multivariate analysis, high M2BPGi was significantly associated with worse RFS and OS. Even after achievement of an SVR, the risk of HCC recurrence cannot be eliminated. Measurement of M2BPGi after an SVR can be applied for risk stratification in the assessment of patients with HCV-related HCC.

Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients.

Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave‐one‐out cross‐validation analysis. When the transcriptome analysis was combined with Milan criteria using the k‐top scoring pairs (k‐TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant.

A New Prognostic Algorithm Predicting HCC Recurrence in Patients With Barcelona Clinic Liver Cancer Stage B Who Received PA-TACE.

BackgroundPostoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) is effective in preventing the recurrence of hepatocellular carcinoma (HCC) in patients treated with surgery. However, there is a lack of reports studying the risk factors associated with recurrence in HCC patients who received PA-TACE. In this study, we identified the independent risk factors for recurrence of HCC patients who received PA-TACE. We also developed a novel, effective, and valid nomogram to predict the individual probability of recurrence, 1, 3, and 5 years after PA-TACE.MethodsA retrospective study was performed to identify the independent risk factors for recurrence of HCC in a group of 502 patients diagnosed in stage B based on the Barcelona Clinic Liver Cancer (BCLC) evaluation system for HCC that underwent curative resections. Then, subgroup analysis was performed for 184 patients who received PA-TACE, who were included in the training cohort. The other 147 HCC patients were included in a validation cohort. A recurrence-free survival (RFS)-predicting nomogram was constructed, and results were assessed using calibration and decision curves and a time-dependent AUC diagram.ResultsPA-TACE was shown to be a significant independent prognostic value for patients with BCLC stage B [p < 0.001, hazard ratio (HR) = 0.508, 95% CI = 0.375–0.689 for OS, p = 0.002; HR = 0.670, 95%CI = 0.517–0.868 for RFS]. Alpha fetoprotein (AFP), tumor number, tumor size, microvascular invasion (MVI), and differentiation were considered as independent risk factors for RFS in the training cohort, and these were further confirmed in the validation cohort. Next, a nomogram was constructed to predict RFS. The C-index for RFS in the nomogram was 0.721 (95% CI = 0.718–0.724), which was higher than SNACOR, HAP, and CHIP scores (0.587, 0.573, and 0.607, respectively). Calibration and decision curve analyses and a time-dependent AUC diagram were used. Our nomogram showed stronger performance than these other nomograms in both the training and validation cohorts.ConclusionsHCC patients diagnosed as stage B according to BCLC may benefit from PA-TACE after surgery. The RFS nomogram presented here provides an accurate and reliable prognostic model to monitor recurrence. Patients with a high recurrence score based on the nomogram should receive additional high-end imaging exams and shorter timeframes in between follow-up visits.

Different Risk Factors for Early and Late Recurrence After Curative Resection of Hepatocellular Carcinoma.

Factors of early and late recurrence after curative resection of hepatocellular carcinoma (HCC) may be different. The aim of this study was to identify clinical factors, including liver stiffness measurement (LSM), which are associated with HCC recurrence after curative resection. Patients who underwent preoperative LSM and primary curative resection for HCC between October 2015 and May 2018 were retrospectively reviewed, with 1year as the cut-off between early and late recurrence. Recurrence was observed in 42/149 (28.2%) patients over a median follow-up of 38.3months (early recurrence: 10 [6.7%] patients; late recurrence: 32 [21.5%] patients). Multivariate analysis identified LSM (P = 0.026) and tumor size (P = 0.010) as the only factors that were significantly associated with recurrence-free survival. Compared with patients without recurrence, those with early recurrence had larger tumor size (P = 0.035) and those with late recurrence had higher LSM (P = 0.024). Receiver-operating characteristic analysis indicated that the optimal LSM cut-off value for predicting HCC recurrence was 7.4kPa. Tumor size was associated with early HCC recurrence after curative resection and LSM was associated with late recurrence. LSM cut-off of 7.4kPa is recommended in predicting recurrence.

Association of Low Serum Albumin Level with Higher Hepatocellular Carcinoma Recurrence in Patients with Hepatitis B Virus Pre-S2 Mutant after Curative Surgical Resection.

Hepatocellular carcinoma (HCC) is, globally, one of the most prevalent and deadly human cancers; despite curative surgical resection, its high recurrence rate after surgery remains a large threat, resulting in poor patient survival. The hepatitis B virus (HBV) pre-S2 mutant that harbors deletions spanning the pre-S2 gene segment has emerged as an important oncoprotein for HCC development and a valuable prognostic biomarker for HCC recurrence; however, its relationship with clinicopathological factors is largely unexplored. In this study, the correlation of the deletion spanning the pre-S2 gene segment with clinicopathological factors and the association of such correlation with HCC recurrence after curative surgical resection were examined in HBV-related HCC patients. Inverse correlation between serum albumin level and the deletion spanning the pre-S2 gene segment was identified. HCC patients with the presence of the deletion spanning the pre-S2 gene segment and a low serum albumin level were associated with higher HCC recurrence than patients with either factor alone or neither factor were. Moreover, a combination of the serum albumin level and the deletion spanning the pre-S2 gene segment exhibited better performance than that of either factor alone in predicting HCC recurrence. Collectively, this study shows an association of low serum albumin level with pre-S2 mutant-positive HCC patients, and validates the prognostic value of this association in identifying patients with higher HCC recurrence after curative surgical resection.

Anticancer Effect of Statins in Patients Undergoing Liver Transplantation for Hepatocellular Carcinoma.

The anticancer effect of statins is drawing attention. However, it is unclear whether statin use reduces the risk of hepatocellular carcinoma (HCC) recurrence in patients who undergo liver transplantation (LT) for HCC. Consecutive patients who underwent LT for HCC between 1995 and 2019 were enrolled. The effects of statins on HCC recurrence and mortality were compared between statin user and statin nonuser groups. We performed the analyses in a variety of ways, including inverse probability treatment weighting (IPTW) methods to balance any confounders and the landmark method to avoid immortal time bias. A total of 430 patients were enrolled, among whom 323 (75.1%) were statin nonusers and 107 (24.9%) were statin users. During a median of 64.9months (IQR, 26.1-122.6 months) of follow-up, 79 patients (18.4%) had HCC recurrence and 111 (25.8%) died. Among those who died, 53 (47.7%) were identified as HCC-related mortalities. Statin use was a predictor of HCC recurrence (adjusted hazard ratio [HR],0.3; 95% confidence interval [CI], 0.1-0.6; P=0.002), all-cause mortality (adjusted HR,0.3; 95% CI, 0.2-0.5; P<0.001), and HCC-related mortality (adjusted HR,0.4; 95% CI, 0.2-0.9; P=0.03). The effects of statin use on clinical outcomes were also identified through IPTW analysis. There was a dose-dependent relationship between statin use and HCC recurrence. The anticancer effect of statins on HCC recurrence was consistently significant across multivariable-stratified and sensitivity analyses. Statin use significantly reduced the risk of HCC recurrence and improved the survival of patients who underwent LT for HCC.

Discoidin Domain Receptor Tyrosine Kinase 1 (DDR1): A Novel Predictor for Recurrence of Hepatocellular Carcinoma After Curative Resection

BackgroundPrevious studies showed that the discoidin domain receptor tyrosine kinase 1 (DDR1) is significantly elevated in a variety of cancers, and it is closely related to the occurrence and development of tumors. However, its clinical significance in hepatocellular carcinoma (HCC) is not fully elucidated. So, in this study, we aimed to systemically evaluate the prognostic value of DDR1 in HCC.Material/MethodsA total of 200 individuals were enrolled in this study (including 120 HCC patients, 40 chronic hepatitis patients, and 40 health individuals). The contents of DDR1 in serum was measured by enzyme-linked immunosorbent assay (ELISA), while the expression level of DDR1 in para-tumor and tumor tissue was detected by immunohistochemistry staining. Kaplan-Meier, Cox regression analyses, and log-rank test were used to assess the prognostic value.ResultsThe contents of DDR1 in serum of HCC patients was significantly higher compared with chronic hepatitis patients (P<0.01) and health individuals (P<0.001). The expression level of DDR1 in tumors was higher than that in normal liver tissue, and it had relatively strong correlation with DDR1 in serum. We next demonstrated that high DDR1 has utility as a prognostic risk factor for tumor recurrence and metastasis, and it still retains its discrimination ability in low-risk groups (BCLC 0+A). Moreover, DDR1 is as an independent predictor of prognosis in HCC patients with microvascular invasion (MVI), and is strongly associated with epithelial-mesenchymal transition (EMT)-related protein.ConclusionsDDR1 is a novel predictor for HCC recurrence. Integration of serum and tumor DDR1 detection into clinical management would provide convenience and enhanced accuracy in clinical practice.

VALUE OF SERUM ALPHA L FUCOSIDASE LEVEL AS A PROGNOSTIC BIOMARKER FOR HEPATOCELLULAR CARCINOMA BEFORE AND AFTER CHEMO-EMBOLIZATION AND RADIO-FREQUENCY, A PROSPECTIVE STUDY

Because hepatocellular carcinoma (HCC) is a complex disease with multiple risk pathogenicmechanisms due to misdiagnosis with a single biomarker. A combination of biomarkers may bemore valuable for the diagnosis, staging and prognosis of HCC. In the near future, identifyingnon-invasive and cost-effective biomarkers for early diagnosis and personalized treatment ofHCC will be one of the most promising fields of biomarker research. This study assessed the alphaL fucosidase (AFU) value as a prognostic biomarker in patients with HCC before and afterchemo-embolization and radio-frequency. A total of 60 subjects were subdivided into 3 groups:GI: 30 HCC patients underwent interventional management (chemo-embolization or radiofrequency),GII: 20 liver cirrhosis (LC) patients and GIII: 10 cross-matched individuals as controlThe results showed that plasma AFU had significantly higher diagnostic performance in HCCdiagnosis than alpha fetoprotein (AFP) at a cut off value of > 2.5u/l, with sensitivity 100%, specificity95%, positive predictive value (PPV) 96.8%, negative predictive value (NPV) 100% anddiagnostic accuracy 98%. Basal pre-intervention AFU) had significantly high diagnostic performanceto predict HCC recurrence after intervention at a cut off value of > 12.5u/l with sensitivity100%, specificity 92%, PPV, 71.4%, NPV 100% and diagnostic accuracy 93.3%. PostinterventionAFU had significant moderate diagnostic performance in predicting recurrence ofHCC at cut off value of > 7.5u/l with sensitivity 80%, specificity 92%, positive predictive value(PPV) 66.7%, negative predictive value (NPV) 95.8% & diagnostic accuracy 90%.

Risk Score Based on Two microRNAs as a Prognostic Marker of Hepatocellular Carcinoma and the Corresponding Competitive Endogenous RNA Network.

PurposeLiver transplantation (LT) currently yields the best outcomes for hepatocellular carcinoma (HCC). However, tumor recurrence still occurs in some patients. Identifying markers that predict HCC recurrence after LT is an unmet medical need.MethodsIn this study, differential expression analysis was used to identify differentially expressed microRNAs (DEmiRs) between HCC and liver tissues in the The Cancer Genome Atlas database and in data from patients with recurrent or non-recurrent HCC in the GSE64989 dataset. The expression profiles of the overlap DEmiRs were used to construct an miRNA-based risk score to predict prognosis using Cox regression analysis. The target genes of the miRNAs of interest were predicted, and they were analyzed for functional enrichment. Furthermore, we used the miRNAs of interest to construct a competitive endogenous RNA (ceRNA) network of long non-coding RNAs (lncRNAs), miRs and mRNAs.ResultsFour up-regulated and three down-regulated miRNAs in HCC and recurrent HCC after LT were considered as candidate miRs. MiR-3200-3p and miR-3690 were selected to construct the miR-based risk score, which was found to be associated with poor overall survival and progression-free survival. Furthermore, it proved to be an independent prognostic factor after adjusting for other clinicopathological factors. The corresponding ceRNA networks of these two miRs that we constructed may help to understand their regulatory mechanisms in HCC.ConclusionWe propose a risk score based on miR-3200-3p and miR-3690 that may be useful as a prognostic marker to predict HCC recurrence after LT. We generated a ceRNA network involving these miRNAs, which may help reveal their regulatory roles in HCC.