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Background: Globally, hepatocellular carcinoma (HCC) is one of the most prevalent and deadly malignant tumors. A recent study proposed disulfidptosis, a novel form of regulated cell death (RCD), offering a new avenue for identifying tumor prognosis biomarkers and developing novel therapeutic targets., Methods: Based on the expression data of 14 disulfideptosis-related genes extracted from public databases, a new molecular classification of HCC called the "disulfidptosis score" was constructed and its relationship to tumor immunity and prognosis was evaluated., Results: Based on the expression of disulfideptosis-related genes, we performed cluster analysis on HCC samples from the TCGA cohort, which classified these patients into three clusters: A, B, and C, and the differentially expressed genes of different clusters were analyzed. A disulfidptosis score model was constructed by differentially expressed genes associated with prognosis. Univariate and multivariate COX regression analysis showed that disulfidptosis score was an independent prognostic factor for HCC. In addition, in various disulfidptosis score groups, notable disparities were observed concerning the tumor immune microenvironment as well as the expression of immune checkpoint., Conclusion: Disulfidptosis score have an important role in predicting HCC prognosis and help guide us in providing better immunotherapy options for patients., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.

Background and Aims: Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for patients with HCC. The clinical applications of documented molecular subtypes are constrained by several issues., Approach and Results: We integrated 3 single-cell data sets to describe the TME landscape and identified 6 prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry, and multiplex immunofluorescence. The prognosis-related score was constructed based on a machine-learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The 5 transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes, and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while classes 2 and 4 were characterized by a lack of T-cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that classes 5 and 3 were sensitive to immune checkpoint blockade and targeted therapy, whereas classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistance to all conventional HCC therapies. Four potential therapeutic agents and 4 targets were further identified for high prognosis-related score patients with HCC., Conclusions: Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients., (© 2024. The Author(s).)

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies., Competing Interests: The authors declare that they have no competing interests., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Liver zonation is a unique phenomenon in which the liver exhibits distinct functions among hepatocytes along the radial axis of the lobule. This phenomenon can cause the sectionalized initiation of several liver diseases, including hepatocellular carcinoma (HCC). However, few studies have explored the zonation features of HCC., Methods: Four single-cell RNA sequencing datasets were used to identify hepatocyte-specific zonation markers. Integrative analysis was then performed with a training RNA-seq cohort (616 HCC samples) and an external validating microarray cohort (285 HCC samples) from the International Cancer Genome Consortium, The Cancer Genome Atlas, Gene Expression Omnibus, and EMBL's European Bioinformatics Institute for clustering using non-negative matrix factorization consensus clustering based on zonation genes. Afterward, we evaluated the prognostic value, clinical characteristics, transcriptome and mutation features, immune infiltration, and immunotherapy response of the HCC subclasses., Results: A total of 94 human hepatocyte-specific zonation markers (39 central markers and 55 portal markers) were identified for the first time. Subsequently, three subgroups of HCC, namely Cluster1, Cluster2, and Cluster3 were identified. Cluster1 exhibited a non-zonational-like signature with the worst prognosis. Cluster2 was intensively associated with a central-like signature and exhibited low immune infiltration and sensitivity toward immune blockade therapy. Cluster3 was intensively correlated with a portal-like signature with the best prognosis. Finally, we identified candidate therapeutic targets and agents for Cluster1 HCC samples., Conclusion: The current study established a novel HCC classification based on liver zonation signature. By classifying HCC into three clusters with non-zonational-like (Cluster1), central-like (Cluster2), and portal-like (Cluster3) features, this study provided new perspectives on the heterogeneity of HCC and shed new light on delivering precision medicine for HCC patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Gu, Wang, Lu, Cai, Li, Nie, Chen and Wang.)

Background: Hepatocellular carcinoma (HCC) is an extensive heterogeneous disease where epigenetic factors contribute to its pathogenesis. Polycomb group (PcG) proteins are a group of subunits constituting various macro-molecular machines to regulate the epigenetic landscape, which contributes to cancer phenotype and has the potential to develop a molecular classification of HCC., Results: Here, based on multi-omics data analysis of DNA methylation, mRNA expression, and copy number of PcG-related genes, we established an epigenetic classification system of HCC, which divides the HCC patients into two subgroups with significantly different outcomes. Comparing these two epigenetic subgroups, we identified different metabolic features, which were related to epigenetic regulation of polycomb-repressive complex 1/2 (PRC1/2). Furthermore, we experimentally proved that inhibition of PcG complexes enhanced the lipid metabolism and reduced the capacity of HCC cells against glucose shortage. In addition, we validated the low chemotherapy sensitivity of HCC in Group A and found inhibition of PRC1/2 promoted HCC cells' sensitivity to oxaliplatin in vitro and in vivo. Finally, we found that aberrant upregulation of CBX2 in Group A and upregulation of CBX2 were associated with poor prognosis in HCC patients. Furthermore, we found that manipulation of CBX2 affected the levels of H3K27me3 and H2AK119ub., Contributions: Our study provided a novel molecular classification system based on PcG-related genes data and experimentally validated the biological features of HCC in two subgroups. Our founding supported the polycomb complex targeting strategy to inhibit HCC progression where CBX2 could be a feasible therapeutic target., (© 2022. The Author(s).)

There have been major advances in the armamentarium for hepatocellular carcinoma (HCC) since the last official update of the Barcelona Clinic Liver Cancer prognosis and treatment strategy published in 2018. Whilst there have been advances in all areas, we will focus on those that have led to a change in strategy and we will discuss why, despite being encouraging, data for select interventions are still too immature for them to be incorporated into an evidence-based model for clinicians and researchers. Finally, we describe the critical insight and expert knowledge that are required to make clinical decisions for individual patients, considering all of the parameters that must be considered to deliver personalised clinical management., Competing Interests: Conflict of interest MR: reports consultancy from Bayer, BMS, Roche, Ipsen, Astra Zeneca, Boston Science and Lilly; lecture fees from Bayer, BMS, Gilead, Lilly,Roche and UniversalDX and travel support from Bayer, BMS, Lilly and Astra Zeneca. Received research funding (to institution) from Bayer and Ipsen. AF: reports lecture fees from Bayer, Boston Science, Gilead, and MSD, consultancy fees from Bayer, Astra Zeneca, Roche, SIRTEX, AB Exact Science and Guerbert. JR: reports lectures and travel grants from Bayer and Roche. JFF: reports lecture fees from Bayer. MB: reports lectures and/or travel support from Boston Science, Terumo, Guerbet and Bayer. AGC: reports lectures fee from Boston Science, Terumo. KRK: reports consultancy fees (to self) from Exact Sciences, Genentech/Roche, Gilead. Received travel support from Ipsen. Received research funding (to institution) from Agios, Astra Zeneca, Bayer, BMS, Eli Lilly, EMD Serono, Exelixis, Genentech/Roche, Merck, Novartis, Partner Therapeutics, QED, Relay Therapeutics, Surface Oncology, Taiho. PRG: consultancy fees and/or travel support from Bayer, Boston Scientific, AstraZeneca, Adaptimmune, BMS, MSD, Sirtex, Lilly, Roche, Guerbet, Ipsen, Eisai. VM: Nothing to disclose. RS: reports consultancy fees from Boston Scientific, Cook, Bard, Genentech, Astrazeneca, Eisai, Sirtex, Siemens, research support from Boston Scientific. BS: reports consultancy fees from Adaptimmune, Astra Zeneca, Bayer, BMS, Boston Scientific, BTG, Eisai, Eli Lilly, H3 Biomedicine, Ipsen, Novartis, Merck, Roche, Sirtex Medical, Terumo; speaker fees from Astra Zeneca, Bayer, BMS, BTG, Eli Lilly, Ipsen, Novartis, Merck, Roche, Sirtex Medical, Terumo; research grants (to Institution) from BMS and Sirtex Medical. AS: has served on advisory boards or consulted for Genentech, Bayer, Eisai, AstraZeneca, BMS, and Exelixis. AV: Speaker, consultancy and advisory role: Amgen, Roche, Bayer, Sanofi, BMS, Lilly, Novartis, EISAI, AstraZeneca, Merck, Incyte, Ipsen, PierreFabre, MSD, Sirtex, BTG, Servier, Terumo, GSK. JFu: Nothing to disclose. CA: reports lectures fee from Bayer. JB: has consulted for Arqule, Bayer-Shering Pharma, Novartis, BMS, BTG- Biocompatibles, Eisai, Kowa, Terumo, Gilead, Bio-Alliance, Roche, AbbVie, MSD, Sirtex, Ipsen, Astra-Medimmune, Incyte, Quirem, Adaptimmune, Lilly, Basilea, Nerviano, Sanofi and UniversalDX; and received research/educational grants from Bayer, and lecture fees from Bayer-Shering Pharma, BTG-Biocompatibles, Eisai, Terumo, Sirtex, Ipsen. Please refer to the accompanying ICMJE disclosure forms for further details., (Copyright © 2021 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Introduction: Considering the narrow window of surgery, early diagnosis of liver cancer is still a fundamental issue to explore. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA) are considered as two different types of liver cancer because of their distinct pathogenesis, pathological features, prognosis, and responses to adjuvant therapies. Qualitative analysis of image is not enough to make a discrimination of liver cancer, especially early-stage HCC or ICCA., Methods: This retrospective study developed a radiomic-based model in a training cohort of 122 patients. Radiomic features were extracted from computed tomography (CT) scans. Feature selection was operated with the least absolute shrinkage and operator (LASSO) logistic method. The support vector machine (SVM) was selected to build a model. An internal validation was conducted in 89 patients., Results: In the training set, the AUC of the evaluation of the radiomics was 0.855 higher than for radiologists at 0.689. In the valuation cohorts, the AUC of the evaluation was 0.847 and the validation was 0.659, which indicated that the established model has a significantly better performance in distinguishing the HCC from ICCA., Conclusion: We developed a radiomic diagnosis model based on CT image that can quickly distinguish HCC from ICCA, which may facilitate the differential diagnosis of HCC and ICCA in the future., Competing Interests: On behalf of all authors, the corresponding authors state that there is no conflict of interest., (Copyright © 2022 Xiaoliang Xu et al.)

Hepatocellular carcinoma (HCC) is a lethal human malignancy with a very low overall and long-term survival rate. Poor prognostic outcomes are predominantly associated with HCC due to a huge landscape of heterogeneity found in the deadliest disease. However, molecular subtyping of HCC has significantly improved the knowledge of the underlying mechanisms that contribute towards the heterogeneity and progression of the disease. In this review, we have extensively summarized the current information available about molecular classification of HCC. This review can be of great significance for providing the insight information needed for development of novel, efficient and personalized therapeutic options for the treatment of HCC patients globally., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: With the development of sequencing technology, several signatures have been reported for the prediction of prognosis in patients with hepatocellular carcinoma (HCC). However, the above signatures are characterized by cumbersome application. Therefore, the study is aimed at screening out a robust stratification system based on only one gene to guide treatment., Methods: Firstly, we used the limma package for performing differential expression analysis on 374 HCC samples, followed by Cox regression analysis on overall survival (OS) and disease-free interval (PFI). Subsequently, hub prognostic genes were found at the intersection of the above three groups. In addition, the topological degree inside the PPI network was used to screen for a unique hub gene. The rms package was used to construct two visual stratification systems for OS and PFI, and Kaplan-Meier analysis was utilized to investigate survival differences in clinical subgroups. The ssGSEA algorithm was then used to reveal the relationship between the hub gene and immune cells, immunological function, and checkpoints. In addition, we also used function annotation to explore into putative biological functions. Finally, for preliminary validation, the hub gene was knocked down in the HCC cell line., Results: We discovered 6 prognostic genes ( SKA1 , CDC20 , AGTRAP , BIRC5 , NEIL3 , and CDC25C ) for constructing a PPI network after investigating survival and differential expression genes. According to the topological degree, AGTRAP was chosen as the basis for the stratification system, and it was revealed to be a risk factor with an independent prognostic value in Kaplan-Meier analysis and Cox regression analysis ( P < 0.05). In addition, we constructed two visualized nomograms based on AGTRAP . The novel stratification system had a robust predictive value for PFI and OS in ROC analysis and calibration curve ( P < 0.05). Meanwhile, AGTRAP upregulation was associated with T staging, N staging, M staging, pathological stage, grade, and vascular invasion ( P < 0.05). Notably, AGTRAP was overexpressed in tumor tissues in all pancancers with paired samples ( P < 0.05). Furthermore, AGTRAP was associated with immune response and may change immune microenvironment in HCC ( P < 0.05). Next, gene enrichment analysis suggested that AGTRAP may be involved in the biological process, such as cotranslational protein targeting to the membrane. Finally, we identified the oncogenic effect of AGTRAP by qRT-PCR, colony formation, western blot, and CCK-8 assay ( P < 0.05)., Conclusion: We provided robust evidences that a stratification system based on AGTRAP can guide survival prediction for HCC patients., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Li Wang et al.)

Hepatocellular carcinoma (HCC) constitutes 80% of all primary liver cancers. Based on key developments in the understanding of its carcinogenesis and the advancement of treatment options, detailed algorithms and practice guidelines have been published to guide the clinical management of HCC. Furthermore, several subclasses of HCC have been described based on molecular profiles and linked to pathological characteristics, clinical features, and disease aggressiveness. Most recently, the combination of the checkpoint inhibitor atezolizumab plus bevacizumab has significantly increased treatment response in the first line systemic treatment of HCC. Unfortunately, rare HCC variants, in particular fibrolamellar liver cancer (FLC), combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA), and sarcomatoid hepatocellular carcinoma (sHCC), were excluded from phase III studies. Therefore, data for decision-making and treatment allocation for these distinct entities, representing 1-5% of all primary liver cancers, is scarce. Moreover, most of the knowledge available for these rare HCC variants is based on registry data and retrospective studies. In this position paper, we briefly summarize the current clinical knowledge regarding FLC, cHCC-CCA, and sHCC. Based on our summary, we propose future clinical research activities within the framework of the European Reference Network on Hepatological Diseases (ERN RARE-LIVER)., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

The prognostic role of adjacent nontumor tissue in hepatocellular carcinoma (HCC) patients is still not clear. The activity changes of immunologic and hallmark gene sets in adjacent nontumor tissues may substantially impact on prognosis by affecting proliferation of liver cells and colonization of circulating tumor cells after HCC treatment measures such as hepatectomy. We aimed to identify HCC subtypes and prognostic gene sets based on the activity changes of gene sets in tumor and nontumor tissues, to improve patient outcomes. We comprehensively revealed the activity changes of immunologic and hallmark gene sets in HCC and nontumor samples by gene set variation analysis (GSVA), and identified three clinically relevant subtypes of HCC by nonnegative matrix factorization method (NMF). Patients with subtype 1 had good overall survival, whereas those with subtype 2 and subtype 3 had poor prognosis. Patients with subtype 1 in the validation group also tended to live longer. We also identified three prognostic gene sets in tumor and four prognostic gene sets in nontumor by least absolute shrinkage and selection operator method (LASSO). Interestingly, functional enrichment analysis revealed that in nontumor tissues, genes from four gene sets correlated with immune reaction, cell adhesion, whereas in tumor tissue, genes from three gene sets closely correlated with cell cycle. Our results offer new insights on accurately evaluating prognosis-the important role of gene sets in both tumor and adjacent nontumor tissues, suggesting that when selecting for HCC treatment modality, changes in tumor and nontumor tissues should also be considered, especially after hepatectomy., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Purpose: To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC., Materials and Methods: Preoperative computed tomography and tumor marker information of 617 primary liver cancer patients were retrospectively collected to develop CNN models categorizing tumors into three categories: moderately differentiated HCC (mHCC), poorly differentiated HCC (pHCC), and ICC, where the histopathological diagnoses were considered as ground truths. The models processed manually cropped tumor with and without tumor marker information (two-input and one-input models, respectively). Overall accuracy was assessed using a held-out dataset (10%). Area under the curve, sensitivity, and specificity for differentiating ICC from HCCs (mHCC + pHCC), and pHCC from mHCC were also evaluated. We assessed two radiologists' performance without tumor marker information as references (overall accuracy, sensitivity, and specificity). The two-input model was compared with the one-input model and radiologists using permutation tests., Results: The overall accuracy was 0.61, 0.60, 0.55, 0.53 for the two-input model, one-input model, radiologist 1, and radiologist 2, respectively. For differentiating pHCC from mHCC, the two-input model showed significantly higher specificity than radiologist 1 (0.68 [95% confidence interval: 0.50-0.83] vs 0.45 [95% confidence interval: 0.27-0.63]; p = 0.04)., Conclusion: Our CNN model with tumor marker information showed feasibility and potential for three-class classification within primary liver cancer.

Background: With high diagnostic accuracy, magnetic resonance elastography (MRE) is a noninvasive tool and can be adopted to measure liver stiffness (LS). In this study, meta-analysis was carried out to further evaluate whether LS measured by MRE can predict early recurrence in patients with hepatocellular carcinoma (HCC)., Methods: PUBMED, EMBASE, Web of Science, China National Knowledge Infrastructure, and Cochrane Library database were searched for studies related to LS measured by MRE in the prediction of recurrence in patients with HCC. Survival outcome was estimated by hazard ratios and 95% confidence intervals. Meta-analysis was conducted with the Stata 16.0., Results: The results of this meta-analysis will be submitted to a peer-reviewed journal for publication., Conclusion: This study will provide evidence support for LS measured by MRE in predicting the recurrence of HCC., Ethics and Dissemination: The private information from individuals will not be published. This systematic review also should not damage participants' rights. Ethical approval is not available. The results may be published in a peer-reviewed journal or disseminated in relevant conferences., Osf Registration Number: DOI 10.17605/ OSF.IO / SURH3., Competing Interests: The authors have no conflicts of interests to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

The morphologic spectrum of hepatocellular carcinoma (HCC) is quite broad. While in about one-third of cases, the neoplasms can be categorized into meaningful subtypes based on morphology, a vast majority of these neoplasms are morphologically heterogeneous. With extensive tumor profiling, data has begun to emerge which can correlate specific morphologic features with underlying molecular signatures. A true morphologic subtype not only has reproducible H & E features, further supported by specific immunohistochemical or molecular signatures, but also has specific clinical implications and prognostic associations. Eight such morphologic subtypes are recognized by the 2019 WHO classification of tumors with a few more additional subtypes described in the literature. The goal of this review is to familiarize the reader with the morphologic subtypes and elaborate on the clinical and molecular associations of these neoplasms., Competing Interests: None

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP)., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2021 S. Murugesan et al.)

Hepatocellular carcinoma is a malignance that remains difficult to cure. Immunotherapy has shown its potential application in a variety of refractory malignancies. Due to the complexity of immune microenvironment of hepatocellular carcinoma, the efficacy of immunotherapy for hepatocellular carcinoma is not as effective as expected. Expression data of hepatocellular carcinoma from the TCGA and ICGC databases were used for classification and verification of hepatocellular carcinoma subtypes. The immune-related functions and pathways were identified via gene set enrichment analysis, while the sections denoting the subsets of the immune cells were estimated using the CIBERSORT algorithm. Immunity low (Immunity&#95;L), immunity medium (Immunity&#95;M), and immunity high (Immunity&#95;H) were specified as the three immune-related subtypes of hepatocellular carcinoma. The quantity of stromal and immune cells was the most substantial in Immunity&#95;H, compared to the other subtypes. Interestingly, the proportion of M0 macrophages decreased from Immunity&#95;L to Immunity&#95;H, while the proportion of CD8 T cells increased. Furthermore, the HLA genes expression levels, as well as those of six immune checkpoint genes were substantially lower in Immunity&#95;L than in Immunity&#95;H. Functional analysis was performed for 1512 differentially expressed genes between Immunity&#95;L and Immunity&#95;H. Finally, the PPI network was constructed with 118 nodes. The highest connectivity degree nodes were B2M, HLA-DRA, and HLA-DRB1. The above results were verified in ICGC-JP and ICGC-FR databases with a consistent trend. In this study, we divided hepatocellular carcinoma into three subtypes and explored the immune-related characteristics of these subtypes. These results may provide new insights for immunotherapy of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with expected increasing frequency in the next few decades. At early stages, HCC is curable, with most common therapeutic modalities to include surgical resection and liver transplantation. The Barcelona Clinic Liver Cancer (BCLC) Staging System is widely adopted tool to guide the therapeutic algorithms of patients with HCC. This classification is guiding the clinical practice for the last 2 decades. However, there are emerging data demonstrating that patients beyond the traditional criteria of operability, resectability or transplantability actually can benefit from surgical treatment, emphasizing the need of refinement or even change of current BCLC criteria.

Background: The Barcelona Clinic Liver Cancer (BCLC) system has been endorsed by international guidelines as a staging algorithm of hepatocellular carcinoma. This analysis was performed to assess the outcome of liver transplantation in patients treated against the BCLC recommendations., Methods: The data of 198 patients who underwent liver transplantation for hepatocellular carcinoma were extracted from a prospectively maintained database to classify the patients according to the BCLC system., Results: BCLC staging was as follows: 0, n = 5; A, n = 77; B, n = 41; C, n = 53; and D, n = 22. Accordingly, liver transplantation was performed in the majority of patients against BCLC recommendations. Surgery (n = 16), radiofrequency ablation (n = 15) and transarterial chemoembolization (n = 151) preceded liver transplantation in 182 patients. Sixteen patients were transplanted without pretreatment. The1-, 5- and 10-year survival rates were 83.8%, 62.4% and 45.9%, and 1-, 5-, and 10-year recurrence rates were 7.7%, 22.7% and 26.7%. The BCLC classification did neither impact survival (P = 0.796) nor recurrence (P = 0.693). In the Cox analysis, RECIST tumor progression and initial alpha fetoprotein were independent predictors of outcome., Conclusions: Neither the oncological nor the functional stratification imposed by the BCLC system was of importance for outcome. Lack of flexibility and disregard of biological parameters hamper its clinical applicability in liver transplantation., (Copyright © 2020. Published by Elsevier B.V.)

Introduction: Prognostic classifications for patients treated with sorafenib for hepatocellular carcinoma (HCC) facilitate stratification in trials and inform clinical decision making. Recently, 3 different prognostic models (hepatoma arterial-embolization prognosis [HAP] score, sorafenib advanced HCC prognosis [SAP] score, and Prediction Of Survival in Advanced Sorafenib-treated HCC [PROSASH]-II) have been proposed specifically for patients treated with sorafenib. This study aimed to compare the prognostic performance of different scores., Methods: We analyzed a large prospective database gathering data of 552 patients treated with sorafenib from 7 Italian centers. The performance of the HAP, SAP, and PROSASH-II models were compared with those of generic HCC prognostic models (including the Barcelona Clinic for Liver Cancer and Italian Liver Cancer staging systems, albumin-bilirubin grade, and Child-Pugh score) to verify whether they could provide additional information., Results: The PROSASH-II model improved discrimination (C-index 0.62) compared with existing prognostic scores (C-index ≤0.59). Its stratification significantly discriminated patients, with a median overall survival of 21.5, 15.3, 9.3, and 6.0 months for risk group 1, 2, 3, and 4, respectively. The HAP and SAP score were also validated but with a poorer performance compared with the PROSASH-II., Discussion: Although suboptimal, PROSASH-II is the most effective prognostic classification model among other available scores in a large Italian population of patients treated with sorafenib., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Background: Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer in dogs. Despite this, relatively few reports of this disease exist pertaining to prognostic factors and outcome., Aim: To evaluate factors associated with survival in dogs with all subtypes of HCC diagnosed on histopathology., Methods: A retrospective single institutional study was carried out on 94 client-owned dogs with a histopathologic diagnosis of HCC between 2007 and 2018 obtained by biopsy (21/94) or attempted definitive resection (73/94). Signalment, preoperative features, surgical findings, and postoperative outcomes were recorded. Associations between survival to discharge data were collected and univariable logistical regression was carried out. Kaplan-Meier survival analysis was carried out to identify negative risk factors for long-term prognosis., Results: The median survival time (MST) for all patients was 707 days (95% CI = 551-842). MST was not significantly different ( p > 0.05) between patients who had suspected versus incidentally diagnosed HCC (695 vs. 775 days), between complete versus incomplete surgical margins (668 vs. 834 days), or between patients with massive subtype versus nodular/diffuse subtype (707 vs. 747 days). Logistical regression identified an association with the excision of the right medial lobe and risk of perioperative death (OR = 9.2, CI 1.5-55.9, p = 0.016). An American Society of Anesthesiologists score ≥4, disease present within the quadrate lobe, and elevated blood urea nitrogen, potassium or gamma-glutamyltransferase were identified as negative prognosticators during multivariable Cox regression. Preoperative imaging (ultrasound or CT) agreed with the surgical location in 91% of the cases. Preoperative cytology was consistent with a diagnosis of HCC in 15/32 (46.9%) cases., Conclusion: Type of diagnosis (incidental vs presumed), completeness of excision, and subtype were not associated with MST in this study. Preoperative identification of tumors within the central division may be related to a less favorable outcome. Results of preoperative cytology were not highly sensitive for identifying a malignancy., Competing Interests: The authors declare that there is no conflict of interest

Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy in adults. Several studies have classified HCC into molecular subtypes aiming at detecting aggressive subtypes. The aim of the present study was to investigate the role of p53, β-catenin, CD133, and Ki-67 in subclassification of HCC into different aggressive subtypes and the correlation between those markers and the clinicopathologic characteristics of HCC patients. This retrospective study was conducted on paraffin-embedded blocks of 114 HCC specimens. Tissue microarray was constructed and immunostaining for p53, β-catenin, CD133, and Ki-67 was performed and HCC score was formulated. P53 expression was associated with old age (P=0.028), large tumor size (P=0.019), poorly differentiated HCC (P=0.012), hepatitis B virus (HBV) positivity (P=0.032), and hepatitis C virus (HCV) negativity (P =0.046). β-catenin expression was associated with small sized tumors (P=0.005), HBV negativity (P=0.027), early-staged tumors (P=0.029), and prolonged recurrence-free survival (P=0.045). High percentage of CD133 expression was associated with old patients (P=0.035) and HBV positivity (P= 0.045). Ki-67 expression was associated with large tumor size (P= 0.049), vascular invasion (P= 0.05), old age (P=0.035), and previous treatment of HCV by direct acting antiviral agents (P=0.005). Cases with high HCC score showed significant association with old patients (P=0.002), previous treatment of HCV by direct acting antiviral agents (P<0.001), large tumor size (P<0.001), and poorly differentiated tumors (P= 0.009). The proposed HCC score can divide HCC patients into subtypes necessitating tailoring of treatment strategy according to this proposed score to target and optimally treat the aggressive subtypes. This score needs to be further validated on large number of patients with longer follow-up period.

Hepatocellular carcinoma (HCC), the primary malignancy of hepatocytes, is a diagnosis with bleak outcome. According to National Cancer Institute's SEER database, the average five-year survival rate of HCC patients in the US is 19.6% but can be as low as 2.5% for advanced, metastatic disease. When diagnosed at early stages, it is treatable with locoregional treatments including surgical resection, Radio-Frequency Ablation, Trans-Arterial Chemoembolization or liver transplantation. However, HCC is usually diagnosed at advanced stages when the tumor is unresectable, making these treatments ineffective. In such instances, systemic therapy with tyrosine kinase inhibitors (TKIs) becomes the only viable option, even though it benefits only 30% of patients, provides only a modest (~3months) increase in overall survival and causes drug resistance within 6months. HCC, like many other cancers, is highly heterogeneous making a one-size fits all option problematic. The selection of liver transplantation, locoregional treatment, TKIs or immune checkpoint inhibitors as a treatment strategy depends on the disease stage and underlying condition(s). Additionally, patients with similar disease phenotype can have different molecular etiology making treatment responses different. Stratification of patients at the molecular level would facilitate development of the most effective treatment option. With the increase in efficiency and affordability of "omics"-level analysis, considerable effort has been expended in classifying HCC at the molecular, metabolic and immunologic levels. This review examines the results of these efforts and the ways they can be leveraged to develop targeted treatment options for HCC., (© 2021 Elsevier Inc. All rights reserved.)

Malignant primary liver tumors are rare in children. Yet a wide histologic spectrum is seen, particularly in hepatoblastoma, the most common malignant liver tumor in children. Furthermore, there can be significant morphologic overlap with hepatocellular carcinoma, the second most common pediatric liver malignancy, and tumors with hybrid features of hepatoblastoma and hepatocellular carcinoma are also reported (currently placed in the provisional category of malignant hepatocellular neoplasm, not otherwise specified). This review provides detailed morphologic descriptions and updates in the evolving clinical context of these tumors, and presents recent molecular advances that may further help in accurate classification of these tumors, which is critical in their management., Competing Interests: Disclosure The author has nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Genetic variations are important drivers of carcinogenesis. It is extremely important to identify molecular distinctions between patients of the same disease for effective cancer treatment. This study aims to understand cellular and molecular differences between hepatocellular carcinoma patients carrying TP53 or CTNNB1 mutations, which could possess clinical significance. For this purpose, DNA sequencing and mRNA expression data for hepatocellular carcinoma patients were analyzed. Differentially expressed genes and the cellular processes that they are involved in were determined for TP53/CTNNB1-altered patient groups. We found that the mutations of TP53/CTNNB1 genes in the patient cohort was almost mutually exclusive and gene expression profiling in these subgroups were unique. Gene Ontology (GO) enrichment analysis of the differentially expressed genes identified several important cellular processes. In line with this, selected histone variants, histone chaperons, as well as the binding partners of TP53/CTNNB1 showed distinct enrichment levels. TP53/CTNNB1-altered patient groups laso showed different prognostic outcomes and tumor infiltration levels. In conclusion, our results strongly imply differential chromatin states and transcriptional regulation in relation to the mutational status of TP53 vs. CTNNB1, suggesting that these genes might be inducing different cellular pathways involving distinct chromatin environments during the course of carcinogenesis., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The Liver Imaging Reporting and Data System (LI-RADS) is widely adopted for the noninvasive diagnosis of hepatocellular carcinoma (HCC). Herein, possible strategies to improve the diagnostic performance of LR-5 without reducing specificity for HCC were investigated. This retrospective study included 792 patients who underwent gadoxetate disodium-enhanced magnetic resonance imaging. Hepatic observations were categorized according to LI-RADS v2018 and categories were readjusted by upgrading LR4 to LR5 using ancillary features, arterial phase hyperenhancement (APHE) interpreted with subtraction images, indication of no washout when APHE was absent, extension of washout to the transitional phase, and subthreshold growth as a major feature. Based on LI-RADS v2018, LR-5 showed a sensitivity of 71.9% and a specificity of 97.9% for the diagnosis of HCC. Category-readjusted LR-5 after upgrading LR-4 to LR-5 using ancillary features favoring HCC in particular, subthreshold growth as a major feature, extending washout to transitional phase and APHE interpreted using subtraction images showed significantly increased sensitivity (P<0.001) without decreased specificity (Ps>0.05). The sensitivity of LR-5 can be improved without loss of specificity via category readjustment using AFs favoring HCC in particular, subthreshold growth as a major feature, extending washout to transitional phase and APHE interpreted with subtraction images., Competing Interests: The authors have declared that no competing interests exist.

Hepatocellular carcinoma (HCC) is a morphologically heterogeneous tumor with variable architectural growth patterns and several distinct histologic subtypes. Large-scale attempts have been made over the past decade to identify targetable genomic alterations in HCC; however, its translation into clinical personalized care remains a challenge to precision oncology. The role of pathology is no longer limited to confirmation of diagnosis when radiologic features are atypical. Pathology is now in a position to predict the underlying molecular alteration, prognosis, and behavior of HCC. This review outlines various aspects of histopathologic diagnosis and role of pathology in cutting-edge diagnostics of HCC., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a primary liver cancer (PLC) with both hepatocytic and cholangiocytic phenotypes. Recently, the World Health Organization (WHO) updated its histological classification system for cHCC-CCA. Compared to the previous WHO histological classification system, the new version no longer recognizes subtypes of cHCC-CCA with stem cell features. Furthermore, some of these cHCC-CCA subtypes with stem cell features have been recategorized as either hepatocellular carcinomas (HCCs) or intrahepatic cholangiocarcinomas (ICCs). Additionally, distinctive diagnostic terms for intermediate cell carcinomas and cholangiolocarcinomas (previous cholangiolocellular carcinoma subtype) are now recommended. It is important for radiologists to understand these changes because of its potential impact on the imaging-based diagnosis of HCC, particularly because cHCC-CCAs frequently manifest as HCC mimickers, ICC mimickers, or as indeterminate on imaging studies. Therefore, in this review, we introduce the 2019 WHO classification system for cHCC-CCA, illustrate important imaging features characteristic of its subtypes, discuss the impact on imaging-based diagnosis of HCC, and address other important considerations., Competing Interests: The authors have no potential conflicts of interest to disclose., (Copyright © 2020 The Korean Society of Radiology.)

As liver hepatocellular carcinoma (LIHC) has high morbidity and mortality rates, improving the clinical diagnosis and treatment of LIHC is an important issue. The advent of the era of precision medicine provides us with new opportunities to cure cancers, including the accumulation of multi-omics data of cancers. Here, we proposed an integration method that involved the Fisher ratio, Spearman correlation coefficient, classified information index, and an ensemble of decision trees (DTs) for biomarker identification based on an unbalanced dataset of LIHC. Then, we obtained 34 differentially expressed genes (DEGs). The ability of the 34 DEGs to discriminate tumor samples from normal samples was evaluated by classification, and a high area under the curve (AUC) was achieved in our studied dataset and in two external validation datasets (AUC = 0.997, 0.973, and 0.949, respectively). Additionally, we also found three subtypes of LIHC, and revealed different biological mechanisms behind the three subtypes. Mutation enrichment analysis showed that subtype 3 had many enriched mutations, including tumor protein p53 (TP53) mutations. Overall, our study suggested that the 34 DEGs could serve as diagnostic biomarkers, and the three subtypes could help with precise treatment for LIHC.

Background: DNA methylation is a common chemical modification of DNA in the carcinogenesis of hepatocellular carcinoma (HCC)., Methods: In this bioinformatics analysis, 348 liver cancer samples were collected from the Cancer Genome Atlas (TCGA) database to analyse specific DNA methylation sites that affect the prognosis of HCC patients., Results: 10,699 CpG sites (CpGs) that were significantly related to the prognosis of patients were clustered into 7 subgroups, and the samples of each subgroup were significantly different in various clinical pathological data. In addition, by calculating the level of methylation sites in each subgroup, 119 methylation sites (corresponding to 105 genes) were selected as specific methylation sites within the subgroups. Moreover, genes in the corresponding promoter regions in which the above specific methylation sites were located were subjected to signalling pathway enrichment analysis, and it was discovered that these genes were enriched in the biological pathways that were reported to be closely correlated with HCC. Additionally, the transcription factor enrichment analysis revealed that these genes were mainly enriched in the transcription factor KROX. A naive Bayesian classification model was used to construct a prognostic model for HCC, and the training and test data sets were used for independent verification and testing., Conclusion: This classification method can well reflect the heterogeneity of HCC samples and help to develop personalized treatment and accurately predict the prognosis of patients.

Objectives: Hepatocellular carcinoma (HCC) is the most common type of primary liver tumour worldwide and is increasing in incidence. This study aimed to describe the clinical characteristics of HCC among Omani patients, along with its major risk factors, outcomes and the role of surveillance., Methods: This retrospective case-series study was conducted between January 2008 and December 2015 at the three main tertiary care hospitals in Oman. All adult Omani patients diagnosed with HCC and visited these hospitals during the study period were included. Relevant data were collected from the patients' electronic medical records., Results: A total of 284 HCC patients were included in the analysis. The mean age was 61.02 ± 11.41 years and 67.6% were male. The majority had liver cirrhosis (79.9%), with the most common aetiologies being chronic hepatitis C (46.5%) and B (43.2%). Only 13.7% of cases were detected by the HCC surveillance programme. Approximately half of the patients (48.5%) had a single liver lesion and 31.9% had a liver tumour of >5 cm in size. Approximately half (49.2%) had alpha-fetoprotein levels of ≥200 ng/mL. The majority (72.5%) were diagnosed using multiphase computed tomography alone. Less than half of the patients (48.9%) were offered one or more HCC treatment modalities., Conclusion: The majority of Omani HCC patients were male and had cirrhosis due to viral hepatitis. In addition, few patients were identified by the national surveillance programme and presented with advanced disease precluding therapeutic or even palliative treatment., Competing Interests: CONFLICT OF INTEREST The authors declare no conflicts of interest., (© Copyright 2020, Sultan Qaboos University Medical Journal, All Rights Reserved.)

Objective: The aim of this study was to develop quantitative feature-based models from histopathological images to distinguish hepatocellular carcinoma (HCC) from adjacent normal tissue and predict the prognosis of HCC patients after surgical resection., Methods: A fully automated pipeline was constructed using computational approaches to analyze the quantitative features of histopathological slides of HCC patients, in which the features were extracted from the hematoxylin and eosin (H&E)-stained whole-slide images of HCC patients from The Cancer Genome Atlas and tissue microarray images from West China Hospital. The extracted features were used to train the statistical models that classify tissue slides and predict patients' survival outcomes by machine-learning methods., Results: A total of 1733 quantitative image features were extracted from each histopathological slide. The diagnostic classifier based on 31 features was able to successfully distinguish HCC from adjacent normal tissues in both the test [area under the receiver operating characteristic curve (AUC) 0.988] and external validation sets (AUC 0.886). The random-forest prognostic model using 46 features was able to significantly stratify patients in each set into longer- or shorter-term survival groups according to their assigned risk scores. Moreover, the prognostic model we constructed showed comparable predicting accuracy as TNM staging systems in predicting patients' survival at different time points after surgery., Conclusions: Our findings suggest that machine-learning models derived from image features can assist clinicians in HCC diagnosis and its prognosis prediction after hepatectomy.

Background: To date, no standard follow-up guidelines exist regarding patients receiving ablation for initial recurrent hepatocellular carcinoma (HCC). We aimed to explore whether intensive follow-up could benefit these patients., Methods: We reviewed the clinical data of patients who received complete ablation for initial HCC recurrence after curative treatments in our institution from January 2005 to June 2017. Risk factors for second recurrence of HCC were identified by univariate and multivariate analyses. Patients were classified into low- and high-risk groups according to the outcome of the classification and regression model. The patients were further categorized into short- (< 3 months) and long-interval (3-6 months) follow-up subgroups based on their surveillance in the first 2 years after complete ablation for initial recurrence. The Kaplan-Meier method with log-rank test was performed to compare the overall survival (OS) based on follow-up intervals in each risk group. We also validated our results by stratifying patients into subgroups with different numbers of risk factors and comparing the OS between patients with different follow-up intervals., Results: A total of 361 patients were enrolled. The risk factors for secondary recurrence included the Barcelona Clinic Liver Cancer (BCLC) stage at initial recurrence and first recurrence-free survival after curative treatments for primary HCC (p < 0.001 and p = 0.002). Two risk groups (low and high) were identified. In both the low- and high-risk groups, the OS of patients was not associated with intervals of follow-up (p = 0.29 and 0.49). No significant difference was found in the rates of BCLC 0/A stage, tumor location or curative treatments for the second recurrence by different follow-up intervals in each risk group (p = 0.34 and 0.87; p = 0.69 and 0.97). The same tendency was found in subgroups with 0/1/2 risk factors for secondary recurrence during validation., Conclusion: The long-interval follow-up did not compromise the survival of patients with complete ablation for initial recurrent HCC.

Background The recently described "macrotrabecular-massive" (MTM) histologic subtype of hepatocellular carcinoma (HCC) (MTM-HCC) represents an aggressive form of HCC and is associated with poor survival. Purpose To investigate whether preoperative MRI can help identify MTM-HCCs in patients with HCC. Materials and Methods This retrospective study included patients with HCC treated with surgical resection between January 2008 and February 2018 and who underwent preoperative multiphase contrast material-enhanced MRI. Least absolute shrinkage and selection operator (LASSO)-penalized and multivariable logistic regression analyses were performed to identify clinical, biologic, and imaging features associated with the MTM-HCC subtype. Early recurrence (within 2 years) and overall recurrence were evaluated by using Kaplan-Meier analysis. Multivariable Cox regression analysis was performed to determine predictors of early and overall recurrence. Results One hundred fifty-two patients (median age, 64 years; interquartile range, 56-72 years; 126 men) with 152 HCCs were evaluated. Twenty-six of the 152 HCCs (17%) were MTM-HCCs. LASSO-penalized logistic regression analysis identified substantial necrosis, high serum α-fetoprotein (AFP) level (>100 ng/mL), and Barcelona Clinic Liver Cancer (BCLC) stage B or C as independent features associated with MTM-HCCs. At multivariable analysis, substantial necrosis (odds ratio = 32; 95% confidence interval [CI] = 8.9, 114; P < .001), high serum AFP level (odds ratio = 4.4; 95% CI = 1.3, 16; P = .02), and BCLC stage B or C (odds ratio = 4.2; 95% CI = 1.2, 15; P = .03) were independent predictors of MTM-HCC subtype. Substantial necrosis helped identify 65% (17 of 26; 95% CI: 44%, 83%) of MTM-HCCs (sensitivity) with a specificity of 93% (117 of 126; 95% CI: 87%, 97%). In adjusted models, only the presence of satellite nodules was independently associated with both early (hazard ratio = 3.7; 95% CI: 1.5, 9.4; P = .006) and overall (hazard ratio = 3.0; 95% CI: 1.3, 7.2; P = .01) tumor recurrence. Conclusion At multiphase contrast-enhanced MRI, substantial necrosis helped identify macrotrabecular-massive hepatocellular carcinoma subtype with high specificity. © RSNA, 2020.

Background/aims: Patients with an intermediate stage of hepatocellular carcinoma (HCC) represent a highly heterogeneous population; therefore, many models have been proposed to predict the survival of these patients. The aim of this study was to evaluate the prognostic performance of a novel subclassification for tumors classified as Barcelona Clinic Liver Cancer (BCLC) stage B using the Model to Estimate Survival in Ambulatory HCC patients (MESIAH)., Methods: This analysis was based on 377 patients with HCC treated at Seoul National University Hospital (training cohort) and 189 patients at the Soonchunhyang University Bucheon Hospital (validation cohort). Four subclassification systems were tested: MESIAH; original BCLC B subclassification (B1, B2, B3, and B4); modified model A (B1, B2, and B3+B4); and modified model B (B1, B2+B3, and B4)., Results: Median survival progressively decreased from stage B1 through stages B2 to B3 according to the new MESIAH subclassification (p<0.001). Moreover, significantly different survival among contiguous stages was observed. In the multivariable Cox regression, the MESIAH subclassification was an independent predictor of overall survival (p<0.001). In terms of discrimination and calibration, MESIAH performed better than the original BCLC B subclassification, modified model A and modified model B., Conclusions: The MESIAH model would be an effective tool for stratifying heterogeneous BCLC stage B cancer, and the ability of this model to predict survival is better than that of the other previously proposed models.

Cell heterogeneity of tumor tissues is one of the causes of cancer recurrence after chemotherapy. Cell subtype identification in tumor tissues of specific cancer is critical for precision medicine and prognosis. As the structural and functional components of cells, lipids are closely related to the apparent morphology of cells. They are potential biomarkers of species of cancers and can be used to classify different cancer cell types, but it remains a challenge to establish a stable cell differentiation model and extend it to tumor tissue cell subtype differentiation. Here we describe a lipid profiling method based on nanostructure assisted laser desorption/ionization mass spectrometry (NALDI-MS), which could classify five hepatocellular carcinoma (HCC) cell lines and discriminate subtype of mixed cells and tumor tissues. The NALDI target was patterned with array of sample spots containing vertical silicon nanowires (Si NWs). Owing to its high ability to absorb laser energy, the vertical Si NWs can help to generate abundant lipid ions of cell extracts without need of organic matrix. Combined with statistical analysis methods, twenty-two ion peaks distributed in four MS peak clusters were selected as potential biomarkers to distinguish the subtype of the five HCC cell lines. Peak normalization was performed within each MS peak cluster to reduce the variation of peak intensity in batch to batch analysis. Compared to full-spectrum normalization method, the inner-cluster normalization method could help to distinguish cell subtype more stably and accurately. The molecular structure of these biomarkers was identified and sorted into two classes including phosphatidylcholine (PE, PI, PG, PA, PS) and glycosphingolipid (LacCer, ST). Furthermore, the established method was successfully applied to identify the major HCC cell subtype in mixed cell samples and xenograft tumor tissues as well as drug response test, showing great potential in precision medicine and prognosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

We aimed to develop an HCC classification model based on the integrated gene expression and methylation data of methylation-driven genes. Genome, methylome, transcriptome, proteomics and clinical data of 369 HCC patients from The Cancer Genome Atlas Network were retrieved and analyzed. Consensus clustering of the integrated gene expression and methylation data from methylation-driven genes identified 4 HCC subclasses with significant prognosis difference. HS1 was well differentiated with a favorable prognosis. HS2 had high serum α-fetoprotein level that was correlated with its poor outcome. High percentage of CTNNB1 mutations corresponded with its activation in WNT signaling pathway. HS3 was well differentiated with low serum α-fetoprotein level and enriched in metabolism signatures, but was barely involved in immune signatures. HS3 also had high percentage of CTNNB1 mutations and therefore enriched in WNT activation signature. HS4 was poorly differentiated with the worst prognosis and enriched in immune-related signatures, but was barely involved in metabolism signatures. Subsequently, a prediction model was developed. The prediction model had high sensitivity and specificity in distributing potential HCC samples into groups identical with the training cohort. In conclusion, this work sheds light on HCC patient prognostication and prediction of response to targeted therapy.

Outcomes for patients with hepatocellular carcinoma (HCC) remain poor because the condition is often unresponsive to the available treatments. Consequently, the early and precise diagnosis of HCC is crucial to achieve improvements in prognosis. For patients with chronic liver disease, the assessment of liver fibrosis is also important to ascertain both the staging of fibrosis and the risk of HCC occurrence. Early HCC was first described in 1991 in Japan and was defined internationally in 2009. As the concept of early HCC spread, the multistage hepatocarcinogenesis process became accepted. Consequently, improvements in imaging technology made the early diagnosis of HCC possible. At present, the most appropriate therapeutic strategy for HCC is determined using an integrated staging system that assesses the tumor burden, the degree of liver dysfunction and the patient performance status; however, pathological and molecular features are not taken into account. The recent introduction of several new therapeutic agents will change the treatment strategy for HCC. Against this background, HCC subclassification based on tumor cellular and microenvironmental characteristics will become increasingly important. In this review, we give an overview of how pathological analysis contributes to understanding the development and progression of HCC and establishing a precision diagnosis of HCC., (© 2020 Japanese Society of Pathology and John Wiley & Sons Australia, Ltd.)

Hepatocellular carcinoma (HCC) arises from hepatocytes through the sequential accumulation of multiple genomic and epigenomic alterations resulting from Darwinian selection. Genes from various signalling pathways such as telomere maintenance, Wnt/β-catenin, P53/cell cycle regulation, oxidative stress, epigenetic modifiers, AKT/mTOR and MAP kinase are frequently mutated in HCC. Several subclasses of HCC have been identified based on transcriptomic dysregulation and genetic alterations that are closely related to risk factors, pathological features and prognosis. Undoubtedly, integration of data obtained from both preclinical models and human studies can help to accelerate the identification of robust predictive biomarkers of response to targeted biotherapy and immunotherapy. The aim of this review is to describe the main advances in HCC in terms of molecular biology and to discuss how this knowledge could be used in clinical practice in the future., (Copyright © 2019 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Objective: This study aimed to classify hepatocellular carcinomas (HCCs) according to their diameter using statistic technology and evaluate the prognosis of the classified groups after the combined use of transarterial chemoembolization (TACE) and radiofrequency ablation (RFA)., Materials and Methods: Electronic medical records of 128 consecutive patients who underwent TACE-RFA as the initial treatment for HCC from January 2010 to April 2018 were retrospectively analyzed. TACE was initially performed with subsequent RFA performed after 3-7 days. The decision tree model was used to classify overall survival (OS), progression-free survival (PFS), local recurrence rate (LRR), and treatment complications in HCC., Results: The tumors were divided into three groups of sizes ≤2.9 cm, 2.9-4.8 cm, and >4.8 cm. The group of tumors >4.8 cm showed inferior OS, PFS, and LRR than the other two groups (P < 0.05) on long-term follow-up but not in thefirst 6 months (P > 0.05). The groups of tumors ≤2.9 cm and 2.9-4.8 cm showed no statistically significant difference in OS, PFS, and LRR (P > 0.05)., Conclusions: The cutoff points of 2.9 and 4.8 cm were achieved using the objective decision tree model rather than the artificial division of 3 and 5 cm. The prognosis was not significantly different between the groups of tumors ≤2.9 cm and 2.9-4.8 cm, and the prognosis of the two groups was better than the group of tumors >4.8 cm in the long-term follow-up but not in thefirst 6 months., Competing Interests: None

Objective: To explore the specific contrast-enhanced ultrasound (CEUS) features of hepatocellular adenomas (HCA) according to their pathological molecular classifications., Methods & Materials: In this retrospective study, fifty-three histopathologically proved HCA lesions (mean size, 39.7±24.9 mm) were included. Final histopathological diagnosis of HCA lesions were identified by surgical resection (n = 51) or biopsy (n = 2) specimens. CEUS imaging features were compared among four subgroups according to World Health Organization (WHO) 2019 pathological molecular classifications standards. Analysis of variance (ANOVA) were used for statistical analysis of continuous variables. Fisher's exact test were used for categorical variables. The sensitivity (SE), specificity (SP), and accuracy of CEUS feature in diagnosis of each HCA subtype were calculated and compared., Results: Final histopathological diagnosis included HNF-1α inactivated HCAs (H-HCA, n = 12), β-catenin activated HCAs (B-HCA, n = 8), inflammatory HCAs (I-HCA, n = 31), and unclassified HCAs (U-HCA, n = 2). During arterial phase of CEUS, all HCAs were hyper-enhanced, 66.6% (8/12) of H-HCAs and 50% (4/8) of B-HCAs displayed complete hyperenhancement, whereas 58.0% (18/31) of I-HCAs showed centripetal filling hyperenhancement pattern (P = 0.016). Hyper-enhanced subcapsular arteries could be detected in 64.5% (20/31) I-HCAs during early arterial phase. During portal venous and late phase, sustained hyper- or iso-enhancement were observed in 91.7% (11/12) of H-HCAs, while most of I-HCAs (61.3%, 19/31) and B-HCAs (7/8, 87.5%) were hypo-enhanced (P = 0.000). Central unenhanced areas were most commonly observed in I-HCAs (29.0%, 9/31) (P = 0.034)., Conclusion: Depending on its unique imaging features including enhancement filling pattern, hyper-enhanced subcapsular artery and presence of washout, CEUS might provide helpful diagnostic information for preoperative prediction of various HCA molecular subtypes.

Background and Aim: Patients with Barcelona Clinic Liver Cancer stage B hepatocellular carcinoma are a heterogeneous population, and the classifications available could not predict the prognosis accurately. Herein, we proposed a new substage classification method, Scoring Method for Intermediate Stage, for precise classification and clinical guidance in hepatocellular carcinoma patients within Barcelona Clinic Liver Cancer stage B., Methods: A total of 1026 stage B patients of hepatocellular carcinoma who underwent transcatheter arterial chemoembolization as a first-line treatment in Liver Cancer Institute, Zhongshan Hospital, Fudan University were retrospectively enrolled. The prognostic evaluation ability of the new substage classification criteria was analyzed, in comparison with the existing substage classification criteria., Results: Using Scoring Method for Intermediate Stage, 1026 stage B patients were subclassified into three subgroups, based on Child-Pugh score and up-to-7 grade, as B1 (scoring 2), B2 (scoring 3 or 4), and B3 (scoring 5 or 6). The median survival time of the three substages was 29 (95% confidence interval [CI]: 25-36), 19 (95% CI: 16-21), and 10 (95% CI: 8-12) months, respectively. More favorable discrimination efficacy was identified by the new criteria in comparison with the existing substage classification criteria, including Bolondi, Kinki, MICAN, and Kim's criteria. Moreover, multivariate analyses indicated that the novel classification was highly associated with prognosis (Hazard ratio(s) = 1.63, 95% CI: 1.43-1.86, P < 0.001)., Conclusions: Scoring Method for Intermediate Stage demonstrates satisfying capacity in classifying patients with stage B hepatocellular carcinoma and predicting prognosis., (© 2019 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Many advances have been made in the imaging diagnosis and in the histopathological evaluation of HCC. However, the classic imaging and histopathological features of HCC are still inadequate to define patient's prognosis. We aimed to find the link between new proposed morphovascular patterns of hepatocellular carcinoma (HCC) and magnetic resonance imaging (MRI) features to identify pre-operatory markers of biologically aggressive HCC. Thirty-nine liver nodules in 22 patients were consecutively identified. Histopathological analysis and immunohistochemistry for CD34 and Nestin were performed to identify the four different HCC morphovascular patterns. MRI was performed using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid. Three out of four morphovascular HCC patterns showed peculiar MRI features: in particular Pattern D (solid aggressive HCCs with CD34+/Nestin+ new-formed arteries) were isointense on T1-WI in 83% of cases and hyperintense on T2-WI in 50%. Five histologically-diagnosed HCC were diagnosed as non-malignant nodules on MRI due to their early vascularization and low aggressiveness (Pattern A). The comparison between histology and MRI confirms that a subclassification of HCC is possible in a pre-operatory setting. MRI seems to reinforce once more the identity of the different morphovascular HCC patterns and the possibility to pre-operatively identify HCCs with features of biological aggressiveness.

Combined hepatocellular-cholangiocarcinoma (cHCC-CC) and intrahepatic cholangiocarcinoma (ICC) are classified into one category, but comparison of prognosis of the two carcinomas remains controversial. The aim of the current study was to investigate surgical outcomes for patients with ICC or cHCC-CC who underwent resection in order to elucidate whether the classification of ICC and cHCC-CC is justified. Subjects were 61 patients with ICC and 29 patients with cHCC-CC who underwent liver resection from 2001 to 2017. Clinic-pathological data from the two groups were compared. Tumor number and vascular invasion were independent risk factors for recurrence-free survival (RFS) in both groups (P < .001 for both). Of note, for patients with ICC, tumor cut-off size of 5 cm showed statistical significance in median RFS (>5 cm vs ≤5 cm, 0.5 years vs 4.0 years, P = .003). For patients with cHCC-CC, tumor cut-off size of 2 cm showed statistical significance in median RFS (>2 cm vs ≤2 cm, 0.6 years vs 2.6 years, P = .038). The median RFS of patients with cHCC-CC was 0.9 years (95% confidence interval: 0.3-1.6), which was poorer than that of patients with ICC (1.3 years, 0.5-2.1) (P = .028); the rate of RFS at 5 years was 0% and 37.7% respectively. Our study supports the concept of classifying ICC and cHCC-CC into different categories because of a significant difference in RFS between the two., (© 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: Although the Barcelona Clinic Liver Cancer (BCLC) staging system has been largely adopted in clinical practice, recent studies have questioned the prognostic stratification of this classification schema, as well as the proposed treatment allocation of patients with a single large tumor., Methods: Patients who underwent curative-intent hepatectomy for histologically proven hepatocellular carcinoma (HCC) between 1998 and 2017 were identified using an international multi-institutional database. Overall survival (OS) among patients with BCLC stage 0, A, and B was examined. Patients with a single large tumor were classified as BCLC stage A1 and were independently assessed., Results: Among 814 patients, 68 (8.4%) were BCLC-0, 310 (38.1%) were BCLC-A, 279 (34.3%) were BCLC-A1, and 157 (19.3%) were BCLC-B. Five-year OS among patients with BCLC stage 0, A, A1, and B HCC was 86.2%, 69.0%, 56.9%, and 49.9%, respectively (p < 0.001). Among patients with very early- and early-stage HCC (BCLC 0, A, and A1), patients with BCLC stage A1 had the worst OS (p = 0.0016). No difference in survival was noted among patients undergoing surgery for BCLC stage A1 and B HCC (5-year OS: 56.9% vs. 49.9%; p = 0.259) even after adjusting for competing factors (hazard ratio 0.83, 95% confidence interval 0.54-1.28; p = 0.40)., Conclusion: Prognosis following liver resection among patients with BCLC-A1 HCC was similar to patients presenting with BCLC-B tumors. Surgery provided acceptable long-term outcomes among select patients with BCLC-B HCC. Designation into BCLC stage B should not be considered an a priori contraindication to surgery.

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer, both at the molecular and histological level. High-throughput sequencing and gene expression profiling have identified distinct transcriptomic subclasses and numerous recurrent genetic alterations; several HCC subtypes characterised by histological features have also been identified. HCC phenotype appears to be closely related to particular gene mutations, tumour subgroups and/or oncogenic pathways. Non-proliferative tumours display a well-differentiated phenotype. Among this molecular subgroup, CTNNB1-mutated HCCs constitute a homogeneous subtype, exhibiting cholestasis and microtrabecular and pseudoglandular architectural patterns. Another non-proliferative subtype has a gene expression pattern similar to that of mature hepatocytes (G4) and displays a steatohepatitic phenotype. In contrast, proliferative HCCs are most often poorly differentiated, and notably include tumours with progenitor features. A novel morphological variant of proliferative HCC - designated "macrotrabecular-massive" - was recently shown to be associated with angiogenesis activation and poor prognosis. Altogether, these findings may help to translate our knowledge of HCC biology into clinical practice, resulting in improved precision medicine for patients with this highly aggressive malignancy. This manuscript reviews the most recent data in this exciting field, discussing future directions and challenges., (Copyright © 2019 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.)

Current clinical classification of hepatocellular carcinoma (HCC) is unable to predict prognosis efficiently. Our aim is to classify HCC into clinically/biologically relevant subtypes according to stromal factors. We detected seven types of stromal features in tumors from 161 HCC patients by immunohistochemical staining and Hematoxylin-eosin staining. Five stromal features were selected out of seven types of stromal features to construct stromal type based on LASSO COX regression model. Then, integrating multiple clinicopathologic characteristics and stromal type, we built two nomograms for overall survival (OS) and disease-free survival (DFS). Further validation of the stromal type and nomograms were performed in the testing cohort (n = 160) and validation cohort (n = 120). Using the LASSO model, we classified HCC patients into stromal type A subgroup (CD34 low TIL-stromal-ratio high Stromal-tumor-ratio low α-SMA weak Stroma mature ) and stromal type B subgroup (CD34 high TIL-stromal-ratio low Stromal-tumor-ratio high α-SMA strong Stroma immature ). The stromal type was an independent prognostic factor for OS and DFS in the training, testing and validation cohorts. Two nomograms (for OS and DFS) that integrated the stromal type and clinicopathologic risk factors also showed good predictive accuracy and discriminatory power. In addition, immune cell recruitment in the tumor microenvironment (TME) was conditioned by the tumor stromal type. In conclusion, the newly developed tumor stromal type was an effective predictor of OS and DFS. Furthermore, the stromal type is associated with the immune phenotype in the TME.