Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling

Deep Learning Model for Predicting Proliferative Hepatocellular Carcinoma Using Dynamic Contrast-Enhanced MRI: Implications for Early Recurrence Prediction Following Radical Resection

RATIONALE AND OBJECTIVES

The proliferative nature of hepatocellular carcinoma (HCC) is closely related to early recurrence following radical resection. This study develops and validates a deep learning (DL) prediction model to distinguish between proliferative and non-proliferative HCCs using dynamic contrast-enhanced MRI (DCE-MRI), aiming to refine preoperative assessments and optimize treatment strategies by assessing early recurrence risk.

MATERIALS AND METHODS

In this retrospective study, 355 HCC patients from two Chinese medical centers (April 2018-February 2023) who underwent radical resection were included. Patient data were collected from medical records, imaging databases, and pathology reports. The cohort was divided into a training set (n = 251), an internal test set (n = 62), and external test sets (n = 42). A DL model was developed using DCE-MRI images of primary tumors. Clinical and radiological models were generated from their respective features, and fusion strategies were employed for combined model development. The discriminative abilities of the clinical, radiological, DL, and combined models were extensively analyzed. The performances of these models were evaluated against pathological diagnoses, with independent and fusion DL-based models validated for clinical utility in predicting early recurrence.

RESULTS

The DL model, using DCE-MRI, outperformed clinical and radiological feature-based models in predicting proliferative HCC. The area under the curve (AUC) for the DL model was 0.98, 0.89, and 0.83 in the training, internal validation, and external validation sets, respectively. The AUCs for the combined DL and clinical feature models were 0.99, 0.86, and 0.83 in these sets, while the AUCs for the combined DL, clinical, and radiological model were 0.99, 0.87, and 0.8, respectively. Among models predicting early recurrence, the DL plus clinical features model showed superior performance.

CONCLUSION

The DL-based DCE-MRI model demonstrated robust performance in predicting proliferative HCC and stratifying patient risk for early postoperative recurrence. As a non-invasive tool, it shows promise in enhancing decision-making for individualized HCC management strategies.

Genomic biomarkers to predict response to atezolizumab plus bevacizumab immunotherapy in hepatocellular carcinoma: Insights from the IMbrave150 trial

BACKGROUND/AIMS

Combination immunotherapy, exemplified by atezolizumab plus bevacizumab, has become the standard of care for inoperable hepatocellular carcinoma (HCC). However, the lack of predictive biomarkers and limited understanding of response mechanisms remain a challenge.

METHODS

Using data from the IMbrave150plus cohort, we applied an immune signature score (ISS) predictor to stratify HCC patients treated with atezolizumab plus bevacizumab or with sorafenib alone into potential high and low response groups. By applying multiple statistical approaches including a Bayesian covariate prediction algorithm, we refined the signature to 10 key genes (ISS10) for clinical use while maintaining similar predictive power to the full model. We further validated ISS10 in an independent HCC cohort treated with nivolumab plus ipilimumab.

RESULTS

The study identified a significant association between the ISS and treatment response. Among patients classified as high responders, those treated with the atezolizumab plus bevacizumab combination exhibited improved overall and progression-free survival as well as better objective response rate compared to those treated with sorafenib. We also observed a significant correlation between ISS10 and response to nivolumab plus ipilimumab treatment. Analysis of immune cell subpopulations revealed distinct characteristics associated with ISS subtypes. In particular, the ISS10 high subtype displayed a more favorable immune environment with higher proportions of antitumor macrophages and activated T-cells, potentially explaining its better response.

CONCLUSION

Our study suggests that ISS and ISS10 are promising predictive biomarkers for enhanced therapeutic outcomes in HCC patients undergoing combination immunotherapy. These markers are crucial for refining patient stratification and personalized treatment approaches to advance the effectiveness of standard-of-care regimens.

A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) is a highly fatal cancer characterized by high intra-tumor heterogeneity (ITH). A panoramic understanding of its tumor evolution, in relation to its clinical trajectory, may provide novel prognostic and treatment strategies.

METHODS

Through the Asia-Pacific Hepatocellular Carcinoma trials group (NCT03267641), we recruited one of the largest prospective cohorts of patients with HCC, with over 600 whole genome and transcriptome samples from 123 treatment-naïve patients.

RESULTS

Using a multi-region sampling approach, we revealed seven convergent genetic evolutionary paths governed by the early driver mutations, late copy number variations and viral integrations, which stratify patient clinical trajectories after surgical resection. Furthermore, such evolutionary paths shaped the molecular profiles, leading to distinct transcriptomic subtypes. Most significantly, although we found the coexistence of multiple transcriptomic subtypes within certain tumors, patient prognosis was best predicted by the most aggressive cell fraction of the tumor, rather than by overall degree of transcriptomic ITH level - a phenomenon we termed the 'bad apple' effect. Finally, we found that characteristics throughout early and late tumor evolution provide significant and complementary prognostic power in predicting patient survival.

CONCLUSIONS

Taken together, our study generated a comprehensive landscape of evolutionary history for HCC and provides a rich multi-omics resource for understanding tumor heterogeneity and clinical trajectories.

IMPACT AND IMPLICATIONS

This prospective study, utilizing comprehensive multi-sector, multi-omics sequencing and clinical data from surgically resected hepatocellular carcinoma (HCC), reveals critical insights into the role of tumor evolution and intra-tumor heterogeneity (ITH) in determining the prognosis of HCC. These findings are invaluable for oncology researchers and clinicians, as they underscore the influence of distinct evolutionary paths and the 'bad apple' effect, where the most aggressive tumor fraction dictates disease progression. These insights not only enhance prognostic accuracy post-surgical resection but also pave the way for personalized treatment strategies tailored to specific tumor evolutionary and transcriptomic profiles. The coexistence of multiple subtypes within the same tumor prompts a re-appraisal of the utilities of depending on single samples to represent the entire tumor and suggests the need for clinical molecular imaging. This research thus marks a significant step forward in the clinical understanding and management of HCC, underscoring the importance of integrating tumor evolutionary dynamics and multi-omics biomarkers into therapeutic decision-making.

CLINICAL TRIAL NUMBER

NCT03267641 (Observational cohort).

LOCC: a novel visualization and scoring of cutoffs for continuous variables with hepatocellular carcinoma prognosis as an example

BACKGROUND

The interpretation of large datasets, such as The Cancer Genome Atlas (TCGA), for scientific and research purposes, remains challenging despite their public availability. In this study, we focused on identifying gene expression profiles most relevant to patient prognosis and aimed to develop a method and database to address this issue. To achieve this, we introduced Luo's Optimization Categorization Curve (LOCC), an innovative tool for visualizing and scoring continuous variables against dichotomous outcomes. To demonstrate the efficacy of LOCC using real-world data, we analyzed gene expression profiles and patient data from TCGA hepatocellular carcinoma samples.

RESULTS

To showcase LOCC, we demonstrate an optimal cutoff for E2F1 expression in hepatocellular carcinoma, which was subsequently validated in an independent cohort. Compared to ROC curves and their AUC, LOCC offered a superior description of the predictive value of E2F1 expression across various cancer types. The LOCC score, comprised of factors representing significance, range, and impact of the biomarker, facilitated the ranking of all gene expression profiles in hepatocellular carcinoma, aiding in the evaluation and understanding of previously published prognostic gene signatures. We also demonstrate that LOCC does not have the same assumptions required of Cox proportional hazards modeling for accurate analysis. Repeated sampling demonstrated that LOCC scores outperformed ROC's AUC in discriminating predictors from non-predictors. Additionally, gene set enrichment analysis revealed significant associations between certain genes and prognosis, such as E2F target genes and G2M checkpoint with poor prognosis, and bile acid metabolism and oxidative phosphorylation with good prognosis.

CONCLUSION

In summary, we present LOCC as a novel visualization tool for the analysis of gene expression in cancer, particularly for understanding and selecting cutoffs. Our findings suggest that LOCC scores, which effectively rank genes based on their prognostic potential, represent a more suitable approach than ROC curves and Cox proportional hazard for prognostic modeling and understanding in cancer gene expression analysis. LOCC holds promise as an invaluable tool for advancing precision medicine and furthering biomarker research. Further research regarding multivariable integration and validation will help LOCC reach its full potential and establish its utility across diverse cancer types and clinical settings.

Specific features of ß-catenin-mutated hepatocellular carcinomas

CTNNB1, encoding the ß-catenin protein, is a key oncogene contributing to liver carcinogenesis. Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer in adult, representing the third leading cause of cancer-related death. Aberrant activation of the Wnt/ß-catenin pathway, mainly due to mutations of the CTNNB1 gene, is observed in a significant subset of HCC. In this review, we first resume the major recent advances in HCC classification with a focus on CTNNB1-mutated HCC subclass. We present the regulatory mechanisms involved in β-catenin stabilisation, transcriptional activity and binding to partner proteins. We then describe specific phenotypic characteristics of CTNNB1-mutated HCC thanks to their unique gene expression patterns. CTNNB1-mutated HCC constitute a full-fledged subclass of HCC with distinct pathological features such as well-differentiated cells with low proliferation rate, association to cholestasis, metabolic alterations, immune exclusion and invasion. Finally, we discuss therapeutic approaches to target ß-catenin-mutated liver tumours and innovative perspectives for future drug developments.

Identification of two heterogeneous subtypes of hepatocellular carcinoma with distinct pathway activities and clinical outcomes based on gene set variation analysis

Background

High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment.

Methods

Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments.

Results

We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells.

Conclusion

The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets.

HN1-mediated activation of lipogenesis through Akt-SREBP signaling promotes hepatocellular carcinoma cell proliferation and metastasis

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide, with more than 800,000 deaths each year, and its 5-year survival rate is less than 12%. The role of the HN1 gene in HCC has remained elusive, despite its upregulation in various cancer types. In our investigation, we identified HN1's heightened expression in HCC tissues, which, upon overexpression, fosters cell proliferation, migration, and invasion, unveiling its role as an oncogene in HCC. In addition, silencing HN1 diminished the viability and metastasis of HCC cells, whereas HN1 overexpression stimulated their growth and invasion. Gene expression profiling revealed HN1 silencing downregulated 379 genes and upregulated 130 genes, and suppressive proteins associated with the lipogenic signaling pathway networks. Notably, suppressing HN1 markedly decreased the expression levels of SREBP1 and SREBP2, whereas elevating HN1 had the converse effect. This dual modulation of HN1 affected lipid formation, hindering it upon HN1 silencing and promoting it upon HN1 overexpression. Moreover, HN1 triggers the Akt pathway, fostering tumorigenesis via SREBP1-mediated lipogenesis and silencing HN1 effectively curbed HCC tumor growth in mouse xenograft models by deactivating SREBP-1, emphasizing the potential of HN1 as a therapeutic target, impacting both external and internal factors, it holds promise as an effective therapeutic strategy for HCC.

The Spatial Extracellular Proteomic Tumor Microenvironment Distinguishes Molecular Subtypes of Hepatocellular Carcinoma

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.

Higher inflammatory response in hepatocellular carcinoma is associated with immune cell infiltration and a better outcome

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) often develops from chronic liver inflammation. Inflammation within a tumor can either promote cancer progression or activate an immune response against it. This study aims to determine the clinical significance of enhanced inflammation in HCC.

METHODS

Data from 655 HCC patients across four cohorts (TCGA, GSE6764, GSE76427, GSE89377) were examined. Inflammatory response was quantified using a scoring system derived from the gene set variation analysis of the "INFLAMMATORY_RESPONSE" gene set.

RESULTS

A stepwise increase in inflammatory response was noted from normal liver to cirrhosis, with consistently lower levels in HCC across both GSE6764 and GSE89377 cohorts (both p < 0.001). Similar trends were observed in interferon response, pathways such as IL6/JAK/STAT3 and complement signaling, coagulation cascade, and allograft rejection (all p < 0.02). HCCs with high inflammatory response were associated with increased immune cell infiltrations (p < 0.01) and cytolytic activity (p < 0.001). Interestingly, these HCCs had reduced mutation rates, no relationship with cell proliferation, and displayed both immune responses and pro-cancerous signals including epithelial-mesenchymal transition, KRAS, and hypoxia. Further, a high inflammatory score correlated with improved disease-free survival in TCGA (p = 0.034) and overall survival in GSE76427 (p = 0.008).

CONCLUSION

HCC with higher levels of inflammatory response demonstrated increased immune cell infiltration, enhanced immune-related and other pro-cancerous-related signaling, and showed a trend toward a better patient prognosis.

Syngeneic mouse model of YES-driven metastatic and proliferative hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a disease of high unmet medical need that has become a global health problem. The development of targeted therapies for HCC has been hindered by the incomplete understanding of HCC pathogenesis and the limited number of relevant preclinical animal models. We recently unveiled a previously uncharacterized YES kinase (encoded by YES1)-dependent oncogenic signaling pathway in HCC. To model this subset of HCC, we established a series of syngeneic cell lines from liver tumors of transgenic mice expressing activated human YES. The resulting cell lines (referred to as HepYF) were enriched for expression of stem cell and progenitor markers, proliferated rapidly, and were characterized by high SRC family kinase (SFK) activity and activated mitogenic signaling pathways. Transcriptomic analysis indicated that HepYF cells are representative of the most aggressive proliferation class G3 subgroup of HCC. HepYF cells formed rapidly growing metastatic tumors upon orthotopic implantation into syngeneic hosts. Treatment with sorafenib or the SFK inhibitor dasatinib markedly inhibited the growth of HepYF tumors. The new HepYF HCC cell lines provide relevant preclinical models to study the pathogenesis of HCC and test novel small-molecule inhibitor and immunotherapy approaches.

Integrated Exploration of Epigenetic Dysregulation Reveals a Stemness/EMT Subtype and MMP12 Linked to the Progression and Prognosis in Hepatocellular Carcinoma

Epigenetic dysregulation drives aberrant transcriptional programs playing a critical role in hepatocellular carcinoma (HCC), which may provide novel insights into the heterogeneity of HCC. This study performed an integrated exploration on the epigenetic dysregulation of miRNA and methylation. We discovered and validated three patterns endowed with gene-related transcriptional traits and clinical outcomes. Specially, a stemness/epithelial-mesenchymal transition (EMT) subtype was featured by immune exhaustion and the worst prognosis. Besides, MMP12, a characteristic gene, was highly expressed in the stemness/EMT subtype, which was verified as a pivotal regulator linked to the unfavorable prognosis and further proven to promote tumor proliferation, invasion, and metastasis in vitro experiments. Proteomic analysis by mass spectrometry sequencing also indicated that the overexpression of MMP12 was significantly associated with cell proliferation and adhesion. Taken together, this study unveils innovative insights into epigenetic dysregulation and identifies a stemness/EMT subtype-specific gene, MMP12, correlated with the progression and prognosis of HCC.

Recent Advances in Pathology of Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICCA) is a malignant epithelial neoplasm characterized by biliary differentiation within the liver. ICCA is molecularly heterogeneous and exhibits a broad spectrum of histopathological features. It is a highly aggressive carcinoma with high mortality and poor survival rates. ICCAs are classified into two main subtypes: the small-duct type and large-duct types. These two tumor types have different cell origins and clinicopathological features. ICCAs are characterized by numerous molecular alterations, including mutations in KRAS, TP53, IDH1/2, ARID1A, BAP1, BRAF, SAMD4, and EGFR, and FGFR2 fusion. Two main molecular subtypes-inflammation and proliferation-have been proposed. Recent advances in high-throughput assays using next-generation sequencing have improved our understanding of ICCA pathogenesis and molecular genetics. The diagnosis of ICCA poses a significant challenge for pathologists because of its varied morphologies and phenotypes. Accurate diagnosis of ICCA is essential for effective patient management and prognostic determination. This article provides an updated overview of ICCA pathology, focusing particularly on molecular features, histological subtypes, and diagnostic approaches.

Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma

Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a "spatially competing distribution" (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.

Precision Oncology in Hepatopancreatobiliary Cancer Surgery

Advances in technology have allowed for the characterization of tumors at the genomic, transcriptomic, and proteomic levels. There are well-established targets for biliary tract cancers, with exciting new targets emerging in pancreatic ductal adenocarcinoma and potential targets in hepatocellular carcinoma. Taken together, these data suggest an important role for molecular profiling for personalizing cancer therapy in advanced disease and need for design of novel neoadjuvant studies to leverage these novel therapeutics perioperatively in the surgical patient.

Identification of Two Distinct Immune Subtypes in Hepatitis B Virus (HBV)-Associated Hepatocellular Carcinoma (HCC)

HBV is the most common risk factor for HCC development, accounting for almost 50% of cases worldwide. Despite significant advances in immunotherapy, there is limited information on the HBV-HCC tumor microenvironment (TME), which may influence the response to checkpoint inhibitors. Here, we characterize the TME in a unique series of liver specimens from HBV-HCC patients to identify who might benefit from immunotherapy. By combining an extensive immunohistochemistry analysis with the transcriptomic profile of paired liver samples (tumor vs. nontumorous tissue) from 12 well-characterized Caucasian patients with HBV-HCC, we identified two distinct tumor subtypes that we defined immune-high and immune-low. The immune-high subtype, seen in half of the patients, is characterized by a high number of infiltrating B and T cells in association with stromal activation and a transcriptomic profile featuring inhibition of antigen presentation and CTL activation. All the immune-high tumors expressed high levels of CTLA-4 and low levels of PD-1, while PD-L1 was present only in four of six cases. In contrast, the immune-low subtype shows significantly lower lymphocyte infiltration and stromal activation. By whole exome sequencing, we documented that four out of six individuals with the immune-low subtype had missense mutations in the CTNNB1 gene, while only one patient had mutations in this gene in the immune-high subtype. Outside the tumor, there were no differences between the two subtypes. This study identifies two distinctive immune subtypes in HBV-associated HCC, regardless of the microenvironment observed in the surrounding nontumorous tissue, providing new insights into pathogenesis. These findings may be instrumental in the identification of patients who might benefit from immunotherapy.

Cancer cell genetics shaping of the tumor microenvironment reveals myeloid cell-centric exploitable vulnerabilities in hepatocellular carcinoma

Myeloid cells are abundant and plastic immune cell subsets in the liver, to which pro-tumorigenic, inflammatory and immunosuppressive roles have been assigned in the course of tumorigenesis. Yet several aspects underlying their dynamic alterations in hepatocellular carcinoma (HCC) progression remain elusive, including the impact of distinct genetic mutations in shaping a cancer-permissive tumor microenvironment (TME). Here, in newly generated, clinically-relevant somatic female HCC mouse models, we identify cancer genetics' specific and stage-dependent alterations of the liver TME associated with distinct histopathological and malignant HCC features. Mitogen-activated protein kinase (MAPK)-activated, NrasG12D-driven tumors exhibit a mixed phenotype of prominent inflammation and immunosuppression in a T cell-excluded TME. Mechanistically, we report a NrasG12D cancer cell-driven, MEK-ERK1/2-SP1-dependent GM-CSF secretion enabling the accumulation of immunosuppressive and proinflammatory monocyte-derived Ly6Clow cells. GM-CSF blockade curbs the accumulation of these cells, reduces inflammation, induces cancer cell death and prolongs animal survival. Furthermore, GM-CSF neutralization synergizes with a vascular endothelial growth factor (VEGF) inhibitor to restrain HCC outgrowth. These findings underscore the profound alterations of the myeloid TME consequential to MAPK pathway activation intensity and the potential of GM-CSF inhibition as a myeloid-centric therapy tailored to subsets of HCC patients.

Periodic changes of cyclin D1 mRNA stability are regulated by PC4 modifications in the cell cycle

The cell cycle is a highly regulated process in which proteins involved in cell cycle progression exhibit periodic expression patterns, controlled by specific mechanisms such as transcription, translation, and degradation. However, the precise mechanisms underlying the oscillations of mRNA levels in cell cycle regulators are not fully understood. In this study, we observed that the stability of cyclin D1 (CCND1) mRNA fluctuates during the cell cycle, with increased stability during interphase and decreased stability during the M phase. Additionally, we identified a key RNA binding protein, positive coactivator 4 (PC4), which plays a crucial role in stabilizing CCND1 mRNA and regulating its periodic expression. Moreover, the binding affinity of PC4 to CCND1 mRNA is modulated by two cell cycle-specific posttranslational modifications: ubiquitination of K68 enhances binding and stabilizes the CCND1 transcript during interphase, while phosphorylation of S17 inhibits binding during the M phase, leading to degradation of CCND1 mRNA. Remarkably, PC4 promotes the transition from G1 to S phase in the cell cycle, and depletion of PC4 enhances the efficacy of CDK4/6 inhibitors in hepatocellular carcinoma, suggesting that PC4 could serve as a potential therapeutic target. These findings provide valuable insights into the intricate regulation of cell cycle dynamics.

Precision treatment in advanced hepatocellular carcinoma

The past decade has witnessed significant advances in the systemic treatment of advanced hepatocellular carcinoma (HCC). Nevertheless, the newly developed treatment strategies have not achieved universal success and HCC patients frequently exhibit therapeutic resistance to these therapies. Precision treatment represents a paradigm shift in cancer treatment in recent years. This approach utilizes the unique molecular characteristics of individual patient to personalize treatment modalities, aiming to maximize therapeutic efficacy while minimizing side effects. Although precision treatment has shown significant success in multiple cancer types, its application in HCC remains in its infancy. In this review, we discuss key aspects of precision treatment in HCC, including therapeutic biomarkers, molecular classifications, and the heterogeneity of the tumor microenvironment. We also propose future directions, ranging from revolutionizing current treatment methodologies to personalizing therapy through functional assays, which will accelerate the next phase of advancements in this area.

Multiomics identifies metabolic subtypes based on fatty acid degradation allocating personalized treatment in hepatocellular carcinoma

BACKGROUND AND AIMS

Molecular classification is a promising tool for prognosis prediction and optimizing precision therapy for HCC. Here, we aimed to develop a molecular classification of HCC based on the fatty acid degradation (FAD) pathway, fully characterize it, and evaluate its ability in guiding personalized therapy.

APPROACH AND RESULTS

We performed RNA sequencing (RNA-seq), PCR-array, lipidomics, metabolomics, and proteomics analysis of 41 patients with HCC, in which 17 patients received anti-programmed cell death-1 (PD-1) therapy. Single-cell RNA sequencing (scRNA-seq) was performed to explore the tumor microenvironment. Nearly, 60 publicly available multiomics data sets were analyzed. The associations between FAD subtypes and response to sorafenib, transarterial chemoembolization (TACE), immune checkpoint inhibitor (ICI) were assessed in patient cohorts, patient-derived xenograft (PDX), and spontaneous mouse model ls. A novel molecular classification named F subtype (F1, F2, and F3) was identified based on the FAD pathway, distinguished by clinical, mutational, epigenetic, metabolic, and immunological characteristics. F1 subtypes exhibited high infiltration with immunosuppressive microenvironment. Subtype-specific therapeutic strategies were identified, in which F1 subtypes with the lowest FAD activities represent responders to compounds YM-155 and Alisertib, sorafenib, anti-PD1, anti-PD-L1, and atezolizumab plus bevacizumab (T + A) treatment, while F3 subtypes with the highest FAD activities are responders to TACE. F2 subtypes, the intermediate status between F1 and F3, are potential responders to T + A combinations. We provide preliminary evidence that the FAD subtypes can be diagnosed based on liquid biopsies.

CONCLUSIONS

We identified 3 FAD subtypes with unique clinical and biological characteristics, which could optimize individual cancer patient therapy and help clinical decision-making.

Molecular profiling in the management of hepatocellular carcinoma

PURPOSE OF REVIEW

The purpose of this review is to both summarize the current knowledge of hepatocellular carcinoma molecular biology and to suggest a framework in which to prospectively translate this knowledge into patient care. This is timely as recent guidelines recommend increased use of these technologies to advance personalized liver cancer care.

RECENT FINDINGS

The main themes covered here address germline and somatic genetic alterations recently discovered in hepatocellular carcinoma, largely owing to next generation sequencing technologies, and nascent efforts to translate these into contemporary practice.

SUMMARY

Early efforts of translating molecular profiling to hepatocellular carcinoma care demonstrate a growing number of potentially actionable alterations. Still lacking are a consensus on what biomarkers and technologies to adopt, at what scale and cost, and how to integrate them most effectively into care.

EGFR core fucosylation, induced by hepatitis C virus, promotes TRIM40-mediated-RIG-I ubiquitination and suppresses interferon-I antiviral defenses

Aberrant N-glycosylation has been implicated in viral diseases. Alpha-(1,6)-fucosyltransferase (FUT8) is the sole enzyme responsible for core fucosylation of N-glycans during glycoprotein biosynthesis. Here we find that multiple viral envelope proteins, including Hepatitis C Virus (HCV)-E2, Vesicular stomatitis virus (VSV)-G, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-Spike and human immunodeficiency virus (HIV)-gp120, enhance FUT8 expression and core fucosylation. HCV-E2 manipulates host transcription factor SNAIL to induce FUT8 expression through EGFR-AKT-SNAIL activation. The aberrant increased-FUT8 expression promotes TRIM40-mediated RIG-I K48-ubiquitination and suppresses the antiviral interferon (IFN)-I response through core fucosylated-EGFR-JAK1-STAT3-RIG-I signaling. FUT8 inhibitor 2FF, N-glycosylation site-specific mutation (Q352AT) of EGFR, and tissue-targeted Fut8 silencing significantly increase antiviral IFN-I responses and suppress RNA viral replication, suggesting that core fucosylation mediated by FUT8 is critical for antiviral innate immunity. These findings reveal an immune evasion mechanism in which virus-induced FUT8 suppresses endogenous RIG-I-mediated antiviral defenses by enhancing core fucosylated EGFR-mediated activation.

Establishment and Validation of Novel Prognostic Subtypes in Hepatocellular Carcinoma Based on Bile Acid Metabolism Gene Signatures Using Bulk and Single-Cell RNA-Seq Data

Hepatocellular carcinoma (HCC) is a highly detrimental cancer type and has limited therapeutic options, posing significant threats to human health. The development of HCC has been associated with a disorder in bile acid (BA) metabolism. In this study, we employed an integrative approach, combining various datasets and omics analyses, to comprehensively characterize the tumor microenvironment in HCC based on genes related to BA metabolism. Our analysis resulted in the classification of HCC samples into four subtypes (C1, C2a, C2b, and C3). Notably, subtype C2a, characterized by the highest bile acid metabolism score (BAMS), exhibited the highest survival probability. This subtype also demonstrated increased immune cell infiltration, lower cell cycle scores, reduced AFP levels, and a lower risk of metastasis compared to subtypes C1 and C3. Subtype C1 displayed poorer survival probability and elevated cell cycle scores. Importantly, the identified subtypes based on BAMS showed potential relevance to the gene expression of drug targets in currently approved drugs and those under clinical research. Genes encoding VEGFR (FLT4 and KDR) and MET were elevated in C2, while genes such as TGFBR1, TGFB1, ADORA3, SRC, BRAF, RET, FLT3, KIT, PDGFRA, and PDGFRB were elevated in C1. Additionally, FGFR2 and FGFR3, along with immune target genes including PDCD1 and CTLA4, were higher in C3. This suggests that subtypes C1, C2, and C3 might represent distinct potential candidates for TGFB1 inhibitors, VEGFR inhibitors, and immune checkpoint blockade treatments, respectively. Significantly, both bulk and single-cell transcriptome analyses unveiled a negative correlation between BA metabolism and cell cycle-related pathways. In vitro experiments further confirmed that the treatment of HCC cell lines with BA receptor agonist ursodeoxycholic acid led to the downregulation of the expression of cell cycle-related genes. Our findings suggest a plausible involvement of BA metabolism in liver carcinogenesis, potentially mediated through the regulation of tumor cell cycles and the immune microenvironment. This preliminary understanding lays the groundwork for future investigations to validate and elucidate the specific mechanisms underlying this potential association. Furthermore, this study provides a novel foundation for future precise molecular typing and the design of systemic clinical trials for HCC therapy.

Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy

Patients with hepatocellular carcinoma (HCC) at the same clinical stage can have extremely different prognoses, and molecular subtyping provides an opportunity for individualized precision treatment. In this study, genomic, transcriptomic, proteomic, and phosphoproteomic profiling of primary tumor tissues and paired para-tumor tissues from HCC patients (N = 160) are integrated. Proteomic profiling identifies three HCC subtypes with different clinical prognosis, which are validated in three publicly available external validation sets. A simplified panel of nine proteins associated with metabolic reprogramming is further identified as a potential subtype-specific biomarker for clinical application. Multi-omics analysis further reveals that three proteomic subtypes have significant differences in genetic alterations, microenvironment dysregulation, kinase-substrate regulatory networks, and therapeutic responses. Patient-derived cell-based drug tests (N = 26) show personalized responses for sorafenib in three proteomic subtypes, which can be predicted by a machine-learning response prediction model. Overall, this study provides a valuable resource for better understanding of HCC subtypes for precision clinical therapy.

Tumor biology, immune infiltration and liver function define seven hepatocellular carcinoma subtypes linked to distinct drivers, survival and drug response

BACKGROUND & AIMS

Tumor heterogeneity is jointly determined by the components of the tumor ecosystem (TES) including tumor cells, immune cells, stromal cells, and non-cellular components. We aimed to identify subtypes using TES-related genes and determine subtype specific drivers and treatments for hepatocellular carcinoma (HCC).

METHODS

We collected 68 genesets depicting tumor biology, immune infiltration, and liver function, totaling 2831 genes, and collected mRNA profiles and clinical data for over 6000 tumors from 65 datasets in the GEO, TCGA, ICGC, and several other databases. We designed a three-step clustering pipeline to identify subtypes. The microenvironment, genomic alteration, and drug response differences were systematically compared among subtypes.

RESULTS

Seven subtypes (TES-1/2/3/4/5/6/7) were revealed in 159 tumors from the CHCC-HBV cohort. We constructed a single sample classifier using paired genes (sscpgsTES). TES subtypes were significantly associated with multiple clinical variables including etiology, and survival in 14 of 17 cohorts and the meta-cohort. TES-1 had the poorest prognosis and highest proliferation level. Both TES-2 and TES-7 were immune-enriched, however, TES-2 had a significantly worse prognosis, and hypoxic and immunosuppressive microenvironment. TES-4 had activated Wnt pathway, driven by CTNNB1 mutation. Good prognosis TES-6 exhibited the best differentiation. TES-5 and TES-3 were considered as novel subclasses by comparing with ten previous subtyping systems. TES-5 tumors had high AFP but good overall survival, and ∼45% of them harbored AXIN1 mutation. TES-3 was immune and stromal desert, may be driven by high copy number alteration burden, and had the poorest response to immune checkpoint inhibitor. TES-1 and TES-2 had significantly lower response to transarterial chemoembolization, but they showed significantly higher sensitivity to compound YM-155.

CONCLUSIONS

Tumor ecosystem subtypes expand existing HCC subtyping systems, have distinct drivers, prognosis, and treatment vulnerabilities.

Clinically conserved genomic subtypes of gastric adenocarcinoma

Gastric adenocarcinoma (GAC) is a lethal disease characterized by genomic and clinical heterogeneity. By integrating 8 previously established genomic signatures for GAC subtypes, we identified 6 clinically and molecularly distinct genomic consensus subtypes (CGSs). CGS1 have the poorest prognosis, very high stem cell characteristics, and high IGF1 expression, but low genomic alterations. CGS2 is enriched with canonical epithelial gene expression. CGS3 and CGS4 have high copy number alterations and low immune reactivity. However, CGS3 and CGS4 differ in that CGS3 has high HER2 activation, while CGS4 has high SALL4 and KRAS activation. CGS5 has the high mutation burden and moderately high immune reactivity that are characteristic of microsatellite instable tumors. Most CGS6 tumors are positive for Epstein Barr virus and show extremely high levels of methylation and high immune reactivity. In a systematic analysis of genomic and proteomic data, we estimated the potential response rate of each consensus subtype to standard and experimental treatments such as radiation therapy, targeted therapy, and immunotherapy. Interestingly, CGS3 was significantly associated with a benefit from chemoradiation therapy owing to its high basal level of ferroptosis. In addition, we also identified potential therapeutic targets for each consensus subtype. Thus, the consensus subtypes produced a robust classification and provide for additional characterizations for subtype-based customized interventions.

Hepatocellular carcinomas, exhibiting intratumor fibrosis, express cancer-specific extracellular matrix remodeling and WNT/TGFB signatures, associated with poor outcome

BACKGROUND AND AIMS

HCC, the third leading cause of cancer-related death, arises in the context of liver fibrosis. Although HCC is generally poorly fibrogenic, some tumors harbor focal intratumor extracellular matrix (ECM) deposits called "fibrous nests." To date, the molecular composition and clinical relevance of these ECM deposits have not been fully defined.

APPROACH AND RESULTS

We performed quantitative matrisome analysis by tandem mass tags mass spectrometry in 20 human cancer specific matrisome (HCCs) with high or low-grade intratumor fibrosis and matched nontumor tissues, as well as in 12 livers from mice treated with vehicle, carbon tetrachloride, or diethylnitrosamine. We found 94 ECM proteins differentially abundant between high and low-grade fibrous nests, including interstitial and basement membrane components, such as several collagens, glycoproteins, proteoglycans, enzymes involved in ECM stabilization and degradation, and growth factors. Pathway analysis revealed a metabolic switch in high-grade fibrosis, with enhanced glycolysis and decreased oxidative phosphorylation. Integrating the quantitative proteomics with transcriptomics from HCCs and nontumor livers (n = 2,285 samples), we identified a subgroup of fibrous nest HCCs, characterized by cancer-specific ECM remodeling, expression of the WNT/TGFB (S1) subclass signature, and poor patient outcome. Fibrous nest HCCs abundantly expressed an 11-fibrous-nest - protein signature, associated with poor patient outcome, by multivariate Cox analysis, and validated by multiplex immunohistochemistry.

CONCLUSIONS

Matrisome analysis highlighted cancer-specific ECM deposits, typical of the WNT/TGFB HCC subclass, associated with poor patient outcomes. Hence, histologic reporting of intratumor fibrosis in HCC is of clinical relevance.

Imaging prognostication and tumor biology in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy, and represents a significant global health burden with rising incidence rates, despite a more thorough understanding of the etiology and biology of HCC, as well as advancements in diagnosis and treatment modalities. According to emerging evidence, imaging features related to tumor aggressiveness can offer relevant prognostic information, hence validation of imaging prognostic features may allow for better noninvasive outcomes prediction and inform the selection of tailored therapies, ultimately improving survival outcomes for patients with HCC.

Hepatocellular Carcinoma Genetic Classification

ABSTRACT

Hepatocellular carcinoma (HCC) represents a significant global burden, with management complicated by its heterogeneity, varying presentation, and relative resistance to therapy. Recent advances in the understanding of the genetic, molecular, and immunological underpinnings of HCC have allowed a detailed classification of these tumors, with resultant implications for diagnosis, prognostication, and selection of appropriate treatments. Through the correlation of genomic features with histopathology and clinical outcomes, we are moving toward a comprehensive and unifying framework to guide our diagnostic and therapeutic approach to HCC.

Hepatocellular Carcinoma: Past and Present Challenges and Progress in Molecular Classification and Precision Oncology

Hepatocellular carcinoma (HCC) is one of the most common solid tumor malignancies in the world and represents roughly 90% of all primary malignancies of the liver. The most common risk factors for HCC include hepatitis B virus, hepatitis C virus, alcohol, and increasingly, fatty liver. Most HCC is diagnosed at advanced stages, excluding the possibility of curative resection, which leaves systemic therapy as the only treatment option. However, given the extreme mutational diversity and heterogenous nature of HCC, efforts to develop new targeted systemic therapies were largely unsuccessful until recently. HCC pathogenesis is thought to be a multistage process driven by a wide array of nonmutually exclusive driver mutations accompanied by many passenger mutations, with the average tumor possessing approximately 40 genomic aberrations. Over the past two decades, several efforts to categorize HCC prognostically and therapeutically according to different molecular subclassifications with the intent to guide treatment and identify drug targets have emerged, though, no single consensus has been reached. Recent breakthroughs in drug development have greatly expanded treatment options, but the ideal of uniting each patient's unique HCC with a targeted systemic therapy remains elusive.

Cancer-testis antigen CEP55 serves as a prognostic biomarker and is correlated with immune infiltration and immunotherapy efficacy in pan-cancer

Background: Centrosomal Protein 55 (CEP55) was initially described as a main participant in the final stage of cytokinesis. Further research identified CEP55 as a cancer-testis antigen (CTA) that is aberrantly expressed in different malignancies and a cancer vaccination candidate. The current study aimed to disclose the complete expression of CEP55, its effect on various malignancy prognoses, and its role in the tumor microenvironment. Methods: Transcriptional information regarding tumor and normal tissues, as well as externally validated and protein expression data were gathered from the Cancer Genome Atlas, Genotype-Tissue Expression project, Gene Expression Omnibus, and Human Protein Atlas. We examined the effect of CEP55 on tumor prognosis using Kaplan-Meier (KM) and univariate Cox regression analyses. In addition, we investigated the connections between CEP55 expression and hallmark cancer pathways, immune cell infiltration, and immune regulator expression across malignancies. We constructed and validated a CEP55-related risk model for hepatocellular carcinoma (HCC) and explored the correlations between CEP55 expression and HCC molecular subtypes. Finally, we investigated putative small-molecule drugs targeting CEP55 using a connectivity map (CMap) database and validated them using molecular docking analysis. Findings: CEP55 was aberrantly expressed in most cancers and revealed a prognostic value for several malignancies. Cancers with high CEP55 expression showed significantly enhanced cell cycle, proliferation, and immune-related pathways. For most malignancies, elevated CEP55 expression was associated with the infiltration of myeloid-derived suppressor cells (MDSCs) and Th2 cells. In addition, CEP55 expression was linked to immunomodulators and the potential prediction of immune checkpoint inhibitor (ICI) responses, and strongly associated with distinct molecular HCC subtypes, whereby the CEP55-based nomogram performed well in predicting short- and long-term HCC survival. Finally, we used connectivity map (CMap) and molecular docking analyses to discover three candidate small-molecule drugs that could directly bind to CEP55. Conclusion: CEP55 affected the occurrence and development of various cancers and possibly the regulation of the tumor immune microenvironment. Our findings suggest that CEP55 is a potential biomarker for prognosis and a powerful biomarker for ICI efficacy prediction.

AUF1 promotes hepatocellular carcinoma progression and chemo-resistance by post-transcriptionally upregulating alpha-fetoprotein expression

BACKGROUND

The target genes of AU-rich Element RNA-binding Protein 1 (AUF1), which is an RNA binding protein, and its role in the progression of hepatocellular carcinoma (HCC) is still elusive. This study aims to investigate the biological function and the underlying target genes of AUF1 in HCC.

METHODS

RNA sequencing data and the Liver Cancer Institute (LCI) database were used to screen candidate targets of AUF1. LCI database, TCGA database, and a retrospective HCC cohort were used to investigate the correlation between AUF1 and alpha-fetoprotein (AFP) and their prognostic values in HCC patients. Huh-7, HepG2, and HepAD38 cell lines were used to investigate the underlying mechanism of AUF1 regulating the AFP expression. Cell Counting Kit-8, colony formation, EdU incorporation, and flow cytometry assays were performed to detect the effect of AUF1-AFP axis on the progression and doxorubicin resistance of HCC cells.

RESULTS

A combined analysis of the transcriptome data from Huh-7 cells after knockdown of AUF1 and gene expression data from LCI database revealed that AFP was the most significantly downregulated gene after AUF1 depletion. AUF1 expression was positively associated with AFP expression in HCC tissues and the high expression of both AUF1 and AFP were correlated with a worse prognosis in HCC patients of LCI and TCGA databases, as well as our retrospective cohort. Mechanistically, AUF1 bound to the 3' untranslation region (UTR) of AFP mRNA to enhance the mRNA stability of AFP, thereby upregulating AFP. Functional tests showed that AFP knockdown inhibited tumor growth and doxorubicin resistance of HCC cells induced by AUF1.

CONCLUSIONS

AFP may be an important target gene of AUF1. AUF1 promoted HCC progression and doxorubicin resistance by upregulating AFP expression via increasing its mRNA stability.

Comprehensive characterization of ferroptosis in hepatocellular carcinoma revealing the association with prognosis and tumor immune microenvironment

Background

Ferroptosis is a type of regulatory cell death (RCD) mode that depends on iron-mediated oxidative damage. It has the potential to improve the efficacy of tumor immunotherapy by modulating the tumor microenvironment (TME). Currently, immunotherapy has significantly improved the overall treatment strategy for advanced hepatocellular carcinoma (HCC), but the distinct immune microenvironment and high tolerance to the immune make massive differences in the immunotherapy effect of HCC patients. As a result, it is imperative to classify HCC patients who may benefit from immune checkpoint therapy. Simultaneously, the predictive value of ferroptosis in HCC and its potential role in TME immune cell infiltration also need to be further clarified.

Methods

Three ferroptosis molecular models were built on the basis of mRNA expression profiles of ferroptosis-related genes (FRGs), with notable variations in immunocyte infiltration, biological function, and survival prediction. In order to further investigate the predictive impact of immunotherapy response in HCC patients, the ferroptosis score was constructed using the principal component analysis (PCA) algorithm to quantify the ferroptosis molecular models of individual tumors.

Results

In HCC, there were three totally different ferroptosis molecular models. The ferroptosis score can be used to assess genetic variation, immunotherapy response, TME characteristics, and prognosis. Notably, tumors with low ferroptosis scores have extensive tumor mutations and immune exhaustion, which are associated with a poor prognosis and enhanced immunotherapy response.

Conclusions

Our study indicates that ferroptosis plays an indispensable role in the regulation of the tumor immune microenvironment. For HCC, the ferroptosis score is an independent prognostic indicator. Assessing the molecular model of ferroptosis in individual tumors will assist us in better understanding the characteristics of TME, predicting the effect of immunotherapy in HCC patients, and thus guiding a more reasonable immunotherapy program.

Molecular characterization-based multi-omics analyses in primary liver cancer using the Japanese version of the genome atlas

BACKGROUND

Primary liver cancer (PLC) is classified into hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular and intrahepatic cholangiocarcinoma (CHC). We investigated the genomic landscape of PLC according to the histological classification and established a cross-histological molecular subtyping for PLC by a multi-omics analysis.

METHODS

We analyzed 265 PLC cases with whole-exome sequencing and DNA copy number analyses and 251 cases with gene expression profiling.

RESULTS

The cohort included HCC (n = 223, 84%), ICC (n = 34, 13%), and CHC (n = 8, 3%). Mutation analyses identified histological type-specific driver genes, such as CTNNB1 in HCC and KRAS, IDH1, and PIK3CA in ICC, and ARID1A and KMT2C in CHC. The tumor suppressor gene TP53 mutation was detected in 21.1% of HCC, 16.1% of ICC, and 25.0% of CHC cases. Other well-characterized tumor suppressor genes included RB1, which was mutated in 2.8% of HCC and 3.2% of ICC; and PTEN, which was mutated in 1.4% of HCC, 3.2% of ICC, and 12.5% of CHC cases. DNA copy number analyses identified focal amplifications, with NUF2 (1q23.3) the most frequently detected as an amplified gene in all 3 types (HCC, 3.8%; CHC, 12.5%, ICC, 3.2%). Molecular subtyping for PLC based on the multi-omics analysis identified three subtypes, one of which was associated with recurrence after resection and amplified genes located at chromosome 8q.

CONCLUSIONS

Our dataset serves as a fundamental resource for genomic medicine for PLC in Japan and identified amplified genes located at chromosome 8q as promising therapeutic targets for the subgroup with a poor prognosis.

Circulating Biomarkers for the Early Diagnosis and Management of Hepatocellular Carcinoma with Potential Application in Resource-Limited Settings

Hepatocellular carcinoma (HCC) is among the world's third most lethal cancers. In resource-limited settings (RLS), up to 70% of HCCs are diagnosed with limited curative treatments at an advanced symptomatic stage. Even when HCC is detected early and resection surgery is offered, the post-operative recurrence rate after resection exceeds 70% in five years, of which about 50% occur within two years of surgery. There are no specific biomarkers addressing the surveillance of HCC recurrence due to the limited sensitivity of the available methods. The primary goal in the early diagnosis and management of HCC is to cure disease and improve survival, respectively. Circulating biomarkers can be used as screening, diagnostic, prognostic, and predictive biomarkers to achieve the primary goal of HCC. In this review, we highlighted key circulating blood- or urine-based HCC biomarkers and considered their potential applications in resource-limited settings, where the unmet medical needs of HCC are disproportionately highly significant.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Classification and survival prediction in early-stage cirrhosis by gene expression profiling

Liver cirrhosis has been increasingly diagnosed at an early stage owing to the non-invasive diagnostic techniques. However, it is difficult to identify patients at high risk of disease progression. Screening cirrhotic patients with poor prognosis who are most in need of surveillance is still challenging. Gene expression data GSE15654 and GSE14520 were downloaded for performing unsupervised clustering analysis. The prognostic differences between the different clusters were explored by Cox regression. Integrative analysis of gene expression signature, immune cell enrichments and clinical characterization was performed for different clusters. Two distinctive subclasses were identified in HCV-related GSE15654, and Kaplan-Meier analysis indicated that subtype 2 had lower survival rates than subtype 1 (p = 0.0399). Further analysis revealed subtype 2 had a higher density of follicular T helper cells, resting natural killer cells and M0, M2 macrophages while subtype 1 with a higher fraction of naive B cells, memory B cells, resting memory CD 4 T cells, activated natural killer cells and monocytes. 226 differentially expressed genes were identified between the two subtypes, and Reactome analysis showed the mainly enriched pathways were biological oxidations and fatty acid metabolism. Five hub genes (AKT1, RPS16, CDC42, CCND1 and PCBP2) and three significant modules were extracted from the PPI network. The results were validated in HBV-related GSE14520 cohort. We identified two subtypes of patients with different prognosis for hepatitis C-related early-stage liver cirrhosis. Bioinformatics analysis of the gene expression and immune cell profile may provide fresh insight into understanding the prognosis difference.

MRI features associated with HCC histologic subtypes: a western American and European bicenter study

OBJECTIVES

To evaluate if preoperative MRI can predict the most frequent HCC subtypes in North American and European patients treated with surgical resection.

METHODS

A total of 119 HCCs in 97 patients were included in the North American group and 191 HCCs in 176 patients were included in the European group. Lesion subtyping was based on morphologic features and immuno-histopathological analysis. Two radiologists reviewed preoperative MRI and evaluated the presence of imaging features including LI-RADS major and ancillary features to identify clinical, biologic, and imaging features associated with the main HCC subtypes.

RESULTS

Sixty-four percent of HCCs were conventional. The most frequent subtypes were macrotrabecular-massive (MTM-15%) and steatohepatitic (13%). Necrosis (OR = 3.32; 95% CI: 1.39, 7.89; p = .0064) and observation size (OR = 1.011; 95% CI: 1.0022, 1.019; p = .014) were independent predictors of MTM-HCC. Fat in mass (OR = 15.07; 95% CI: 6.57, 34.57; p < .0001), tumor size (OR = 0.97; 95% CI: 0.96, 0.99; p = .0037), and absence of chronic HCV infection (OR = 0.24; 95% CI: 0.084, 0.67; p = .0068) were independent predictors of steatohepatitic HCC. Independent predictors of conventional HCCs were viral C hepatitis (OR = 3.20; 95% CI: 1.62, 6.34; p = .0008), absence of fat (OR = 0.25; 95% CI: 0.12, 0.52; p = .0002), absence of tumor in vein (OR = 0.34; 95% CI: 0.13, 0.84; p = .020), and higher tumor-to-liver ADC ratio (OR = 1.96; 95% CI: 1.14, 3.35; p = .014) CONCLUSION: MRI is useful in predicting the most frequent HCC subtypes even in cohorts with different distributions of liver disease etiologies and tumor subtypes which might have future treatment and management implications.

KEY POINTS

• Representation of both liver disease etiologies and HCC subtypes differed between the North American and European cohorts of patients. • Retrospective two-center study showed that liver MRI is useful in predicting the most frequent HCC subtypes.

Transcriptomic Analysis of Hepatitis B Infected Liver for Prediction of Hepatocellular Carcinoma

Hepatocellular cancer (HCC) is a leading cause of cancer-related mortality worldwide, and chronic hepatitis B virus infection (CHB) has been a major risk factor for HCC development. The pathogenesis of HBV-related HCC has been a major focus revealing the interplay of a multitude of intracellular signaling pathways, yet the precise mechanisms and their implementations to clinical practice remain to be elucidated. This study utilizes publicly available transcriptomic data from the livers of CHB patients in order to identify a population with a higher risk of malignant transformation. We report the identification of a novel list of genes (PCM1) which can generate clear transcriptomic sub-groups among HBV-infected livers. PCM1 includes genes related to cell cycle activity and liver cancer development. In addition, markers of inflammation, M1 macrophages and gamma delta T cell infiltration are present within the signature. Genes within PCM1 are also able to differentiate HCC from normal liver, and some genes within the signature are associated with poor prognosis of HCC at the mRNA level. The analysis of the immunohistochemical stainings validated that proteins coded by a group of PCM1 genes were overexpressed in liver cancer, while minimal or no expression was detected in normal liver. Altogether, our findings suggest that PCM1 can be developed into a clinically applicable method to identify CHB patients with a higher risk of HCC development.

Anticancer properties of bromelain: State-of-the-art and recent trends

Bromelain is a key enzyme found in pineapple (Ananas comosus (L.) Merr.); a proteolytic substance with multiple beneficial effects for human health such as anti-inflammatory, immunomodulatory, antioxidant and anticarcinogenic, traditionally used in many countries for its potential therapeutic value. The aim of this updated and comprehensive review focuses on the potential anticancer benefits of bromelain, analyzing the cytotoxic, apoptotic, necrotic, autophagic, immunomodulating, and anti-inflammatory effects in cancer cells and animal models. Detailed information about Bromelain and its anticancer effects at the cellular, molecular and signaling levels were collected from online databases such as PubMed/MedLine, TRIP database, GeenMedical, Scopus, Web of Science and Google Scholar. The results of the analyzed studies showed that Bromelain possesses corroborated pharmacological activities, such as anticancer, anti-edema, anti-inflammatory, anti-microbial, anti-coagulant, anti-osteoarthritis, anti-trauma pain, anti-diarrhea, wound repair. Nonetheless, bromelain clinical studies are scarce and still more research is needed to validate the scientific value of this enzyme in human cancer diseases.

Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis

Objective

To provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study.

Methods

In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles.

Results

1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer.

Conclusion

AI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.

Clinical Significance of Glycolytic Metabolic Activity in Hepatocellular Carcinoma

High metabolic activity is a hallmark of cancers, including hepatocellular carcinoma (HCC). However, the molecular features of HCC with high metabolic activity contributing to clinical outcomes and the therapeutic implications of these characteristics are poorly understood. We aimed to define the features of HCC with high metabolic activity and uncover its association with response to current therapies. By integrating gene expression data from mouse liver tissues and tumor tissues from HCC patients (n = 1038), we uncovered three metabolically distinct HCC subtypes that differ in clinical outcomes and underlying molecular biology. The high metabolic subtype is characterized by poor survival, the strongest stem cell signature, high genomic instability, activation of EPCAM and SALL4, and low potential for benefitting from immunotherapy. Interestingly, immune cell analysis showed that regulatory T cells (Tregs) are highly enriched in high metabolic HCC tumors, suggesting that high metabolic activity of cancer cells may trigger activation or infiltration of Tregs, leading to cancer cells' evasion of anti-cancer immune cells. In summary, we identified clinically and metabolically distinct subtypes of HCC, potential biomarkers associated with these subtypes, and a potential mechanism of metabolism-mediated immune evasion by HCC cells.

Genetic Predisposition to Hepatocellular Carcinoma

Liver preneoplastic and neoplastic lesions of the genetically susceptible F344 and resistant BN rats cluster, respectively, with human HCC with better (HCCB) and poorer prognosis (HCCP); therefore, they represent a valid model to study the molecular alterations determining the genetic predisposition to HCC and the response to therapy. The ubiquitin-mediated proteolysis of ERK-inhibitor DUSP1, which characterizes HCC progression, favors the unrestrained ERK activity. DUSP1 represents a valuable prognostic marker, and ERK, CKS1, or SKP2 are potential therapeutic targets for human HCC. In DN (dysplastic nodule) and HCC of F344 rats and human HCCP, DUSP1 downregulation and ERK1/2 overexpression sustain SKP2-CKS1 activity through FOXM1, the expression of which is associated with a susceptible phenotype. SAM-methyl-transferase reactions and SAM/SAH ratio are regulated by GNMT. In addition, GNMT binds to CYP1A, PARP1, and NFKB and PREX2 gene promoters. MYBL2 upregulation deregulates cell cycle and induces the progression of premalignant and malignant liver. During HCC progression, the MYBL2 transcription factor positively correlates with cells proliferation and microvessel density, while it is negatively correlated to apoptosis. Hierarchical supervised analysis, regarding 6132 genes common to human and rat liver, showed a gene expression pattern common to normal liver of both strains and BN nodules, and a second pattern is observed in F344 nodules and HCC of both strains. Comparative genetics studies showed that DNs of BN rats cluster with human HCCB, while F344 DNs and HCCs cluster with HCCP.

Consensus subtypes of hepatocellular carcinoma associated with clinical outcomes and genomic phenotypes

BACKGROUND AND AIMS

Although many studies revealed transcriptomic subtypes of HCC, concordance of the subtypes are not fully examined. We aim to examine a consensus of transcriptomic subtypes and correlate them with clinical outcomes.

APPROACH AND RESULTS

By integrating 16 previously established genomic signatures for HCC subtypes, we identified five clinically and molecularly distinct consensus subtypes. STM (STeM) is characterized by high stem cell features, vascular invasion, and poor prognosis. CIN (Chromosomal INstability) has moderate stem cell features, but high genomic instability and low immune activity. IMH (IMmune High) is characterized by high immune activity. BCM (Beta-Catenin with high Male predominance) is characterized by prominent β-catenin activation, low miRNA expression, hypomethylation, and high sensitivity to sorafenib. DLP (Differentiated and Low Proliferation) is differentiated with high hepatocyte nuclear factor 4A activity. We also developed and validated a robust predictor of consensus subtype with 100 genes and demonstrated that five subtypes were well conserved in patient-derived xenograft models and cell lines. By analyzing serum proteomic data from the same patients, we further identified potential serum biomarkers that can stratify patients into subtypes.

CONCLUSIONS

Five HCC subtypes are correlated with genomic phenotypes and clinical outcomes and highly conserved in preclinical models, providing a framework for selecting the most appropriate models for preclinical studies.

Impact of WTAP in small HCC and paired adjacent non-neoplastic liver tissue on recurrence: A cohort study with external extension analysis

Background: To evaluate prognostic value of WTAP levels in tumor and paired adjacent non-neoplastic liver tissues (PANLT) for cases of hepatitis B virus (HBV)-positive Asian small hepatocellular carcinoma (sHCC) patients who received curative partial hepatectomy. Method: The investigation with two external cohorts were included. Associations between hazard risk of recurrence and continuous WTAP levels were investigated with restricted cubic spline models. Cox and inverse probability weighting models were established for survival analysis. Based on interaction effects, further stratification analysis was performed. Landmark analysis was employed to analyze cases of late recurrence. Finally, sensitivity analysis was performed to assess unmeasured confounders. Findings: In an investigation cohort of 307 patients, restricted cubic spline models indicated that hazard risk of recurrence increases with elevated WTAP levels for sHCC and PANLT. However, using Cox and inverse probability weighting models, no significant differences were observed in recurrence-free survival (RFS) between groups with different WTAP levels in sHCC. Multivariate analysis showed that patients with high PANLT WTAP levels had significantly worse RFS (HR 1.567, 95% CI 1.065-2.307; p = 0.023). Based on the significant interaction effect between WTAP levels in sHCC and PANLT, stratification analysis revealed that recurrence risk is more pronounced in patients with high WTAP levels in both PANLT and sHCC. Landmark analysis showed that late recurrence was more likely to occur in patients with high PANLT WTAP levels (HR 2.058, 95% CI 1.113-3.805; p = 0.021). Moreover, the detrimental effects of elevated PANLT WTAP levels on RFS were validated with two external cohorts. Sensitivity analysis confirmed the robustness of results. Conclusion: Increased PANLT WTAP expression levels independently predict high recurrence risk in HBV-positive Asian sHCC patients. Both tumor tissues and PANLT need to be considered together in future clinical practice to obtain a more comprehensive and accurate evaluation for recurrence risk.

Clinical value of identifying genes that inhibit hepatocellular carcinomas

INTRODUCTION

Primary liver cancer is a major health problem being the sixth most frequent cancer in the world and the fourth most frequent cause of cancer-related death in the world. The most common histological type of liver cancer is hepatocellular carcinoma (HCC, 75-80%).

AREAS COVERED

Based on primary literature, this review provides an updated analysis of studies of genetic characterization of HCC at the level of gene mutation profiling, copy number alterations and gene expression, with definition of molecular subgroups and identification of some molecular biomarkers and therapeutic targets.

EXPERT OPINION

A detailed and comprehensive study of the genetic abnormalities characterizing different HCC subsets represents a fundamental tool for a better understanding of the disease heterogeneity and for the identification of subgroups of patients responding or resistant to targeted treatments and for the discovery of new therapeutic targets. It is expected that a comprehensive characterization of these tumors may provide a fundamental contribution to improve the survival of a subset of HCC patients. Immunotherapy represents a new fundamental strategy for the treatment of HCC.

Gene coexpression network analysis identifies hubs in hepatitis B virus-associated hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related death worldwide. The molecular pathogenesis of HCC involves multiple signaling pathways. This study utilizes systems and bioinformatic approaches to investigate the pathogenesis of HCC.

METHODS

Gene expression microarray data were obtained from 50 patients with chronic hepatitis B and HCC. There were 1649 differentially expressed genes inferred from tumorous and nontumorous datasets. Weighted gene coexpression network analysis (WGCNA) was performed to construct clustered coexpressed gene modules. Statistical analysis was used to study the correlation between gene coexpression networks and demographic features of patients. Functional annotation and pathway inference were explored for each coexpression network. Network analysis identified hub genes of the prognostic gene coexpression network. The hub genes were further validated with a public database.

RESULT

Five distinct gene coexpression networks were identified by WGCNA. A distinct coexpressed gene network was significantly correlated with HCC prognosis. Pathway analysis of this network revealed extensive integration with cell cycle regulation. Ten hub genes of this gene network were inferred from protein-protein interaction network analysis and further validated in an external validation dataset. Survival analysis showed that lower expression of the 10-gene signature had better overall survival and recurrence-free survival.

CONCLUSION

This study identified a crucial gene coexpression network associated with the prognosis of hepatitis B virus-related HCC. The identified hub genes may provide insights for HCC pathogenesis and may be potential prognostic markers or therapeutic targets.

Transcriptome sequencing of hepatocellular carcinoma uncovers multiple types of dysregulated ncRNAs

Transcriptome profiling of hepatocellular carcinoma (HCC) by next-generation sequencing (NGS) technology has been broadly performed by previous studies, which facilitate our understanding of the molecular mechanisms of HCC formation, progression, and metastasis. However, few studies jointly analyze multiple types of noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and micro-RNAs (miRNAs), and further uncover their implications in HCC. In this study, we observed that the circRNA cZRANB1 and lncRNA DUXAP10 were not only significantly upregulated in tumor tissues, but also higher expressed in blood exosomes of HCC as compared with healthy donors. From the analysis of subclass-associated dysregulated ncRNAs, we observed that DLX6-AS1, an antisense RNA of DLX6, and the sense gene DLX6 were highly expressed in S1, a subclass with a more invasive/disseminative phenotype. High correlation between DLX6-AS1 and DLX6 suggested that DLX6-AS1 may function via promoting the transcription of DLX6. Integrative analysis uncovers circRNA–miRNA, lncRNA–miRNA, and competing endogenous RNA networks (ceRNAs). Specifically, cZRANB1, LINC00501, CTD-2008L17.2, and SLC7A11-AS1 may function as ceRNAs that regulate mRNAs by competing the shared miRNAs. Further prognostic analysis demonstrated that the dysregulated ncRNAs had the potential to predict HCC patients’ overall survival. In summary, we identified some novel circRNAs and miRNAs, and dysregulated ncRNAs that could participate in HCC tumorigenesis and progression by inducing transcription of their neighboring genes, increasing their derived miRNAs, or acting as miRNA sponges. Moreover, our systematic analysis provides not only rich data resources for related researchers, but also new insights into the molecular basis of how different ncRNAs coordinately or antagonistically participate in the pathogenesis process of diseases.

Deep View of HCC Gene Expression Signatures and Their Comparison with Other Cancers

BACKGROUND

Gene expression signatures correlate genetic alterations with specific clinical features, providing the potential for clinical usage. A plethora of HCC-dependent gene signatures have been developed in the last two decades. However, none of them has made its way into clinical practice. Thus, we investigated the specificity of public gene signatures to HCC by establishing a comparative transcriptomic analysis, as this may be essential for clinical applications.

METHODS

We collected 10 public HCC gene signatures and evaluated them by utilizing four different (commercial and non-commercial) gene expression profile comparison tools: Oncomine Premium, SigCom LINCS, ProfileChaser (modified version), and GENEVA, which can assign similar pre-analyzed profiles of patients with tumors or cancer cell lines to our gene signatures of interests. Among the query results of each tool, different cancer entities were screened. In addition, seven breast and colorectal cancer gene signatures were included in order to further challenge tumor specificity of gene expression signatures.

RESULTS

Although the specificity of the evaluated HCC gene signatures varied considerably, none of the gene signatures showed strict specificity to HCC. All gene signatures exhibited potential significant specificity to other cancers, particularly for colorectal and breast cancer. Since signature specificity proved challenging, we furthermore investigated common core genes and overlapping enriched pathways among all gene signatures, which, however, showed no or only very little overlap, respectively.

CONCLUSION

Our study demonstrates that specificity, independent validation, and clinical use of HCC genetic signatures solely relying on gene expression remains challenging. Furthermore, our work made clear that standards in signature generation and statistical methods but potentially also in tissue preparation are urgently needed.

Comprehensive characterization of enhancer RNA in hepatocellular carcinoma reveals three immune subtypes with implications for immunotherapy

Hepatocellular carcinoma (HCC) is highly heterogeneous. Molecular subtyping for guiding immunotherapy is warranted. Previous studies have indicated that enhancer RNAs (eRNAs) are involved in tumor heterogeneity and immune infiltration. However, the eRNA landscape and its correlation with immune infiltration in HCC remain unknown. Here we first revealed the genome-wide eRNA landscape in two HCC cohorts. Then we divided individuals with HCC into three immune-related clusters (C1, C2, and C3) based on eRNA expression profiles. The prognosis, biological properties, immune infiltration, clinical features, genomic features, and drug response were analyzed. C1 was enriched in immune infiltration and potentially sensitive to immune checkpoint inhibitors (ICIs). C2 displayed features of immune depletion, high proliferation activity, malignant clinical features, and the worst prognosis. C2 may benefit from targeted therapy. C3 presented moderate immune infiltration, metabolism-related signatures, and the best prognosis. Transarterial chemoembolization (TACE) may be effective for C3. Finally, we constructed a 51-eRNA classifier for subtype prediction and validated its efficacy in The Cancer Genome Atlas (TCGA) cohort and Sun Yat-sen University Cancer Center (SYSUCC) cohort. Our results provide a novel method for immune classification of HCC, shed new light on tumor heterogeneity, and may aid in HCC immunotherapy.