The association between diabetes mellitus, hyperglycemia, and cholangiocarcinoma (CCA) development and progression has been established. One speculation of the effects of high glucose levels promoting CCA progression is via the feeding of substrate to the aerobic glycolysis or so-called Warburg effects in CCA cells. Several glycolytic enzymes and glucose transporters are upregulated in CCA and further activated by high glucose conditions. However, the increased glucose uptake and the increased aggressive phenotypes of CCA under high glucose conditions might not be solely due to this aberrant energy metabolism. High glucose conditions have been proven to be the activator of the other signaling pathways, as well as the precursors for dysregulated glycosylation of oncoproteins in CCA. The higher requirement of glucose and the abundant glucose availability in diabetic conditions then synergize to promote aggressive CCA phenotypes. Additionally, the glucose avidity could also become the Achilles heel of CCA cells, as they could be sensitive to glucose deprivation. The development of therapeutic agents targeting glucose metabolisms or glucose-activated pathways is promising for CCA treatments. This article reviews and discusses the up-to-date research on how high glucose is involved in CCA progression, both via Warburg effects and other mechanisms.
Aim: Our objective was to describe variations in attributable fractions (AFs) of hepatitis B virus (HBV) and hepatitis C virus (HCV) to primary liver cancer (PLC) across mainland China.
Methods: We conducted a systematic review and meta-analysis of studies published up to July 2024 in PubMed, Embase, WanFang, and China National Knowledge Infrastructure. Eligible studies reported the prevalence of HBV and HCV infection, alone and in combination, in PLC. AFs of HBV, HCV, and non-viral etiology in PLC were estimated by province and, when possible, by sex, age, histological diagnosis, and study periods. Regional and overall AFs were estimated by weighting by provincial and regional population size. Publication bias and heterogeneity were assessed by funnel plots of AFs of HBV, HCV, and non-viral etiology in PLC cases, using Egger test by study size.
Results: We included 240 studies with 71,905 PLC cases from 31 provinces, autonomous regions and municipalities across mainland China. AFs of HBV, HCV, and non-viral etiology for PLC across mainland China were 76.5%, 6.6%, and 16.9%, respectively. The AFs of HBV and HCV varied by region, with HBV AFs ranging from 68.7% in Northwest China to 82.9% in South Central China. Respective HCV AFs ranged from 12.7% in Northeast China down to 3.7% in South Central China. Non-viral AFs ranged from 13.4% to 21.0% by region. AFs of non-viral etiology were 57.9% in intrahepatic cholangiocarcinoma (ICC), and AF of HBV was double prevalence ratio (PR), 2.11 (95%CI: 1.89-2.34) in hepatocellular carcinoma (HCC, 76.3%) than ICC (37.3%) cases. Prevalence of HBV was higher in male (83.9%) than female (74.6%) PLC cases, and the mean age at diagnosis was 10 years higher for PLC with HCV (64.9 years) than HBV (54.4 years).
Conclusion: HBV, HCV, and non-viral AFs varied substantially by region across China, as well as by sex and age, which can inform strategies for liver cancer prevention and control.
Minimally invasive anatomical liver resection is increasingly feasible due to the improved availability of preoperative 3-dimensional (3D) reconstruction of the liver as well as endoscopic cameras with near-infrared indocyanine green fluorescence overlay visualization. In this article, we share our experience and perceived benefits of anatomical liver resection with preoperative 3D liver reconstruction, provide technical tips to improve success at pedicle isolation and address common pitfalls that may be encountered by adopters early on their learning curve.
Artificial intelligence (AI) is rapidly advancing in hepatocellular carcinoma (HCC) management, offering promising applications across diagnosis, prognosis, and treatment. In histopathology, deep learning models have shown impressive accuracy in differentiating liver lesions and extracting prognostic information from tissue samples. For biomarker discovery, AI techniques applied to multi-omics data have identified novel prognostic signatures and predictors of immunotherapy response. In radiology, convolutional neural networks have demonstrated high performance in classifying hepatic lesions, grading tumors, and predicting microvascular invasion from computed tomography (CT) and magnetic resonance imaging (MRI) images. Multimodal AI models integrating histopathology, genomics, and clinical data are emerging as powerful tools for risk stratification. Large language models (LLMs) show potential to support clinical decision making and patient education, though concerns about accuracy remain. While AI holds immense promise, several challenges must be addressed, including algorithmic bias, data privacy, and regulatory compliance. The successful implementation of AI in HCC care will require ongoing collaboration between clinicians, data scientists, and ethicists. As AI technologies continue to evolve, they are expected to enable more personalized approaches to HCC management, potentially improving diagnosis, treatment selection, and patient outcomes. However, it is crucial to recognize that AI tools are designed to assist, not replace, clinical expertise. Continuous validation in diverse, real-world settings will be essential to ensure the reliability and generalizability of AI models in HCC care.
Primary liver cancer (PLC), which includes hepatocellular carcinoma (HCC, 93% in China; 75%-80% worldwide), intrahepatic cholangiocarcinoma (ICC, 4.3% in China; 10%-15% worldwide), and combined hepatocellular cholangiocarcinoma (CHC, 1.6% in China), is a global disease that brings a heavy burden to the world and the number of incidence cases is on the rise. Chronic liver injury caused by factors such as exposure to aflatoxin B1, infection with Clonorchis sinensis, heavy alcohol consumption, chronic infection with hepatitis C virus (HCV), and metabolic syndrome are all known risk factors for PLC. Notably, chronic infection with hepatitis B virus (HBV) is the major risk factor for HCC. Globally, PLC risk factors are changing from infectious causes to metabolic factors. Here, we update the mechanisms of HBV-related HCC (HBV-HCC) development, especially the effect of HBV evolution on the development of HCC. The HBV mutations, viral load, and HBV integration, together with parameters of poor liver function, are key components to define the highest-risk population of HBV-HCC. Antiviral therapy has been proven to be effective for the prevention of HBV-HCC in the highest-risk population. Non-invasive imaging combined with key markers is economical and convenient for screening early PLC. Surgical resection and liver transplantation are therapeutic options for HCC; however, postoperative recurrence reaches 70% in five years. Targeted therapy, immunotherapy, and radiotherapy can improve the survival of PLC. Active prophylaxes, including HBV vaccination, antiviral treatment, improving lifestyle to decrease chronic inflammation, and surveillance, are cost-effective in decreasing the disease burden of PLC.
Hepatocellular carcinoma (HCC) represents a significant global health challenge, with a complex etiology and limited treatment options. The integration of multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has revolutionized our understanding of HCC, offering novel insights into its molecular underpinnings. This comprehensive review synthesizes the current knowledge on the application of multi-omics in HCC, highlighting its role in disease classification, early detection, and the development of targeted therapies. We discuss the identification of key driver mutations and single nucleotide polymorphisms (SNPs) that enhance risk prediction models, with implications for personalized medicine. The multi-omics approach has facilitated the discovery of distinct HCC subtypes, each with unique molecular signatures and tumor microenvironments (TME), which are critical for predicting prognosis and guiding treatment strategies. Furthermore, we explore the implications of these findings for precision medicine, emphasizing the potential of biomarker identification and targeted therapies, including immune checkpoint blockade (ICB). The review concludes by underscoring the transformative impact of multi-omics on HCC research and clinical practice, heralding a new era of personalized medicine with the promise of improved patient outcomes.
Chronic liver disease (CLD) and cirrhosis are leading contributors to global morbidity and mortality, with hepatocellular carcinoma (HCC) frequently arising in patients with advanced liver damage. This review explores the interplay between immune dysfunction and the progression of cirrhosis to HCC, emphasizing the pivotal role of immune status in HCC pathogenesis. Chronic inflammation, cirrhosis-associated immune dysfunction syndrome (CAIDS), and immunosenescence create a permissive environment for tumorigenesis by impairing immune surveillance and promoting hepatocyte stress. Key mechanisms include T cell exhaustion, dysregulated cytokine signaling, and gut-liver axis dysfunction, which collectively drive malignant transformation. Emerging immune biomarkers, such as PD-1/PD-L1, LAG-3, TIGIT, and soluble CD14/CD163, offer promise for refining HCC risk stratification and improving early detection. Integrating these biomarkers into existing surveillance protocols could enhance screening efficacy, particularly in high-risk populations such as the elderly and immunocompromised. Current guidelines recommend biannual ultrasound and alpha-fetoprotein testing for cirrhotic patients, but adherence remains suboptimal, and late diagnoses are common. A personalized approach combining clinical risk factors, immune markers, and patient demographics may improve surveillance outcomes. Future research should focus on validating immune biomarkers in large cohorts, exploring novel therapeutic targets like LAG-3 and TIGIT, and developing tools to quantify immune dysfunction for risk stratification. By addressing immune dysregulation as a critical factor in HCC development, this review highlights the potential for improved HCC screening adherence and early detection for better patient outcomes through immune-based strategies.
Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer and a leading cause of cancer-related mortality globally. The heterogeneity of HCC complicates prognostic, management, and predictive strategies across different patient populations. Recent advancements in artificial intelligence (AI) and machine learning (ML) offer transformative opportunities to improve HCC management. This review consolidates findings from various studies regarding integrating AI in detecting, diagnosing, and treating HCC, leveraging diverse data sources such as radiological imaging, genomics, and clinical records. AI-based approaches have shown potential to improve the accuracy and efficiency of HCC screening, early detection, tumor characterization, and treatment response evaluation, surpassing traditional methods. However, the deployment of AI technologies is hindered by challenges, including data standardization, validation across multiple centers, and ethical considerations regarding AI applications. This review emphasizes the need to establish comprehensive multimodal datasets and collaborative research efforts to validate AI applications in HCC management. By addressing these challenges, the integration of AI technology has the potential to revolutionize HCC care, ultimately leading to improved patient outcomes and a more personalized approach to treatment strategies.
Surveillance for hepatocellular carcinoma (HCC) improves early tumor detection, increases access to curative therapies, and reduces mortality by about 40%. Early diagnosis through surveillance is essential and should be extended to as many at-risk patients as possible to maximize the benefits of multidisciplinary tumor board evaluations. Current guidelines recommend semi-annual abdominal ultrasonography (US), with/without serum alpha-fetoprotein measurement, for patients with cirrhosis and certain subgroups of individuals with pre-cirrhotic chronic liver disease. However, the populations eligible for surveillance include subsets with varying degrees of HCC risk, which may change over time in some individuals. As risk level is a key determinant of cost-effectiveness, the rigid, “one-size-fits-all” strategy appears inadequate. Moreover, certain non-cirrhotic patients - particularly those with advanced liver fibrosis - are currently excluded from surveillance but may benefit from risk stratification to identify those for whom surveillance would be cost-effective. Surveillance strategies must also consider potential harms, and the limitations of US as a screening test. In response, alternative approaches such as biomarkers-based tests and abbreviated magnetic resonance imaging are under investigation. This article reviews the literature advocating for a transition from the current “one-size-fits-all” approach to programs tailored to individual oncological risk (risk-stratified surveillance) or those that also consider the main factors (sex, etiology, phenotype) that influence screening test performance (precision surveillance). Additionally, it presents a seminal proposal for a risk-stratified algorithm designed to optimize cost-effectiveness and the risk-benefit balance by integrating variable screening intervals and modality selection.
Aim: This study aimed to investigate the prognostic and diagnostic value of autophagy-related genes (ATGs) in hepatocellular carcinoma (HCC).
Methods: The expression profiles of differentially expressed ATGs (DEAs) were extracted from The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), International Cancer Genome Consortium (ICGC), and TIMER databases. The biological functions and enrichment pathways related to the DEAs were determined. In the TCGA training cohort, univariate Cox regression was used to define HCC subtypes, and prognostic ATGs were submitted to LASSO Cox analyses to generate overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), and disease-specific survival (DSS)-related models. The ICGC validation cohort [the Liver Cancer-RIKEN JP (LIRI-JP)] was used to examine the predictive models. The Kaplan Meier, nomogram, and receiver operating characteristic (ROC) analyses were used to confirm the accuracy of the prediction. The relationship between prognostic signature and clinicopathologic parameters, genetic alterations, tumor microenvironment, and subcellular location annotation was also examined by multiple databases.
Results: A total of 50 DEAs were identified and enriched in autophagosome membrane, programmed cell death, and PI3K-Akt signaling pathways. BIRC5, GAPDH, FKBP1A, and RAC1 were significantly correlated with poor prognosis and were identified to be independent predictors for HCC OS and DSS (HR > 1.8, P < 0.05). The risk score of prognostic models has confirmed that BIRC5 was an independent prognostic factor. The calibration curve has validated the accuracy and reliability of the nomogram for survival years, as well as ICGC validation using the same risk models. BIRC5 may influence the mortality of hepatitis B and C patients. Immunohistochemistry showed that BIRC5 protein was moderately expressed in HCC and may influence tumor detection and genetic mutations. High amplification of BIRC5 may inhibit the immune infiltrates as CD4 T cells, macrophages, and neutrophils.
Conclusion:BIRC5 is overexpressed in HCC tissues, indicating a poor prognosis that could be a new prognostic biomarker, treatment target, and diagnostic signature for HCC.
Metabolic dysfunction-associated steatotic liver disease (MASLD) and its inflammatory form, metabolic dysfunction-associated steatohepatitis (MASH), are emerging as leading causes of hepatocellular carcinoma (HCC) development. This has important implications for evaluating patients with these conditions, including the potential for early diagnosis through screening techniques. Imaging techniques for the noninvasive diagnosis of HCC in the context of MASLD also present unique considerations. Notably, HCC development in patients without cirrhosis is more frequent in MASLD compared to other chronic liver disease etiologies. Moreover, the presence of liver steatosis, a common feature in MASLD patients, can modify the radiological appearance of the liver, giving HCC in MASLD/MASH uncommon imaging characteristics. Additionally, certain histological subtypes, particularly the steatohepatitic HCC, are more prevalent in MASLD/MASH, which may influence both diagnostic strategies and therapeutic decisions in these patients. This review article focuses on the radiological characteristics of HCC developed in patients with MASLD/MASH. It specifically addresses the roles of screening and surveillance, the radiological features of HCC in MASLD/MASH, the histological subtypes associated with these conditions, and the impact of imaging on treatment decisions. Finally, a brief summary of future directions and the role of new technologies in HCC diagnosis within the context of MASLD is provided.
Aim: To construct and validate a multitask deep learning (DL) model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) contrast-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) plus cytokeratin 19 (CK19) positivity in patients with hepatocellular carcinoma (HCC).
Methods: A total of 145 pathologically confirmed HCC patients undergoing preoperative enhanced MRI were assessed between January 2012 and January 2023. A predictive model, whose skeleton structure was an expert shared network based on spatial transformations and relational reasoning, was established based on hepatobiliary phase (HBP) images using a training set (n = 66, Center 1) and validated using an external (n = 79, Centers 2 and 3) test set. A receiver operating characteristic (ROC) curve was used to evaluate MVI CK19 positivity.
Results: The area under the ROC curve (AUC) of the new model, named Expert Sharing Network, for the prediction of the CK19 and MVI expression was 0.87 and 0.88 in the training set and 0.80 and 0.85 in the validation set, respectively, which was superior to the ResNeSt50-based model, EfficientNet-b0-based model, and ResNet50-based model. The AUC of the DL model for the prediction of the MVI was 0.88 in the training set and 0.85 in the validation set, which was superior to the other three models.
Conclusion: This new model can accurately predict CK19 expression and MVI in patients with HCC.
Aim: The reprogramming of lipid metabolism can markedly enhance the nutritional adaptability of tumor cells to the glucose-deficient and hypoxic tumor microenvironment, which holds profound significance for the development and metastasis of liver cancer. Nevertheless, the alterations of lipid metabolism under stress conditions and the specific mechanisms remain ambiguous. The current study aimed to explore the molecular interaction between endoplasmic reticulum (ER) stress and lipid metabolism in hepatocellular carcinoma (HCC) using bioinformatics analysis, and further verify the role of core hub genes and offer potential targets for diagnosing and treating HCC.
Methods: Differentially expressed lipid-related genes (DLRGs) were identified via cross-crossing differentially expressed genes (DEGs) in the TCGA-LIHC program and lipid metabolism-related genes in the Genecards database. Identification of hub genes was achieved by constructing a protein-protein interaction (PPI) network, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Disease correlation analysis was performed in the Comparison of Toxicology Database, receiver operating characteristic (ROC) curve and Kaplan-Meier curve analyses were performed for the hub genes, and the CIBERSORT algorithm was employed to assess immune infiltration. The role of acetyl-CoA carboxylase 1 (ACACA) in HCC was evaluated by Western blotting, polymerase chain reaction, immunohistochemistry, and CCK-8 assay.
Results: In total, 131 DLRGs were identified, comprising 70 upregulated and 61 downregulated. PPI analysis identified 20 hub DLRGs, while ROC curve analysis and Kaplan-Meier analysis further revealed that ACACA, LCAT, APOC3, LPA and PON1 may hold diagnostic and prognostic value for HCC patients. More importantly, ACACA overexpression was related to unfavorable overall survival (OS) and adverse pathological characteristics in HCC patients. In addition, both free fatty acid (FFA) and tunicamycin (TM) could activate ER stress and enhance the expression of ACACA in HCC. Interestingly, inhibition of ER stress or fatty acid synthesis using
Conclusion: The study identifies a novel core lipid metabolism-related gene called ACACA, which has prognostic and therapeutic potential for HCC. We also provide a deep understanding of lipid metabolism correlated with ER stress in the progression of HCC, offering new opportunities for the identification of biological targets and the development of drugs and treatments for HCC patients.
Hepatocellular carcinoma (HCC) is a complex malignancy that necessitates a multidisciplinary approach to optimize diagnosis, treatment, and management. The Barcelona Clinic Liver Cancer (BCLC) staging system remains a cornerstone for clinical decision making, yet its real-world application often requires a more personalized strategy. A multi-parametric framework integrating tumor morphology, biological markers, imaging characteristics, and inflammatory responses has gained traction in refining therapeutic selection. Key factors such as tumor location, size, and vascular involvement critically impact treatment feasibility and efficacy. Biomarkers like alpha-fetoprotein (AFP) provide prognostic value, while novel markers such as des-gamma-carboxy prothrombin (DCP) enhance risk stratification for curative therapies. Inflammation-based indices, including neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios, contribute to recurrence and survival predictions. Advanced imaging modalities, such as positron emission tomography (PET), offer valuable insights into tumor biology and treatment response, guiding both surgical and non-surgical interventions. Despite its promise, implementing this multi-parametric, patient-tailored approach in clinical practice presents challenges, including variability in biomarker reliability, accessibility of advanced imaging, and the need for interdisciplinary coordination. Overcoming these limitations requires seamless collaboration among hepatologists, oncologists, radiologists, and surgeons to integrate diverse data streams into cohesive treatment algorithms. By leveraging individualized, data-driven strategies, this evolving paradigm aims to improve patient outcomes and advance precision medicine in HCC care.
Hepatocellular carcinoma (HCC), which accounts for 75%-80% of primary liver cancers, is recognized worldwide as a highly lethal cancer. Adjunctive diagnostic biomarkers for HCC are α-fetoprotein (AFP), induced by the absence of vitamin K or its antagonist (PIVKA-II), and AFP-L3. Using multiple biomarkers, or biomarkers in combination with other clinical tests, is recommended from the perspective of sensitivity and specificity; however, this approach does not lend itself to early detection. Therefore, novel biomarkers are needed urgently. Non-coding RNAs, mainly circular RNAs (circRNAs), are key players in the progression and suppression of diseases, including cancer. Unlike linear RNAs, circRNAs are cyclic RNAs generated by 3’ to 5’ back-splicing. Advancements in RNA sequencing technology and bioinformatic pipelines mean that numerous circRNAs have been predicted and identified. These RNA molecules play a role in the progression/suppression of cancer through direct or indirect epigenetic regulation via microRNAs and proteins in cancer cells, as well as in the surrounding tumor microenvironment. Some circRNAs are tissue- or cell line-specific and are detected in exosomes, blood, and saliva. Importantly, circRNAs are more stable than linear cognate RNAs in vivo. This unique characteristic positions circRNAs as versatile materials with potential for use as biomarkers, in clinical tests, and as therapeutic applications. In the context of HCC, numerous circRNAs have been identified. Here, we describe the functions of circRNAs in HCC, the experimental methods in which they can be used, and their potential clinical applications.
Cholangiocarcinoma (CCA) is a rare primary cancer of the bile duct epithelium, accounting for about 3% of all gastrointestinal cancers worldwide. CCA incidence is notably higher in Southeast and East Asia, particularly in northeastern Thailand. The early diagnosis of CCA is limited, while the cancer tends to metastasize rapidly, contributing to high mortality rates. Current treatments for CCA, including conventional chemotherapies, often cause drug resistance and induce significant side effects due to the drug going off-target. This underlines the need for novel therapeutic strategies, including chemopreventive and adjuvant treatments. Targeted drug delivery systems using nano-based technologies offer a promising approach to enhance treatment specificity and effectiveness, thereby minimizing side effects. This review provides an overview of nanomedicine’s application in the treatment of CCA. Polymeric and lipid-based nanoparticles (NPs), as examples of passive targeting mechanisms such as the enhanced permeability and retention effect, are discussed. Additionally, functionalized NPs are described, focusing on their role in active targeting strategies in CCA therapy. This summary will support the development of more effective drugs for CCA.
Cholangiocarcinoma (CCA) is a highly aggressive liver cancer associated with a poor prognosis. Cancer stem cells (CSCs) play critical roles in CCA by driving tumor initiation, metastasis, recurrence, and treatment resistance. CSCs possess unique properties, including self-renewal, quiescence, and metabolic plasticity, enabling them to evade conventional treatments such as chemotherapy and radiation. Several key CCA stem cell markers are involved in interactions with immune cells within the tumor microenvironment (TME) and in metabolic adaptations that support CSC survival and resistance through complex mechanisms. Despite advancements in chemotherapy and targeted therapies, CSCs remain a major challenge in CCA treatment. In this review, we summarize new insights, updated information, emerging therapeutic strategies, and future directions for CSC research in CCA.
Cholangiocarcinoma (CCA) is an aggressive malignancy marked by profound glucose metabolic dysregulation and oxidative stress. Central to this reprogramming is the upregulation of glucose transporters such as GLUT1, driving enhanced glycolytic flux, activation of the pentose phosphate pathway (PPP), increased lactate production, and alterations in the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS). These metabolic shifts support tumor proliferation, redox balance, and stemness, and are closely linked to recurrent oncogenic mutations, including KRAS, TP53, IDH1/2, ARID1A, FGFR2, and HER2. These mutations converge on key signaling networks that promote metabolic plasticity and therapeutic resistance. Recent evidence suggests that targeting metabolic vulnerabilities offers promising avenues for intervention. Inhibitors of glycolytic enzymes (HKII, PKM2), PPP regulators (G6PD, TKT), TCA cycle components [IDH, glutaminase (GLS)], lactate metabolism (LDHA), and OXPHOS machinery (Complex I) have demonstrated potential in preclinical models. Additionally, repurposing antidiabetic drugs such as metformin and SGLT2 inhibitors may offer novel metabolic therapies. Regulatory non-coding RNAs, including microRNAs and long non-coding RNAs, further modulate key enzymes and transporters, highlighting their emerging roles as both biomarkers and therapeutic targets. However, challenges such as tumor heterogeneity, metabolic redundancy, off-target toxicity, and resistance mechanisms continue to hinder clinical translation. Integrated therapeutic approaches combining metabolic inhibitors with chemotherapy, immunotherapy, or targeted agents are likely necessary to overcome these barriers. This review synthesizes the current understanding of glucose metabolism and redox dysregulation in CCA, emphasizing the molecular drivers, therapeutic opportunities, and translational challenges, with the goal of guiding future research toward more effective and personalized treatment strategies.
Aim: Globally, primary liver cancer ranks as the third most common cause of mortality among cancer patients. China reports the highest number of cases in the world, with chronic hepatitis B virus (HBV) infection being the most common cause. Recent studies have shown an increasing risk among adolescents and young adults aged 15-49 years, posing a significant public health challenge.
Methods: This study uses the latest data from the Global Burden of Disease (GBD) 2021 to systematically evaluate the disease burden of liver cancer among individuals aged 15-49 years in China from 1990 to 2021.
Results: The results show an increase in liver cancer cases and deaths in this age group, with a decreasing trend in incidence and mortality rates. The highest incidence and mortality rates were observed in the 45-49-year age group, with a significant increase in disease burden in men and a decrease in women. The annual net decreases in incidence and mortality rates are 1.65% and 2.31%, respectively. In the Chinese adolescent population aged 15-49 years, more than 80% of liver cancer cases are attributable to HBV. Other major risk factors include hepatitis C virus (HCV), alcohol consumption, non-alcoholic steatohepatitis (NASH), smoking, high body mass index (BMI), and high fasting plasma glucose.
Conclusion: Our study underscores the potential of HBV vaccination and lifestyle interventions in controlling Chinese adolescents’ liver cancer incidence, and provides valuable insights for other countries and regions facing similar stages of development and major health challenges related to liver cancer.
Hepatocellular carcinoma (HCC) management is a challenging task. Despite continuous efforts to develop more effective treatments, patient prognosis often remains poor. Recently, a new management concept - the “multiparametric therapeutic Hierarchy” - has been proposed, with the hypothetical advantage of avoiding the risk of undertreatment inherent in “classical” HCC management modalities while also minimizing the risk of overtreatment that may occur when inexperienced clinicians adopt overly aggressive treatment hierarchies. The “multiparametric” concept emphasizes a more comprehensive evaluation of each patient, focusing on their individual clinical presentation. Among the various factors considered, patient fitness plays a critical role. Here, fitness is understood as the set of patient-specific characteristics that can influence the outcomes of therapies within the hierarchical framework (or even preclude certain therapies altogether). This multifaceted concept extends beyond traditional “Performance Status” measurements, incorporating considerations of frailty and comorbidity. In this review, we explore the available evidence regarding the role of patient fitness within the multiparametric therapeutic hierarchy for HCC. Finally, we discuss fitness considerations across different levels of the therapeutic hierarchy, highlighting current evidence, existing challenges, and limitations, with the aim of providing new insights for physicians involved in HCC management.
The study titled “Oncolytic virus VG161 in refractory hepatocellular carcinoma” presents a promising advancement in third-line therapy for advanced hepatocellular carcinoma (HCC). VG161, derived from an oncolytic herpes simplex virus, is engineered to express interleukin (IL)-12, IL-15, IL-15R, and a fusion protein that blocks the programmed cell death protein-1 (PD-1)/programmed death-ligand-1 (PD-L1) pathway. In the multicenter phase 1 clinical trial, VG161 exhibited both strong efficacy and safety. This paper summarizes the study’s findings, discusses potential limitations, and proposes directions for future research.
Aim: Novel biomarker panels may have comparable performance to ultrasound with alpha-fetoprotein (US-AFP) in the surveillance of hepatocellular carcinoma (HCC) among individuals with cirrhosis. We performed an economic modeling study to estimate the threshold price at which biomarker-based surveillance would be cost-effective in the Australian healthcare setting.
Methods: We constructed a Markov model to evaluate three strategies: no surveillance, US-AFP surveillance, and biomarker surveillance based on the recently reported GAAD algorithm (gender, age, AFP and des-γ-carboxy prothrombin). The threshold price of biomarker surveillance was estimated relative to US-AFP, using a willingness-to-pay threshold of A$50,000 per quality-adjusted life year (QALY). The base-case analysis assumed an annual HCC incidence of 2% and 70% adherence to surveillance. Sensitivity analyses were performed to evaluate uncertainty in biomarker performance, HCC incidence, and surveillance adherence.
Results: In the base-case scenario, biomarker surveillance yielded a 0.02 QALY gain compared with US-AFP surveillance and a 0.07 QALY gain compared with no surveillance. Relative to US-AFP, biomarker surveillance was cost-effective if priced at ≤ A$454 and dominant if priced at ≤ A$310. Sensitivity analyses showed that the biomarker threshold price ranged from A$165 (with 50% adherence) to A$838 (with an annual HCC incidence of 5%).
Discussion: Biomarker surveillance is a promising intervention for HCC surveillance in people with cirrhosis. Our results suggest that, in the Australian context, it is likely to be cost-effective - and potentially cost-saving - when compared with the existing standard of US-AFP surveillance.
Driven by the global pandemic of obesity and metabolic syndrome, metabolic-associated fatty liver disease (MAFLD) has emerged as a principal driver of hepatocellular carcinoma (HCC). MAFLD-related HCC (MAFLD-HCC) exhibits distinct pathological features and a unique immune microenvironment, which collectively contribute to its poorer response to immune checkpoint inhibitors (ICIs) compared to HCC of other etiologies. This review systematically outlines the immune microenvironment in MAFLD-HCC, focusing on the dynamic changes and interactions among dysfunctional CD8+ T cells, regulatory T cells, myeloid cells, and B cells, as well as their crosstalk with metabolic reprogramming. These changes collectively result in a highly immunosuppressive tumor microenvironment, leading to resistance to ICIs. Addressing the challenges in treating MAFLD-HCC, this paper discusses the current progress in immunotherapy for MAFLD-HCC and explores potential directions for future immunotherapy research.
Thermal ablation, including radiofrequency ablation and microwave ablation, is a critical treatment modality for early-stage hepatocellular carcinoma (HCC); however, the high postoperative recurrence rate remains a significant challenge. Underlying mechanisms of post-ablation recurrence - including autophagy, epithelial-mesenchymal transition, hypoxic microenvironment formation, and immune suppression - are being progressively elucidated. Insufficient ablation is considered a key initiating factor for these processes. Advanced imaging techniques have thus been developed to improve intraprocedural monitoring and margin assessment, aiming to prevent insufficient ablation. For cases with confirmed residual or recurrent tumors, integrated therapies such as transarterial chemoembolization, radiotherapy, immunotherapy, and nanomedicine offer promising salvage options to mitigate further recurrence and improve survival. This review summarizes the mechanisms of tumor recurrence following HCC ablation and discusses potential countermeasures, aiming to provide valuable insights for clinical practice.
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related deaths worldwide, characterised by significant clinical heterogeneity and therapeutic complexity. The Barcelona Clinic Liver Cancer system has long been the primary framework for staging and treatment allocation; however, its 2025 update, while introducing important refinements, retains the structural limitations inherent to stage-based algorithms. Recent guidelines from international organisations - including the European Association for the Study of the Liver (2025), the European Society for Medical Oncology (2025), the American Association for the Study of Liver Diseases (2023), and various national bodies - have shifted towards flexible, patient-centred approaches that emphasise multidisciplinary tumour board decision making, feasibility assessment, and dynamic therapeutic adaptation. The multiparametric therapeutic hierarchy (MTH) has been introduced as an expert opinion framework to formalise this evolving approach. MTH maintains the prognostic value of staging while separating it from treatment decisions, replacing inflexible algorithms with a tri-axial model: an ordinal hierarchy of therapies ranked by survival benefit, a structured multiparametric feasibility assessment, and a converse therapeutic hierarchy allowing upward movement through curative-intent strategies over time. The model aligns with the conceptual and methodological directions of current guidelines, offering an auditable, adaptable, and ethically consistent decision-making tool for expert multidisciplinary teams. Although based on strong evidence supporting its conceptual foundations, MTH remains a “checklist” that requires prospective validation and additional detail with evidence-based parameters, including biomarkers, imaging criteria, patient-reported outcomes, and integration of artificial intelligence. By providing the conceptual basis for this Special Issue “The Multiparametric Therapeutic Hierarchy: A Multidisciplinary Approach to HCC Management”, MTH aims to support a coherent, multidisciplinary, and future-oriented framework for personalised management of HCC.
Hepatocellular carcinoma (HCC) exhibits high incidence and mortality rates in China, posing a significant public health burden. Established risk factors, including hepatitis B virus, hepatitis C virus, aflatoxin B1 exposure, alcohol consumption, and smoking, shape the unique epidemiological profile of HCC in China and exacerbate its marked tumor heterogeneity. This complexity leads to highly intricate prognostic assessment, management strategies, and predictive approaches across diverse patient populations. The updated “Guidelines for Diagnosis and Treatment of Primary Liver Cancer (2024 Edition)” reflect significant advancements in screening, diagnosis, staging, treatment, and follow-up, with particular emphasis on management strategies tailored to the Chinese context. The multidisciplinary tumor board (MDTB) serves as a cornerstone of modern oncology care. By integrating expertise from diverse medical specialties, the MDTB is crucial for developing individualized treatment plans for complex HCC cases. However, current MDTB practice faces significant challenges, primarily stemming from the rapid evolution of treatment options and the swift advancement of emerging technologies, particularly artificial intelligence (AI). This necessitates continuous learning among MDTB members to effectively integrate cutting-edge therapies and tools. This review focuses on the disease characteristics of HCC in China and the unmet needs within its clinical management. It delves into how AI technologies can enhance the capabilities of MDTBs, aiming to elucidate the transformative potential and persisting challenges of AI-driven multidisciplinary care models for HCC in China.
Hepatitis B virus (HBV) infection remains a major cause of hepatocellular carcinoma (HCC). It is estimated that 254 million people are chronically infected with HBV, with 1.1 million deaths projected in 2025. Functional cure, defined as sustained loss of hepatitis B surface antigen (HBsAg), is important for the prevention of HCC. While nucleos(t)ide analogs maintain viral suppression in over 95% of patients, HBsAg clearance is achieved in only 2%-11%. The functional cure rate with interferon therapy is highly variable in different populations, ranging from 2% to over 40%. Consequently, functional cure remains the primary focus of novel therapeutic development. Here, we analyze the virological and immunological barriers to functional cure and summarize current therapeutic methods. Among these, novel RNA-interference-based therapeutics reduce HBsAg to below 10% of baseline in most patients. However, monotherapy with these agents results in HBsAg loss in fewer than 10% of cases. However, sequential interferon or immunomodulatory agents raise the HBsAg loss to 15%-30%. To achieve the > 50% cure rate likely required for the World Health Organization’s 2030 elimination goals, we analyze promising strategies focused on multi-target combination approaches and precision-medicine frameworks.
Aim: This study aimed to construct and validate a nomogram model to predict the occurrence of severe immune-related adverse events (sirAEs) (≥ 3 grade) in hepatocellular carcinoma (HCC) patients receiving transcatheter arterial chemoembolization (TACE) plus immunotherapy.
Methods: Data regarding lymphocyte subpopulations and clinical characteristics of 130 HCC patients treated from January 2020 to June 2024 were retrospectively analyzed, including 46 patients with sirAEs and 84 patients without sirAEs. Univariate logistic regression analysis was used to summarize the factors that might affect the occurrence of sirAEs in patients, and then these factors were included in the multivariate logistic regression analysis. These influencing factors were then included in the construction of the nomogram prediction model. Receiver operating characteristic (ROC) and calibration curves were used to verify the nomogram prediction model.
Results: Multivariate logistic regression analysis indicated that liver cirrhosis, lower value of Neutrophil-to-Lymphocyte Ratio and Regulatory T cell, and higher value of lymphocytes and creatinine were significantly associated with sirAEs (P < 0.05). Based on these factors, a nomogram prediction model for predicting sirAEs after TACE plus immunotherapy in HCC patients was constructed. The area under the ROC curve (AUC) for the prediction model was 0.885 (95% confidence interval: 0.820-0.951), with a cut-off value of 137.786. The model demonstrated a sensitivity of 0.812 and a specificity of 0.889.
Conclusion: The nomogram model developed in this study shows promising predictive performance for sirAEs in HCC patients receiving TACE plus immunotherapy; however, further validation in larger, multi-center prospective cohorts is needed to confirm its generalizability and clinical utility.
For hepatocellular carcinoma (HCC) patients, the clinical efficacy of immune checkpoint inhibitors (ICIs) remains limited by low response rates. The gut microbiome as a critical modulator of ICI responsiveness in HCC. We systematically analyze the relevant gut microbial signatures distinguishing programmed death 1 therapy in responders and non-responders, with particular emphasis on prognostic taxa. Microbiome-targeted interventions, encompassing antibiotic modulation, probiotic supplementation, prebiotic administration, and fecal microbiota transplantation, may synergistically enhance the efficacy of ICIs by leveraging the immunomodulatory potential of gut-derived microbial metabolites. The mechanisms governing microbiome-mediated immunotherapeutic regulation involve multifaceted interactions, particularly through microbiota-driven immunomodulation within the tumor microenvironment. We identify key translational challenges of tumor heterogeneity in microbiomes. Future research directions emphasize the need for standardized protocols, longitudinal cohort studies, and innovative preclinical models to bridge existing knowledge gaps.