Search Results (4,093)

Background: Early detection of hepatocellular carcinoma (HCC) is critical for improving patient outcomes; however, ultrasound-based surveillance has limited sensitivity and variable performance across patient populations. We evaluated the performance of a multitarget HCC blood test (mt-HBT), incorporating methylated DNA markers, alpha-fetoprotein (AFP), and patient sex, across clinically relevant subgroups. Methods: We performed a subgroup analysis of a multicenter, prospective case-control study that included 159 patients with early-stage HCC (Barcelona Clinic Liver Cancer Stage 0/A) and 649 control patients with cirrhosis or chronic hepatitis B without HCC. The mt-HBT combined methylated HOXA1, TSPYL5, and B3GALT6 markers with AFP and sex. Sensitivity and specificity were evaluated overall and according to age, sex, obesity, liver disease etiology, Child Pugh class, and tumor size. Performance was compared with AFP and GALAD. Results: Overall sensitivity and specificity of mt-HBT for early-stage HCC detection were 76.7% (95% CI, 69.6–82.6) and 87.5% (95% CI, 84.8–89.8), respectively. Sensitivity was significantly higher than that of AFP (35.2%, p < 0.001) and comparable to that of GALAD (78.6%, p = 0.56), whereas specificity was lower than that of AFP (98.5%, p < 0.001) but higher than that of GALAD (76.9%, p < 0.001). Sensitivity was maintained across key subgroups, including patients with obesity (68.9%), Child Pugh B cirrhosis (75.0%), hepatitis C (80.0%), hepatitis B (72.2%), alcohol-associated liver disease (80.5%), and metabolic dysfunction-associated steatotic disease (69.0%) (all p > 0.05 between subgroups). Specificity exceeded 80% in all examined populations and was significantly higher in women than in men (92.5% vs. 83.9%, p < 0.001). Sensitivity increased with tumor size, ranging from 60.0% for tumors < 2 cm to 100% for tumors > 5 cm. Conclusions: The mt-HBT demonstrated robust, consistent performance for early-stage HCC detection across diverse patient populations, including subgroups in which ultrasound surveillance commonly underperforms. These findings support the prospective validation of mt-HBT as a blood-based surveillance strategy for HCC. Full article

Hepatocellular carcinoma (HCC) remains difficult to control because recurrence, impaired hepatic reserve, and treatment resistance limit durable benefit. Natural products and traditional Chinese medicine (TCM) provide resources that range from drug-lead discovery to adjunctive multicomponent therapy. This review integrates pharmacological and clinical evidence for purified compounds, semisynthetic derivatives, extracts, formulas, and delivery systems. It focuses on metabolic reprogramming and redox homeostasis, stress responses and regulated cell death, tumor cell plasticity and vascular remodeling, and the immune microenvironment and host response. Recent studies have strengthened selected mechanistic claims through chemical probes, functional perturbation, and resistance models. Clinical research has concentrated on recurrence control after surgery or minimally invasive treatment and on combinations with transarterial chemoembolization, targeted agents, and immunotherapy. Randomized trials and prospective cohorts suggest potential benefit in specific settings, although product standardization, external validation, and long-term follow-up remain limited. Major translational barriers include uncertain active constituents, inadequate batch comparability, missing tumor-exposure data, and sparse herb–drug interaction studies. Future development should match target validation, pharmacokinetics, safety assessment, and clinical endpoints to each product class and clarify whether a candidate is best positioned as a drug lead, adjunctive therapy, or supportive intervention. Full article

Background: Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality, and responses to immune checkpoint blockade are often limited. Casein kinase 2 beta (CK2beta), encoded by CSNK2B, has established tumor-intrinsic functions, but its relationship with immune states in HCC remains unclear. Methods: We integrated TCGA-LIHC, six GEO cohort-platform combinations, GSE149614 single-cell RNA sequencing data, CIBERSORTx deconvolution, broad-compartment CellChat inference, and public proteomic resources. We evaluated CSNK2B expression, overall survival, a prespecified GDF15-associated Treg/checkpoint transcriptional module, tumor microenvironment features, compartment-level localization, and protein-level support. Results: CSNK2B was upregulated in TCGA-LIHC and all six external cohort-platform comparisons but was not a robust prognostic marker. The strongest immune association was with the GDF15-associated Treg/checkpoint module (Spearman rho = 0.371, q = 2.9 × 10⁻¹²); the direction was positive in all six GEO analyses and four passed BH-FDR. Deconvolution results were small and heterogeneous. CSNK2B and GDF15 were most prominent in hepatocyte-like compartments, while the candidate hepatocyte-to-T/NK GDF15–TGFBR2 pair was not reproduced in pooled data or in any of 10 patient-stratified CellChat runs. Public proteomic datasets supported tumor-side elevation of both proteins. Conclusions: CSNK2B expression aligns with a reproducible GDF15-associated transcriptional state in HCC, accompanied by selective stromal, endothelial, and immune-context changes. The evidence is associative and provides hypotheses for perturbation, spatial, and co-culture validation rather than a causal or treatment-prediction claim. Full article

Background/Objectives: Hepatocellular carcinoma (HCC) is the most prevalent primary liver tumor and is often diagnosed at advanced stages with very poor therapeutic response, leading to high mortality. Thus, new therapeutic strategies and biomarkers are urgently needed. We previously showed that the combination of loratadine, raloxifene, and sorafenib exerts synergistic cytotoxicity on HCC cells. Here, we explored potential molecular mechanisms underlying the anticancer effects of this combination using multiomics analyses. Methods: We performed proteomic analyses based on mass spectrometry, transcriptomic analyses using the Clariom D Plus human microarray (Affymetrix), and metabolomic analyses based on nuclear magnetic resonance to investigate the profile changes induced by the drug combination in HuH7 cells. Bioinformatic analyses were applied to associate the omics changes with biological functions, molecular interactions, and clinical relevance in terms of patient survival. Results: We identified several molecules whose expression changed in response to treatment across the three omics profiles analyzed. Some of them were found to be involved in hallmarks of cancer, including sustained proliferation, evasion of growth suppressors, and resistance to cell death. Integrated multi-omics analyses revealed that the drug combination suppresses critical oncogenic drivers (C7orf50, NUP188, and HS2ST1) and that the mitotic cell cycle process, DNA synthesis and cholesterol biosynthesis are the primary pathways affected. Protein–protein interaction analysis revealed five key hubs (KIF2C, PCNA, TRIP13, NDC80, and RPA3), whose expression in HCC is associated with poor clinical prognosis. Conclusions: The combined treatment rewired molecular networks involved in HCC progression. These findings identify clinically relevant molecular targets associated with poor prognosis and provide mechanistic insights into the synergistic anticancer activity of this drug combination. Full article

With the advent of the new definition, fatty liver disorders have been reframed into metabolic dysfunction-associated steatotic liver disease (MASLD), alcohol-related liver disease (ALD), and the mixed phenotype referred to as MetALD (MASLD and increased alcohol intake). This change reflects the real-world clinical practice, where metabolic dysfunction and alcohol frequently coexist and synergize to increase risks of steatohepatitis, fibrosis, and hepatocellular carcinoma (HCC). While conventional non-invasive tests (NITs) remain the backbone of risk stratification, lipidomics and metabolomics can capture biological information on disease mechanisms and may improve early detection and prognosis. Here, we summarize the current evidence on circulating and tissue lipidomic and metabolomic signatures across MASLD, ALD and MetALD, discuss how the new definitions affect clinical risk assessment, and highlight recent studies which partially distinguish molecular fingerprints for mixed etiology disease. Full article

Background: Portal vein tumor thrombus (PVTT) marks advanced hepatocellular carcinoma (HCC) and carries a dismal prognosis. Survival varies widely even within this stage, yet simple tools for individualized risk stratification remain scarce. Methods: We retrospectively enrolled 134 HCC patients with PVTT and randomly divided them into a training set (n = 94) and a validation set (n = 40). Clinical predictors were selected by variance inflation factor screening and backward elimination Cox regression. A radiomics score (Rad-score) was constructed from portal-venous phase computed tomography (CT) images using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression with 10-fold cross-validation. Three Cox models were built: a clinical model, an imaging model based solely on the Rad-score, and a combined model integrating both. Discrimination was assessed by C-index and time-dependent area under the curve (AUC). Calibration was examined with bootstrap-based calibration curves. Decision curve analysis evaluated net benefit. A nomogram was developed from the combined model. Results: Four clinical variables (alpha-fetoprotein (AFP), body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), and alkaline phosphatase (ALP)) and two CT texture features (GLRLM_SRHGE and GLZLM_SZHGE) were retained as independent predictors. The combined model gave the highest C-index in both the training set (0.843) and the internal validation set (0.815). Its 1-year AUC reached 0.953 and 0.947 in the two sets. Calibration slopes ranged from 1.044 to 1.291 across time points, indicating a tendency toward mild overdispersion; nevertheless, decision curve analysis confirmed net benefit across clinically relevant thresholds. The combined model offered greater net benefit than either single-domain model across a 0–50% threshold range. A nomogram incorporating all five predictors was generated for individualized 12- and 24-month survival prediction. Conclusions: A combined model integrating routine laboratory variables and a CT-based radiomics score improved survival prediction over clinical or imaging models alone. The corresponding nomogram uses inputs from a basic blood panel and a single portal-venous phase CT, suggesting its potential as a low-cost prognostic stratification tool for HCC patients with PVTT, although external validation in prospective multicenter cohorts is required before clinical implementation. Full article

Background/Objectives: Stereotactic body radiotherapy (SBRT) is increasingly used for hepatocellular carcinoma (HCC) that is unsuitable for surgery, radiofrequency ablation (RFA), or transplantation. Although current evidence for imaging assessment after SBRT is largely based on contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), repeated contrast-enhanced imaging may be difficult to perform at every routine follow-up visit. Thus, we evaluated whether conventional abdominal ultrasound (US) can monitor tumor size after SBRT for HCC. Methods: We retrospectively reviewed 67 consecutive patients who underwent SBRT for HCC at our institution between January 2015 and October 2020. After excluding patients treated for local recurrence after RFA or transarterial chemoembolization, those whose lesions were not visible on pretreatment US, and those without follow-up US within one year, 32 patients with 32 nodules were analyzed. Tumor visibility and size changes on US were assessed before treatment and at <6, 6–12, and 12–18 months after SBRT. Results: The treated lesion was identified as a discrete nodule on US in 100% (15/15; 95% CI, 78.2–100%), 75.0% (18/24; 95% CI, 53.3–90.2%), and 50.0% (8/16; 95% CI, 24.7–75.3%) of examinations at <6, 6–12, and 12–18 months, respectively. In all cases in which the lesion was no longer measurable on US, contrast-enhanced CT/MRI showed complete or partial response. Local tumor progression occurred in one patient (3.1%) during a median follow-up of 24.1 months; in this patient, interval enlargement was first detected by US 3.7 months after SBRT and was subsequently confirmed by dynamic CT/MRI. Conclusions: These descriptive findings suggest that in selected patients with lesions clearly visible on pretreatment US, conventional abdominal US may provide complementary morphologic information during the first year after SBRT when used alongside periodic dynamic CT/MRI. Prospective validation is required before routine implementation. Full article

Chronic hepatitis B virus (CHB) infection remains a major cause of hepatocellular carcinoma (HCC), yet the premalignant microenvironment that links to HBV-associated HCC (HBV-HCC) is still poorly defined. This review synthesizes evidence that HBV-infected hepatocytes function as signaling hubs that, through microRNA (miRNA)-regulated crosstalk with Kupffer cells, liver sinusoidal endothelial cells, hepatic stellate cells and cancer-associated fibroblasts (CAFs), progressively remodel the liver from an antiviral tissue into a premalignant and early tumor microenvironment. Across the HBV-HCC continuum, a core set of dysregulated miRNAs, including miR-21, miR-29a/b, miR-122, miR-146a, miR-155, miR-200a, miR-126, miR-210 and the miR-130/301 family, coordinates transition from innate antiviral responses to HSC activation, extracellular matrix deposition, mechanotransduction, angiogenesis, chronic inflammation and cancer-associated CAF programing. By mapping these stage-specific miRNA networks onto acute infection, CHB, early fibrogenesis, advanced fibrosis and CAF-rich dysplastic states, the review reframes HBV-HCC pathogenesis as a sequence of miRNA-guided hepatocyte–stromal states rather than a purely hepatocyte-intrinsic process. This perspective suggests that composite, cell-type-resolved miRNA signatures in serum or liver tissue could serve as biomarkers for identifying CHB patients who are entering a premalignant microenvironment before conventional surveillance markers become abnormal. It further highlights miRNA hubs that couple antiviral, fibrogenic, angiogenic and CAF-associated signaling as potential therapeutic targets for reprograming the HBV-driven premalignant microenvironment, with the long-term goal of intercepting HBV-HCC development at earlier, microenvironmentally defined stages. Full article

Aflatoxin B1 (AFB1) is a potent foodborne mycotoxin classified as a Group 1 human carcinogen by the International Agency for Research on Cancer (IARC) and is widely recognized for its causal role in hepatocellular carcinoma (HCC). Beyond its well-established hepatotoxicity, increasing evidence indicates that AFB1 also disrupts intestinal homeostasis by impairing epithelial barrier integrity, reducing mucus production, altering gut microbial composition, and disturbing microbial metabolism. These changes promote increased intestinal permeability, facilitating the translocation of bacteria and microbial products and contributing to chronic inflammation through the gut–liver axis. Recent experimental studies further suggest that alterations in short-chain fatty acid (SCFA) production and microbiota-dependent signaling pathways may actively mediate AFB1-induced intestinal and hepatic injury. Although direct evidence linking AFB1-induced dysbiosis to extrahepatic carcinogenesis remains limited, growing evidence indicates that persistent barrier dysfunction, microbial imbalance, and chronic inflammation may create a microenvironment favorable for tumor initiation and progression in tissues beyond the liver. This review critically summarizes current evidence regarding the effects of AFB1 on intestinal barrier function, gut microbiota dysbiosis, bacterial translocation, microbial metabolites, and the gut–liver axis while evaluating the mechanistic evidence supporting these interactions and highlighting the major knowledge gaps that should be addressed in future research. Full article

Geranylgeranoic acid (GGA) is an endogenous acyclic diterpenoid metabolite of the mevalonate pathway that has been implicated in programmed cell death in hepatoma cells and may contribute to the elimination of premalignant hepatocytes. Recent metabolomic studies have identified 2,3-dihydrogeranylgeranoic acid (2,3-diGGA), an α-saturated derivative of GGA with reduced cell-death-inducing activity compared with GGA. This concise review examines the hypothesis that diversion of bioactive GGA toward 2,3-diGGA formation represents a metabolic shift that may attenuate hepatic tumor surveillance and influence hepatocellular carcinoma susceptibility. The review summarizes current evidence for endogenous GGA biosynthesis through the mevalonate pathway, MAO-B- and CYP3A4-associated GGA formation, and the tissue- and age-associated distribution of GGA and 2,3-diGGA. It also discusses the proposed 2,3-diGGA-forming activity, whose molecular identity remains unknown, and evaluates the potential utility of the GGA/2,3-diGGA ratio as a candidate biomarker. Finally, future experimental strategies are outlined to identify the responsible enzyme, clarify causality in cellular and animal models, and validate this metabolic framework in human liver tissues, chronic liver disease cohorts, and HCC-associated settings. Full article

Hepatocellular carcinoma (HCC) remains a major global health challenge and one of the leading causes of cancer-related mortality, with advanced disease continuing to be associated with limited therapeutic options and substantial heterogeneity in response to systemic treatment. Recent evidence has established the gut microbiota, through the gut–liver axis, as a critical determinant of immunotherapy efficacy, while also influencing antitumor immunity and liver carcinogenesis. Microbial dysbiosis may promote chronic inflammation, intestinal barrier disruption, bacterial translocation, and immune dysfunction, thereby contributing to hepatocarcinogenesis. Moreover, gut microbial composition and microbial-derived metabolites, including bile acids, short-chain fatty acids (SCFAs), and inosine, have been associated with modulation of antitumor immune responses and differential outcomes to immune checkpoint inhibitors (ICIs). Emerging clinical evidence in HCC has identified distinct gut microbial signatures associated with response to nivolumab, pembrolizumab, and atezolizumab-based regimens, including enrichment of Akkermansia muciniphila and SCFA-producing taxa such as Ruminococcaceae, Roseburia, and Prevotella in responders. However, these findings remain inconsistent across studies, with no reproducible microbial signature identified because of small cohort sizes, heterogeneous patient populations, geographic variation, cirrhosis-related confounding factors, and methodological differences in microbiome analysis. This review summarizes the current understanding of microbiome–immune interactions in HCC, examines mechanistic pathways linking the microbiota to immunotherapy response, critically evaluates available clinical evidence, and discusses current limitations and future therapeutic strategies, including fecal microbiota transplantation, probiotics, dietary modulation, and engineered bacterial platforms. Collectively, microbiome-based approaches may contribute to the development of personalized immunotherapeutic strategies in HCC, although larger standardized prospective studies are required before microbiome-derived biomarkers can be implemented in routine clinical practice. Full article

Hepatocellular carcinoma (HCC) is one of the malignant tumors with high incidence and mortality rates worldwide. Given the poor prognosis of patients with HCC, it is crucial to explore the molecular mechanisms underlying HCC development and to evaluate prognostic markers. Differential expression analysis followed by univariate Cox, LASSO, and multivariate Cox regression identified four genes (EPO, SOCS2, IL18RAP, and KPNA2), and a Cox-based risk score was evaluated in the TCGA-LIHC cohort and externally in GSE14520 using Kaplan–Meier and time-dependent ROC analyses. Bulk, single-cell, and protein resources provided convergent expression context. Survival machine-learning analysis using observed overall-survival time and censoring status identified Cox–Ridge as the best-performing model in TCGA-LIHC, with more modest performance in GSE14520, and immune profiling revealed risk-group-associated differences in estimated immune and stromal components, immune-cell composition, and immune-checkpoint expression. The oncoPredict/GDSC2 screen highlighted five potential drug candidates for experimental prioritization. Because the drug screen is based on computationally predicted sensitivities, these findings should be regarded as hypothesis-generating and require validation in prospective cohorts and experimental systems before clinical translation. Full article

Microvascular invasion (MVI) is a critical prognostic indicator for recurrence and survival in hepatocellular carcinoma (HCC); however, its accurate preoperative assessment remains clinically challenging. Postoperative histopathology is subject to sampling bias and time delays, while traditional imaging techniques lack the molecular specificity required to predict MVI. Liquid biopsy, through the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), circulating tumor RNA (ctRNA), and extracellular vesicles (EVs), provides a minimally invasive approach for capturing tumor-derived molecular and cellular signals associated with vascular invasion. This narrative review comprehensively summarizes the current evidence linking these four liquid biopsy analyte categories to MVI in HCC, evaluates their integration into multi-omics predictive models, including multi-marker, clinicopathological-integrated, and imaging-integrated strategies, and proposes an evidence-level framework that categorizes blood biomarkers according to the strength of their support for MVI prediction, distinguishing direct histopathological validation from indirect associations with aggressive tumor biology. Key challenges are critically examined, including the variable specificity of individual biomarkers for MVI, the lack of head-to-head comparative studies, the absence of standardized pre-analytical and analytical protocols, and the methodological limitations of current prediction models. As a narrative review, this work does not employ systematic review methodology, and the evidence synthesis should be interpreted accordingly. The review provides a framework for understanding how liquid biopsy-based MVI risk stratification may inform surgical and perioperative decision-making following prospective validation. Full article

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide and exhibits considerable biological heterogeneity in both molecular and clinical characteristics. The diverse molecular alterations and clinical manifestations of HCC indicate substantial heterogeneity across patient subgroups. This study aimed to identify novel therapeutic targets and predictive biomarkers associated with HCC using an integrative bioinformatics approach. High-throughput genomic datasets were obtained from the UCSC Xena browser to retrieve mRNA HTSeq-count data from the TCGA-HCC cohort. Gene co-expression network (GCN), protein–protein interaction network (PPIN), and enrichment analyses were performed to identify key dysregulated genes and their biological significance. Integrated network analyses identified three dysregulated hub genes, namely CXCR2, TLR2, and TLR4. Genomic alterations in these genes were further evaluated across tumor samples in the TCGA-HCC cohort. Kaplan–Meier (KM) survival analysis demonstrated that lower CXCR2 mRNA expression was significantly associated with poorer overall survival (OS) and recurrence-free survival (RFS). Furthermore, TIMER and UALCAN analyses revealed significant associations between CXCR2 expression and tumor purity, as well as immune cell infiltration levels, including T cells, macrophages, dendritic cells (DCs), and neutrophils. These findings suggest that CXCR2 is significantly associated with the immune microenvironment of HCC and represents a potential prognostic biomarker whose biological role warrants further mechanistic investigation. Full article

Background: Predicting recurrence after liver transplantation (LT) for hepatocellular carcinoma (HCC) remains important for post-transplant risk stratification and surveillance planning. The Milan criteria discriminate only moderately and some machine-learning re-analyses report overly optimistic results because of information leakage. Aim: The current study aimed to re-evaluate a previously published transplant cohort under a leakage-free survival-analysis framework and to benchmark post-transplant, explant-informed survival learners against the Milan criteria as a fixed pre-transplant reference. We hypothesised moderate rather than near-perfect discrimination, similar performance across learners of differing complexity, and better discrimination than the Milan criteria. Methods: This secondary analysis included 356 patients with HCC who underwent LT. The primary endpoint was recurrence-free survival, analysed from the observed event indicator and follow-up time rather than from a derived risk label. Seven survival learners were benchmarked with repeated nested cross-validation, using three repeats of a five-fold outer loop with a three-fold inner tuning loop. All data-dependent preprocessing, including robust multivariable outlier handling and imputation, was fitted within training folds only. Performance was assessed by the concordance indices of Harrell and Uno, the time-dependent area under the curve, the integrated Brier score, calibration, decision-curve analysis and descriptive competing-risk assessment. Results: Recurrence developed in 183 of the 356 patients over a median follow-up of 52 months. Discrimination was moderate rather than near-perfect and similar across learners; the random survival forest ranked highest and the Elastic-Net Cox model performed comparably. All learners showed higher descriptive concordance than the Milan criteria, and dependency-corrected comparisons supported higher concordance for the full-feature Cox model than for the Milan criteria, whereas the random survival forest and Cox did not differ materially. Out-of-fold calibration of the Elastic-Net Cox model at 36 months was acceptable, decision-curve analysis indicated positive net benefit across clinically relevant thresholds, and tumour size and alpha-fetoprotein were the leading contributors to prediction. Findings were stable in ablation and threshold-sensitivity analyses. Conclusions: Leakage-free survival modelling gave moderate but internally validated prediction of post-transplant recurrence and higher concordance than the Milan criteria in this cohort, supporting the stated hypotheses. Careful study design may matter more than architectural complexity in this setting, and leakage-free survival analysis is a practical standard for prognostic modelling in transplant oncology. Full article