Gastroenterology. 2026 Apr 1:S0016-5085(26)00312-4. doi: 10.1053/j.gastro.2026.03.020. Online ahead of print.

ABSTRACT

The burden of chronic liver disease is rapidly increasing worldwide, driven primarily by metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic and alcohol-associated liver disease (MetALD), and alcohol-associated liver disease (ALD). Genetic predisposition contributes substantially to variability in disease onset, progression, and outcomes, and recent advances in genomic discovery have brought polygenic risk scores (PRS) and targeted sequencing closer to clinical relevance. This review summarizes the role of genetic testing in clinical hepatology, including monogenic drivers of disease and the growing role of common variants and PRS. Specific populations, including cryptogenic cirrhosis and lean MASLD patients, may be enriched for monogenic drivers of disease. In addition, patients with chronic liver disease may benefit from incorporation of genetic risk scores including PNPLA3, TM6SF2, HSD17B13, and other key variants in determining risk for fibrosis progression and cirrhosis. Across MASLD and ALD, PRS demonstrate modest improvements in predicting fibrosis progression and liver-related events, especially when integrated with clinical risk factors and comorbidities. However, their performance remains limited for population-level screening. Similarly, PRS alone has limited diagnostic accuracy for hepatocellular carcinoma and more complex models with clinical features and multi-omic biomarkers are likely needed. Emerging therapies targeting PNPLA3 and HSD17B13 variants represent a paradigm shift toward genetically informed treatment. Yet challenges remain, including limited ancestral diversity in genomic datasets, pleiotropic effects of variants, cost-effectiveness, and the need for integration with other omics and electronic medical records. As evidence matures, combining genetic risk with clinical and environmental factors may enable more personalized approaches to prognostication and therapy in liver disease.

PMID:41932449 | DOI:10.1053/j.gastro.2026.03.020

Discov Oncol. 2026 Apr 2. doi: 10.1007/s12672-026-04740-8. Online ahead of print.

NO ABSTRACT

PMID:41927922 | DOI:10.1007/s12672-026-04740-8

Curr Opin Gastroenterol. 2026 May 1;42(3):121-128. doi: 10.1097/MOG.0000000000001165. Epub 2026 Mar 6.

ABSTRACT

PURPOSE OF REVIEW: Discuss advances in genomics, metabolomics, and proteomics in steatotic liver disease.

RECENT FINDINGS: Common genetic variants in genes including PNPLA3, TM6SF2, and HSD17B13 are associated with risk of hepatic steatosis, metabolic dysfunction-associated steatotic liver disease (MASLD) and alcohol-associated liver disease (ALD) cirrhosis, and hepatocellular carcinoma. In contrast, variants in other genes such as GCKR are strongly associated with steatosis but much more weakly associated with advanced liver disease. The cirrhosis-associated variants typically drive steatosis through reduction of lipid export from the liver, potentially highlighting this mechanism as a driver of fibrosis though not ruling out alternative pathways. Alterations in amino acids, lipids, bile acids, and other metabolites have been observed in both MASLD and ALD reflecting insulin resistance, altered bile acid metabolism, and increased fatty acid flux and de novo lipogenesis (for MASLD) or mitochondrial dysfunction (for ALD). Also seen are characteristic changes in serum/plasma protein levels reflecting fibrosis, systemic inflammation, and hepatic synthetic function are also seen with MASLD and ALD. Predictive models incorporating genomics, metabolomic, and proteomic biomarkers may improve upon existing clinical models, but nearly all studies on this topic have been retrospective or post hoc.

SUMMARY: Genomics, metabolomics, proteomics, and multiomics may improve our understanding of disease pathophysiology. They may also have implications for clinical care, but further prospective studies are required to establish whether they provide sufficient benefit over clinical biomarkers to be routinely used.

PMID:41912348 | DOI:10.1097/MOG.0000000000001165

Cancer Lett. 2026 Mar 27;648:218461. doi: 10.1016/j.canlet.2026.218461. Online ahead of print.

ABSTRACT

Tertiary lymphoid structures (TLSs) have emerged as key regulators of anti-tumor immunity and biomarkers for immunotherapy response in liver cancer, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and combined hepatocellular-cholangiocarcinoma (cHCC-iCCA). Advances in single-cell and spatial multi-omics technologies have revealed unprecedented complexity in TLSs, challenging the traditional binary classification of TLSs as simply "good" or "bad". Their functional diversity appears to be shaped by spatiotemporal context, cellular composition, and maturation status. This review provides a comprehensive synthesis of TLSs in liver cancer, employing the Yin-Yang paradigm to navigate their functional dualism and prognostic contradictions through a detailed analysis of their identification, classification, and spatiotemporal interactions within the TME. Mechanistically, we elucidate how TLS functions are orchestrated by complex interactions between tumor cells, immune cell subsets, stromal components, and systemic factors. Within this framework, key metabolic drivers, notably ATP citrate lyase (ACLY), and signaling axes such as cGAS-STING/mTOR have emerged as pivotal regulators of TLS ontogeny. In addition, we evaluate current preclinical animal models and therapeutic strategies for clinical TLS induction. Furthermore, we have discussed the key unanswered questions in the field, including the three-dimensional architecture of TLSs and the mechanisms by which they establish durable immunological memory independent of the primary tumor. Clinically, TLSs exhibit great promise as prognostic and predictive biomarkers, particularly in the context of immune checkpoint blockade and locoregional therapies. Finally, we identify challenges in standardization, mechanistic understanding, and translational applications, providing directions for future research to harness TLSs for improving liver cancer outcomes.

PMID:41905709 | DOI:10.1016/j.canlet.2026.218461

Crit Rev Oncol Hematol. 2026 Mar 27;222:105305. doi: 10.1016/j.critrevonc.2026.105305. Online ahead of print.

ABSTRACT

The management of hepatocellular carcinoma (HCC) faces substantial and evolving challenges, driven by its aggressive biology, drug resistance, and the clinical urgency to detect recurrence. The treatment paradigm has undergone a profound transformation, evolving from surgical interventions and molecular targeted agents to the current era dominated by immunotherapy. Immune checkpoint inhibitors, particularly when used in combination with anti-angiogenic drugs or as part of dual-checkpoint blockade regimens, have established a new first-line standard of treatment for advanced HCC, delivering unprecedented survival improvements. Despite this progress, significant obstacles remain, including primary and acquired resistance, variable patient responses, and notably reduced efficacy in specific etiological subgroups. This comprehensive review synthesizes the emerging modalities such as bispecific antibodies, adoptive cell therapies, and innovative rational combinations that integrate systemic immunotherapy with locoregional treatments or novel targeted agents. Furthermore, we delve into the critical search for predictive biomarkers, encompassing liquid biopsy and multi-omics approaches, and dissect the complex cellular and molecular mechanisms underlying therapeutic resistance within the immunosuppressive tumor microenvironment. Finally, we outline future translational directions, emphasizing the expansion of immunotherapy, the development of tailored strategies for therapy-resistant disease, and the imperative move towards a personalized, biomarker-driven treatment framework. This review provides a cohesive overview of the field and charts a roadmap for future research to overcome the current challenges in HCC immunotherapy.

PMID:41905572 | DOI:10.1016/j.critrevonc.2026.105305

Molecules. 2026 Mar 16;31(6):997. doi: 10.3390/molecules31060997.

ABSTRACT

CONTEXT: S-phase kinase-associated protein 2 (SKP2) is an oncogene and cell cycle regulator that mediates the ubiquitination of cell cycle regulators. Curcumol, a sesquiterpene natural product, has been reported to regulate SKP2-mediated ubiquitination degradation to overcome drug resistance in cancer cells. However, whether the cell cycle arrest effect of curcumol is related to SKP2's function in cancer cells and its mechanisms are still unclear.

OBJECTIVE: To investigate the role of SKP2 in curcumol-induced cell cycle arrest and its underlying mechanisms.

MATERIALS AND METHODS: Transcriptomic and proteomic analyses were used to screen the ubiquitination-related factors in curcumol treated hepatocellular carcinoma cells. Lentiviral overexpression, co-immunoprecipitation assays, ubiquitination analysis, and cell-line-derived xenograft (CDX) models were used to dissect the role and mechanisms of the identified ubiquitination-related factor in the cell cycle arrest effect of curcucmol.

RESULTS: Curcumol modulated the expression of CDK4, CDK6, Cyclin D1, p27 and SKP2. SKP2 was one candidate target of curcumol selected by multi-omics. Overexpressed SKP2 partially reversed curcumol-induced growth inhibition and G1-phase arrest. The increased expression of p27 induced by curcumol was attenuated by overexpressed SKP2. Curcumol impaired the interaction between SKP2 and p27, and led to the ubiquitination and degradation of p27. In vivo, curcumol effectively reduced tumor growth, and its antitumor effect was significantly mitigated by SKP2 overexpression.

DISCUSSION AND CONCLUSIONS: Curcumol reduced SKP2 expression, weakened the interaction between SKP2 and p27, inhibited degradation of p27, and then induced G1 phase cell-cycle arrest in SK-Hep-1 cells.

PMID:41900096 | PMC:PMC13029316 | DOI:10.3390/molecules31060997

Int J Mol Sci. 2026 Mar 21;27(6):2860. doi: 10.3390/ijms27062860.

ABSTRACT

Patients with Liver Hepatocellular carcinoma (LIHC) have a poor prognosis due to late-stage diagnosis and the limited efficacy of drug treatments. Dysregulation of nuclear pore complex (NPC) components, particularly nucleoporins (NUPs), may play a role in tumor progression. However, the specific role of NUP205 in LIHC has not been comprehensively investigated. We evaluated the expression, prognostic significance, epigenetic regulation, microRNA(miRNA) interactions, drug sensitivity, and biological functions of NUP205 in LIHC. Comprehensive bioinformatics analyses were performed using publicly available databases and web-based analysis platforms, including The Cancer Genome Atlas (TCGA), UALCAN, and the Kaplan-Meier Plotter (KM Plotter), among others. In vitro validation was performed using small interfering RNA (siRNA)-mediated knockdown of NUP205 in HepG2 cells, followed by quantitative reverse transcription PCR (RT-qPCR), apoptosis assay and wound-healing assay. NUP205 expression was significantly elevated in patients with LIHC and was associated with advanced clinicopathological features and poor prognosis. Promoter hypomethylation and miRNAs were identified as regulatory mechanisms influencing NUP205 expression. Increased NUP205 levels were associated with resistance to multiple chemotherapeutic agents. NUP205 knockdown significantly reduced messenger RNA (mRNA) expression in HepG2 and PLC/PRF/5 cells, and also reduced the expression of Transmembrane protein 209 (TMEM209) in HepG2 cells and improved sensitivity to doxorubicin. NUP205 expression was consistently associated with adverse clinicopathological features, poor prognosis, and altered drug sensitivity in LIHC. Integrative analyses suggest that NUP205 dysregulation may be linked to epigenetic and miRNA-associated regulatory mechanisms. These findings support NUP205 as a candidate prognostic biomarker and a potential regulatory factor in LIHC, warranting further mechanistic and protein-level validation. Further research is necessary to fully elucidate its underlying mechanisms and potential clinical applications.

PMID:41898718 | PMC:PMC13026649 | DOI:10.3390/ijms27062860

Biomedicines. 2026 Mar 12;14(3):645. doi: 10.3390/biomedicines14030645.

ABSTRACT

Background: Hepatocellular carcinoma (HCC) stands as a prevalent global health issue with increasing incidence and mortality rates. Hepatocellular carcinoma (HCC) exhibits profound molecular and clinical heterogeneity, which limits the effectiveness of current therapeutic strategies. Circadian rhythm disruption has been implicated in metabolic reprogramming, proliferation, and immune modulation in cancer, but its role in shaping HCC heterogeneity remains poorly defined. Methods: Four public HCC transcriptomic cohorts (TCGA-LIHC, CHCC, LIRI, LICA) were integrated using RMA normalization and ComBat for batch correction. Consensus clustering based on 31 core circadian clock genes (CCGs) identified robust molecular subtypes. Multi-omics characterization-including genomic alterations, pathway activity (GSEA/GSVA), immune microenvironment profiling (CIBERSORT, EPIC, MCP-counter, xCell), and drug-sensitivity prediction (pRRophetic/oncoPredict)-was performed to delineate subtype-specific biological properties. A nine-gene CCG-based RiskScore model was constructed using LASSO Cox regression to internally validate subtype robustness and intra-subtype risk stratification. Results: Using consensus clustering of 31 core CCGs in TCGA-LIHC and three independent validation cohorts (CHCC, LIRI, LICA), we identified three reproducible subtypes-Cluster-1 (metabolic-quiescent), Cluster-2 (transition-intermediate), and Cluster-3 (proliferation-inflammatory)-which were recapitulated across cohorts and showed distinct overall survival (Cluster-3 worst; log-rank p values significant across datasets). Multi-omic characterization revealed that Cluster-3 exhibits the highest tumor mutational burden and CNV burden with enrichment of TP53/AXIN1/TERT alterations, strong activation of cell-cycle, E2F, and G2M programs, and an immune-hot yet immunosuppressed microenvironment enriched for TAMs, Tregs and MDSCs. By contrast, Cluster-1 shows relative genomic stability, dominant hepatic metabolic signatures (fatty-acid oxidation, bile-acid and xenobiotic metabolism) and an immune-cold phenotype. Single-cell mapping linked ALAS1 expression to malignant hepatocytes predominating in Cluster-1, whereas NONO and CSNK1D localized to stromal (CAFs/TECs) and both malignant/immune compartments respectively in Cluster-3, providing a cellular mechanism for subtype-specific metabolism, angiogenesis and immune modulation. Finally, a nine-gene CCG-based RiskScore validated prognostic stratification and drug-sensitivity predictions indicated subtype-specific therapeutic vulnerabilities (notably increased predicted TKI sensitivity in Cluster-3). Conclusion: In conclusion, this study proposes a robust circadian rhythm-based molecular classification of hepatocellular carcinoma, revealing three biologically and clinically distinct subtypes characterized by divergent genomic alterations, metabolic programs, immune microenvironment states, and prognostic patterns. By integrating bulk and single-cell transcriptomic data, we identify subtype-specific roles of key circadian regulators-including ALAS1, NONO, and CSNK1D-in shaping tumor metabolism, proliferation, stromal remodeling, and immune suppression. These findings highlight circadian dysregulation as a potential upstream factor associated with HCC heterogeneity and provide a conceptual framework for developing subtype-tailored mechanistic studies and circadian-informed therapeutic strategies.

PMID:41898292 | PMC:PMC13024568 | DOI:10.3390/biomedicines14030645

Med Sci (Basel). 2026 Feb 25;14(1):110. doi: 10.3390/medsci14010110.

ABSTRACT

BACKGROUND/OBJECTIVES: Hepatocellular carcinoma (HCC) commonly arises from chronic liver diseases that show progressing fibrosis and cirrhosis. The molecular mechanisms driving the transition from advanced fibrosis to overt malignancy remain poorly defined, representing a key knowledge gap in current hepatology research. This review delineates shared pathways like TGFβ/SMAD, WNT/β-catenin, Hedgehog, NOTCH, Hippo/YAP-TAZ and MAPK, linking fibrosis to HCC and opening avenues for dual antifibrotic/antitumor therapies.

RESULTS AND CONCLUSIONS: So far, validated biomarker tools for fibrosis, like FIB-4, Enhanced Liver Fibrosis (ELF) and combined direct/indirect markers of liver damage and tissue remodeling, are used for fibrosis staging, while HCC detection leverages serum parameters like α-fetoprotein (AFP) or, more recently, multi-omics approaches (miRNA, cfDNA, metabolomics). Understanding the interconnection of these pathways can lead to novel targeted therapies (e.g., TGFβ inhibitors) that may show dual antifibrotic and antitumor activity in future studies.

PMID:41892825 | PMC:PMC13027833 | DOI:10.3390/medsci14010110

Biology (Basel). 2026 Mar 19;15(6):492. doi: 10.3390/biology15060492.

ABSTRACT

BACKGROUND: Tumor neoantigens are key targets for personalized vaccines and T-cell therapies, yet most pipelines focus on neoantigens derived from SNV/small indel and often yield a limited number of high-quality candidates. SVs are prevalent in tumors and can generate novel chimeric sequences and neopeptides, making them a promising additional source of neoantigens. However, SV-derived neoantigen prediction remains challenging due to breakpoint uncertainty, isoform-dependent coding inference, and limited integration of multi-dimensional evidence and reproducibility.

METHODS: We developed SVNeoPP (Structural Variant Neoantigen Prediction and Prioritization), an end-to-end workflow for SV-derived neoantigen analysis. SVNeoPP takes WGS and RNA-seq as inputs, performs SV calling and annotation, and reconstructs altered transcripts and coding sequences in a traceable, isoform-aware manner to generate candidate peptides. Candidates are prescreened by integrating antigen-processing features with HLA binding prediction, and then hierarchically filtered and prioritized based on transcript expression, LC-MS/MS proteomics evidence, immunogenicity predictions, and sequence similarity to experimentally validated neoantigen databases. SVNeoPP is implemented in Snakemake to enable modular extension, checkpoint-based restarts, and end-to-end reproducibility.

RESULTS: Using a hepatocellular carcinoma (HCC) multi-omics dataset as a proof of concept, we demonstrated the performance of SVNeoPP and obtained a high-priority shortlist of candidate peptides. Compared with other methods, SVNeoPP substantially expanded the candidate search space for SV-derived neoantigens and showed more favorable distributions of antigen-processing and HLA binding features.

CONCLUSIONS: SVNeoPP provides a reusable, traceable, and interpretable multi-dimensional evidence-driven framework for SV-derived neoantigens. As a complementary module to SNV/small-indel pipelines, it broadens the neoantigen candidate repertoire and generates ranked candidates with interpretable evidence to facilitate downstream prioritization and decision-making.

PMID:41892252 | PMC:PMC13024079 | DOI:10.3390/biology15060492

Environ Health (Wash). 2025 Nov 14;4(3):513-521. doi: 10.1021/envhealth.5c00401. eCollection 2026 Mar 20.

ABSTRACT

Prolonged exposure to fine particulate matter (PM_2.5) has been linked to chronic liver injury and cancer. However, an alternative risk assessment method to prospective longitudinal studies of exposome-metabolome interactions for liver inflammation-associated hepatocellular carcinoma (HCC) is lacking. This study investigates the risk of long-term real-world PM_2.5 exposure in hepatocarcinogenesis through machine learning techniques. Shotgun mass spectrometry (MS) imaging data were acquired from mouse models across a continuum of fibrosis, cirrhosis, and HCC for training a multiclass classification model to identify "No Risk", "Cancer Risk", and "Cancer". Direct infusion-MS data from PM_2.5-exposed mouse livers were analyzed to classify risk. By integrating data-driven and knowledge-based approaches, 14 disease progression biomarkers were identified for modeling. Our results suggest that chronic real-world PM_2.5 exposure can induce liver fibrosis, presenting cancer risk. Incorporating metabolomics, lipidomics, and transcriptomics, we propose PM_2.5 exposure induces mitochondrial dysfunction, activates AMPK signaling, and increases ceramide accumulation, potentially mediating insulin resistance that contributes to nonalcoholic fatty liver disease and HCC progression. This work represents a significant advancement in assessing hepatotoxicity of environmental toxicants by reducing reliance on traditional animal testing methods. It also underscores the potential of emerging technologies in transforming our understanding of PM_2.5 exposure, paving the way for targeted interventions.

PMID:41883379 | PMC:PMC13010293 | DOI:10.1021/envhealth.5c00401

JHEP Rep. 2026 Mar 23:101824. doi: 10.1016/j.jhepr.2026.101824. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality globally. Radiofrequency ablation (RFA) is a widely used treatment for HCC, but its efficacy is often limited by tumor relapse. Neutrophils, serve as a double-edged sword in tumor immunology, have recently been implicated in anti-tumor immunity post-RFA. Shear wave elastography (SWE) is a non-invasive examination for liver tissue, and associated with immune response. This study investigates the correlation between dynamic change of SWE values and neutrophils response following RFA, and explores potential adjuvant strategies for RFA.

METHODS: We conducted a comprehensive analysis using both clinical data from patients undergoing RFA (n=102) and experimental studies in mouse models (n=4-6 per group). Single-cell RNA sequencing (scRNA-seq) and multi-omics analyses including multiplex immunofluorescence staining and flow cytometric analysis were performed to identify neutrophil subsets. To assess the therapeutic potential of neutrophils-activating therapy for enhancing anti-tumor immunity post-RFA, we tested CD40 agonist in combination with RFA in preclinical models.

RESULTS: We noticed that rising liver SWE values following RFA were significantly associated with reduce relapse (n=102, p<0.001), and demonstrated that this phenomenon was linked to the inflammatory environment induced by the infiltration of neutrophils (2.5-fold increase, p<0.001). scRNA-seq analysis identified neutrophil subsets characterized by high expression of interferon-stimulated genes, which exhibited potent anti-tumor activity via nitric oxide. Importantly, treatment with CD40 agonist significantly augmented this immune response, leading to reduced tumor growth in mice (149.6±38.12 mm³ vs 23.92±4.43 mm³, p=0.008).

CONCLUSIONS: We linked clinical features to neutrophil-mediated immunity post-RFA. Neutrophil-activating therapy like CD40 agonists may prevent HCC relapse after RFA.

PMID:41881314 | DOI:10.1016/j.jhepr.2026.101824

Ecotoxicol Environ Saf. 2026 Apr 1;314:120063. doi: 10.1016/j.ecoenv.2026.120063. Epub 2026 Mar 23.

NO ABSTRACT

PMID:41875554 | DOI:10.1016/j.ecoenv.2026.120063

Technol Cancer Res Treat. 2026 Jan-Dec;25:15330338261435022. doi: 10.1177/15330338261435022. Epub 2026 Mar 23.

ABSTRACT

IntroductionHepatocellular carcinoma (HCC) screening in patients with hepatic cirrhosis (HC) relies on ultrasound and alpha-fetoprotein (US + AFP), which has limitations in sensitivity, particularly for early-stage HCC detection. This study aims to evaluate the performance of a novel multi-omics blood test, HCCscreen, with its individual components (methylation, AFP, Des-γ-Carboxy Prothrombin (DCP), mutations) and the standard US + AFP for HCC screening in a hepatic cirrhotic population.MethodsA total of 5078 patients with known high-risk for HCC were recruited. A prospective screening study was conducted on 650 patients with hepatic cirrhosis identified by ultrasound. Blood samples were collected from all patients before the confirmation of diagnosis by imaging and/or pathological examinations. The performance of HCCscreen, individual markers and US + AFP were calculated and compared. Statistics was performed with Graphpad Prism 5.0.ResultsHCCscreen exhibited a sensitivity of 86.3% at a specificity of 81.3%, with a positive predictive value (PPV) of 28.2% and a negative predictive value (NPV) of 98.6%. The positive likelihood ratio (LR+) was 4.61 and the negative LR (LR-) was 0.17. The positive detection rate (PDR) for all markers increased with more advanced HCC stages, whether Barcelona Clinic Liver Cancer (BCLC) or clinical staging. Among the single-omics, methylation showed the highest PDR, followed by AFP, DCP and mutations. HCCscreen demonstrated superior overall performance with an AUC of 0.87, outperforming individual markers like methylation (AUC = 0.76), AFP (AUC = 0.83), and DCP (AUC = 0.77). Crucially, HCCscreen's PDR was significantly higher than US + AFP in early-stage HCC (BCLC-0 and clinical stage I). Furthermore, while AFP's PDR varied significantly by sex, HCCscreen's performance remained consistent across all demographics. Correlation analysis revealed a significant association only between the HCCscreen score and the methylation score.ConclusionsThe multi-omics approach of HCCscreen significantly enhances early HCC detection in patients with hepatic cirrhosis compared to both its individual components and the current standard of US + AFP. Its robust and consistent performance across patient demographics underscores its potential as a superior tool for population-wide early HCC screening.

PMID:41869803 | PMC:PMC13009828 | DOI:10.1177/15330338261435022

Neoplasia. 2026 May;75:101298. doi: 10.1016/j.neo.2026.101298. Epub 2026 Mar 20.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) exhibits high recurrence rates and limited therapeutic options. Endothelial cell-specific molecule 1 (ESM1) and angiopoietin-like 4 (ANGPTL4) are implicated in tumor progression, yet their synergistic role in HCC lipid metabolism and angiogenesis remains unexplored.

METHODS: We integrated multi-omics approaches, including RNA sequencing, metabolomics, and immunoprecipitation-mass spectrometry, in HCC cell lines and patient-derived xenograft models. Key experiments involved Co-IP, Western blotting, tube formation assays, and clinical tissue microarray analysis to validate the ESM1-ANGPTL4-FASN-trioleate axis.

RESULTS: ESM1 and ANGPTL4 formed a positive feedback loop, stabilizing fatty acid synthase (FASN) to promote trioleate synthesis. Trioleate activated the NF-κB/IL-17 pathway in HCC cells and upregulated CD99 in endothelial cells, driving angiogenesis. In vivo, ESM1/ANGPTL4 knockdown suppressed tumor growth, which was rescued by trioleate supplementation. Clinical data revealed elevated ESM1/ANGPTL4 expression in bevacizumab-resistant HCC, correlating with poor prognosis.

CONCLUSIONS: The ESM1-ANGPTL4-FASN-trioleate axis orchestrates metabolic reprogramming and endothelial activation, representing a promising therapeutic target. Future studies should explore combination therapies targeting this axis and overcoming bevacizumab resistance in HCC.

PMID:41864037 | PMC:PMC13019581 | DOI:10.1016/j.neo.2026.101298

Front Oncol. 2026 Mar 4;16:1771313. doi: 10.3389/fonc.2026.1771313. eCollection 2026.

ABSTRACT

N⁶-methyladenosine (m⁶A), as the most abundant RNA epitranscriptional modification in eukaryotes, its key component of the methyltransferase complex, METTL14, not only cooperates in catalyzing m⁶A deposition but also has functions independent of methyltransferase activity. This article systematically reviews the dual regulatory role of METTL14 in tumors and its molecular mechanisms, mainly organizing the relevant research in a logical sequence of "tumor suppressive effect - tumor promoting effect - controversial or context-dependent". Studies have shown that METTL14 often plays a tumor suppressive role in tumors such as hepatocellular carcinoma and colorectal cancer, while in pancreatic cancer and nasopharyngeal carcinoma, it mostly promotes malignant progression, showing a high degree of context dependence. This article focuses on two key mechanisms: on the one hand, METTL14 precisely regulates the processing, stability, and function of non-coding RNAs (including miRNAs, lncRNAs, and circRNAs) through m⁶A modification, reshaping the competitive endogenous RNA (ceRNA) network; on the other hand, it shapes an immunosuppressive tumor microenvironment by directly upregulating immune checkpoints such as PD-L1, mediating metabolism-immune interactions, and regulating the function of immune cells. Its functional duality also stems from the selective regulation of key pathways such as PI3K/AKT, as well as the differential interpretation by different m⁶A readers (such as YTHDF2 and IGF2BPs). Given the close association of these mechanisms with clinical prognosis, the expression level of METTL14 shows significant potential as a prognostic marker and therapeutic target; in the future, it is necessary to combine single-cell multi-omics and other technologies to analyze its dynamic regulatory network in specific tumor contexts and explore precise treatment strategies based on synthetic lethality or targeting downstream effector molecules.

PMID:41858346 | PMC:PMC12995618 | DOI:10.3389/fonc.2026.1771313

Gut. 2026 Mar 30:gutjnl-2025-337938. doi: 10.1136/gutjnl-2025-337938. Online ahead of print.

ABSTRACT

BACKGROUND: Survival outcomes after transarterial chemoembolisation (TACE) vary in hepatocellular carcinoma (HCC) patients, and existing prognostic scores and imaging models often lack generalisability and biological interpretability.

OBJECTIVE: To develop and validate a multimodal prognostication model for HCC that allows for a precise assessment of survival outcomes of HCC patients receiving TACE therapy.

DESIGN: This study enrolled 1448 HCC patients, including a TACE cohort (n=1349), a biomarker subset from a randomised trial (n=41), a single-cell RNA sequencing cohort and The Cancer Genome Atlas (TCGA) HCC cohort (n=50). Pre-treatment contrast-enhanced CT images were used to construct deep learning and conventional radiomic models. The early-fusion and late-fusion models (LFMs) were compared, and a clinical-radiologic model (CRM) was formed by integrating the better-performing LFM with clinical variables. Using TCGA data and single-cell transcriptomic profiles, the differences between high-score and low-score groups in tumour immune microenvironment, cellular functional states and key signalling pathways were investigated.

RESULTS: The CRM effectively stratified patients' survival across multiple independent cohorts and achieved more granular risk stratification than the existing clinical models. Multi-omic analyses revealed that in the LFM high-score group, myelocytomatosis oncogene was activated, epithelial-mesenchymal transition enhanced, glycolysis upregulated and hypoxia pathway activated. Single-cell transcriptomic data confirmed that virtually all cell types in high-risk patients scored high in hypoxia, and cytotoxic T cells had a reduced cytotoxic activity.

CONCLUSION: The CRM model can non-invasively predict the prognosis of HCC patients treated by TACE therapy.

PMID:41856522 | DOI:10.1136/gutjnl-2025-337938

Cell Mol Life Sci. 2026 Mar 19. doi: 10.1007/s00018-026-06167-4. Online ahead of print.

NO ABSTRACT

PMID:41854876 | DOI:10.1007/s00018-026-06167-4

BMC Gastroenterol. 2026 Mar 19. doi: 10.1186/s12876-026-04756-y. Online ahead of print.

NO ABSTRACT

PMID:41851828 | DOI:10.1186/s12876-026-04756-y

J Hepatocell Carcinoma. 2026 Mar 11;13:574690. doi: 10.2147/JHC.S574690. eCollection 2026.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) exhibits significant molecular heterogeneity and complex immune microenvironment, which to some extent limits the accuracy of prognosis assessment and the formulation of individualized treatment strategies. This study aims to identify immune-derived molecular signatures based on multi-omics data and machine learning methods for the prognosis prediction and risk stratification of HCC.

METHODS: Based on weighted gene co-expression network analysis(WGCNA) and differential gene analysis,immune-derived molecular signature (IDMS) were screened in both single-cell and bulk transcriptomes. Prognostic model was constructed by multi-machine learning approachs. Subsequently, we investigated the differences in mutations, biological functions, and immune cell infiltration within the tumor microenvironment between the high- and low-risk groups.In addition, we comprehensively analyzed the drug sensitivity of IDMS and predicted potential drugs.

RESULTS: We identified seven hub genes at the single-cell and bulk transcriptome levels. Based on multiple machine learning, we constructed a prognostic model that demonstrated excellent performance in predicting overall survival for patients with HCC. IDMS -integrated normograms provide a promising and quantitative tool for clinical risk management.Notably, a significant difference in microsatellite instability (MSI) was observed between the high- and low-risk groups. This indicates that patients in the high-risk group might have a better response to immunotherapy. Additionally, we predicted potential drugs targeting to these risk subgroups.

CONCLUSION: Our research developed an IDMS that could serve as an effective tool for patient stratification management and prognosis prediction. This signature could provide a reference for immunotherapy for patients with HCC and improve their prognosis.

PMID:41847219 | PMC:PMC12991065 | DOI:10.2147/JHC.S574690