Journal of Hepatocellular Carcinoma最新文献

Purpose: The explosive progression of residual hepatocellular carcinoma (HCC) following incomplete thermal ablation is challenging, and the underlying mechanisms require further exploration. We investigated the mechanism by which Forkhead box P4 (FOXP4) promotes the malignant transformation of residual HCC cells through N-deacetylase and N-sulfotransferase 2 (NDST2) after incomplete thermal ablation.

Methods: The clinical significance of FOXP4 and NDST2 in HCC was evaluated using big data analysis. FOXP4 expression was detected in clinical samples of HCC. The gene expression levels in an in vitro heat-stressed HCC cell model were determined using quantitative real-time PCR (RT-qPCR) and Western blotting. The effects of the genes on heat-stressed HCC cells were investigated using Cell Counting Kit-8 (CCK-8), scratch, Transwell migration, and invasion assays. Additionally, the regulatory relationship between FOXP4 and NDST2 was validated using the Cleavage Under Targets and Tagmentation (CUT&Tag) experiments and phenotypic assays.

Results: High FOXP4 expression was correlated with liver cancer occurrence and development. In the heat-stressed HCC cell model, downregulating FOXP4 inhibited cancer cell progression. Besides, there was a positive association between FOXP4 and NDST2 in liver cancer. Suppressing FOXP4 reduced NDST2 expression in the heat-stressed HCC cells. Furthermore, reducing NDST2 expression weakened the biological behavior of heat-stressed HCC cells.

Conclusion: FOXP4 and NDST2 are crucial in the incomplete thermal ablation of residual cancer. FOXP4 might regulate the biological progression of residual HCC after incomplete thermal ablation through NDST2.

Purpose: Non-invasive methods are urgently needed to assess the efficacy of transarterial chemoembolization (TACE) and to identify patients with hepatocellular carcinoma (HCC) who may benefit from this procedure. This study, therefore, aimed to investigate the predictive ability of tumor growth patterns and radiomics features from contrast-enhanced magnetic resonance imaging (CE-MRI) in predicting tumor response to TACE among patients with HCC.

Patients and methods: A retrospective study was conducted on 133 patients with HCC who underwent TACE at three centers between January 2015 and April 2023. Enrolled patients were divided into training, testing, and validation cohorts. Rim arterial phase hyperenhancement (Rim APHE), tumor growth patterns, nonperipheral washout, markedly low apparent diffusion coefficient (ADC) value, intratumoral arteries, and clinical baseline features were documented for all patients. Radiomics features were extracted from the intratumoral and peritumoral regions across the three phases of CE-MRI. Seven prediction models were developed, and their performances were evaluated using receiver operating characteristic (ROC) and decision curve analysis (DCA).

Results: Tumor growth patterns and albumin-bilirubin (ALBI) score were significantly correlated with tumor response. Tumor growth patterns also showed a positive correlation with tumor burden (r = 0.634, P = 0.000). The Peritumor (AUC = 0.85, 0.71, and 0.77), Clinics_Peritumor (AUC = 0.86, 0.77, and 0.81), and Tumor_Peritumor (AUC = 0.87, 0.77, and 0.80) models significantly outperformed the Clinics and Tumor models (P < 0.05), while the Clinics_Tumor_Peritumor model (AUC = 0.88, 0.81, and 0.81) outperformed the Clinics (AUC = 0.67, 0.77, and 0.75), Tumor (AUC = 0.78, 0.72, and 0.68), and Clinics_Tumor (AUC = 0.82, 0.83, and 0.78) models (P < 0.05 or 0.053, respectively). The DCA curve demonstrated better predictive performance within a specific threshold probability range for Clinics_Tumor_Peritumor.

Conclusion: Combining tumor growth patterns, intra- and peri-tumoral radiomics features, and ALBI score could be a robust tool for non-invasive and personalized prediction of treatment response to TACE in patients with HCC.

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy within the digestive system, known for its poor prognosis. Gluconeogenesis, a critical metabolic pathway, is responsible for the synthesis of glucose in the normal liver. This study aimed to examine the role of gluconeogenesis-related genes (GRGs) in HCC and evaluate their impact on the tumor microenvironment infiltration and drug sensitivity in HCC.

Methods: We retrieved gene expression and clinical pathological data of HCC from The Cancer Genome Atlas (TCGA) database. This dataset was utilized to develop a prognosis model. The data from The International Cancer Genome Consortium (ICGC) served as an independent validation cohort. A least absolute shrinkage and selection operator (LASSO) regression analysis was applied to a curated panel of GRGs to construct and validate the predictive model. Furthermore, unsupervised consensus clustering, based on the expression levels of GRGs, categorized HCC patients into distinct subgroups.

Results: A four-gene prognostic model, referred to as GRGs, has been successfully developed with high accuracy and stability for the prediction of HCC patient prognosis. This model enables the stratification of patients into high or low risk groups based on individual risk scores, revealing significant differences in immune infiltration patterns and anti-tumor drug responses. Unsupervised consensus clustering analysis delineated four distinct subgroups of patients, each characterized by a unique prognosis and tumor immune microenvironment (TIME).

Conclusion: This study is the first to develop a prognostic model incorporating 4-GRGs that effectively predicts the prognosis, tumor microenvironment infiltration, and drug sensitivity in HCC patients. The model based on 4 GRGs may contribute to predict the prognosis, immunotherapy and chemotherapy response of HCC patients.

Purpose: Hepatocellular carcinoma (HCC) related to metabolic dysfunction-associated steatotic liver disease (MASLD) is often diagnosed at a late stage, and its incidence is increasing. Predictive biomarkers are therefore needed to identify individuals at high risk of HCC. We aimed to characterize the gut microbiome and hepatic transcriptome associated with HCC development in female mice with hepatocyte-deletion of Pten (HepPten ^-). These mice present with large variations in HCC development, making them a powerful model for biomarker discovery.

Methods & results: Sequencing of stool 16S and hepatic RNA was performed on a first set of mice. Among all liver histology parameters measured, the strongest association with microbiome composition changes was with the number of tumors detected at necropsy, followed by inflammation. The gut microbiome of mice with more than 2 tumors was enriched with Lachnospiraceae UCG and depleted of Palleniella intestinalis and Odoribacter. In contrast, hepatic transcriptomic changes were most strongly associated with tumor burden, followed by liver fibrosis. The 840 differentially expressed genes correlating with tumor burden were enriched in leukocyte extravasation and interleukin 10 receptor A (IL10RA) pathways. In addition, the abundance of Spp1-high epithelial cells is correlated with tumor burden. Association between tumor number and depletion of Palleniella intestinalis, and between tumor burden and circulating levels of C-X-C motif chemokine ligand 13 (CXCL13) and stem cell factor (SCF), was further validated in an independent set of mice.

Conclusion: We identified microbiome components contributing to liver carcinogenesis by inducing inflammation, and changes in hepatic gene expression and hepatic cells distribution that contribute to tumor growth. Such information can be highly valuable for the development of new prevention strategies as well as of new biomarkers for risk modeling in HCC.

Background: Hepatitis often occurs after initiating immune checkpoint inhibitor (ICI) treatment. The time and grade of hepatitis after ICI starts and the prognostic role of immune-related hepatitis in patients with advanced hepatocellular carcinoma (aHCC) remain unclear.

Methods: In this real-world analysis, we enrolled aHCC patients receiving ICIs, documented the highest level of liver enzymes during/after ICIs, and analyzed the survival impact of different hepatitis patterns.

Results: One hundred and ninety-three aHCC patients receiving ICIs were recruited. During ICIs, 88.6% of patients experienced aspartate transaminase (AST) elevations (Grade III/IV: 7.8%). For alanine transaminase (ALT), 81.3% had elevated levels (Grade III/IV: 3.6%), and 41.5% of patients had elevated bilirubin levels (Grade 3/4: 6.7%). The median AST, ALT, and total bilirubin values significantly increased after ICI treatment initiated (all p < 0.001) and, similarly, after excluding progressive disease (p = 0.014, p = 0.002, p < 0.001). The median time of hepatitis occurrence is from the 4.0th to 15.9th weeks. Multivariable analysis showed that patterns of liver enzyme change of AST and total bilirubin in patients receiving ICIs significantly correlate to overall survival (OS, p = 0.009 and 0.001, respectively). After ICI termination, patients with elevated bilirubin (p = 0.003) and AST (p = 0.005) would indicate poor survival, with adjustment of viral hepatitis and ICI responses.

Conclusion: Hepatitis emerges between the 4th and 20th weeks post-ICI initiation. Changes in liver enzymes during ICI therapy do not directly affect OS, implying the safety of ICI use when corticosteroids are promptly administered if clinically indicated.

Background: To develop and validate an innovative predictive model that integrates multisequence magnetic resonance (MR) radiomics, deep learning features, and clinical indicators to accurately predict the recurrence of hepatocellular carcinoma (HCC) after thermal ablation.

Methods: This retrospective multicenter cohort study enrolled patients who were diagnosed with HCC and treated via thermal ablation. We extracted radiomic features from multisequence 3T MR images, analyzed these images using a 3D convolutional neural network (3D CNN), and incorporated clinical data into the model. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.

Results: The study included 535 patients from three hospitals, comprising 462 males and 43 females. The RDC model, which stands for the Radiomics-Deep Learning-Clinical data model, demonstrated high predictive accuracy, achieving AUCs of 0.794 in the training set, 0.777 in the validation set, and 0.787 in the test set. Statistical analysis confirmed the model's robustness and the significant contribution of the integrated features to its predictive capabilities.

Conclusion: The RDC model effectively predicts HCC recurrence after thermal ablation by synergistically combining advanced imaging analysis and clinical parameters. This study highlights the potential of such integrative approaches to enhance prognostic assessments in HCC patients and offers a promising tool for clinical decision-making.

Purpose: To examine the relationship between lactate dehydrogenase (LDH) levels and soluble programmed cell death-ligand 1 (sPD-L1) levels in hepatocellular carcinoma (HCC) patients undergoing transarterial chemoembolization (TACE).

Methods: A total of 83 hCC patients participated in this study. Patients were categorized into subgroups based on their alpha-fetoprotein (AFP) levels, presence or absence of extrahepatic metastasis, vascular invasion, Barcelona Clinic Liver Cancer (BCLC) stage, tumor response, tumor size, and number LDH and sPD-L1 levels were compared before and after TACE (3, 7, and 30 days post-TACE).

Results: LDH and sPD-L1 levels were significantly higher at 3 and 7 days post-TACE than at baseline. Positive correlations were observed between changes in LDH levels and sPD-L1 levels at 3 and 7 days post-TACE. LDH levels were higher in patients with elevated AFP compared to those in the normal AFP group at 3 and 7 days post-TACE, in the stable disease (SD) group compared to complete response (CR) and partial response (PR) groups at 7 days post-TACE, and in those with tumor > 5 cm compared with those with tumor ≤ 5 cm at 3 and 7 days after TACE (all P < 0.05). sPD-L1 levels were higher in patients with vascular invasion than those without vascular invasion at 3 and 7 days post-TACE, in the SD group compared to CR and PR groups at 3 and 7 days post-TACE, and in those with tumor > 5 cm compared to those with tumor < 5 cm at 3 and 7 days after TACE (all P < 0.05).

Conclusion: A positive correlation was found between LDH expression and sPD-L1 levels, suggesting LDH as a potential biomarker for assessing immune status in HCC patients following TACE.

Background: Angiogenesis and ferroptosis are both linked to hepatocellular carcinoma (HCC) development, recurrence, and medication resistance. As a result, a thorough examination of the link between genes associated with angiogenesis and ferroptosis and immunotherapy efficacy is required to improve the dismal prognosis of HCC patients.

Methods: The molecular subtypes were found using a non-negative matrix factorization technique (NMF) based on the genes associated with angiogenesis and ferroptosis. Based on the differentially expressed genes (DEGs) screed between different molecular subtypes, an angiogenesis and ferroptosis-related prognostic stratification model was built using LASSO-COX regression, random forest technique, and extreme gradient boosting (XGBoost), which was further validated in the ICGC and GSE14520 databases. The impact of this model on tumor microenvironment (TME) and immunotherapy sensitivity was also investigated. The expression levels of candidate genes were detected and validated by Real-Time PCR and immunohistochemistry between liver cancer tissues and adjacent non-tumor liver tissues.

Results: Both angiogenesis and ferroptosis-related genes can significantly divide HCC patients into two subgroups with different survival outcomes, mutation profiles, and immune microenvironments. We screened six core genes (SLC10A1, PAEP, DPYSL4, MSC, NQO1, and CD24) for the construction of prognostic models by three machine learning methods after intersecting DEGs between angiogenesis and ferroptosis-related subgroups. In both the TCGA, ICGC, and GSE14520 datasets, the model exhibits high prediction efficiency based on the analysis of KM survival curves and ROC curves. Immunomodulatory genes analysis suggested that the model could be used to predict which patients are most likely to benefit from immunotherapy. Furthermore, the transcriptional expression levels of SLC10A1 in the validation experiment matched the outcomes derived from public datasets.

Conclusions: We identified a new angiogenesis and ferroptosis-related signature that might offer the molecular characteristic information needed for an efficient prognostic assessment and perhaps tailored treatment for HCC patients.

Purpose: This study aimed to investigate the benefit of conversion therapy for patients with unresectable hepatocellular carcinoma (HCC).

Patients and methods: A retrospective cohort study was conducted involving 40 patients initially deemed unresectable HCC (uHCC). They received surgery following successful conversion therapy involving lenvatinib. The patients were matched in a 1:1 ratio to with a control group who underwent direct surgery, based on pre-treatment clinical data.

Results: The median recurrence-free survival (RFS) duration for the conversion therapy cohort was notably longer than that of the direct surgery cohort (25 months vs 11 months). Furthermore, the 1- and 2-year RFS rates were significantly higher in the conversion therapy group compared to the direct surgery group (1 year: 70.5% vs 40.1%; 2 years: 49.0% vs 19.1%). The survival curves indicated a statistically significantly longer RFS in the conversion therapy cohort compared to the direct surgery cohort (P = 0.007). While patients achieving good remission based on both RECIST 1.1 and mRECIST criteria showed superior median RFS, no significant disparity was observed in the survival curves. The subgroup analysis revealed significantly improved prognosis among patients in the conversion therapy group who were male, older, had a history of alcohol consumption, were non-smokers, had liver cirrhosis, possessed Child-Pugh A liver function, had a tumor diameter exceeding 5 cm, and had an AFP ≥ 400 ng/mL. Among the cohort of 40 patients, only 8 individuals encountered severe adverse reactions, which were managed through dose reduction. None of the patients experienced multiple severe adverse reactions concurrently.

Conclusion: For patients with unresectable hepatocellular carcinoma, conversion therapy offers a significantly better prognosis than direct surgery for uHCC patients.

Objective: To investigate the role of eosinophil counts (EC) in microvascular invasion (MVI) for enhancing the radiomics based diagnostic model. Additionally, its correlation with early recurrence and tumor immune microenvironment was explored.

Methods: Propensity score matching was employed to evaluate on 462 cases whether EC was an independent risk factor for MVI. Subgroup analyses examined EC's effect on MVI across varying hypersplenism degrees. Univariate-multivariate logistic regression identified MVI's independent factors to develop a diagnostic model. Univariate-multivariate COX regression determined early recurrence factors. Co-detection by indexing (CODEX) constructed the immune score (IS), and Spearman correlation analyzed its association with peripheral immunity.

Results: EC was an independent risk factor for MVI (p=0.038, OR=1.304 (95% CI: 1.014-1.677)), and its effect on MVI disappeared with the severity of hypersplenism. The diagnostic model with EC was significantly improved (AUC=0.787 (95% CI: 0.737-0.836) vs AUC=0.748(95% CI: 0.694-0.802, p=0.005)). MVI was an independent risk factor for early recurrence (p<0.001, HR = 2.254 (95% CI: 1.557-3.263)). IS was negatively correlated with lymphocyte counts (R=-0.311, p=0.022), and positively correlated with EC (R=0.301, p=0.027) and RS (R = 0.315, p = 0.018).

Conclusion: EC was an independent risk factor for MVI and was related to the tumor immune microenvironment. EC should be included in the diagnosis of MVI to improve diagnostic efficiency.