Hepatocellular Carcinoma Model Research Articles

A male mouse model for metabolic dysfunction-associated steatotic liver disease and hepatocellular carcinoma

The lack of an appropriate preclinical model of metabolic dysfunction-associated steatotic liver disease (MASLD) that recapitulates the whole disease spectrum impedes exploration of disease pathophysiology and the development of effective treatment strategies. Here, we develop a mouse model (Streptozotocin with high-fat diet, STZ + HFD) that gradually develops fatty liver, metabolic dysfunction-associated steatohepatitis (MASH), hepatic fibrosis, and hepatocellular carcinoma (HCC) in the context of metabolic dysfunction. The hepatic transcriptomic features of STZ + HFD mice closely reflect those of patients with obesity accompanying type 2 diabetes mellitus, MASH, and MASLD-related HCC. Dietary changes and tirzepatide administration alleviate MASH, hepatic fibrosis, and hepatic tumorigenesis in STZ + HFD mice. In conclusion, a murine model recapitulating the main histopathologic, transcriptomic, and metabolic alterations observed in MASLD patients is successfully established.

Development of mutated β-catenin gene signature to identify CTNNB1 mutations from whole and spatial transcriptomic data in patients with HCC

Background & AimsPatients with β-catenin (encoded by CTNNB1)-mutated hepatocellular carcinoma (HCC) demonstrate heterogenous responses to first-line immune checkpoint inhibitors (ICIs). Precision-medicine based treatments for this subclass are currently in clinical development. Here, we report derivation of Mutated β-catenin Gene Signature (MBGS) to predict CTNNB1-mutational status in patients for future personalized medicine. MethodsCo-expression of mutant-Nrf2 and hMet ± mutant-β-catenin in murine livers in mice led to HCC development. Using bulk RNA-seq and intersectional transcriptomic analysis of various β-catenin-mutated and non-mutated HCC models, MBGS was derived. Integrated RNA/whole-exome-sequencing and spatial transcriptomic data from multiple HCC patient cohorts was assessed to address ability of MBGS to detect CTNNB1 mutation, tumor immune microenvironment and/or predict therapeutic responses. ResultsBulk RNA-seq comparing HCCs in mutant β-catenin-Nrf2, β-catenin-Met and β-catenin-Nrf2-Met to Nrf2-Met HCC model yielded 95 common upregulated genes. In TCGA-LIHC, differential gene expression analysis with FDR=0.05 and log2FC>1.5 on the 95 common genes comparing CTNNB1-mutated vs wild-type patients narrowed the gene panel to 13-genes MBGS. MBGS predicted CTNNB1-mutations in TCGA (n=374) and French (n=398) patient cohorts with ROC AUC of 0.90 and 0.94, respectively. Additionally, increased MBGS expression score was associated with lack of significant overall survival or progression-free survival effects in the atezolizumab-bevacizumab arm versus sorafenib arm in IMbrave150 cohort. MBGS performed comparable or superior to other CTNNB1-mutant classifiers. MBGS overlapped with Hoshida S3, Boyault G5/G6, and Chiang CTNNB1 subclass tumors in TCGA and in HCC spatial transcriptomic datasets visually depicting these tumors to be situated in an immune excluded tumor microenvironment. ConclusionsMBGS will aid in patient stratification to guide precision medicine therapeutics for CTNNB1-mutated HCC subclass as a companion diagnostic, as anti-β-catenin therapies become available.

Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients.

The activities and products of carbohydrate metabolism are involved in key processes of cancer. However, its relationship with hepatocellular carcinoma (HCC) is unclear. The cancer genome atlas (TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases. Differentially expressed genes (DEGs) between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes (CRGs) to obtain differentially expressed CRGs (DE-CRGs). Then, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were applied to identify risk model genes, and HCC samples were divided into high/low-risk groups according to the median risk score. Next, gene set enrichment analysis (GSEA) was performed on the risk model genes. The sensitivity of the risk model to immunotherapy and chemotherapy was also explored. A total of 8 risk model genes, namely, G6PD, PFKFB4, ACAT1, ALDH2, ACYP1, OGDHL, ACADS, and TKTL1, were identified. Moreover, the risk score, cancer status, age, and pathologic T stage were strongly associated with the prognosis of HCC patients. Both the stromal score and immune score had significant negative/positive correlations with the risk score, reflecting the important role of the risk model in immunotherapy sensitivity. Furthermore, the stromal and immune scores had significant negative/positive correlations with risk scores, reflecting the important role of the risk model in immunotherapy sensitivity. Eventually, we found that high-/low-risk patients were more sensitive to 102 drugs, suggesting that the risk model exhibited sensitivity to chemotherapy drugs. The results of the experiments in HCC tissue samples validated the expression of the risk model genes. Through bioinformatic analysis, we constructed a carbohydrate metabolism-related risk model for HCC, contributing to the prognosis prediction and treatment of HCC patients.

Identification of pyroptosis subtypes and prognosis model of hepatocellular carcinoma based on pyroptosis-related genes.

Hepatocellular carcinoma (HCC) is a common malignant tumor with poor prognosis. Pyroptosis, a type of programmed cell death, regulates tumor cell development. However, the role of pyroptosis-related genes (PRGs) in HCC and their association with prognosis are unclear. We conducted bioinformatics analysis to identify PRGs in The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) patients. Consensus clustering classified patients into different subtypes. We used LASSO regression to established a pyroptosis subtype-related score (PSRS) related to prognosis. OncoPredict identified potential pharmaceuticals based on PSRS. We found 20 HCC-related PRGs in 335 TCGA-LIHC patients. Consensus clustering classified patients into two subtypes. Subtype I had better overall survival and higher response to anti-PD1 treatment. The prognostic model involving 20 genes predicted poorer prognosis for high-PSRS group. The model was validated in two external cohorts. OncoPredict identified 65 potential pharmaceuticals based on PSRS. Our investigation revealed a correlation between pyroptosis and HCC. We established PSRS as independent risk factors for predicting prognosis. The study paves the way for using PRGs as prognostic biomarkers and exploring personalized therapy for HCC.

SOX12 Facilitates Hepatocellular Carcinoma Progression and Metastasis through Promoting Regulatory T-Cells Infiltration and Immunosuppression.

Despite the success of immunotherapy in treating hepatocellular carcinoma (HCC), HCC remains a severe threat to health. Here, a crucial transcription factor, SOX12, is revealed that induces the immunosuppression of liver tumor microenvironment. Overexpressing SOX12 in HCC syngeneic models increases intratumoral regulatory T-cell (Treg) infiltration, decreases CD8+T-cell infiltration, and hastens HCC metastasis. Hepatocyte-specific SOX12 knockout attenuates DEN/CCl4-induced HCC progression and metastasis, whereas hepatocyte-specific SOX12 knock-in accelerates these effects. Mechanistically, SOX12 transcriptionally activates C-C motif chemokine ligand 22 (CCL22) expression to promote the recruitment and suppressive activity of Tregs. Moreover, SOX12 transcriptionally upregulates CD274 expression to suppress CD8+T-cell infiltration. Either knockdown of CCL22 or PD-L1 dampens SOX12-mediated HCC metastasis. Blocking of CC chemokine receptor 4 (CCR4), a receptor for CCL22, by inhibitor C-021 or Treg-specific knockout of CCR4 inhibits SOX12-mediated HCC metastasis. Transforming growth factor-β1 (TGF-β1)/TGFβR1-Smad2/3/4 is identified as a key upstream signaling for SOX12 overexpression in HCC cells. Combining C-021 or TGFβR1 inhibitor galunisertib with anti-PD-L1 exhibits an enhanced antitumor effect in two HCC models. Collectively, the findings demonstrate that SOX12 contributes to HCC immunosuppression through the CCL22/CCR4-Treg and PD-L1-CD8+T axes. Blocking of CCR4 or TGFβR1 improves the efficacy of anti-PD-L1 in SOX12-mediated HCC.

Preclinical Models of Hepatocellular Carcinoma: Current Utility, Limitations, and Challenges.

Hepatocellular carcinoma (HCC), the predominant primary liver tumor, remains one of the most lethal cancers worldwide, despite the advances in therapy in recent years. In addition to the traditional chemically and dietary-induced HCC models, a broad spectrum of novel preclinical tools have been generated following the advent of transgenic, transposon, organoid, and in silico technologies to overcome this gloomy scenario. These models have become rapidly robust preclinical instruments to unravel the molecular pathogenesis of liver cancer and establish new therapeutic approaches against this deadly disease. The present review article aims to summarize and discuss the commonly used preclinical models for HCC, evaluating their strengths and weaknesses.

Cinobufacini Inhibits Survival and Metastasis of Hepatocellular Carcinoma via c-Met Signaling Pathway.

To investigate the anti-tumor effects of cinobufacini (CINO) on hepatocellular carcinoma (HCC) induced by des-gamma-carboxy-prothrombin (DCP) and to uncover the underlying mechanisms. The inhibitory effect of CINO on HCC cell proliferation was evaluated using the cell counting kit-8 method, and the apoptosis rate was quantified using flow cytometry. Immunofluorescence and Western blot analyses were used to investigate the differential expression of proteins associated with cell growth, apoptosis, migration, and invasion pathways after CINO treatment. The therapeutic potential of CINO for HCC was confirmed, and the possibility of combining cinobufacini with c-Met inhibitor for the treatment of primary HCC was further validated by in vivo experiments. Under the induction of DCP, CINO inhibited the activity of HCC cells, induced apoptosis, and inhibited migration and invasion. Upon the induction of DCP, CINO regulated c-Met activation and the activation of the phosphatidylinositol-3 kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinase kinase/extracellular signal-regulated kinase (MEK/ERK) pathways. In a mouse model of HCC, CINO exhibited significant antitumor effects by inhibiting the phosphorylation of c-Met and the downstream PI3K/AKT and MEK/ERK pathways in tumor tissues. CINO inhibited HCC cell growth, promoted apoptosis, and suppressed HCC cell invasion and migration by targeting c-Met and PI3K/AKT and MEK/ERK signaling pathways under DCP induction.

Establishing an oxidative stress mitochondria-related prognostic model in hepatocellular carcinoma based on multi-omics characteristics and machine learning computational framework

Hepatocellular carcinoma (HCC) has high incidence and mortality rates worldwide. Damaged mitochondria are characterized by the overproduction of reactive oxygen species (ROS), which can promote cancer development. The prognostic value of the interplay between mitochondrial function and oxidative stress in HCC requires further investigation. Gene expression data of HCC samples were collected from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). We screened prognostic oxidative stress mitochondria-related (OSMT) genes at the bulk transcriptome level. Based on multiple machine learning algorithms, we constructed a consensus oxidative stress mitochondria-related signature (OSMTS), which contained 26 genes. In addition, we identified six of these genes as having a suitable prognostic value for OSMTS to reduce the difficulty of clinical application. Univariate and multivariate analyses verified the OSMTS as an independent prognostic factor for overall survival (OS) in HCC patients. The OSMTS-related nomogram demonstrated to be a powerful tool for the clinical diagnosis of HCC. We observed differences in biological function and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. The highest expression of the OSMTS was detected in hepatocytes at the single-cell transcriptome level. Hepatocytes in the high- and low-risk groups differed significantly in terms of biological function and intercellular communication. Moreover, at the spatial transcriptome level, high expression of OSMTS was mainly in regions enriched in hepatocytes and B cells. Potential drugs targeting specific risk subgroups were identified. Our study revealed that the OSMTS can serve as a promising tool for prognosis prediction and precise intervention in HCC patients.

FOXM1/DEPDC1 feedback loop promotes hepatocarcinogenesis and represents promising targets for cancer therapy.

Forkhead box M1 (FOXM1) is a key regulator of mitosis and is identified as an oncogene involved in several kinds of human malignancies. However, how it induces carcinogenesis and related therapeutic approaches remains not fully understood. In this study, we aimed to identify a regulatory axis involving FOXM1 and its target gene DEP domain containing 1 (DEPDC1) and investigate their biological functions. FOXM1 bound to the promoter and transcriptionally induced DEPDC1 expression, in turn, DEPDC1 physically interacted with FOXM1, promoted its nuclear translocation, and reinforced its transcriptional activities. The FOXM1/DEPDC1 axis was indispensable for cancer cells, as evidenced by the fact that DEPDC1 rescued cell growth inhibition caused by FOXM1 knockdown, and silencing DEPDC1 efficiently attenuated tumor growth in a murine hepatocellular carcinoma model. Furthermore, strong positive associations between FOXM1/DEPDC1 axis and poor clinical outcome were observed in human hepatocellular carcinoma samples, further indicating their significance for hepatocarcinogenesis. Finally, we attempted to exploit immunotherapy approaches to target the FOXM1/DEPDC1 axis. Several HLA-A24:02-restricted T-cell epitopes targeting FOXM1 or DEPDC1 were identified through bioinformatic analysis. Then, T cell receptor (TCR)-engineered T cells targeting FOXM1262-270 or DEPDC1294-302 were successfully established and proved to efficiently eradicate tumor cells. Our findings highlight the significance of the FOXM1/DEPDC1 axis in the process of oncogenesis and indicate their potential as immunotherapy targets.

Reprogramming of Glutamine Amino Acid Transporters Expression and Prognostic Significance in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver malignancy and is a major cause of cancer-related mortality in the world. This study aimed to characterize glutamine amino acid transporter expression profiles in HCC compared to those of normal liver cells. In vitro and in vivo models of HCC were studied using qPCR, whereas the prognostic significance of glutamine transporter expression levels within patient tumors was analyzed through RNAseq. Solute carrier (SLC) 1A5 and SLC38A2 were targeted through siRNA or gamma-p-nitroanilide (GPNA). HCC cells depended on exogenous glutamine for optimal survival and growth. Murine HCC cells showed superior glutamine uptake rate than normal hepatocytes (p < 0.0001). HCC manifested a global reprogramming of glutamine transporters compared to normal liver: SLC38A3 levels decreased, whereas SLC38A1, SLC7A6, and SLC1A5 levels increased. Also, decreased SLC6A14 and SLC38A3 levels or increased SLC38A1, SLC7A6, and SLC1A5 levels predicted worse survival outcomes (all p < 0.05). Knockdown of SLC1A5 and/or SLC38A2 expression in human Huh7 and Hep3B HCC cells, as well as GPNA-mediated inhibition, significantly decreased the uptake of glutamine; combined SLC1A5 and SLC38A2 targeting had the most considerable impact (all p < 0.05). This study revealed glutamine transporter reprogramming as a novel hallmark of HCC and that such expression profiles are clinically significant.

Babaodan inhibits cell proliferation and metastasis and enhances anti-tumor effects of camrelizumab by inhibiting M2 phenotype macrophages in hepatocellular carcinoma

Ethnopharmacological relevanceBabaodan (BBD) is a unique Chinese medication utilized in traditional Chinese medicine. It can eliminate toxins, induce diuresis, and eliminate yellowish hue. In addition to treating acute and chronic viral hepatitis, cholecystitis, cholangitis, and urinary tract infections, BBD has garnered popularity as a substitution treatment for several malignant cancers, particularly hepatocellular carcinoma (HCC). Aim of the studyTo elucidate the efficacy and mechanism of BBD alone and combined with camrelizumab (CLM) for treating HCC. MethodsWe investigated the effects of BBD on the HCC tumor microenvironment in vivo. Furthermore, we evaluated its effects on tumor growth and metastasis induced by M2 macrophages in vitro. ResultsIn a mouse model of orthotopic HCC, BBD decreased tumor growth. Furthermore, it increased the M1/M2 macrophage ratio and CD8+ T-cell abundance in mice. In addition, BBD reversed HCC cell proliferation and metastasis induced by M2 macrophages, increased the anti-HCC effect of low-dose CLM, and attenuated organ damage induced by high-dose CLM. Lastly, BBD enhanced the efficacy of CLM via the PI3K/AKT/mTOR signaling pathway. ConclusionBBD increases the antitumor effect of CLM by modulating the tumor immune microenvironment and attenuating its the toxic side effects of CLM.

Developing an advanced diagnostic model for hepatocellular carcinoma through multi-omics integration leveraging diverse cell-death patterns.

Hepatocellular carcinoma (HCC), representing more than 80% of primary liver cancer cases, lacks satisfactory etiology and diagnostic methods. This study aimed to elucidate the role of programmed cell death-associated genes (CDRGs) in HCC by constructing a diagnostic model using single-cell RNA sequencing (scRNA-seq) and RNA sequencing (RNA-seq) data. Six categories of CDRGs, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA-seq data from blood-derived exosomes were sourced from the exoRBase database, RNA-seq data from cancer tissues from the TCGA database, and scRNA-seq data from the GEO database. Subsequently, we intersected the differentially expressed genes (DEGs) of the HCC cohort from exoRBase and TCGA databases with CDRGs, as well as DEGs obtained from single-cell datasets. Candidate biomarker genes were then screened using clinical indicators and a machine learning approach, resulting in the construction of a seven-gene diagnostic model for HCC. Additionally, scRNA-seq and spatial transcriptome sequencing (stRNA-seq) data of HCC from the Mendeley data portal were used to investigate the underlying mechanisms of these seven key genes and their association with immune checkpoint blockade (ICB) therapy. Finally, we validated the expression of key molecules in tissues and blood-derived exosomes through quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry experiments. Collectively, we obtained a total of 50 samples and 104,288 single cells. Following the meticulous screening, we established a seven-gene diagnostic model for HCC, demonstrating high diagnostic efficacy in both the exoRBase HCC cohort (training set: AUC = 1; testing set: AUC = 0.847) and TCGA HCC cohort (training set: AUC = 1; testing set: AUC = 0.976). Subsequent analysis revealed that HCC cluster 3 exhibited a higher stemness index and could serve as the starting point for the differentiation trajectory of HCC cells, also displaying more abundant interactions with other cell types in the microenvironment. Notably, key genes TRIB3 and NQO1 displayed elevated expression levels in HCC cells. Experimental validation further confirmed their elevated expression in both tumor tissues and blood-derived exosomes of cancer patients. Additionally, stRNA analysis not only substantiated these findings but also suggested that patients with high TRIB3 and NQO1 expression might respond more favorably to ICB therapy. The seven-gene diagnostic model demonstrated remarkable accuracy in HCC screening, with TRIB3 emerging as a promising diagnostic tool and therapeutic target for HCC.

Dual Enzyme-Locked Activation Reporter for Accurate Liver Cancer Surveillance.

Activatable probes with a higher signal-to-background ratio and accuracy are essential for monitoring liver cancer as well as intraoperative fluorescence navigation. However, the presence of only one biomarker is usually not sufficient to meet the high requirement of a signal-to-background ratio in cancer surveillance, leading to the risk of misdiagnosis. In this work, a dual-locked activation response probe, Si-NTR-LAP, for nitroreductase and leucine aminopeptidase was reported. This dual-locked probe provides better tumor recognition and a higher signal-to-noise ratio than that of single-locked probes (Si-LAP and Si-NTR). In both the subcutaneous tumor model and the more complex orthotopic hepatocellular carcinoma model, the probe was able to identify tumor tissue with high specificity and accurately differentiate the boundaries between tumor tissue and normal tissue. Therefore, the dual-locked probe may provide a new and practical strategy for applying to real patient tumor tissue samples.

Neutrophil extracellular traps promote immune escape in hepatocellular carcinoma by up-regulating CD73 through Notch2

Immune escape is the main reason that immunotherapy is ineffective in hepatocellular carcinoma (HCC). Here, this study illustrates a pathway mediated by neutrophil extracellular traps (NETs) that can promote immune escape of HCC. Mechanistically, we demonstrated that NETs up-regulated CD73 expression through activating Notch2 mediated nuclear factor kappa B (NF-κB) pathway, promoting regulatory T cells (Tregs) infiltration to mediate immune escape of HCC. In addition, we found the similar results in mouse HCC models by hydrodynamic plasmid transfection. The treatment of deoxyribonuclease I (DNase I) could inhibit the action of NETs and improve the therapeutic effect of anti-programmed cell death protein 1 (PD-1). In summary, our results revealed that targeting of NETs was a promising treatment to improve the therapeutic effect of anti-PD-1.

Multicompartment Polyion Complex Micelles Based on Triblock Polypept(o)ides Mediate Efficient siRNA Delivery to Cancer-Associated Fibroblasts for Antistromal Therapy of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is the most frequent type of primary liver cancer and the third leading cause for cancer-related death worldwide. The tumor is difficult-to-treat due to its inherent resistance to chemotherapy. Antistromal therapy is a novel therapeutic approach, targeting cancer-associated fibroblasts (CAF) in the tumor microenvironment. CAF-derived microfibrillar-associated protein 5 (MFAP-5) is identified as a novel target for antistromal therapy of HCC with high translational relevance. Biocompatible polypept(o)ide-based polyion complex micelles (PICMs) constructed with a triblock copolymer composed of a cationic poly(l-lysine) complexing anti-MFAP-5 siRNA (siMFAP-5) via electrostatic interaction, a poly(γ-benzyl-l-glutamate) block loading cationic amphiphilic drug desloratatine (DES) via π-πinteraction as endosomal escape enhancer and polysarcosine poly(N-methylglycine) for introducing stealth properties, are generated for siRNA delivery. Intravenous injection of siMFAP-5/DES PICMs significantly reduces the hepatic tumor burden in a syngeneic implantation model of HCC, with a superior MFAP-5 knockdown effect over siMFAP-5 PICMs or lipid nanoparticles. Transcriptome and histological analysis reveal that MFAP-5 knockdown inhibited CAF-related tumor vascularization, suggesting the anti-angiogenic effect of RNA interference therapy. In conclusion, multicompartment PICMs combining siMFAP-5 and DES in a single polypept(o)ide micelle induce a specific knockdown of MFAP-5 and demonstrate a potent antitumor efficacy (80% reduced tumor burden vs untreated control) in a clinically relevant HCC model.

Cryoablation combined with dual immune checkpoint blockade enhances antitumor efficacy in hepatocellular carcinoma model mice

Background Cryoablation (Cryo) is a minimally invasive treatment for tumors. Cryo can activate the body’s immune response, although it is typically weak. The immune response induced by Cryo in hepatocellular carcinoma (HCC) is poorly understood. PD-1 and CTLA-4 monoclonal antibodies are immune checkpoint inhibitors used in immunotherapy for tumors. The combined use of these antibodies with Cryo may enhance the immune effect. Methods A Balb/c mouse model of HCC was established and treated with Cryo, immune checkpoint blockade (ICB), or Cryo + ICB (combination therapy). The growth trend of right untreated tumors and survival time of mice were determined. The expression of apoptosis-related proteins was detected by Western blot (WB) assay. The percentages of immune cells and immunosuppressive cells were analyzed by flow cytometry. The numbers of infiltrating T lymphocytes were checked by immunohistochemistry, and the levels of T-cell-associated cytokines were detected by Quantitative real-time Polymerase Chain Reaction (qRT-PCR) assays and Enzyme-Linked Immunosorbent Assays (ELISA) assays. Results Cryo + ICB inhibited the growth of right untreated tumors, promoted tumor cell apoptosis, and prolonged the survival time of mice. Local T-cell infiltration in right tumor tissues increased after the combination therapy, while the number of immunosuppressive cells was significantly reduced. In addition, the combination therapy may induce the production of multiple Th1-type cytokines but reduce the production of Th2-type cytokines. Conclusions Cryo can activate CD8+ and CD4+ T-cell immune responses. Cryo + ICB can relieve the immunosuppressive tumor microenvironment and shift the Th1/Th2 balance toward Th1 dominance, further enhancing the Cryo-induced T-cell immune response and resulting in a stronger antitumor immune response.

Investigation on the mechanisms of scorpion venom in hepatocellular carcinoma model mice via untargeted metabolomics profiling

Metabolic reprogramming is frequently accompanied by hepatocellular carcinoma (HCC) progression. Disrupted metabolites act as potential biomarkers and drug therapeutic targets for HCC. Peptide extract of scorpion venom (PESV) induces cytotoxic anti-proliferative effects and apoptosis in tumors. However, the action mechanisms of PESV remain unknown. This study aimed to explore the serum metabolic profiles of tumor-bearing mouse model. We generated an orthotopic HCC xenograft mouse model by implanting H22 cells into the left hepatic lobe of male C57BL/6 mice. After surgery, the mice were assigned to two groups randomly: PESV (PESV-treated 40 mg/kg daily, i.g.; n = 6) and control (treated with the solvent equally for 14 d, n = 6) groups. Based on an untargeted metabolomics approach using ultra-high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry, differential metabolites were screened via univariate and multivariate data analyses. A total of 48 differential metabolites in negative ion mode and 63 in positive ion mode were identified in the serum samples. Furthermore, metabolic pathway analysis revealed that aminoacyl-tRNA biosynthesis, amino acid pathway, glutathione metabolism, protein transports, protein digestion and absorption, and cAMP signaling pathways play vital roles in PESV-induced inhibition of tumors. These findings highlight the distinct changes in the metabolic profiles of HCC-bearing mice after PESV treatment, suggesting the potential of the identified metabolic molecules as therapeutic targets for HCC.

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

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

Constructing an immune-related prognostic signature for predicting prognosis and immune response in hepatocellular carcinoma

BackgroundCurrently, there are few studies on immune-related prognostic analysis of hepatocellular carcinoma (HCC). Our aim was to establish an immune-correlated prognostic model for HCC. MethodsImmune-associated cells were obtained from the scRNA-seq dataset (GSE149614) of HCC. Differentially expressed genes between normal and tumor cells from immune-associated cells and the immune-related genes from the ImmPort database were used to identify immune-related differentially expressed genes (IRDEGs). Subsequently, the risk model was established in the TCGA-LIHC cohort (n = 438) from the Cancer Genome Atlas (TCGA) database by using Kaplan-Meier (K-M) survival curve, univariate/multivariate Cox regression analysis. Subsequently, we further analyzed tumor immune microenvironment characteristics, somatic mutation, immune checkpoint and its ligand expression levels between high- and low-risk groups, as well as drug sensitivity prediction. ICGC cohort was set as the validation cohort. TCGA-LIHC cohort and three independent the Gene Expression Omnibus (GEO) datasets (GSE54236, GSE14520, and GSE64041) was used to verify IRDEGs expression, as well as PCR assays using clinical samples. ResultsThe IRDEGs was composed of 4 genes, namely B2M, SPP1, PPIA, and HRG. The 438 HCC patients were divided into high- and low-risk group. The high-risk group was associated with poor prognosis, including higher T stage, advanced pathological stages, less immune cell infiltration, higher TP53 mutation rate, the high expression of CTLA4 and HAVCR2. Besides, high-risk populations benefit from most chemotherapy drugs. Similarly, the performance of the risk model was validated in the ICGC. All four datasets (TCGA-LIHC cohort, GSE54236, GSE14520, and GSE64041) and clinical q-PCR results demonstrated that, compared with normal samples, the expressions of B2M and HRG were lower in tumor samples, and the expression of SPP1 was higher. ConclusionIn summary, the immune-related prognostic signature had a good predictive performance on prognosis and immunotherapy for HCC patients.

A Novel Amino Acid-Related Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is an extremely harmful malignant tumor in the world. Since the energy metabolism and biosynthesis of HCC cells are closely related to amino acids, it is necessary to further explore the relationship between amino acid-related genes and the prognosis of HCC to achieve individualized treatment. We herein aimed to develop a prognostic model for HCC based on amino acid genes. In this study, RNA-sequencing data of HCC patients were downloaded from the TCGA-LIHC cohort as the training cohort and the GSE14520 cohort as the validation cohort. Amino acid-related genes were derived from the Molecular Signatures Database. Univariate Cox and Lasso regression analysis were used to construct an amino acid-related signature (AARS). The predictive value of this risk score was evaluated by Kaplan-Meier (K-M) curve, receiver operating characteristic (ROC) curve, univariate and multivariate Cox regression analysis. Gene set variation analysis (GSVA) and immune characteristics evaluation were used to explore the underlying mechanisms. Finally, a nomogram was established to help the personalized prognosis assessment of patients with HCC. The AARS comprises 14 amino acid-related genes to predict overall survival (OS) in HCC patients. HCC patients were divided into AARS-high group and AARS-low group according to the AARS scores. The K-M curve, ROC curve, and univariate and multivariate Cox regression analysis verified the good prediction efficiency of the risk score. Using GSVA, we found that AARS variants were concentrated in four pathways, including cholesterol metabolism, delayed estrogen response, fatty acid metabolism, and myogenesis metabolism. Our results suggest that the AARS as a prognostic model based on amino acid-related genes is of great value in the prediction of survival of HCC, and can help improve the individualized treatment of patients with HCC.