Deficiency of betaine-homocysteine methyltransferase activates glucose-6-phosphate dehydrogenase (G6PD) by decreasing arginine methylation of G6PD in hepatocellular carcinogenesis.

Betaine-homocysteine methyltransferase (BHMT) regulates protein methylation and is correlated with tumorigenesis; however, the effects and regulation of BHMT in hepatocarcinogenesis remain largely unexplored. Here, we determined the clinical significance of BHMT in the occurrence and progression of hepatocellular carcinoma (HCC) using tissue samples from 198 patients. BHMT was to be frequently found (86.6%) expressed at relatively low levels in HCC tissues and was positively correlated with the overall survival of patients with HCC. Bhmt overexpression effectively suppressed several malignant phenotypes in hepatoma cells in vitro and in vivo, whereas complete knockout of Bhmt (Bhmt-/-) produced the opposite effect. We combined proteomics, metabolomics, and molecular biological strategies and detected that Bhmt-/- promoted hepatocarcinogenesis and tumor progression by enhancing the activity of glucose-6-phosphate dehydrogenase (G6PD) and PPP metabolism in DEN-induced HCC mouse and subcutaneous tumor-bearing models. In contrast, restoration of Bhmt with an AAV8-Bhmt injection or pharmacological inhibition of G6PD attenuated hepatocarcinogenesis. Additionally, coimmunoprecipitation identified monomethylated modifications of the G6PD, and BHMT regulated the methylation of G6PD. Protein sequence analysis, generation and application of specific antibodies, and site-directed mutagenesis indicated G6PD methylation at the arginine residue 246. Furthermore, we established bidirectionally regulated BHMT cellular models combined with methylation-deficient G6PD mutants to demonstrate that BHMT potentiated arginine methylation of G6PD, thereby inhibiting G6PD activity, which in turn suppressed hepatocarcinogenesis. Taken together, this study reveals a new methylation-regulatory mechanism in hepatocarcinogenesis owing to BHMT deficiency, suggesting a potential therapeutic strategy for HCC treatment.

Acetyl-CoA metabolic accumulation promotes hepatocellular carcinoma metastasis via enhancing CXCL1-dependent infiltration of tumor-associated neutrophils.

High levels of acetyl-CoA are considered a key metabolic feature of metastatic cancers. However, the impacts of acetyl-CoA metabolic accumulation on cancer microenvironment remodeling are poorly understood. In this study, using human hepatocellular carcinoma (HCC) tissues and orthotopic xenograft models, we found a close association between high acetyl-CoA levels in HCCs, increased infiltration of tumor-associated neutrophils (TANs) in the cancer microenvironment and HCC metastasis. Cytokine microarray and enzyme-linked immunosorbent assays (ELISA) revealed the crucial role of the chemokine (C-X-C motif) ligand 1(CXCL1). Mechanistically, acetyl-CoA accumulation induces H3 acetylation-dependent upregulation of CXCL1 gene expression. CXCL1 recruits TANs, leads to neutrophil extracellular traps (NETs) formation and promotes HCC metastasis. Collectively, our work linked the accumulation of acetyl-CoA in HCC cells and TANs infiltration, and revealed that the CXCL1-CXC receptor 2 (CXCR2)-TANs-NETs axis is a potential target for HCCs with high acetyl-CoA levels.

The Landscape of Using Glycosyltransferase Gene Signatures for Overall Survival Prediction in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a heterogeneous disease that occurs in the setting of chronic liver diseases. The role of glycosyltransferase (GT) genes has recently been the focus of research associated with tumor development. However, the prognostic value of GT genes in HCC remains unclear. Therefore, this study aimed to identify GT genes related to HCC prognosis through bioinformatics analysis. We firstly constructed a prognostic signature based on four GT genes using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses in The Cancer Genome Atlas (TCGA) dataset. Next, the risk score of each patient was calculated, and HCC patients were divided into high- and low-risk groups. Kaplan-Meier analysis showed that the survival rate of high-risk patients was significantly lower than that of low-risk patients. Receiver operating characteristic (ROC) curves assessed that risk scores calculated with a four-gene signature could predict 3- and 5-year overall survival (OS) of HCC patients, revealing the prognostic ability of this gene signature. Moreover, univariate and multivariate Cox regression analyses demonstrated that the risk score was an independent prognostic factor of HCC. Finally, functional analysis revealed that immune-related pathways were enriched and the immune status was different between the two risk groups in HCC. In summary, the novel GT gene signature could be used for prognostic prediction of HCC. Thus, targeting the GT genes may serve as an alternative treatment strategy for HCC.

Identification of Context-Specific Fitness Genes Associated With Metabolic Rearrangements for Prognosis and Potential Treatment Targets for Liver Cancer.

Liver cancer is the most frequent fatal malignancy. Furthermore, there is a lack of effective therapeutics for this cancer type. To construct a prognostic model for potential beneficiary screens and identify novel treatment targets, we used an adaptive daisy model (ADaM) to identify context-specific fitness genes from the CRISPR-Cas9 screens database, DepMap. Functional analysis and prognostic significance were assessed using data from TCGA and ICGC cohorts, while drug sensitivity analysis was performed using data from the Liver Cancer Model Repository (LIMORE). Finally, a 25-gene prognostic model was established. Patients were then divided into high- and low-risk groups; the high-risk group had a higher stemness index and shorter overall survival time than the low-risk group. The C-index, time-dependent ROC curves, and multivariate Cox regression analysis confirmed the excellent prognostic ability of this model. Functional enrichment analysis revealed the importance of metabolic rearrangements and serine/threonine kinase activity, which could be targeted by trametinib and is the key pathway in regulating liver cancer cell viability. In conclusion, the present study provides a prognostic model for patients with liver cancer and might help in the exploration of novel therapeutic targets to ultimately improve patient outcomes.

Significance of Liver Zonation in Hepatocellular Carcinoma.

Liver zonation is fundamental to normal liver function, and numerous studies have investigated the microstructure of normal liver lobules. However, only a few studies have explored the zonation signature in hepatocellular carcinoma (HCC). In this study, we investigated the significance of liver zonation in HCC with the help of single-cell RNA sequencing (scRNA-seq) and multicolor immunofluorescence staining. Liver zonation-related genes were extracted from the literature, and a three-gene model was established for HCC prognosis. The model reliability was validated using bulk RNA and single-cell RNA-level data, and the underlying biological mechanism was revealed by a functional enrichment analysis. The results showed that the signaling pathways of high-risk groups were similar to those of perivenous zones in the normal liver, indicating the possible regulating role of hypoxia in HCC zonation. Furthermore, the co-staining results showed that the low-grade tumors lost their zonation features whereas the high-grade tumors lost the expression of zonation-related genes, which supported the results obtained from the sequencing data.

Spatial transcriptome profiling of normal human liver.

The comprehensive study of the spatial-cellular anatomy of the human liver is critical to addressing the cellular origins of liver disease. Here we conducted spatial transcriptomics on normal human liver tissue sections, providing detailed information of liver zonation at the transcriptional level. We present 6581 high-quality spots from normal livers of two human donors. In this dataset, cells were mainly hepatocytes, and we classified them into four sub-groups. Collectively, these data provide a reliable reference for studies on spatial heterogeneity of liver lobules.

The Prognostic Model Based on Tumor Cell Evolution Trajectory Reveals a Different Risk Group of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and heterogeneity of HCC is the major barrier in improving patient outcome. To stratify HCC patients with different degrees of malignancy and provide precise treatment strategies, we reconstructed the tumor evolution trajectory with the help of scRNA-seq data and established a 30-gene prognostic model to identify the malignant state in HCC. Patients were divided into high-risk and low-risk groups. C-index and receiver operating characteristic (ROC) curve confirmed the excellent predictive value of this model. Downstream analysis revealed the underlying molecular and functional characteristics of this model, including significantly higher genomic instability and stronger proliferation/progression potential in the high-risk group. In summary, we established a novel prognostic model to overcome the barriers caused by HCC heterogeneity and provide the possibility of better clinical management for HCC patients to improve their survival outcomes.

An Integrated Fibrosis Signature for Predicting Survival and Immunotherapy Efficacy of Patients With Hepatocellular Carcinoma.

Introduction: Fibrosis, a primary cause of hepatocellular carcinoma (HCC), is intimately associated with inflammation, the tumor microenvironment (TME), and multiple carcinogenic pathways. Currently, due to widespread inter- and intra-tumoral heterogeneity of HCC, the efficacy of immunotherapy is limited. Seeking a stable and novel tool to predict prognosis and immunotherapy response is imperative. Methods: Using stepwise Cox regression, least absolute shrinkage and selection operator (LASSO), and random survival forest algorithms, the fibrosis-associated signature (FAIS) was developed and further validated. Subsequently, comprehensive exploration was conducted to identify distinct genomic alterations, clinical features, biological functions, and immune landscapes of HCC patients. Results: The FAIS was an independent prognostic predictor of overall survival and recurrence-free survival in HCC. In parallel, the FAIS exhibited stable and accurate performance at predicting prognosis based on the evaluation of Kaplan-Meier survival curves, receiver operator characteristic curves, decision curve analysis, and Harrell's C-index. Further investigation elucidated that the high-risk group presented an inferior prognosis with advanced clinical traits and a high mutation frequency of TP53, whereas the low-risk group was characterized by superior CD8+ T cell infiltration, a higher TIS score, and a lower TIDE score. Additionally, patients in the low-risk group might yield more benefits from immunotherapy. Conclusion: The FAIS was an excellent scoring system that could stratify HCC patients and might serve as a promising tool to guide surveillance, improve prognosis, and facilitate clinical management.

Identifying the Key Genes in Mouse Liver Regeneration After Partial Hepatectomy by Bioinformatics Analysis and in vitro/vivo Experiments.

BACKGROUND: The liver is the only organ that can completely regenerate after various injuries or tissue loss. There are still a large number of gene functions in liver regeneration that have not been explored. This study aimed to identify key genes in the early stage of liver regeneration in mice after partial hepatectomy (PH). MATERIALS AND METHODS: We first analyzed the expression profiles of genes in mouse liver at 48 and 72 h after PH from Gene Expression Omnibus (GEO) database. Gene ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) analysis were performed to identify key genes in liver regeneration. Finally, we validated key genes in vivo and in vitro. RESULTS: We identified 46 upregulated genes and 19 downregulated genes at 48 h after PH, and 223 upregulated genes and 40 downregulated genes at 72 h after PH, respectively. These genes were mainly involved in cell cycle, DNA replication, and p53 signaling pathway. Among of these genes, cycle-related genes (Ccna2, Cdkn1a, Chek1, and Mcm5) and Ube2c were highly expressed in the residual liver both at 48 and 72 h after PH. Furthermore, Ube2c knockdown not only caused abnormal expression of Ccna2, Cdkn1a, Chek1, and Mcm5, but also inhibited transition of hepatocytes from G1 to S phase of the cell cycle in vitro. CONCLUSION: Mouse hepatocytes enter the proliferation phase at 48 h after PH. Ube2c may mediate cell proliferation by regulating or partially regulating Ccna2, Cdkn1a, Chek1, and Mcm5.

A signature of 33 immune-related gene pairs predicts clinical outcome in hepatocellular carcinoma.

OBJECTIVE: Hepatocellular carcinoma (HCC) has become the second most common tumor type that contributes to cancer-related death worldwide. The study aimed to establish a robust immune-related gene pair (IRGP) signature for predicting the prognosis of HCC patients. METHODS: Two RNA-seq datasets (The Cancer Genome Atlas Program and International Cancer Genome Consortium) and one microarray dataset (GSE14520) were included in this study. We used a series of immune-related genes from the ImmPort database to construct gene pairs. Lasso penalized Cox proportional hazards regression was employed to develop the best prognostic signature. We assigned patients into two groups with low immune risk and high immune risk. Then, the prognostic ability of the signature was evaluated by a log-rank test and a Cox proportional hazards regression model. RESULTS: After 1000 iterations, the 33-immune gene pair model obtained the highest frequency. As a result, we chose the 33 immune gene pairs to establish the immune-related prognostic signature. As we expected, the immune-related signature accurately predicted the prognosis of HCC patients, and high-risk groups showed poor prognosis in the training datasets and testing datasets as well as in the validation datasets. Furthermore, the immune-related gene pair (IRGP) signature also showed higher predictive accuracy than three existing prognostic signatures. CONCLUSION: Our prognostic signature, which reflects the link between the immune microenvironment and HCC patient outcome, is promising for prognosis prediction in HCC.