Down-regulation of solute carrier family 10 member 1 is associated with early recurrence and poorer prognosis of hepatocellular carcinoma

Exploring bile acid transporters as key players in cancer development and treatment: Evidence from preclinical and clinical studies

Bile acid transporters (BATs) are integral membrane proteins belonging to various families, such as solute carriers, organic anion transporters, and ATP-binding cassette families. These transporters play a crucial role in bile acid transportation within the portal and systemic circulations, with expression observed in tissues, including the liver, kidney, and small intestine. Bile acids serve as signaling molecules facilitating the absorption and reabsorption of fats and lipids. Dysregulation of bile acid concentration has been implicated in tumorigenesis, yet the role of BATs in this process remains underexplored. Emerging evidence suggests that BATs may modulate various stages of cancer progression, including initiation, development, proliferation, metastasis, and tumor microenvironment regulation. Targeting BATs using siRNAs, miRNAs, and small compound inhibitors in preclinical models and their polymorphisms are well-studied for transporters like BSEP, MDR1, MRP2, OATP1A2, etc., and have shed light on their involvement in tumorigenesis, particularly in cancers such as those affecting the liver and gastrointestinal tract. While BATs' role in diseases like Alagille syndrome, biliary atresia, and cirrhosis have been extensively studied, their implications in cancer warrant further investigation. This review highlights the expression and function of BATs in cancer development and emphasizes the potential of targeting these transporters as a novel therapeutic strategy for various malignancies.

The loss of hepatitis B virus receptor NTCP/SLC10A1 in human liver cancer cells is due to epigenetic silencing

Human Na+-taurocholate cotransporting polypeptide (hNTCP) is predominantly expressed in hepatocytes, maintaining bile salt homeostasis and serving as a receptor for hepatitis B virus (HBV). hNTCP expression is downregulated during hepatocellular carcinoma (HCC) development. In this study, we investigated the molecular mechanisms underlying hNTCP dysregulation using HCC tissues and cell lines, and primary human hepatocytes (PHHs). Firstly, we observed a significant reduction of hNTCP in HCC tumors compared to adjacent and normal tissues. Additionally, hNTCP mRNA levels were markedly lower in HepG2 cells compared to PHHs, which was corroborated at the protein level by immunoblotting. Sanger sequencing confirmed identical sequences for hNTCP promoter, exons, and mRNA coding sequences between PHH and HepG2 cells, indicating no mutations or splicing alterations. We then assessed the epigenetic status of hNTCP. The hNTCP promoter, with low CG content, showed no significant methylation differences between PHH and HepG2 cells. Chromatin immunoprecipitation coupled with qPCR (ChIP-qPCR) revealed a loss of activating histone posttranslational modification (PTM) H3K27ac near the hNTCP transcription start site (TSS) in HepG2 cells. This loss was also confirmed in HCC tumor cells compared to adjacent and background cells. Treating HepG2 cells with histone deacetylase inhibitors enhanced H3K27ac accumulation and glucocorticoid receptor (GR) binding at the hNTCP TSS, significantly increasing hNTCP mRNA and protein levels, and rendering the cells susceptible to HBV infection. In summary, histone PTM-related epigenetic mechanisms play a critical role in hNTCP dysregulation in liver cancer cells, providing insights into hepatocarcinogenesis and its impact on chronic HBV infection.

IMPORTANCE

HBV is a hepatotropic virus that infects human hepatocytes expressing the viral receptor hNTCP. Without effective antiviral therapy, chronic HBV infection poses a high risk of liver cancer. However, most liver cancer cell lines, including HepG2 and Huh7, do not support HBV infection due to the absence of hNTCP expression, and the mechanism underlying this defect remains unclear. This study demonstrates a significant reduction of hNTCP in hepatocellular carcinoma samples and HepG2 cells compared to normal liver tissues and primary human hepatocytes. Despite identical hNTCP genetic sequences, epigenetic analyses revealed a loss of the activating histone modification H3K27ac near the hNTCP transcription start site in cancer cells. Treatment with histone deacetylase inhibitors restored H3K27ac levels, reactivated hNTCP expression, and rendered HepG2 cells susceptible to HBV infection. These findings highlight the role of epigenetic modulation in hNTCP dysregulation, offering insights into hepatocarcinogenesis and its implications for chronic HBV infection.

Comprehensive Analysis of Angiogenesis and Ferroptosis Genes for Predicting the Survival Outcome and Immunotherapy Response of Hepatocellular Carcinoma

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.

Involvement of lncRNAs in the regulation of aerobic glycolysis in hepatocellular carcinoma: Main functions, regulatory mechanisms and potential therapeutic implications (Review)

Even under aerobic conditions, tumor cells can reprogram their metabolism to preferentially metabolize glucose into lactic acid. This abnormal metabolic pattern, known as the 'Warburg' effect or aerobic glycolysis, promotes cancer progression. Long non‑coding RNAs (lncRNAs) are RNAs that are >200 nucleotides in length and do not have protein‑coding capabilities. However, these RNAs play a key role in tumor development. There is increasing evidence to indicate that lncRNAs regulate glucose metabolism in tumor cells by affecting metabolic enzymes and some signaling pathways, thereby regulating the occurrence and progression of hepatocellular carcinoma (HCC). Therefore, it is crucial to understand which lncRNAs play a regulatory role in HCC glycolysis and to determine the related molecular mechanisms. The present review summarized and discussed the functions of lncRNAs, focusing on the regulatory mechanisms of lncRNAs in the process of glycolysis in HCC. In addition, the present review suggests the importance of lncRNAs as future therapeutic targets for antitumor cell metabolism.

Immunotherapy and drug sensitivity predictive roles of a novel prognostic model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most significant causes of cancer-related deaths in the worldwide. Currently, predicting the survival of patients with HCC and developing treatment drugs still remain a significant challenge. In this study, we employed prognosis-related genes to develop and externally validate a predictive risk model. Furthermore, the correlation between signaling pathways, immune cell infiltration, immunotherapy response, drug sensitivity, and risk score was investigated using different algorithm platforms in HCC. Our results showed that 11 differentially expressed genes including UBE2C, PTTG1, TOP2A, SPP1, FCN3, SLC22A1, ADH4, CYP2C8, SLC10A1, F9, and FBP1 were identified as being related to prognosis, which were integrated to construct a prediction model. Our model could accurately predict patients' overall survival using both internal and external datasets. Moreover, a strong correlation was revealed between the signaling pathway, immune cell infiltration, immunotherapy response, and risk score. Importantly, a novel potential drug candidate for HCC treatment was discovered based on the risk score and also validated through ex vivo experiments. Our finds offer a novel perspective on prognosis prediction and drug exploration for cancer patients.

Hypoxia-driven immunosuppression by Treg and type-2 conventional dendritic cells in HCC

BACKGROUND AND AIMS

Hypoxia is one of the central players in shaping the immune context of the tumor microenvironment (TME). However, the complex interplay between immune cell infiltrates within the hypoxic TME of HCC remains to be elucidated.

APPROACH AND RESULTS

We analyzed the immune landscapes of hypoxia-low and hypoxia-high tumor regions using cytometry by time of light, immunohistochemistry, and transcriptomic analyses. The mechanisms of immunosuppression in immune subsets of interest were further explored using in vitro hypoxia assays. Regulatory T cells (Tregs) and a number of immunosuppressive myeloid subsets, including M2 macrophages and human leukocyte antigen-DR isotype (HLA-DR^lo ) type 2 conventional dendritic cell (cDC2), were found to be significantly enriched in hypoxia-high tumor regions. On the other hand, the abundance of active granzyme B^hi PD-1^lo CD8⁺ T cells in hypoxia-low tumor regions implied a relatively active immune landscape compared with hypoxia-high regions. The up-regulation of cancer-associated genes in the tumor tissues and immunosuppressive genes in the tumor-infiltrating leukocytes supported a highly pro-tumorigenic network in hypoxic HCC. Chemokine genes such as CCL20 (C-C motif chemokine ligand 20) and CXCL5 (C-X-C motif chemokine ligand 5) were associated with recruitment of both Tregs and HLA-DR^lo cDC2 to hypoxia-high microenvironments. The interaction between Tregs and cDC2 under a hypoxic TME resulted in a loss of antigen-presenting HLA-DR on cDC2.

CONCLUSIONS

We uncovered the unique immunosuppressive landscapes and identified key immune subsets enriched in hypoxic HCC. In particular, we identified a potential Treg-mediated immunosuppression through interaction with a cDC2 subset in HCC that could be exploited for immunotherapies.

Machine learning algorithm and deep neural networks identified a novel subtype in hepatocellular carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Due to the lack of specific characteristics in the early stage of the disease, patients are usually diagnosed in the advanced stage of disease progression. OBJECTIVE: This study used machine learning algorithms to identify key genes in the progression of hepatocellular carcinoma and constructed a prediction model to predict the survival risk of HCC patients. METHODS: The transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differential expression analysis and COX proportional-hazards model participated in the identification of survival-related genes. K-Means, Random forests, and LASSO regression are involved in identifying novel subtypes of HCC and screening key genes. The prediction model was constructed by deep neural networks (DNN), and Gene Set Enrichment Analysis (GSEA) reveals the metabolic pathways where key genes are located. RESULTS: Two subtypes were identified with significantly different survival rates (p< 0.0001, AUC = 0.720) and 17 key genes associated with the subtypes. The accuracy rate of the deep neural network prediction model is greater than 93.3%. The GSEA analysis found that the survival-related genes were significantly enriched in hallmark gene sets in the MSigDB database. CONCLUSIONS: In this study, we used machine learning algorithms to screen out 17 genes related to the survival risk of HCC patients, and trained a DNN model based on them to predict the survival risk of HCC patients. The genes that make up the model are all key genes that affect the formation and development of cancer.