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

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.