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.

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.