The necroptosis related gene LGALS3 can be used as a biomarker for the adverse progression from chronic HBV infection to HCC

Role of ACSL4 in modulating farnesoid X receptor expression and M2 macrophage polarization in HBV-induced hepatocellular carcinoma

The intricate relationship between bile acid (BA) metabolism, M2 macrophage polarization, and hepatitis B virus-hepatocellular carcinoma (HBV-HCC) necessitates a thorough investigation of ACSL4's (acyl-CoA synthetase long-chain family member 4) role. This study combines advanced bioinformatics and experimental methods to elucidate ACSL4's significance in HBV-HCC development. Using bioinformatics, we identified differentially expressed genes in HBV-HCC. STRING and gene set enrichment analysis analyses were employed to pinpoint critical genes and pathways. Immunoinfiltration analysis, along with in vitro and in vivo experiments, assessed M2 macrophage polarization and related factors. ACSL4 emerged as a pivotal gene influencing HBV-HCC. In HBV-HCC liver tissues, ACSL4 exhibited upregulation, along with increased levels of M2 macrophage markers and BA. Silencing ACSL4 led to heightened farnesoid X receptor (FXR) expression, reduced BA levels, and hindered M2 macrophage polarization, thereby improving HBV-HCC conditions. This study underscores ACSL4's significant role in HBV-HCC progression. ACSL4 modulates BA-mediated M2 macrophage polarization and FXR expression, shedding light on potential therapeutic targets and novel insights into HBV-HCC pathogenesis.

Transcriptome Analyses of Liver Sinusoidal Endothelial Cells Reveal a Consistent List of Candidate Genes Associated with Endothelial Dysfunction and the Fibrosis Progression

Liver fibrosis is an important step in the transformation of chronic liver disease into cirrhosis and liver cancer, and structural changes and functional disorders of liver sinusoidal endothelial cells (LSECs) are early events in the occurrence of liver fibrosis. Therefore, it is necessary to identify the key regulatory genes of endothelial dysfunction in the process of liver fibrosis to provide a reference for the diagnosis and treatment of liver fibrosis. In this study, we identified 230 common differentially expressed genes (Co-DEGs) by analyzing transcriptomic data of primary LSECs from three different liver fibrosis mouse models (carbon tetrachloride; choline-deficient, l-amino acid-defined diet; and nonalcoholic steatohepatitis). Enrichment analysis revealed that the Co-DEGs were mainly involved in regulating the inflammatory response, immune response, angiogenesis, formation and degradation of the extracellular matrix, and mediating chemokine-related pathways. A Venn diagram analysis was used to identify 17 key genes related to the progression of liver cirrhosis. Regression analysis using the Lasso-Cox method identified genes related to prognosis among these key genes: SOX4, LGALS3, SERPINE2, CD52, and LPXN. In mouse models of liver fibrosis (bile duct ligation and carbon tetrachloride), all five key genes were upregulated in fibrotic livers. This study identified key regulatory genes for endothelial dysfunction in liver fibrosis, namely SOX4, LGALS3, SERPINE2, CD52, and LPXN, which will provide new targets for the development of therapeutic strategies targeting endothelial dysfunction in LSECs and liver fibrosis.

Galectin-3-ITGB1 Signaling Mediates Interleukin 10 Production of Hepatic Conventional Natural Killer Cells in Hepatitis B Virus Transgenic Mice and Correlates with Hepatocellular Carcinoma Progression in Patients

BACKGROUND AND AIMS

The outcomes of HBV infections are related to complex immune imbalances; however, the precise mechanisms by which HBV induces immune dysfunction are not well understood.

METHODS

HBV transgenic (HBs-Tg) mice were used to investigate intrahepatic NK cells in two distinct subsets: conventional NK (cNK) and liver-resident NK (LrNK) cells during a chronic HBV infection.

RESULTS

The cNK cells, but not the LrNK cells, were primarily responsible for the increase in the number of bulk NK cells in the livers of ageing HBs-Tg mice. The hepatic cNK cells showed a stronger ability to produce IL-10, coupled with a higher expression of CD69, TIGIT and PD-L1, and lower NKG2D expression in ageing HBs-Tg mice. A lower mitochondrial mass and membrane potential, and less polarized localization were observed in the hepatic cNK cells compared with the splenic cNK cells in the HBs-Tg mice. The enhanced galectin-3 (Gal-3) secreted from HBsAg+ hepatocytes accounted for the IL-10 production of hepatic cNK cells via ITGB1 signaling. For humans, LGALS3 and ITGB1 expression is positively correlated with IL-10 expression, and negatively correlated with the poor clinical progression of HCC.

CONCLUSIONS

Gal-3-ITGB1 signaling shapes hepatic cNK cells but not LrNK cells during a chronic HBV infection, which may correlate with HCC progression.

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma

INTRODUCTION

Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC).

METHODS

Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm.

RESULTS

Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment.

CONCLUSIONS

TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

The Characteristics of Transcription Factors Regulating T Cell Exhaustion Were Analyzed to Predict the Prognosis and Therapeutic Effect in Patients with HCC

Purpose

Hepatocellular carcinoma (HCC) ranks as the third leading cause of cancer-related deaths, posing a significant threat to people in diverse regions. T-cell exhaustion (Tex) can hinder the efficacy of immunotherapy in patients with HCC, and the transcription factors that regulate Tex in HCC have not yet been fully elucidated.

Patients and Methods

We used the single sample gene set enrichment analysis (ssGSEA) method to define the transcription factor pathway that regulates Tex and employed LASSO regression analysis to establish Tex related genes (TEXRS). To predict differences in immunotherapy efficacy between the two groups, we used the immunophenotype score and submap algorithm. RT-qPCR was used to detect the expression levels of the model genes in 21 pairs of HCC tissues. Finally, we assessed the cell communication strength and identified ligand receptors using the "CellChat" R package.

Results

Nine Tex transcription factors were identified as regulators of the HCC immune microenvironment, with Tex scores affecting patient survival. Patients with a high Tex Risk Score (TEXRS) had significantly worse overall survival compared to patients with low TEXRS. After adjusting for confounding factors, TEXRS remained an independent prognostic factor. Importantly, TEXRS performed well in multiple independent external validation cohorts. Various algorithms have shown that patients in the low-TEXRS group might benefit more from immunotherapy. Finally, RT-qPCR analysis of 21 HCC samples showed that C7, CD5L, and SDS were significantly downregulated in HCC tissues, consistent with the bioinformatics analysis results.

Conclusion

TEXRS proved to be a valuable predictor of immunotherapy and transcatheter arterial chemoembolization efficacy in patients with HCC. This holds promise for enhancing the prognosis and treatment outcomes of patients with HCC.