CLEC1B is a Promising Prognostic Biomarker and Correlated with Immune Infiltration in Hepatocellular Carcinoma

Harnessing immunotherapy for hepatocellular carcinoma: Principles and emerging promises

HCC is considered as one of the leadin causes of death worldwide, with the ability of resistance towards therapeutics. Immunotherapy, particularly ICIs, have provided siginficant insights towards harnessing the immune system. The present review introduces the concepts and possibilities of immunotherapy for HCC treatment, emphasizing its underlying mechanisms and capacity to enhance patient results, focusing on both pre-clinical and clinical insights. The functions of TME and immune evasion mechanisms typical of HCC would be evaluated along with how contemporary immunotherapeutic approaches are designed to address these challenges. Furthermore, the clinical application of immunotherapy in HCC is discussed, emphasizing recent trial findings demonstrating the effectiveness and safety of drugs. In addition, the problems caused by immune evasion and resistance would be discussed to increase potential of immunotherapy along with combination therapy.

Development of a novel diagnostic model to monitor the progression of metabolic dysfunction-associated steatotic liver disease to hepatocellular carcinoma in females

BACKGROUND AND AIMS

The onset of metabolic dysfunction-associated steatotic liver disease-associated hepatocellular carcinoma (MASLD-HCC) is insidious and exhibits sex-specific variations. Effective methods for monitoring MASLD-HCC progression in females have not yet been developed.

METHODS

Transcriptomic data of female liver tissue samples were obtained from multiple public databases. Differentially expressed genes (DEGs) in MASLD-HCC were identified using differential expression and robust rank aggregation analyses. Diagnostic prediction models for MASLD (DP.MASLD) and HCC (DP.HCC) were developed and validated using elastic net analysis, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Bioinformatics was used to assess the pathogenesis of MASLD-HCC.

RESULTS

Seven overlapping DEGs were identified in female patients with MASLD and HCC: AKR1B10, CLEC1B, CYP2C19, FREM2, MT1H, NRG1, and THBS1). The area under the ROC curve (AUC) values for the training and validation groups of the DP.MASLD model were 0.864 and 0.782, 0.932 and 1.000, and 0.920 and 0.969 when differentiating between the steatosis and normal liver, steatohepatitis and steatosis, and steatohepatitis and normal liver groups, respectively. The AUCs for DP.HCC were 0.980 and 0.997 in the training and validation groups, respectively. The oncogenesis of female MASLD-HCC is associated with molecular pathways, including cytochrome P450-associated drug metabolism, tyrosine metabolism, fatty acid degradation, focal adhesion, extracellular matrix receptor interactions, and protein digestion and absorption.

CONCLUSION

A novel and effective method to quantitatively assess the risk of MASLD-HCC progression in female patients was developed, and this method will aid in the generation of precise diagnostic, preventive, and therapeutic strategies.

Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Macrophage Polarization-Related Genes

Background

The progression of hepatocellular carcinoma (HCC) is related to macrophage polarization (MP). Our aim was to identify genes associated with MP in HCC patients and develop a prognostic model based on these genes.

Results

We successfully developed a prognostic model consisting of six MP-related genes (SCN4A, EBF3, ADGRB2, HOXD9, CLEC1B, and MSC) to calculate the risk score for each patient. Patients were then classified into high- and low-risk groups based on their median risk score. The performance of the MP-related prognostic model was evaluated using Kaplan-Meier and ROC curves, which yielded favorable results. Additionally, the nomogram demonstrated good clinical effectiveness and displayed consistent survival predictions with actual observations. Gene Set Enrichment Analysis (GSEA) revealed enrichment of pathways related to KRAS signaling downregulation, the G2M checkpoint, and E2F targets in the high-risk group. Conversely, pathways associated with fatty acid metabolism, xenobiotic metabolism, bile acid metabolism, and adipogenesis were enriched in the low-risk group. The risk score positively correlated with the number of invasion-related genes. Immune checkpoint expression differed significantly between the two groups. Patients in the high-risk group exhibited increased sensitivity to mitomycin C, cisplatin, gemcitabine, rapamycin, and paclitaxel, while those in the low-risk group showed heightened sensitivity to doxorubicin. These findings suggest that the high-risk group may have more invasive HCC with greater susceptibility to specific drugs. IHC staining revealed higher expression levels of SCN4A in HCC tissues. Furthermore, experiments conducted on HepG2 cells demonstrated that supernatants from cells with reduced SCN4A expression promoted M2 macrophage polarization marker, CD163 in THP-1 cells. Reduced SCN4A expression induced HCC-related genes, while increased SCN4A expression reduced their expression in HepG2 cells.

Conclusion

The MP-related prognostic model comprising six MPRGs can effectively predict HCC prognosis, infer invasiveness, and guide drug therapy. SCN4A is identified as a suppressor gene in HCC.

Gene expression signatures of stepwise progression of Hepatocellular Carcinoma

The molecular pathogenesis of Hepatocellular Carcinoma (HCC) is a complex process progressing from premalignant stages to cancer in a stepwise manner. Mostly, HCC is detected at advanced stages, leading to high mortality rates. Hence, characterising the molecular underpinnings of HCC from normal to cancer state through precancerous state may help in early detection and improve its prognosis and treatment. In this work, we analysed the transcriptomic profile of tumour and premalignant samples from HCC or chronic liver disease patients, who had undergone either total or partial hepatectomy. The normal samples from patients with metastatic cancer/polycystic liver disease/ cholangiocarcinoma were also included. A gene co-expression network approach was applied to identify hierarchical changes: modules, pathways, and genes related to different trajectories of HCC and patient survival. Our analysis shows that the progression from premalignant conditions to tumour is accompanied by differences in the downregulation of genes associated with HNF4A activity and the immune system and upregulation of cell cycle genes, bringing about variability in patient outcomes. However, an increase in immune and cell cycle activity is observed in premalignant samples. Interestingly, co-expression modules and genes from premalignant stages are associated with survival. THBD, a classical marker for dendritic cells, is a predictor of survival at the premalignant stage. Further, genes linked to microtubules, kinetochores, and centromere are altered in both premalignant and tumour conditions and are associated with survival. Our analysis revealed a three-way molecular axis of liver function, immune pathways, and cell cycle driving HCC pathogenesis.

Comprehensive analysis identifies CLEC1B as a potential prognostic biomarker in hepatocellular carcinoma

BACKGROUND

C-type lectin domain family 1 member B (CLEC1B, encoding the CLEC-2 protein), a member of the C-type lectin superfamily, is a type II transmembrane receptor involved in platelet activation, angiogenesis, and immune and inflammatory responses. However, data regarding its function and clinical prognostic value in hepatocellular carcinoma (HCC) remain scarce.

METHODS

The expression of CLEC1B was explored using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RT-qPCR, western blot, and immunohistochemistry assays were employed to validate the downregulation of CLEC1B. Univariate Cox regression and survival analyses were used to evaluate the prognostic value of CLEC1B. Gene Set Enrichment Analysis (GSEA) was conducted to investigate the potential association between cancer hallmarks and CLEC1B expression. The TISIDB database was applied to search for the correlation between immune cell infiltration levels and CLEC1B expression. The association between CLEC1B and immunomodulators was conducted by Spearman correlation analysis based on the Sangerbox platform. Annexin V-FITC/PI apoptosis kit was used for the detection of cell apoptosis.

RESULTS

The expression of CLEC1B was low in various tumors and exhibited a promising clinical prognostic value for HCC patients. The expression level of CLEC1B was tightly associated with the infiltration of various immune cells in the HCC tumor microenvironment (TME) and positively correlated with a bulk of immunomodulators. In addition, CLEC1B and its related genes or interacting proteins are implicated in multiple immune-related processes and signaling pathways. Moreover, overexpression of CLEC1B significantly influenced the treatment effects of sorafenib on HCC cells.

CONCLUSIONS

Our results reveal that CLEC1B could serve as a potential prognostic biomarker and may be a novel immunoregulator for HCC. However, its function in immune regulation should be further explored.

A novel stemness-hypoxia-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma

Background

The specific differentiation potential, unlimited proliferation, and self-renewal capacity of cancer stem cells (CSCs) are closely related to the occurrence, recurrence, and drug resistance of hepatocellular carcinoma (HCC), as well as hypoxia. Therefore, an in-depth analysis of the relationship between HCC stemness, oxygenation status, and the effectiveness of immunotherapy is necessary to improve the poor prognosis of HCC patients.

Methods

The weighted gene co-expression network analysis (WGCNA) was utilized to find hypoxia-related genes, and the stemness index (mRNAsi) was evaluated using the one-class logistic regression (OCLR) technique. Based on stemness-hypoxia-related genes (SHRGs), population subgroup categorization using NMF cluster analysis was carried out. The relationship between SHRGs and survival outcomes was determined using univariate Cox regression. The LASSO-Cox regression strategy was performed to investigate the quality and establish the classifier associated with prognosis. The main effect of risk scores on the tumor microenvironment (TME) and its response to immune checkpoint drugs was also examined. Finally, qRT-PCR was performed to explore the expression and prognostic value of the signature in clinical samples.

Results

After identifying tumor stemness- and hypoxia-related genes through a series of bioinformatics analyses, we constructed a prognostic stratification model based on these SHRGs, which can be effectively applied to the prognostic classification of HCC patients and the prediction of immune checkpoint inhibitors (ICIs) efficacy. Independent validation of the model in the ICGC cohort yielded good results. In addition, we also constructed hypoxic cell models in Herp3B and Huh7 cells to verify the expression of genes in the prognostic model and found that C7, CLEC1B, and CXCL6 were not only related to the tumor stemness but also related to hypoxia. Finally, we found that the constructed signature had a good prognostic value in the clinical sample.

Conclusions

We constructed and validated a stemness-hypoxia-related prognostic signature that can be used to predict the efficacy of ICIs therapy. We also verified that C7, CLEC1B, and CXCL6 are indeed associated with stemness and hypoxia through a hypoxic cell model, which may provide new ideas for individualized immunotherapy.