Landscape of infiltrating B cells and their clinical significance in human hepatocellular carcinoma

M6A-mediated molecular patterns and tumor microenvironment infiltration characterization in nasopharyngeal carcinoma

N6-methyladenosine (m6A) is the most predominant RNA epigenetic regulation in eukaryotic cells. Numerous evidence revealed that m6A modification exerts a crucial role in the regulation of tumor microenvironment (TME) cell infiltration in several tumors. Nevertheless, the potential role and mechanism of m6A modification in nasopharyngeal carcinoma (NPC) remains unknown. mRNA expression data and clinical information from GSE102349, and GSE53819 datasets obtained from Gene Expression Omnibus (GEO) was used for differential gene expression and subsequent analysis. Consensus clustering was used to identify m6A-related molecular patterns of 88 NPC samples based on prognostic m6A regulators using Univariate Cox analysis. The TME cell-infiltrating characteristics of each m6A-related subclass were explored using single-sample gene set enrichment (ssGSEA) algorithm and CIBERSORT algotithm. DEGs between two m6A-related subclasses were screened using edgeR package. The prognostic signature and predicated nomogram were constructed based on the m6A-related DEGs. The cell infiltration and expression of prognostic signature in NPC was determined using immunohistochemistry (IHC) analysis. Chi-square test was used to analysis the significance of difference of the categorical variables. And survival analysis was performed using Kaplan-Meier plots and log-rank tests. The NPC samples were divided into two m6A-related subclasses. The TME cell-infiltrating characteristics analyses indicated that cluster 1 is characterized by immune-related and metabolism pathways activation, better response to anit-PD1 and anti-CTLA4 treatment and chemotherapy. And cluster 2 is characterized by stromal activation, low expression of HLA family and immune checkpoints, and a worse response to anti-PD1 and anti-CTLA4 treatment and chemotherapy. Furthermore, we identified 1558 DEGs between two m6A-related subclasses and constructed prognostic signatures to predicate the progression-free survival (PFS) for NPC patients. Compared to non-tumor samples, REEP2, TMSB15A, DSEL, and ID4 were upregulated in NPC samples. High expression of REEP2 and TMSB15A showed poor survival in NPC patients. The interaction between REEP2, TMSB15A, DSEL, ID4, and m6A regulators was detected. Our finding indicated that m6A modification plays an important role in the regulation of TME heterogeneity and complexity.

Integration of Clinical Trial Spatial Multiomics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery

Due to the lack of treatment options, there remains a need to advance new therapeutics in hepatocellular carcinoma (HCC). The traditional approach moves from initial molecular discovery through animal models to human trials to advance novel systemic therapies that improve treatment outcomes for patients with cancer. Computational methods that simulate tumors mathematically to describe cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico, potentially greatly accelerating delivery of new therapeutics to patients. To facilitate the design of dosing regimens and identification of potential biomarkers for immunotherapy, we developed a new computational model to track tumor progression at the organ scale while capturing the spatial heterogeneity of the tumor in HCC. This computational model of spatial quantitative systems pharmacology was designed to simulate the effects of combination immunotherapy. The model was initiated using literature-derived parameter values and fitted to the specifics of HCC. Model validation was done through comparison with spatial multiomics data from a neoadjuvant HCC clinical trial combining anti-PD1 immunotherapy and a multitargeted tyrosine kinase inhibitor cabozantinib. Validation using spatial proteomics data from imaging mass cytometry demonstrated that closer proximity between CD8 T cells and macrophages correlated with nonresponse. We also compared the model output with Visium spatial transcriptomics profiling of samples from posttreatment tumor resections in the clinical trial and from another independent study of anti-PD1 monotherapy. Spatial transcriptomics data confirmed simulation results, suggesting the importance of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. Our findings demonstrate that incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides a novel approach for patient outcome prediction and biomarker discovery. Significance: Incorporating mathematical modeling and computer simulations with high-throughput spatial multiomics data provides an effective approach for patient outcome prediction and biomarker discovery.

The Role and Clinical Relevance of Glycolysis-Associated Genes on Immune Infiltration in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) poses a significant challenge with dismal survival rates, necessitating a deeper understanding of its molecular mechanisms and the development of improved therapies. Metabolic reprogramming, particularly heightened glycolysis, plays a crucial role in HCC progression. Glycolysis-associated genes (GAGs) emerge as key players in HCC pathogenesis, influencing the tumor microenvironment and immune responses. This study aims to investigate the intricate interplay between GAGs and the immune landscape within HCC, offering valuable insights into potential prognostic markers and therapeutic targets to enhance treatment strategies and patient outcomes. Through the exploration of GAGs, we have identified two distinct molecular glycolytic subtypes in HCC patients, each exhibiting significant differences in both the immune microenvironment and prognosis. A risk model comprising five key GAGs was formulated and subsequently evaluated for their predictive accuracy. Our findings underscore the diverse tumor microenvironment and immune responses associated with the varying glycolytic subtypes observed in HCC. The identified key GAGs hold promise as prognostic indicators for evaluating HCC risk levels, predicting patient outcomes, and guiding clinical treatment decisions, particularly in the context of anticipating responses to immunotherapy drugs.

A novel risk scoring system predicts overall survival of hepatocellular carcinoma using cox proportional hazards machine learning method

BACKGROUND

Robust and practical prognosis prediction models for hepatocellular carcinoma (HCC) patients play crucial roles in personalized precision medicine.

MATERIAL AND METHODS

We recruited two independent HCC cohorts (discovery cohort and validation cohort), totally consisting of 222 HCC patients undergone surgical resection. We quantified the expressions of immune-related proteins (CD8, CD68, CD163, PD-1 and PD-L1) in paired HCC tissues and non-tumor liver tissues from these HCC patients using immunohistochemistry (mIHC) assays. We constructed the HCC prognosis prediction model using five different machine learning methods based on the patients in the discovery cohort, such as Cox proportional hazards (CoxPH).

RESULTS

We identified 19 features that were associated with overall survival of HCC patients in the discovery cohort (p < 0.1), such as immune-related features CD68+ and CD8+ cell infiltration. We constructed five HCC prognosis prediction models using five different machine learning methods. Among the five different machine learning models, the CoxPH model achieved the best performance (area under the curve [AUC], 0.839; C-index, 0.779). According to the risk score from CoxPH model, we divided HCC patients into high-risk group/low-risk group. In both discovery cohort and validation cohort, the patients in low-risk group showed longer overall survival compared with those in high-risk group (p = 1.8 × 10-7 and 3.4 × 10-5, respectively). Moreover, our novel scoring system efficiently predicted the 6, 12, and 18 months survival rate of HCC patients with AUC >0.75 in both discovery cohort and validation cohort. In addition, we found that the scoring system could also distinguish the patients with high/low risks of relapse in both discovery cohort and validation cohort (p = 0.00015 and 0.00012).

CONCLUSION

The novel CoxPH-based risk scoring model on clinical, laboratory-testing and immune-related features showed high prediction efficiencies for overall survival and recurrence of HCCs undergone surgical resection. Our results may be helpful to optimize clinical follow-up or therapeutic interventions.

A Bibliometric Analysis of Metabolic Reprogramming in the Tumor Microenvironment From 2003 to 2022

BACKGROUND

Despite considerable progress in cancer immunotherapy, it is not available for many patients. Resistance to immune checkpoint blockers arises from the intricate interactions between cancer and its microenvironment. Metabolic reprogramming in tumor and immune cells in the tumor microenvironment (TME) influences anti-tumor immune responses by remodeling the immune microenvironment. Metabolic reprogramming has emerged as an important hallmark of tumorigenesis. However, few studies have focused on the TME and metabolic reprogramming. Therefore, we aimed to explore the current research status and popular topics in TME-related metabolic reprogramming over a 20 years using a bibliometric approach.

METHODS

Studies focusing on metabolic reprogramming and TME were searched using the Web of Science Core Collection database. Bibliometric and visual analyses of the articles and reviews were performed using Bibliometrix, VOSviewer, and CiteSpace.

RESULTS

In total, 4726 articles published between 2003 and 2022 were selected. The number of publications and citations has increased annually. Cooperation network analysis indicated that the United States holds the foremost position in metabolic reprogramming and TME research with the highest volume of publications and citations, thus exerting the greatest influence. Among these institutions, Fudan University displayed the highest level of productivity. Frontiers in Immunology showed the highest degree of productivity in this field. Ho Ping-Chih made the most article contributions, and Pearce Edward J. was the most co-cited author. Four clusters were obtained after a cluster analysis of the authors' keywords: TME, metabolic reprogramming, immunometabolism, and immunity. Immunometabolism, glycolysis, immune cells, and tumor-associated macrophages are relatively recent keywords that have attracted increasing attention.

CONCLUSIONS

A comprehensive landscape of advancements in metabolic reprogramming and the TME was evaluated, which provided crucial information for scholars to further advance this promising field. Further research should explore new topics related to immunometabolism in the TME using a transdisciplinary approach.

Unraveling pancreatic ductal adenocarcinoma immune prognostic signature through a naive B cell gene set

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood.

METHODS

We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC.

RESULTS

Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk.

CONCLUSION

Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.

Causal associations of BAFF-R on IgD+ CD24- B cell immune cell trait with hepatocellular carcinoma and the mediating role of phenylacetylglutamate levels: a Mendelian randomization study

We conducted a bi-directional two-sample Mendelian randomization (MR) analysis to investigate the causal associations between immune cell traits and hepatocellular carcinoma (HCC) and identified the mediating factor of metabolites. The exposure factors were immune cell traits, the mediators were metabolites, and the outcome variable was HCC. Inverse-variance weighted method (IVW) was the main method. Weighted median, MR-Egger regression, weighted mode, simple mode, and MR pleiotropy residual sum and outlier (MRPRESSO) methods were used as complementary methods. The results were tested by using the Bayesian weighted Mendelian randomization (BWMR) approach in our MR study. Subsequently, the potential mediating effect was investigated by conducting a two-step mediation analysis. We identified 26 traits with suggestive correlations between immune cell traits and HCC, with 4 immune cell traits among them having causal correlations with HCC. There were no causal correlations between HCC and immune cell traits in the reverse MR analysis. In the mediation analysis, we found a positive causal association between B cell-activating factor receptors (BAFF-R) on IgD+ CD24- B cell and HCC [IVW: odd ratio (OR), 0.845; 95% CI, 0.759-0.942; p = 0.002]. Phenylacetylglutamate (PAG) levels mediated 7.353% of the causal pathway from BAFF-R on IgD+ CD24- B cell and HCC. In conclusion, BAFF-R on IgD+ CD24- B cell lowers risk of HCC, with PAG levels playing a mediating role.

B cells present a double-sided effect in digestive system tumors: a review for tumor microenvironment

Over the past few years, there has been an increasing interest in investigating tumor-infiltrating lymphocytes. B lymphocytes (B cells) are extensively distributed within tertiary lymphoid structure (TLS) as multifaceted subgroups and are intimately linked to the anti-tumor properties of TLS, as well as the survival and prognostication of individuals. While the investigation of T lymphocytes in the TLS has advanced to the level of clinical practice, the study of B cells remains limited. The principal impediment to the utilization of B cells in immunotherapy is their notable dual impact on tumors. Compared with tumors in other parts and systems, the function of B cells in the microenvironment of digestive system tumors to promote tumors proliferation, differentiation and migration cannot be ignored. Therefore, this review collects the studies of B cell subsets in tumor microenvironments, particularly related single cell sequencing research. The multifaceted role and function of B cells are investigated in esophageal, liver, colorectal, gastric and pancreatic cancers. And through the identification of B cell subsets and specific markers, this review attempts to explain the reasons why B cells produce different tumor-promoting effects in those tumors. The insights gleaned from this review may provide potential help and support the development of B cell-based immunotherapies.

A Four Amino Acid Metabolism-Associated Genes (AMGs) Signature for Predicting Overall Survival Outcomes and Immunotherapeutic Efficacy in Hepatocellular Carcinoma

Metabolites are important indicators of cancer and mutations in genes involved in amino acid metabolism may influence tumorigenesis. Immunotherapy is an effective cancer treatment option; however, its relationship with amino acid metabolism has not been reported. In this study, RNA-seq data for 371 liver cancer patients were acquired from TCGA and used as the training set. Data for 231 liver cancer patients were obtained from ICGC and used as the validation set to establish a gene signature for predicting liver cancer overall survival outcomes and immunotherapeutic responses. Four reliable groups based on 132 amino acid metabolism-related DEGs were obtained by consistent clustering of 371 HCC patients and a four-gene signature for prediction of liver cancer survival outcomes was developed. Our data show that in different clinical groups, the overall survival outcomes in the high-risk group were markedly low relative to the low-risk group. Univariate and multivariate analyses revealed that the characteristics of the 4-gene signature were independent prognostic factors for liver cancer. The ROC curve revealed that the risk characteristic is an efficient predictor for 1-, 2-, and 3-year HCC survival outcomes. The GSVA and KEGG pathway analyses revealed that high-risk score tumors were associated with all aspects of the degree of malignancy in liver cancer. There were more mutant genes and greater immune infiltrations in the high-risk groups. Assessment of the three immunotherapeutic cohorts established that low-risk score patients significantly benefited from immunotherapy. Then, we established a prognostic nomogram based on the TCGA cohort. In conclusion, the 4-gene signature is a reliable diagnostic marker and predictor for immunotherapeutic efficacy.

Identification of prognostic risk model based on plasma cell markers in hepatocellular carcinoma through single-cell sequencing analysis

Hepatocellular carcinoma (HCC) represents a substantial global health burden. Tumorinfiltrating B lymphocytes (TIL-Bs) contribute to tumor progression and significantly impact the efficacy of tumor therapy. However, the characteristics of TIL-Bs in HCC and their effect on HCC therapy remain elusive. Single-cell RNA sequencing (scRNAseq) was applied to investigate the heterogeneity, cellular differentiation and cell-cell communication of TIL-Bs in HCC. Further, the Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) and liver cancer institutes (LCI) cohorts were applied to construct and validate the plasma cell marker-based prognostic risk model. The relationship between the prognostic risk model and the responsiveness of immunotherapy and chemotherapy in patients with HCC were estimated by OncoPredict and tumor immune dysfunction and exclusion (TIDE) algorithm. Finally, we established nomogram and calibration curves to evaluate the precision of the risk score in predicating survival probability. Our data identified five subtypes of TIL-Bs in HCC, each exhibiting varying levels of infiltration in tumor tissues. The interactions between TIL-Bs and other cell types contributed to shaping distinct tumor microenvironments (TME). Moreover, we found that TIL-Bs subtypes had disparate prognostic values in HCC patients. The prognostic risk model demonstrated exceptional predictive accuracy for overall survival and exhibited varying sensitivities to immunotherapy and chemotherapy among patients with HCC. Our data demonstrated that the risk score stood as an independent prognostic predictor and the nomogram results further affirmed its strong prognostic capability. This study reveals the heterogeneity of TIL-Bs and provides a prognostic risk model based on plasma cell markers in HCC, which could prove valuable in predicting prognosis and guiding the choice of suitable therapies for patients with HCC.

Hepatocellular Carcinoma and the Multifaceted Relationship with Its Microenvironment: Attacking the Hepatocellular Carcinoma Defensive Fortress

Hepatocellular carcinoma is a malignant tumor that originates from hepatocytes in an inflammatory substrate due to different degrees of liver fibrosis up to cirrhosis. In recent years, there has been growing interest in the role played by the complex interrelationship between hepatocellular carcinoma and its microenvironment, capable of influencing tumourigenesis, neoplastic growth, and its progression or even inhibition. The microenvironment is made up of an intricate network of mesenchymal cells, immune system cells, extracellular matrix, and growth factors, as well as proinflammatory cytokines and translocated bacterial products coming from the intestinal microenvironment via the enterohepatic circulation. The aim of this paper is to review the role of the HCC microenvironment and describe the possible implications in the choice of the most appropriate therapeutic scheme in the prediction of tumor response or resistance to currently applied treatments and in the possible development of future therapeutic perspectives, in order to circumvent resistance and break down the tumor's defensive fort.

Heterogeneity in Liver Cancer Immune Microenvironment: Emerging Single-Cell and Spatial Perspectives

Primary liver cancer is a solid malignancy with a high mortality rate. The success of immunotherapy has shown great promise in improving patient care and highlights a crucial need to understand the complexity of the liver tumor immune microenvironment (TIME). Recent advances in single-cell and spatial omics technologies, coupled with the development of systems biology approaches, are rapidly transforming the landscape of tumor immunology. Here we review the cellular landscape of liver TIME from single-cell and spatial perspectives. We also discuss the cellular interaction networks within the tumor cell community in regulating immune responses. We further highlight the challenges and opportunities with implications for biomarker discovery, patient stratification, and combination immunotherapies.

Identification and immunological characteristics of anoikis-associated molecular clusters in lung adenocarcinoma

Anoikis plays a crucial role in the progression, prognosis, and immune response of lung adenocarcinoma (LUAD). However, its specific impact on LUAD remains unclear. In this study, we investigated the intricate interplay of nesting apoptotic factors in LUAD. By analyzing nine key nesting apoptotic factors, we categorized LUAD patients into two distinct clusters. Further examination of immune cell profiles revealed that Cluster A exhibited greater infiltration of innate immune cells than did Cluster B. Additionally, we identified two genes closely associated with prognosis and developed a predictive model to differentiate patients based on molecular clusters. Our findings suggest that the loss of specific anoikis-related genes could significantly influence the prognosis, tumor microenvironment, and clinical features of LUAD patients. Furthermore, we validated the expression and functional roles of two pivotal prognostic genes, solute carrier family 2 member 1 (SLC2A1) and sphingosine kinase 1 (SPHK1), in regulating tumor cell viability, migration, apoptosis, and anoikis. These results offer valuable insights for future mechanistic investigations. In conclusion, this study provides new avenues for advancing our understanding of LUAD, improving prognostic assessments, and developing more effective immunotherapy strategies.

Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a molecularly heterogeneous solid malignancy, and its fitness may be shaped by how its tumor cells evolve. However, ability to monitor tumor cell evolution is hampered by the presence of numerous passenger mutations that do not provide any biological consequences. Here we develop a strategy to determine the tumor clonality of three independent HCC cohorts of 524 patients with diverse etiologies and race/ethnicity by utilizing somatic mutations in cancer driver genes. We identify two main types of tumor evolution, i.e., linear, and non-linear models where non-linear type could be further divided into classes, which we call shallow branching and deep branching. We find that linear evolving HCC is less aggressive than other types. GTF2IRD2B mutations are enriched in HCC with linear evolution, while TP53 mutations are the most frequent genetic alterations in HCC with non-linear models. Furthermore, we observe significant B cell enrichment in linear trees compared to non-linear trees suggesting the need for further research to uncover potential variations in immune cell types within genomically determined phylogeny types. These results hint at the possibility that tumor cells and their microenvironment may collectively influence the tumor evolution process.

Pan-cancer molecular signatures connecting aspartate transaminase (AST) to cancer prognosis, metabolic and immune signatures

Background

Serum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels.

Methods

We mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors.

Results

We show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors.

Conclusions

Our findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

B cell responses and antibody-based therapeutic perspectives in human cancers

BACKGROUND

Immuno-oncology has been focused on T cell-centric approaches until the field recently started appreciating the importance of tumor-reactive antibody production by tumor-infiltrating plasma B cells, and the necessity of developing novel therapeutic antibodies for the treatment of different cancers.

RECENT FINDINGS

B lymphocytes often infiltrate solid tumors and the extent of B cell infiltration normally correlates with stronger T cell responses while generating humoral responses against malignant progression by producing tumor antigens-reactive antibodies that bind and coat the tumor cells and promote cytotoxic effector mechanisms, reiterating the fact that the adaptive immune system works by coordinated humoral and cellular immune responses. Isotypes, magnitude, and the effector functions of antibodies produced by the B cells within the tumor environment differ among cancer types. Interestingly, apart from binding with specific tumor antigens, antibodies produced by tumor-infiltrating B cells could bind to some non-specific receptors, peculiarly expressed by cancer cells. Antibody-based immunotherapies have revolutionized the modalities of cancer treatment across the world but are still limited against hematological malignancies and a few types of solid tumor cancers with a restricted number of targets, which necessitates the expansion of the field to have newer effective targeted antibody therapeutics.

CONCLUSION

Here, we discuss about recent understanding of the protective spontaneous antitumor humoral responses in human cancers, with an emphasis on the advancement and future perspectives of antibody-based immunotherapies in cancer.

Statin therapy: a potential adjuvant to immunotherapies in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide and accounts for more than 90% of primary liver cancer. The advent of immune checkpoint inhibitor (ICI)-related therapies combined with angiogenesis inhibition has revolutionized the treatment of HCC in late-stage and unresectable HCC, as ICIs alone were disappointing in treating HCC. In addition to the altered immune microenvironment, abnormal lipid metabolism in the liver has been extensively characterized in various types of HCC. Stains are known for their cholesterol-lowering properties and their long history of treating hypercholesterolemia and reducing cardiovascular disease risk. Apart from ICI and other conventional therapies, statins are frequently used by advanced HCC patients with dyslipidemia, which is often marked by the abnormal accumulation of cholesterol and fatty acids in the liver. Supported by a body of preclinical and clinical studies, statins may unexpectedly enhance the efficacy of ICI therapy in HCC patients through the regulation of inflammatory responses and the immune microenvironment. This review discusses the abnormal changes in lipid metabolism in HCC, summarizes the clinical evidence and benefits of stain use in HCC, and prospects the possible mechanistic actions of statins in transforming the immune microenvironment in HCC when combined with immunotherapies. Consequently, the use of statin therapy may emerge as a novel and valuable adjuvant for immunotherapies in HCC.

Identification of CD8+ T-cell exhaustion signatures for prognosis in HBV-related hepatocellular carcinoma patients by integrated analysis of single-cell and bulk RNA-sequencing

BACKGROUND

HBV infection is the leading risk factor for HCC. HBV infection has been confirmed to be associated with the exhaustion status of CD8+ T cells and immunotherapeutic efficacy in HCC. In this study, we aimed to investigate the prognostic value of the CD8+ T-cell exhaustion signature and immunotherapy response in patients with HBV-related HCC.

METHODS

We identified different clusters of HBV-related HCC cells by single-cell RNA sequencing (scRNA-seq) and identified CD8+ T-cell exhaustion-related genes (TERGs) by pseudotime analysis. We conducted differential expression analysis and LASSO Cox regression to detect genes and construct a CD8+ T-cell exhaustion index (TEI). We next combined the TEI with other clinicopathological factors to design a prognostic nomogram for HCC patients. We also analysed the difference in the TEI between the non-responder and responder groups during anti-PD-L1 therapy. In addition, we investigated how HBV induces CD8+ T lymphocyte exhaustion through the inhibition of tyrosine metabolism in HCC using gene set enrichment analysis and RT‒qPCR.

RESULTS

A CD8+ T-cell exhaustion index (TEI) was established with 5 TERGs (EEF1E1, GAGE1, CHORDC1, IKBIP and MAGOH). An AFP level > 500 ng, vascular invasion, histologic grade (G3-G4), advanced TNM stage and poor five-year prognosis were related to a higher TEI score, while HBV infection was related to a lower TEI score. Among those receiving anti-PD-L1 therapy, responders had lower TEIs than non-responders did. The TEI also serves as an independent prognostic factor for HCC, and the nomogram incorporating the TEI, TNM stage, and vascular invasion exhibited excellent predictive value for the prognosis in HCC patients. RT‒qPCR revealed that among the tyrosine metabolism-associated genes, TAT (tyrosine aminotransferase) and HGD (homogentisate 1,2 dioxygenase) were expressed at lower levels in HBV-HCC than in non-HBV HCC.

CONCLUSION

Generally, we established a novel TEI model by comprehensively analysing the progression of CD8+ T-cell exhaustion, which shows promise for predicting the clinical prognosis and potential immunotherapeutic efficacy in HBV-related HCC patients.

High CD49a+ NK cell infiltrate is associated with poor clinical outcomes in Hepatocellular Carcinoma

NK cells infiltrating Hepatocellular Carcinoma (HCC) may express residency markers such as Integrin Subunit Alpha 1 (CD49a) that have been associated with nurturing functions in the decidua, and characterized by the production of angiogenic factors as well as loss of cytotoxicity. CIBERSORT, a computational analysis method for quantifying cell fractions from bulk tissue gene expression profiles, was used to estimate the infiltrating immune cell composition of the tumor microenvironment from gene expression profiles of a large cohort of 225 HCCs in the public GEO database. Decidual-like CD49a+ NK cells, in addition to another 22 immune cell populations, were characterized and thoroughly investigated so that HCC cell heterogeneity in a large cohort of 225 HCCs from the public GEO database could be studied. An inverse correlation of the expression of CD49a+ NK-cells and CD8+ T-cells suggested a negative association with clinical outcomes. This result was confirmed in a further validation cohort of 100 HCC patients from The Cancer Genome Atlas, Liver Hepatocellular Carcinoma (TCGA-LIHC). Cox regression analysis did not identify CD49a+ cells as a variable independently associated with survival. However, a more abundant infiltrate of this subset was present in patients at a more advanced pathological and clinical HCC stage. In conclusion, we found that NK cells, with a decidual-like gene expression profile, are enriched in HCC, and their abundance increases not only in tumor size but also at advanced stages of the disease suggesting that these cells play a role in tumor growth. For this reason, these NK cells may represent a possible new target for immunotherapeutic approaches in HCC.

Immune landscape and immunotherapy of hepatocellular carcinoma: focus on innate and adaptive immune cells

Hepatocellular carcinoma (HCC) is responsible for roughly 90% of all cases of primary liver cancer, and the cases are on the rise. The treatment of advanced HCC is a serious challenge. Immune checkpoint inhibitor (ICI) therapy has marked a watershed moment in the history of HCC systemic treatment. Atezolizumab in combination with bevacizumab has been approved as a first-line treatment for advanced HCC since 2020; however, the combination therapy is only effective in a limited percentage of patients. Considering that the tumor immune microenvironment (TIME) has a great impact on immunotherapies for HCC, an in-depth understanding of the immune landscape in tumors and the current immunotherapeutic approaches is extremely necessary. We elaborate on the features, functions, and cross talk of the innate and adaptive immune cells in HCC and highlight the benefits and drawbacks of various immunotherapies for advanced HCC, as well as future projections. HCC consists of a heterogeneous group of cancers with distinct etiologies and immune microenvironments. Almost all the components of innate and adaptive immune cells in HCC have altered, showing a decreasing trend in the number of tumor suppressor cells and an increasing trend in the pro-cancer cells, and there is also cross talk between various cell types. Various immunotherapies for HCC have also shown promising efficacy and application prospect. There are multilayered interwoven webs among various immune cell types in HCC, and emerging evidence demonstrates the promising prospect of immunotherapeutic approaches for HCC.

B cells: The many facets of B cells in allergic diseases

B cells play a key role in our immune system through their ability to produce antibodies, suppress a proinflammatory state, and contribute to central immune tolerance. We aim to provide an in-depth knowledge of the molecular biology of B cells, including their origin, developmental process, types and subsets, and functions. In allergic diseases, B cells are well known to induce and maintain immune tolerance through the production of suppressor cytokines such as IL-10. Similarly, B cells protect against viral infections such as severe acute respiratory syndrome coronavirus 2 that caused the recent coronavirus disease 2019 pandemic. Considering the unique and multifaceted functions of B cells, we hereby provide a comprehensive overview of the current knowledge of B-cell biology and its clinical applications in allergic diseases, organ transplantation, and cancer.

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

The integration of machine learning and multi-omics analysis provides a powerful approach to screen aging-related genes and predict prognosis and immunotherapy efficacy in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a highly malignant tumor with high incidence and mortality rates. Aging-related genes are closely related to the occurrence and development of cancer. Therefore, it is of great significance to evaluate the prognosis of HCC patients by constructing a model based on aging-related genes.

METHOD

Non-negative matrix factorization (NMF) clustering analysis was used to cluster the samples. The correlation between the risk score and immune cells, immune checkpoints, and Mismatch Repair (MMR) was evaluated through Spearman correlation test. Real Time Quantitative PCR (RT-qPCR) and immunohistochemistry were used to validate the expression levels of key genes in tissue and cells for the constructed model.

RESULT

By performing NMF clustering, we were able to effectively group the liver cancer samples into two distinct clusters. Considering the potential correlation between aging-related genes and the prognosis of liver cancer patients, we used aging-related genes to construct a prognostic model. Spearman correlation analysis showed that the model risk score was closely related to MMR and immune checkpoint expression. Drug sensitivity analysis also provided guidance for the clinical use of chemotherapy drugs. RT-qPCR showed that TFDP1, NDRG1, and FXR1 were expressed at higher levels in different liver cancer cell lines compared to normal liver cells.

CONCLUSION

In summary, we have developed an aging-related model to predict the prognosis of hepatocellular carcinoma and guide clinical drug treatment for different patients.

Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.

Immune System and Hepatocellular Carcinoma (HCC): New Insights into HCC Progression

According to the WHO's recently released worldwide cancer data for 2020, liver cancer ranks sixth in morbidity and third in mortality among all malignancies. Hepatocellular carcinoma (HCC), the most common kind of liver cancer, accounts approximately for 80% of all primary liver malignancies and is one of the leading causes of death globally. The intractable tumor microenvironment plays an important role in the development and progression of HCC and is one of three major unresolved issues in clinical practice (cancer recurrence, fatal metastasis, and the refractory tumor microenvironment). Despite significant advances, improved molecular and cellular characterization of the tumor microenvironment is still required since it plays an important role in the genesis and progression of HCC. The purpose of this review is to present an overview of the HCC immune microenvironment, distinct cellular constituents, current therapies, and potential immunotherapy methods.

Effect of PTGES3 on the Prognosis and Immune Regulation in Lung Adenocarcinoma

Background

PTGES3 is upregulated in multiple cancer types and promotes tumorigenesis and progression. However, the clinical outcome and immune regulation of PTGES3 in lung adenocarcinoma (LUAD) are not fully understood. This study aimed to explore the expression level and prognostic value of PTGES3 and its correlation with potential immunotherapy in LUAD.

Methods

All data were obtained from several databases, including the Cancer Genome Atlas database. Firstly, gene and protein expression of PTGES3 were analyzed using Tumor Immune Estimation Resource (TIMER), R software, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA). Thereafter, survival analysis was conducted using the R software, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Kaplan-Meier Plotter. In addition, gene alteration and mutation analyses were conducted using the cBio Cancer Genomics Portal (cBioPortal) and Catalog of Somatic Mutations in Cancer (COSMIC) databases. The molecular mechanisms associated with PTGES3 were assessed via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), GeneMANIA, GEPIA2, and R software. Lastly, the role of PTGES3 in immune regulation in LUAD was investigated using TIMER, Tumor-Immune System Interaction Database (TISIDB), and SangerBox.

Results

The gene and protein expression of PTGES3 were elevated in LUAD tissues and compared to the normal tissues, and the high expression of PTGES3 was correlated with cancer stage and tumor grade. Survival analysis revealed that overexpression of PTGES3 was associated with poor prognosis of LUAD patients. Moreover, gene alteration and mutation analysis revealed the occurrence of several types of PTGES3 gene alterations in LUAD. Moreover, co-expression analysis and cross-analysis revealed that three genes, including CACYBP, HNRNPC, and TCP1, were correlated and interacted with PTGES3. Functional analysis of these genes revealed that PTGES3 was primarily enriched in oocyte meiosis, progesterone-mediated oocyte maturation, and arachidonic acid metabolism pathways. Furthermore, we found that PTGES3 participated in a complex immune regulation network in LUAD.

Conclusion

The current study indicated the crucial role of PTGES3 in LUAD prognosis and immune regulation. Altogether, our results suggested that PTGES3 could serve as a promising therapeutic and prognosis biomarker for the LUAD.

Impact of immune tolerance mechanisms on the efficacy of immunotherapy in primary and secondary liver cancers

The liver is a functionally unique organ with an immunosuppressive microenvironment. The liver is the sixth most common site of primary cancer in humans and is a frequent site of metastasis from other solid tumors. The development of effective therapies for primary and metastatic liver cancer has been challenging due to the complex metabolic and immune microenvironment of the liver. The liver tumor microenvironment (TME) in primary and secondary (metastatic) liver cancers is heterogenous and consists of unique immune and stromal cell populations. Crosstalk between these cell populations and tumor cells creates an immunosuppressive microenvironment within the liver which potentiates cancer progression. Immune checkpoint inhibitors (ICIs) are now clinically approved for the management of primary and secondary liver cancer and can partially overcome liver immune tolerance, but their efficacy is limited. In this review, we describe the liver microenvironment and the use of immunotherapy in primary and secondary liver cancer. We discuss emerging combination strategies utilizing locoregional and systemic therapy approaches which may enhance efficacy of immunotherapy in primary and secondary liver cancer. A deeper understanding of the immunosuppressive microenvironment of the liver will inform novel therapies and therapeutic combinations in order to improve outcomes of patients with primary and secondary liver cancer.

A spliceosome-associated gene signature aids in predicting prognosis and tumor microenvironment of hepatocellular carcinoma

Splicing alterations have been shown to be key tumorigenesis drivers. In this study, we identified a novel spliceosome-related genes (SRGs) signature to predict the overall survival (OS) of patients with hepatocellular carcinoma (HCC). A total of 25 SRGs were identified from the GSE14520 dataset (training set). Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were utilized to construct the signature using genes with predictive significance. We then constructed a risk model using six SRGs (BUB3, IGF2BP3, RBM3, ILF3, ZC3H13, and CCT3). The reliability and predictive power of the gene signature were validated in two validation sets (TCGA and GSE76427 dataset). Patients in training and validation sets were divided into high and low-risk groups based on the gene signature. Patients in high-risk groups exhibited a poorer OS than in low-risk groups both in the training set and two validation sets. Next, risk score, BCLC staging, TNM staging, and multinodular were combined in a nomogram for OS prediction, and the decision curve analysis (DCA) curve exhibited the excellent prediction performance of the nomogram. The functional enrichment analyses demonstrated high-risk score patients were closely related to multiple oncology characteristics and invasive-related pathways, such as Cell cycle, DNA replication, and Spliceosome. Different compositions of the tumor microenvironment and immunocyte infiltration ratio might contribute to the prognostic difference between high and low-risk score groups. In conclusion, a spliceosome-related six-gene signature exhibited good performance for predicting the OS of patients with HCC, which may aid in clinical decision-making for individual treatment.

Identifying a novel cuproptosis-related necroptosis gene subtype-related signature for predicting the prognosis, tumor microenvironment, and immunotherapy of hepatocellular carcinoma

Background: Cuproptosis and necroptosis represent two distinct programmed cell death modalities implicated in neoplastic progression; however, the role of combining cuproptosis and necroptosis in hepatocellular carcinoma (HCC) remains to be elucidated. Methods: A total of 29 cuproptosis-related necroptosis genes (CRNGs) were identified, followed by an extensive analysis of their mutational characteristics, expression patterns, prognostic implications, and associations with the tumor microenvironment (TME). Subsequently, a CRNG subtype-related signature was developed, and its value of prognostic prediction, TME, and therapeutic responses in HCC were thoroughly investigated. Last, quantitative real-time PCR and Western blotting were employed for investigating the signature gene expression in 15 paired clinical tissue samples. Results: Two distinct CRNG subtypes were discerned, demonstrating associations between CRNG expression patterns, clinicopathological attributes, prognosis, and the TME. A CRNG subtype-related prognostic signature, subjected to external validation, was constructed, serving as an independent prognostic factor for HCC patients, indicating poor prognosis for high-risk individuals. Concurrently, the signature's correlations with an immune-suppressive TME, mutational features, stemness properties, immune checkpoint genes, chemoresistance-associated genes, and drug sensitivity were observed, signifying its utility in predicting treatment responses. Subsequently, highly accurate and clinically convenient nomograms were developed, and the signature genes were validated via quantitative real-time PCR and Western blotting, further substantiating the stability and dependability of the CRNG subtype-related prognostic signature. Conclusion: Overall, this investigation presented an extensive panorama of CRNGs and developed the CRNG subtype-related prognostic signature, which holds potential for implementation in personalized treatment strategies and prognostic forecasting for HCC patients.

Decoding Immune Signature to Detect the Risk for Early-Stage HCC Recurrence

Hepatocellular carcinoma (HCC) is often recognized as an inflammation-linked cancer, which possesses an immunosuppressive tumor microenvironment. Curative treatments such as surgical resection, liver transplantation, and percutaneous ablation are mainly applicable in the early stage and demonstrate significant improvement of survival rate in most patients. However, 70-80% of patients report HCC recurrence within 5 years of curative treatment, representing an important clinical issue. However, there is no effective recurrence marker after surgical and locoregional therapies, thus, tumor size, number, and histological features such as cancer cell differentiation are often considered as risk factors for HCC recurrence. Host immunity plays a critical role in regulating carcinogenesis, and the immune microenvironment characterized by its composition, functional status, and density undergoes significant alterations in each stage of cancer progression. Recent studies reported that analysis of immune contexture could yield valuable information regarding the treatment response, prognosis and recurrence. This review emphasizes the prognostic value of tumors associated with immune factors in HCC recurrence after curative treatment. In particular, we review the immune landscape and immunological factors contributing to early-stage HCC recurrence, and discuss the immunotherapeutic interventions to prevent tumor recurrence following curative treatments.

Navigating the immunometabolic heterogeneity of B cells in murine hepatocellular carcinoma at single cell resolution

Induction of antitumor immunity is critical for the therapeutic efficacy of hepatocellular carcinoma (HCC) immunotherapy. The cellular metabolic state underpins the effector function of immune cells, yet our understanding of the phenotypic and metabolic heterogeneity of B cells within HCC microenvironment is poorly developed. Herein, we investigated the composition, distribution, phenotype, function and metabolic profiles of B-cell subsets in HCC and adjacent liver tissues from an orthotopic HCC mouse model using single-cell RNA sequencing (scRNA-seq). Our results identified six B-cell clusters, which can be classified into plasma cells and activated and exhausted B cells according to marker expression, functional and temporal distribution. Exhausted B cells exhibited low metabolic activities and impaired effector functions. Activated B and plasma cells showed higher metabolic activity than exhausted B cells, but there were clear differences in their metabolic profiles. In addition, we found that the effector function of exhausted B cells was further diminished in HCC tissues compared with adjacent liver tissues, but their metabolic activity was significantly enhanced. Collectively, we comprehensively characterized the metabolic profile and alterations in B-cell subsets in HCC, which contributes to the understanding of B-cell immunology in HCC and lays the foundation for exploring novel targets in HCC immunotherapy.

Tumor-Infiltrating B Lymphocytes: Promising Immunotherapeutic Targets for Primary Liver Cancer Treatment

Hepatocellular carcinoma and cholangiocarcinoma are the fourth most lethal primary cancers worldwide. Therefore, there is an urgent need for therapeutic strategies, including immune cell targeting therapies. The heterogeneity of liver cancer is partially explained by the characteristics of the tumor microenvironment (TME), where adaptive and innate immune system cells are the main components. Pioneering studies of primary liver cancers revealed that tumor-infiltrating immune cells and their dynamic interaction with cancer cells significantly impacted carcinogenesis, playing an important role in cancer immune evasion and responses to immunotherapy treatment. In particular, B cells may play a prominent role and have a controversial function in the TME. In this work, we highlight the effect of B lymphocytes as tumor infiltrates in relation to primary liver cancers and their potential prognostic value. We also present the key pathways underlying B-cell interactions within the TME, as well as the way that a comprehensive characterization of B-cell biology can be exploited to develop novel immune-based therapeutic approaches.

Prognostic value of autophagy-related genes based on single-cell RNA-sequencing in colorectal cancer

Background: Colorectal cancer (CRC) is the second most common cancer in China. Autophagy plays an important role in the initiation and development of CRC. Here, we assessed the prognostic value and potential functions of autophagy-related genes (ARGs) using integrated analysis using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA).

Methods: We analyzed GEO-scRNA-seq data from GEO using various single-cell technologies, including cell clustering, and identification of differentially expressed genes (DEGs) in different cell types. Additionally, we performed gene set variation analysis (GSVA). The differentially expressed ARGs among different cell types and those between CRC and normal tissues were identified using TCGA-RNA-seq data, and the hub ARGs were screened. Finally, a prognostic model based on the hub ARGs was constructed and validated, and patients with CRC in TCGA datasets were divided into high- and low-risk groups based on their risk-score, and immune cells infiltration and drug sensitivity analyses between the two groups were performed.

Results: We obtained single-cell expression profiles of 16,270 cells, and clustered them into seven types of cells. GSVA revealed that the DEGs among the seven types of cells were enriched in many signaling pathways associated with cancer development. We screened 55 differentially expressed ARGs, and identified 11 hub ARGs. Our prognostic model revealed that the 11 hub ARGs including CTSB, ITGA6, and S100A8, had a good predictive ability. Moreover, the immune cell infiltrations in CRC tissues were different between the two groups, and the hub ARGs were significantly correlated with the enrichment of immune cell infiltration. The drug sensitivity analysis revealed that the patients in the two risk groups had difference in their response to anti-cancer drugs.

Conclusion: We developed a novel prognostic 11-hub ARG risk model, and these hubs may act as potential therapeutic targets for CRC.

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

Background

Hepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients.

Methods

The International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR.

Result

11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong.

Conclusion

TEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.

Ten-eleven translocation-2 inactivation restrains IL-10-producing regulatory B cells to enable antitumor immunity in hepatocellular carcinoma

BACKGROUND AND AIMS

IL-10-producing regulatory B cells (IL-10 + B cells), a dominant regulatory B cell (Breg) subset, foster tumor progression. However, the mechanisms underlying their generation in HCC are poorly understood. Ten-eleven translocation-2 (TET2), a predominant epigenetic regulatory enzyme in B cells, regulates gene expression by catalyzing demethylation of 5-methylcytosine into 5-hydroxymethyl cytosine (5hmC). In this study, we investigated the role of TET2 in IL-10 + B cell generation in HCC and its prospects for clinical application.

APPROACH AND RESULTS

TET2 activation in B cells triggered by oxidative stress from the HCC microenvironment promoted IL-10 expression, whereas adoptive transfer of Tet2 -deficient B cells suppressed HCC progression. The aryl hydrocarbon receptor is required for TET2 to hydroxylate Il10 . In addition, high levels of IL-10, TET2, and 5hmc in B cells indicate poor prognosis in patients with HCC. Moreover, we determined TET2 activity using 5hmc in B cells to evaluate the efficacy of anti-programmed death 1 (anti-PD-1) therapy. Notably, TET2 inhibition in B cells facilitates antitumor immunity to improve anti-PD-1 therapy for HCC.

CONCLUSIONS

Our findings propose a TET2-dependent epigenetic intervention targeting IL-10 + B cell generation during HCC progression and identify the inhibition of TET2 activity as a promising combination therapy with immune checkpoint inhibitors for HCC.

Development and validation of a mitochondrial energy metabolism-related risk model in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers and ranks third inmortality. Mitochondria are the energy manufacturers of cells. Disruption of mitochondrial energy metabolism pathways is strongly correlated with the onset and progression of HCC. Aberrant genes in mitochondrial energy metabolism pathways may represent a unique diagnostic and therapeutic targets that act as indicators for HCC.

METHODS

Gene expression data from 374 HCC patients and 50 controls were acquired from TCGA database. A total of 188 mitochondrial energy metabolism-related genes (MMRGs) were obtained from KEGG PATHWAY database. A total of 368 patients with survival data were randomly split into training and validation groups in a 7: 3 ratio. Prognosis-related MMRGs were selected by univariate Cox and LASSO analyses. Kaplan-Meier and ROC curves were employed to analyze the model precision, whereas the validation set was used for model verification. Furthermore, clinical examinations, immune infiltration analysis, GSVA, and immunotherapy analysis were conducted in the high- and low-risk groups. Finally, the risk model was combined with the clinical variables of HCC patients to perform univariate and multivariate Cox regression analyses to obtain independent risk indicators and draw a nomogram. Therefore, we evaluated the accuracy of the predictions using calibration curves.

RESULTS

A total of 6032 differentially expressed genes (DEGs) were detected in the HCC and control samples. After overlapping DEGs with 188 MMRGs, 42 mitochondrial energy metabolism-related DEGs (DEMMRGs) were identified. A 17 specific genes-based risk score model of HCC was created, which revealed effectiveness in each TCGA training and validation dataset. Moreover, patients categorized by risk scores exhibited distinct immune infiltration status, immunotherapy responsiveness, and functional properties. Finally, univariate and multivariate Cox regression analyses revealed that risk score and stage T were independent predictive variables. Based on the T stage and risk score, a nomogram for estimating the survival of HCC patients was created. The calibration curves demonstrated that the prediction model had a high level of accuracy.

CONCLUSIONS

Our study constructed a mitochondrial energy metabolism-related risk model, that may be utilized to anticipate HCC prognosis and represent the immunological microenvironment of HCC.

Identification and Validation of Ferroptosis-Related Subtypes and a Predictive Signature in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with an immunosuppressive Tumor microenvironment (TME). Ferroptosis plays an essential role in tumor proliferation, invasion, and metastasis. However, the relationship between ferroptosis and TME of HCC has remained elusive.

Methods

Differentially expressed ferroptosis-related genes (DE FRGs) between normal liver tissues and HCC tissues were obtained from The Cancer Genome Atlas (TCGA). On this basis, we identified the molecular subtypes mediated by DE FRGs and TME cell infiltration. Next, a predictive signature was established to quantity the ferroptosis-related characteristics by performing the least absolute shrinkage and selection operator Cox regression analyses. Univariate and multivariate COX analyses determined the independent prognostic factors. Finally, the expression stability of 3 ferroptosis-related signature genes was verified in cancer and paracancerous normal tissues of HCC.

Results

We identified three different molecular subtypes and found that the subtype with the better prognosis was associated with high enrichment of immune- and metabolic-related hallmark signaling pathways and high infiltration of immune cells in TME. The signature was considered to be an independent prognostic factor. We also found that the signature can reflect the infiltration characteristics of different immune cells in TME. Immunosuppressive cells such as myeloid-derived suppressor cells (MDSCs), regulatory T cells, and type 17 T helper cells were significantly enriched in the high-risk group. The analysis data of immune checkpoints and tumor mutation load indicated that the signature had great potential in predicting Immunotherapy response and chemotherapeutic sensitivity. In addition, the overexpression of 3 ferroptosis-related signature genes was confirmed in HCC tissues and HCC cell lines. Ferroptosis inducer RSL3 inhibited the proliferation of HCC cells and was a potential cancer immunotherapy agent.

Conclusion

These findings enhanced our understanding of the regulatory mechanism of ferroptosis in HCC and provided new insights into evaluating prognosis and developing more effective Immunotherapy and chemotherapy strategies.

Neglected no more: B cell-mediated anti-tumor immunity

Immuno-oncology has traditionally focused on the cellular arm of the adaptive immune response, while attributing tumor-promoting activity to humoral responses in tumor-bearing hosts. This view stems from mouse models that do not necessarily recapitulate the antibody response process consistently observed in most human cancers. In recent years, the field has reconsidered the coordinated action of T and B cell responses in the context of anti-tumor immunity, as in any other immune response. Thus, recent studies in human cancer identify B cell responses with better outcome, typically in association with superior T cell responses. An area of particular interest is tertiary lymphoid structures, where germinal centers produce isotype switched antibodies and B cells and T lymphocytes interact with other immune cell types. The presence of these lymphoid structures is associated with better immunotherapeutic responses and remain poorly understood. Here, we discuss recent discoveries on how coordination between humoral and cellular responses is required for effective immune pressure against malignant progression, providing a perspective on the role of tertiary lymphoid structures and interventions to elicit their formation in unresectable tumors.

The prognostic impact of tumor-infiltrating B lymphocytes in patients with solid malignancies: A systematic review and meta-analysis

This study reviewed the prognostic effect of tumor-infiltrating B lymphocytes (TIBLs) on solid malignancies, to determine the potential role of TIBLs in predicting cancer patient's prognosis and their response to immunotherapy. A total of 45 original papers involving 11,099 individual patients were included in this meta-analysis covering 7 kinds of cancer. The pooled results suggested that high levels of TIBLs were correlated with favorable OS in lung, esophageal, gastric, colorectal, liver, and breast cancer; improved RFS in lung cancer; and improved DFS in gastrointestinal neoplasms. Additionally, TIBLs were significantly correlated with negative lymphatic invasion in gastric cancer, small tumor size in hepatocellular carcinoma, and negative distant metastasis in colorectal cancer. Additionally, TIBLs were reported as a discriminative feature of patients treated with immunotherapy with improved survival. We concluded that TIBLs play a favorable prognostic role among the common solid malignancie, providing theoretical evidence for further prognosis prediction for solid tumors.

Anoikis-related genes combined with single cell sequencing: Insights into model specification of lung adenocarcinoma and applicability for prognosis and therapy

Background: Anoikis has therapeutic potential against different malignancies including lung adenocarcinoma. This study used anoikis and bioinformatics to construct a prognostic model for lung adenocarcinoma and explore new therapeutic strategies. Methods: Several bioinformatic algorithms (co-expression analysis, univariate Cox analysis, multivariate Cox analysis, and cross-validation) were used to screen anoikis-related genes (ARGs) to construct a risk model. Lung adenocarcinoma patients were divided into training and testing groups at a ratio of 1:1. The prognostic model was validated by risk score comparison between high- and low-risk groups using receiver operating characteristic curve (ROC), nomograms, independent prognostic analysis and principal component analysis. In addition, two anoikis-related genes patterns were classified utilizing consensus clustering method and were compared with each other in survival time, immune microenvironment, and regulation in pathway. Single cell sequencing was applied to analyze anoikis-related genes constructed the model. Results: This study demonstrated the feasibility of the model based on seven anoikis-related genes, as well as identifying axitinib, nibtinib and sorafenib as potential therapeutic strategies for LUAD. Risk score based on this model had could be used as an independent prognostic factor for lung adenocarcinoma (HR > 1; p < 0.001) and had the highest accuracy to predict survival compared with the clinical characteristics. Single cell sequencing analysis discovered Keratin 14 (KRT14, one of the seven anoikis-related genes) was mainly expressed in malignant cells in various cancers. Conclusion: We identified seven anoikis-related genes and constructed an accurate risk model based on bioinformatics analysis that can be used for prognostic prediction and for the design of therapeutic strategies in clinical practice.

ADAM19 and TUBB1 Correlate with Tumor Infiltrating Immune Cells and Predicts Prognosis in Osteosarcoma

BACKGROUND

Osteosarcoma is the most common type of primary malignant bone tumor.

INTRODUCTION

This study aimed to explore potential key prognostic genes and their roles in osteosarcoma.

METHODS

Three microarray datasets for osteosarcoma were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened by the Limma package. Functional enrichment analysis was performed based on DAVID, GeneMANIA, and Metascape databases. Prognostic value of DEGs was elevated by survival analysis. CIBERSORT was used to assess the infiltrating abundance of 22 immune cells, followed by the Pearson correlation analysis between immune cells and prognosis-related genes. Gene set enrichment analysis and drug-gene interactions prediction were performed for prognosis-related genes.

RESULTS

A total of 8 common up-regulated DEGs and 13 common down-regulated DEGs were screened in the GSE36001 and GSE56001 datasets. Enrichment analysis showed these DEGs were implicated in platelet activation, SMAD protein phosphorylation, lymphocyte/leukocyte/T cells activation, and cell migration. Survival analysis indicated that elevated expression of ADAM19 and TUBB1 were associated with a favorable prognosis. CIBERSORT algorithm revealed the higher infiltrating level of CD8 T cells, macrophages M0, and M2 in osteosarcoma. ADAM19 expression positively correlated with naïve B cells and negatively correlated with activated dendritic cells infiltrating abundance. TUBB1 expression positively correlated with gamma delta T cells while negatively correlated with helper follicular T cells infiltrating abundance. A total of 56 drugs were found to target TUBB1.

CONCLUSION

ADAM19 and TUBB1 could be prognostic biomarkers in osteosarcoma. Both their expression correlates with tumor infiltrating immune cells. TUBB1 was a multi-drug target that might be a therapeutic target in osteosarcoma.

Understanding the Immunoenvironment of Primary Liver Cancer: A Histopathology Perspective

One of the most common cancers worldwide, primary liver cancer remains a major cause of cancer-related mortality. Hepatocellular carcinoma and cholangiocarcinoma represent the majority of primary liver cancer cases. Despite advances in the development of novel anti-cancer therapies that exploit targets within the immune system, survival rates from liver cancer remain poor. Furthermore, responses to immunotherapies, such as immune checkpoint inhibitors, have revealed limited and variable responses amongst patients with hepatocellular carcinoma, although combination immunotherapies have shown recent breakthroughs in clinical trials. This has shifted the focus towards improving our understanding of the underlying immune and molecular characteristics of liver tumours that may influence their response to immune-modulating treatments. In this review, we outline the complex interactions that occur in the tumour microenvironment of hepatocellular carcinoma and cholangiocarcinoma, respectively, from a histopathological perspective. We explore the potential role of a classification system based on immune-specific characteristics within each cancer type, the importance of understanding inter- and intra-tumoural heterogeneity and consider the future role of histopathology and novel technologies within this field.

Tumor-infiltrating lymphocytes for treatment of solid tumors: It takes two to tango?

Tumor-infiltrating lymphocytes (TILs), frontline soldiers of the adaptive immune system, are recruited into the tumor site to fight against tumors. However, their small number and reduced activity limit their ability to overcome the tumor. Enhancement of TILs number and activity against tumors has been of interest for a long time. A lack of knowledge about the tumor microenvironment (TME) has limited success in primary TIL therapies. Although the advent of engineered T cells has revolutionized the immunotherapy methods of hematologic cancers, the heterogeneity of solid tumors warrants the application of TILs with a wide range of specificity. Recent advances in understanding TME, immune exhaustion, and immune checkpoints have paved the way for TIL therapy regimens. Nowadays, TIL therapy has regained attention as a safe personalized immunotherapy, and currently, several clinical trials are evaluating the efficacy of TIL therapy in patients who have failed conventional immunotherapies. Gaining favorable outcomes following TIL therapy of patients with metastatic melanoma, cervical cancer, ovarian cancer, and breast cancer has raised hope in patients with refractory solid tumors, too. Nevertheless, TIL therapy procedures face several challenges, such as high cost, timely expansion, and technical challenges in selecting and activating the cells. Herein, we reviewed the recent advances in the TIL therapy of solid tumors and discussed the challenges and perspectives.

A prognostic pyroptosis-related LncRNA classifier associated with the immune landscape and therapy efficacy in glioma

Background: Glioma has the highest fatality rate among intracranial tumours. Besides, the heterogeneity of gliomas leads to different therapeutic effects even with the same treatment. Developing a new signature for glioma to achieve the concept of “personalised medicine” remains a significant challenge.

Method: The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were searched to acquire information on glioma patients. Initially, correlation and univariate Cox regression analyses were performed to screen for prognostic pyroptosis-related long noncoding RNAs (PRLs). Secondly, 11 PRLs were selected to construct the classifier using certain algorithms. The efficacy of the classifier was then detected by the “timeROC” package for both the training and validation datasets. CIBERSORT and ESTIMATE packages were applied for comparing the differences (variations) in the immune landscape between the high- and low-risk groups. Finally, the therapeutic efficacy of the chemotherapy, radiotherapy, and immunotherapy were assessed using the “oncoPredict” package, survival analysis, and the tumour immune dysfunction and exclusion (TIDE) score, respectively.

Results: A classifier comprising 11 PRLs was constructed. The PRL classifier exhibits a more robust prediction capacity for the survival outcomes in patients with gliomas than the clinical characteristics irrespective of the dataset (training or validation dataset). Moreover, it was found that the tumour landscape between the low- and high-risk groups was significantly different. A high-risk score was linked to a more immunosuppressive tumour microenvironment. According to the outcome prediction and analysis of the chemotherapy, patients with different scores showed different responses to various chemotherapeutic drugs and immunotherapy. Meanwhile, the patient with glioma of WHO grade Ⅳ or aged >50 years in the high risk group had better survival following radiotherapy.

Conclusion: We constructed a PRL classifier to roughly predict the outcome of patients with gliomas. Furthermore, the PRL classifier was linked to the immune landscape of glioma and may guide clinical treatments.

Role of B Lymphocytes in the Pathogenesis of NAFLD: A 2022 Update

Non-alcoholic fatty liver disease and its related complications are becoming one of the most important health problems globally. The liver functions as both a metabolic and an immune organ. The crosstalk between hepatocytes and intrahepatic immune cells plays a key role in coordinating a dual function of the liver in terms of the protection of the host from antigenic overload as a result of receiving nutrients and gut microbiota antigenic stimulation via facilitating immunologic tolerance. B cells are the most abundant lymphocytes in the liver. The crucial role of intrahepatic B cells in energy metabolism under different immune conditions is now emerging in the literature. The accumulating evidence has demonstrated that the antibodies and cytokines produced by B cells in the microenvironment play key and distinct roles in the pathogenesis of non-alcoholic fatty liver disease (NAFLD). Herein, we have aimed to consolidate and update the current knowledge about the pathophysiological roles of B cells as well as the underlying mechanisms in energy metabolism. Understanding how B cells can exacerbate and suppress liver damage by exploiting the antibodies and cytokines they produce will be of great importance for designing B-cell targeting therapies to treat various liver diseases.

STMN1 as a novel prognostic biomarker in HCC correlating with immune infiltrates and methylation

Background

Upregulation of Stathmin 1 (STMN1), a cytoplasmic phosphoprotein that controls the dynamics of cellular microtubules, is linked to malignant behavior and poor prognosis in a range of malignancies. However, little research has been done on STMN1’s potential role in HCC as a single factor in DNA methylation, m⁶A, or immunological modulation.

Results

STMN1 is overexpressed in hepatocellular carcinoma, where it is related to clinicopathological parameters and affects the prognosis of HCC patients. STMN1 overexpression plays an important role in the diagnosis and prognosis of hepatocellular carcinoma. Meanwhile, methylation of 7 CpG sites of STMN1 in HCC was correlated with prognosis, and STMN1 expression was closely related to m⁶A modification. In addition, STMN1 expression is associated with immune cell infiltration, immune molecules, and immune checkpoints in HCC.

Conclusion

STMN1 has a significant role in hepatocellular carcinoma diagnosis and prediction. STMN1 is implicated not just in the onset and course but also in the immunological modulation of the disease. DNA methylation and m⁶A are both linked to STMN1. Therefore, STMN1 could be used as a diagnostic and prognostic biomarker for HCC, as well as a target for immunotherapy.

Predicting Prognosis of Hepatocellular Carcinoma Patients Based on the Expression Signatures of Mitophagy Genes

Background. The unbalance of mitophagy was closely related to hepatocellular carcinoma (HCC) progression. At present, it has not been uncovered about the influence of mitophagy genes on HCC prognosis and their potential pathogenesis. Materials and Methods. The expression and clinical information of HCC in TCGA cohort were used to identify mitophagy differentially expressed genes (MDEGs) with prognostic value. The prognostic model of mitophagy genes was built and externally validated by LASSO regression in TCGA cohort and ICGC cohort, respectively. The function of the prognostic signature and its association with immune cell infiltration were explored. The profile of MDEGs was validated with 39 pairs HCC and paracarcinoma tissues by quantitative reverse transcription-PCR (qRT-PCR). Results. A total of 18 mitophagy genes that were upregulated and contributed to poor prognosis in HCC were identified. These genes could interact with each other. The correlation analysis showed that there was positively correlation among mitophagy genes. According to optimal $\lambda$ value, 8 mitophagy gene signatures were involved in prognostic model. Based on median risk scores, HCC patients were divided into high-risk group and low-risk group. Compared with the low-risk group, the high-risk group has worse overall survival in TCGA cohort and ICGC cohort. The univariate and multivariate Cox regression analysis suggested that risk score was an independent prognostic factor of HCC patients. Time-dependent ROC curve was used to identify and validate good predicting performance of the prognostic model. Enrichment analysis showed that risk differentially expressed genes were enriched in various metabolism and cell division processes. The immune cell infiltration score and immune function were significantly different in two groups. qRT-PCR validation result showed that QSTM1, CSNK2B, PGAM5, and ATG5 were upregulated. Conclusion. Mitophagy genes could influence HCC progression through regulating the metabolism and immune functions and could be used to predict prognosis and considered as potential prognostic biomarker and precise therapeutic target of HCC.

Approaches to spatially resolving the tumour immune microenvironment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common and deadly cancer worldwide. Many factors contribute to mortality and place an individual at high risk of developing HCC, including viral infection, alcohol intake, metabolic-associated disease, autoimmunity and genetic liver disorders. Although there are many therapeutics available, much about this disease remains to be understood. This is most evident when investigating the tumour microenvironment (TME). Both innate and adaptive immune cells have been associated with carcinogenesis within the TME of HCC patients. The ability to interrogate the TME more thoroughly with spatial technologies continues to improve, both at the experimental and analytical stages. This review provides insight into technologies available to investigate the TME, and how such technologies are beneficial for improving our understanding of HCC carcinogenesis.

Immunosuppressive landscape in hepatocellular carcinoma revealed by single-cell sequencing

Background/Aims

Hepatocellular carcinoma (HCC), accounting for 75-85% of primary liver cancer cases, is the third leading cause of cancer-related death worldwide. The purpose of this research was to examine the tumor immune microenvironment (TIME) in HCC.

Methods

We investigated the HCC TIME by integrated analysis of single-cell and bulk-tissue sequencing data to reveal the landscape of major immune cell types.

Results

Regulatory T(Treg) cells were found to be specifically distributed in the TIME of HCC. Several immune checkpoints, including TNFRSF4, TIGIT and CTLA4, were found to be uniquely overexpressed in Treg cells, and the glycolysis/gluconeogenesis pathway was enriched in Treg cells. We also discovered the presence of two NK-cell subsets with different cytotoxic capacities, one in an activated state with antitumor effects and another with an exhausted status. In addition, memory B cells in HCC were found to exist in a unique state, with high proliferation, low differentiation, and low activity, which was induced by overexpression of PRAP1 and activation of the MIF-CD74 axis.

Conclusions

We revealed the TIME landscape in HCC, highlighting the heterogeneity of major immune cell types and their potential mechanisms in the formation of an immunosuppressive environment. Hence, blocking the formation of the TIME could be a useful therapeutic strategy for HCC.

Natural Killer Group 2D Receptor and B1a Cells Crosstalk in Post-Hepatitis C Virus Infection Hepatocellular Carcinoma and Cirrhosis

Background

Natural killer (NK) and B1a cells are implicated in innate immune surveillance against chronic hepatitis C virus (CHCV). NK group 2D (NKG2D) receptor is important for B cell differentiation. This study was designed to assess whether B1a cells and NK Cells expressing NKG2D are implicated in post-hepatitis C infection hepatocellular carcinoma (post-HCV HCC) and cirrhosis using flow cytometry and investigate the association between NK-expressing NKG2D and B1a in complications of CHCV infection.

Methods

In this cross-sectional study, 111 participants were included and divided into the post-HCV HCC (n = 50), post-HCV liver cirrhosis (n = 31), and CHCV (n = 30) groups.

Results

The percentage of B1a cells (B1a%) and the mean fluorescence intensity (MFI) of NKG2D (NKG2D MFI) showed a significant increase in the CHCV group compared with those in the post-HCV liver cirrhosis and post-HCV HCC groups (P < 0.05). A positive correlation was observed between NKG2D MFI and B1a% (r = 0.6, P < 0.001). The receiver operating characteristic (ROC) curve revealed that NKG2D MFI and B1a% differentiated between patients with CHCV infection and those with HCC with a sensitivity of 92% and 98%, respectively, and differentiated between patients with CHCV infection and those with liver cirrhosis with a sensitivity of 94% and 90%, respectively.

Conclusion

Downregulation of B1a frequency and NKG2D intensity is implicated in the progression of CHCV infection to cirrhosis and HCC. NKG2D receptor is associated with the frequency of circulating B1a cells. NKG2D intensity and B1a% can be used as indicators of CHCV progression.