ANXA5: A Key Regulator of Immune Cell Infiltration in Hepatocellular Carcinoma.

BACKGROUND Hepatocellular carcinoma (HCC) poses a significant threat to human life and is the most prevalent form of liver cancer. The intricate interplay between apoptosis, a common form of programmed cell death, and its role in immune regulation stands as a crucial mechanism influencing tumor metastasis. MATERIAL AND METHODS Utilizing HCC samples from the TCGA database and 61 anoikis-related genes (ARGs) sourced from GeneCards, we analyzed the relationship between ARGs and immune cell infiltration in HCC. Subsequently, we identified long non-coding RNAs (lncRNAs) associated with ARGs, using the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a robust prognostic model. The predictive capabilities of the model were then validated through examination in a single-cell dataset. RESULTS Our constructed prognostic model, derived from lncRNAs linked to ARGs, comprised 11 significant lncRNAs: NRAV, MCM3AP-AS1, OTUD6B-AS1, AC026356.1, AC009133.1, DDX11-AS1, AC108463.2, MIR4435-2HG, WARS2-AS1, LINC01094, and HCG18. The risk score assigned to HCC samples demonstrated associations with immune indicators and the infiltration of immune cells. Further, we identified Annexin A5 (ANXA5) as the pivotal gene among ARGs, with it exerting a prominent role in regulating the lncRNA gene signature. Our validation in a single-cell database elucidated the involvement of ANXA5 in immune cell infiltration, specifically in the regulation of mononuclear cells. CONCLUSIONS This study delves into the intricate correlation between ARGs and immune cell infiltration in HCC, culminating in the development of a novel prognostic model reliant on 11 ARGs-associated lncRNAs. Furthermore, our findings highlight ANXA5 as a promising target for immune regulation in HCC, offering new perspectives for immune therapy in the context of HCC.

Transcriptomic and genomic characteristics of intrahepatic metastases of primary liver cancer.

BACKGROUND: Patients with primary multifocal hepatocellular carcinoma (HCC) have a poor prognosis and often experience a high rate of treatment failure. Multifocal HCC is mainly caused by intrahepatic metastasis (IM), and though portal vein tumor thrombosis (PVTT) is considered a hallmark of IM, the molecular mechanism by which primary HCC cells invade the portal veins remains unclear. Therefore, it is necessary to recognize the early signs of metastasis of HCC to arrange better treatment for patients. RESULTS: To determine the differential molecular features between primary HCC with and without phenotype of metastasis, we used the CIBERSORTx software to deconvolute cell types from bulk RNA-Seq based on a single-cell transcriptomic dataset. According to the relative abundance of tumorigenic and metastatic hepatoma cells, VEGFA+ macrophages, effector memory T cells, and natural killer cells, HCC samples were divided into five groups: Pro-T, Mix, Pro-Meta, NKC, and MemT, and the transcriptomic and genomic features of the first three groups were analyzed. We found that the Pro-T group appeared to retain native hepatic metabolic activity, whereas the Pro-Meta group underwent dedifferentiation. Genes highly expressed in the group Pro-Meta often signify a worse outcome. CONCLUSIONS: The HCC cohort can be well-typed and prognosis predicted according to tumor microenvironment components. Primary hepatocellular carcinoma may have obtained corresponding molecular features before metastasis occurred.

Hepatitis B-related hepatocellular carcinoma: classification and prognostic model based on programmed cell death genes.

Instruction: Hepatitis B virus (HBV) infection is a major risk factor for hepatocellular carcinoma (HCC). Programmed cell death (PCD) is a critical process in suppressing tumor growth, and alterations in PCD-related genes may contribute to the progression of HBV-HCC. This study aims to develop a prognostic model that incorporates genomic and clinical information based on PCD-related genes, providing novel insights into the molecular heterogeneity of HBV-HCC through bioinformatics analysis and experimental validation. Methods: In this study, we analyzed 139 HBV-HCC samples from The Cancer Genome Atlas (TCGA) and validated them with 30 samples from the Gene Expression Omnibus (GEO) database. Various bioinformatics tools, including differential expression analysis, gene set variation analysis, and machine learning algorithms were used for comprehensive analysis of RNA sequencing data from HBV-HCC patients. Furthermore, among the PCD-related genes, we ultimately chose DLAT for further research on tissue chips and patient cohorts. Besides, immunohistochemistry, qRT-PCR and Western blot analysis were conducted. Results: The cluster analysis identified three distinct subgroups of HBV-HCC patients. Among them, Cluster 2 demonstrated significant activation in DNA replication-related pathways and tumor-related processes. Analysis of copy number variations (CNVs) of PCD-related genes also revealed distinct patterns in the three subgroups, which may be associated with differences in pathway activation and survival outcomes. DLAT in tumor tissues of HBV-HCC patients is upregulated. Discussion: Based on the PCD-related genes, we developed a prognostic model that incorporates genomic and clinical information and provided novel insights into the molecular heterogeneity of HBV-HCC. In our study, we emphasized the significance of PCD-related genes, particularly DLAT, which was examined in vitro to explore its potential clinical implications.

AKR1B10 expression characteristics in hepatocellular carcinoma and its correlation with clinicopathological features and immune microenvironment.

Hepatocellular carcinoma (HCC) represents a major global health threat with diverse and complex pathogenesis. Aldo-keto reductase family 1 member B10 (AKR1B10), a tumor-associated enzyme, exhibits abnormal expression in various cancers. However, a comprehensive understanding of AKR1B10's role in HCC is lacking. This study aims to explore the expression characteristics of AKR1B10 in HCC and its correlation with clinicopathological features, survival prognosis, and tumor immune microenvironment, further investigating its role and potential regulatory mechanisms in HCC. This study conducted comprehensive analyses using various bioinformatics tools and databases. Initially, differentially expressed genes related to HCC were identified from the GEO database, and the expression of AKR1B10 in HCC and other cancers was compared using TIMER and GEPIA databases, with validation of its specificity in HCC tissue samples using the HPA database. Furthermore, the relationship of AKR1B10 expression with clinicopathological features (age, gender, tumor size, staging, etc.) of HCC patients was analyzed using the TCGA database's LIHC dataset. The impact of AKR1B10 expression levels on patient prognosis was evaluated using Kaplan-Meier survival analysis and the Cox proportional hazards model. Additionally, the correlation of AKR1B10 expression with tumor biology-related signaling pathways and tumor immune microenvironment was studied using databases like GSEA, Targetscan, and others, identifying microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that regulate AKR1B10 expression to explore potential regulatory mechanisms. Elevated AKR1B10 expression was significantly associated with gender, primary tumor size, and fibrosis stage in HCC tissues. High AKR1B10 expression indicated poor prognosis and served as an independent predictor for patient outcomes. Detailed mechanism analysis revealed a positive correlation between high AKR1B10 expression, immune cell infiltration, and pro-inflammatory cytokines, suggesting a potential DANCR-miR-216a-5p-AKR1B10 axis regulating the tumor microenvironment and impacting HCC development and prognosis. The heightened expression of AKR1B10 in HCC is not only related to significant clinical-pathological traits but may also influence HCC progression and prognosis by activating key signaling pathways and altering the tumor immune microenvironment. These findings provide new insights into the role of AKR1B10 in HCC pathogenesis and highlight its potential as a biomarker and therapeutic target.

Cell division cyclin 25C knockdown inhibits hepatocellular carcinoma development by inducing endoplasmic reticulum stress.

BACKGROUND: Cell division cyclin 25C (CDC25C) is a protein that plays a critical role in the cell cycle, specifically in the transition from the G2 phase to the M phase. Recent research has shown that CDC25C could be a potential therapeutic target for cancers, particularly for hepatocellular carcinoma (HCC). However, the specific regulatory mechanisms underlying the role of CDC25C in HCC tumorigenesis and development remain incompletely understood. AIM: To explore the impact of CDC25C on cell proliferation and apoptosis, as well as its regulatory mechanisms in HCC development. METHODS: Hepa1-6 and B16 cells were transduced with a lentiviral vector containing shRNA interference sequences (LV-CDC25C shRNA) to knock down CDC25C. Subsequently, a xenograft mouse model was established by subcutaneously injecting transduced Hepa1-6 cells into C57BL/6 mice to assess the effects of CDC25C knockdown on HCC development in vivo. Cell proliferation and migration were evaluated using a Cell Counting Kit-8 cell proliferation assays and wound healing assays, respectively. The expression of endoplasmic reticulum (ER) stress-related molecules (glucose-regulated protein 78, X-box binding protein-1, and C/EBP homologous protein) was measured in both cells and subcutaneous xenografts using quantitative real-time PCR (qRT-PCR) and western blotting. Additionally, apoptosis was investigated using flow cytometry, qRT-PCR, and western blotting. RESULTS: CDC25C was stably suppressed in Hepa1-6 and B16 cells through LV-CDC25C shRNA transduction. A xenograft model with CDC25C knockdown was successfully established and that downregulation of CDC25C expression significantly inhibited HCC growth in mice. CDC25C knockdown not only inhibited cell proliferation and migration but also significantly increased the ER stress response, ultimately promoting ER stress-induced apoptosis in HCC cells. CONCLUSION: The regulatory mechanism of CDC25C in HCC development may involve the activation of ER stress and the ER stress-induced apoptosis signaling pathway.

Constructing immune and prognostic features associated with ADCP in hepatocellular carcinoma and pan-cancer based on scRNA-seq and bulk RNA-seq.

Aim: Despite the significant therapeutic outcomes achieved in systemic treatments for liver hepatocellular carcinoma (LIHC), it is an objective reality that only a low proportion of patients exhibit an improved objective response rate (ORR) to current immunotherapies. Antibody-dependent cellular phagocytosis (ADCP) immunotherapy is considered the new engine for precision immunotherapy. Based on this, we aim to develop an ADCP-based LIHC risk stratification system and screen for relevant targets. Method: Utilizing a combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, we screened for ADCP modulating factors in LIHC and identified differentially expressed genes along with their involved functional pathways. A risk scoring model was established by identifying ADCP-related genes with prognostic value through LASSO Cox regression analysis. The risk scoring model was then subjected to evaluations of immune infiltration and immunotherapy relevance, with pan-cancer analysis and in vitro experimental studies conducted on key targets. Results: Building on the research by Kamber RA et al., we identified GYPA, CLDN18, and IRX5 as potential key target genes regulating ADCP in LIHC. These genes demonstrated significant correlations with immune infiltration cells, such as M1-type macrophages, and the effectiveness of immunotherapy in LIHC, as well as a close association with clinical pathological staging and patient prognosis. Pan-cancer analysis revealed that CLDN18 was prognostically and immunologically relevant across multiple types of cancer. Validation through tissue and cell samples confirmed that GYPA and CLDN18 were upregulated in liver cancer tissues and cells. Furthermore, in vitro knockdown of CLDN18 inhibited the malignancy capabilities of liver cancer cells. Conclusion: We have identified an ADCP signature in LIHC comprising three genes. Analysis based on a risk scoring model derived from these three genes, coupled with subsequent experimental validation, confirmed the pivotal role of M1-type macrophages in ADCP within LIHC, establishing CLDN18 as a critical ADCP regulatory target in LIHC.

DNA methylation regulator-based molecular subtyping and tumor microenvironment characterization in hepatocellular carcinoma.

Backgroud: Although recent studies have reported the regulation of the immune response in hepatocellular carcinoma (HCC) through DNA methylation, the comprehensive impact methylation modifications on tumor microenvironment characteristics and immunotherapy efficacy has not been fully elucidated. Methods: In this research, we conducted a comprehensive assessment of the patterns of DNA methylation regulators and the profiles of the tumor microenvironment (TME) in HCC, focusing on 21 specific DNA methylation regulators. We subsequently developed a unique scoring system, a DNA methylation score (DMscore), to assess the individual DNA methylation modifications among the three distinct methylation patterns for differentially expressed genes (DEGs). Results: Three distinct methylation modification patterns were identified with distinct TME infiltration characteristics. We demonstrated that the DMscore could predict patient subtype, TME infiltration, and patient prognosis. A low DMscore, characterized by an elevated tumor mutation burden (TMB), hepatitis B virus (HBV)/hepatitis C virus (HCV) infection, and immune activation, indicates an inflamed tumor microenvironment phenotype with a 5-year survival rate of 7.8%. Moreover, a low DMscore appeared to increase the efficacy of immunotherapy in the anti-CTLA-4/PD-1/PD-L1 cohort. Conclusions: In brief, this research has enhanced our understanding of the correlation between modifications in DNA methylation patterns and the profile of the tumor microenvironment in individuals diagnosed with HCC. The DMscore may serve as an alternative biomarker for survival and efficacy of immunotherapy in patients with HCC.

Metabolic dysfunction-associated steatotic liver disease: Navigating terminological evolution, diagnostic frontiers and therapeutic horizon-an editorial exploration.

Metabolic dysfunction-associated steatotic liver disease (MASLD), once known as non-alcoholic fatty liver disease (NAFLD), represents a spectrum of liver disorders characterized by lipid accumulation within hepatocytes. The redefinition of NAFLD in 2023 marked a significant reposition in terminology, emphasizing a broader understanding of liver steatosis and its associated risks. MASLD is now recognized as a major risk factor for liver cirrhosis, hepatocellular carcinoma, and systemic complications such as cardiovascular diseases or systemic inflammation. Diagnostic challenges arise, particularly in identifying MASLD in lean individuals, necessitating updated diagnostic protocols and investing in non-invasive diagnostic tools. Therapeutically, there is an urgent need for effective treatments targeting MASLD, with emerging pharmacological options focusing on, among others, carbohydrate and lipid metabolism. Additionally, understanding the roles of bile acid metabolism, the microbiome, and dietary interventions in MASLD pathogenesis and management holds promise for innovative therapeutic approaches. There is a strong need to emphasize the importance of collaborative efforts in understanding, diagnosing, and managing MASLD to improve physicians' approaches and patient outcomes.

DCAF1 interacts with PARD3 to promote hepatocellular carcinoma progression and metastasis by activating the Akt signaling pathway.

BACKGROUND: Hepatocellular carcinoma (HCC) is a fatal malignancy with poor prognosis due to lack of effective clinical interference. DCAF1 plays a vital role in regulating cell growth and proliferation, and is involved in the progression of various malignancies. However, the function of DCAF1 in HCC development and the underlying mechanism are still unknown. This study aimed to explore the effect of DCAF1 in HCC and the corresponding molecular mechanism. METHODS: Quantitative real-time PCR, Western blot and immunostaining were used to determine DCAF1 expression in tumor tissues and cell lines. Subsequently, in vitro and in vivo experiments were conducted to explore the function of DCAF1 in tumor growth and metastasis in HCC. Coimmunoprecipitation, mass spectrometry and RNA sequencing were performed to identify the underlying molecular mechanisms. RESULTS: In this study, we found that DCAF1 was observably upregulated and associated with poor prognosis in HCC. Knockdown of DCAF1 inhibited tumor proliferation and metastasis and promoted tumor apoptosis, whereas overexpressing DCAF1 yielded opposite effects. Mechanistically, DCAF1 could activate the Akt signaling pathway by binding to PARD3 and enhancing its expression. We also found that the combined application of DCAF1 knockdown and Akt inhibitor could significantly suppress subcutaneous xenograft tumor growth. CONCLUSIONS: Our study illustrates that DCAF1 plays a crucial role in HCC development and the DCAF1/PARD3/Akt axis presents a potentially effective therapeutic strategy for HCC.

Exploring the impact of insufficient thermal ablation on hepatocellular carcinoma: NDST2 overexpression mechanism and its role in facilitating growth and invasion of residual cancer cells.

OBJECTIVE: Incomplete thermal ablation (ITA) fosters the malignancy of residual cells in Hepatocellular carcinoma (HCC) with unclear mechanisms now. This study aims to investigate the expression changes of NDST2 following ITA of HCC and its impact on residual cancer cells. METHODS: An in vitro model of heat stress-induced liver cancer was constructed to measure the expression of NDST2 using Quantitative Real-Time PCR and Western blotting experiments. The sequencing data from nude mice were used for validation. The clinical significance of NDST2 in HCC was evaluated by integrating datasets. Gene ontology and pathway analysis were conducted to explore the potential signaling pathways regulated by NDST2. Additionally, NDST2 was knocked down in heat stress-induced HCC cells, and the effects of NDST2 on these cells were verified using Cell Counting Kit-8 assays, scratch assays, and Transwell assays. RESULTS: NDST2 expression levels are elevated in HCC, leading to a decrease in overall survival rates of HCC patients. Upregulation of immune checkpoint levels in high NDST2-expressing HCC may contribute to immune evasion by liver cancer cells. Additionally, the low mutation rate of NDST2 in HCC suggests a relatively stable expression of NDST2 in this disease. Importantly, animal and cell models treated with ITA demonstrate upregulated expression of NDST2. Knockdown of NDST2 in heat stress-induced liver cancer cells results in growth inhibition associated with gene downregulation. CONCLUSION: The upregulation of NDST2 can accelerate the progression of residual HCC after ITA, suggesting a potential role for NDST2 in the therapeutic efficacy and prognosis of residual HCC.

The clinical significance of long non-coding RNAs MALAT1 and CASC2 in the diagnosis of HCV-related hepatocellular carcinoma.

BACKGROUND: Globally, hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death due to a lack of early predictive and/or diagnostic tools. Thus, research for a new biomarker is important. LncRNAs play a functional role in target gene regulation and their deregulation is associated with several pathological conditions including HCC. OBJECTIVE: This study aimed to explore the diagnostic potential of two LncRNAs MALAT1 and CASC2 in HCC compared to the routinely used diagnostic biomarker. MATERIALS AND METHODS: The current study is a case-control study carried out at Fayoum University Hospital and conducted on 89 individuals. The study included three groups of 36 HCC patients on top of HCV(HCC/HCV), 33 HCV patients, and 20 healthy volunteers as a control group. All study subjects were subjected to radiological examinations. The determination of CBC was performed by the automated counter and liver function tests by the enzymatic method were performed. In addition, HCV RNA quantification and the expression level of two LncRNAs (MALAT1 and CASC2) were performed by qRT-PCR. RESULTS: The results revealed a statistically significant difference between study groups regarding liver function tests with a higher mean in HCC/HCV group. Also, serum MALAT1 significantly up-regulated in HCV (11.2±2.8) and HCC/HCV (4.56±1.4) compared to the control group. Besides, serum CASC2 levels in the HCV group were significantly upregulated (14.9±3.6), while, downregulated in the HCC group (0.16± 0.03). Furthermore, The ROC analysis for diagnostic efficacy parameters indicated that CASC2 has higher accuracy (94.6%) and sensitivity (97.2%) for HCC diagnosis than AFP with an accuracy of (90.9%), sensitivity (69.4%), and MALAT1 showed an accuracy of (56.9%), sensitivity (72.2%). CONCLUSION: Our study results indicated that CASC2 is a promising biomarker and is considered better and could help in HCC diagnosis on top of HCV than MALAT1 and the routine biomarker AFP.

Identification of PANoptosis-related subtypes, construction of a prognosis signature, and tumor microenvironment landscape of hepatocellular carcinoma using bioinformatic analysis and experimental verification.

Background: Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide. PANoptosis is a recently unveiled programmed cell death pathway, Nonetheless, the precise implications of PANoptosis within the context of HCC remain incompletely elucidated. Methods: We conducted a comprehensive bioinformatics analysis to evaluate both the expression and mutation patterns of PANoptosis-related genes (PRGs). We categorized HCC into two clusters and identified differentially expressed PANoptosis-related genes (DEPRGs). Next, a PANoptosis risk model was constructed using LASSO and multivariate Cox regression analyses. The relationship between PRGs, risk genes, the risk model, and the immune microenvironment was studies. In addition, drug sensitivity between high- and low-risk groups was examined. The expression profiles of these four risk genes were elucidate by qRT-PCR or immunohistochemical (IHC). Furthermore, the effect of CTSC knock down on HCC cell behavior was verified using in vitro experiments. Results: We constructed a prognostic signature of four DEPRGs (CTSC, CDCA8, G6PD, and CXCL9). Receiver operating characteristic curve analyses underscored the superior prognostic capacity of this signature in assessing the outcomes of HCC patients. Subsequently, patients were stratified based on their risk scores, which revealed that the low-risk group had better prognosis than those in the high-risk group. High-risk group displayed a lower Stromal Score, Immune Score, ESTIMATE score, and higher cancer stem cell content, tumor mutation burden (TMB) values. Furthermore, a correlation was noted between the risk model and the sensitivity to 56 chemotherapeutic agents, as well as immunotherapy efficacy, in patient with. These findings provide valuable guidance for personalized clinical treatment strategies. The qRT-PCR analysis revealed that upregulated expression of CTSC, CDCA8, and G6PD, whereas downregulated expression of CXCL9 in HCC compared with adjacent tumor tissue and normal liver cell lines. The knockdown of CTSC significantly reduced both HCC cell proliferation and migration. Conclusion: Our study underscores the promise of PANoptosis-based molecular clustering and prognostic signatures in predicting patient survival and discerning the intricacies of the tumor microenvironment within the context of HCC. These insights hold the potential to advance our comprehension of the therapeutic contribution of PANoptosis plays in HCC and pave the way for generating more efficacious treatment strategies.

Epigenetic silencing of LDHB promotes hepatocellular carcinoma by remodeling the tumor microenvironment.

Lactate dehydrogenase B (LDHB) reversibly catalyzes the conversion of pyruvate to lactate or lactate to pyruvate and expressed in various malignancies. However, the role of LDHB in modulating immune responses against hepatocellular carcinoma (HCC) remains largely unknown. Here, we found that down-regulation of lactate dehydrogenase B (LDHB) was coupled with the promoter hypermethylation and knocking down the DNA methyltransferase 3A (DNMT 3A) restored LDHB expression levels in HCC cell lines. Bioinformatics analysis of the HCC cohort from The Cancer Genome Atlas revealed a significant positive correlation between LDHB expression and immune regulatory signaling pathways and immune cell infiltrations. Moreover, immune checkpoint inhibitors (ICIs) have shown considerable promise for HCC treatment and patients with higher LDHB expression responded better to ICIs. Finally, we found that overexpression of LDHB suppressed HCC growth in immunocompetent but not in immunodeficient mice, suggesting that the host immune system was involved in the LDHB-medicated tumor suppression. Our findings indicate that DNMT3A-mediated epigenetic silencing of LDHB may contribute to HCC progression through remodeling the tumor immune microenvironment, and LDHB may become a potential prognostic biomarker and therapeutic target for HCC immunotherapy.

Early detection and prognosis evaluation for hepatocellular carcinoma by circulating tumour DNA methylation: A multicentre cohort study.

BACKGROUND: Early diagnosis of hepatocellular carcinoma (HCC) can significantly improve patient survival. We aimed to develop a blood-based assay to aid in the diagnosis, detection and prognostic evaluation of HCC. METHODS: A three-phase multicentre study was conducted to screen, optimise and validate HCC-specific differentially methylated regions (DMRs) using next-generation sequencing and quantitative methylation-specific PCR (qMSP). RESULTS: Genome-wide methylation profiling was conducted to identify DMRs distinguishing HCC tumours from peritumoural tissues and healthy plasmas. The twenty most effective DMRs were verified and incorporated into a multilocus qMSP assay (HepaAiQ). The HepaAiQ model was trained to separate 293 HCC patients (Barcelona Clinic Liver Cancer (BCLC) stage 0/A, 224) from 266 controls including chronic hepatitis B (CHB) or liver cirrhosis (LC) (CHB/LC, 96), benign hepatic lesions (BHL, 23), and healthy controls (HC, 147). The model achieved an area under the curve (AUC) of 0.944 with a sensitivity of 86.0% in HCC and a specificity of 92.1% in controls. Blind validation of the HepaAiQ model in a cohort of 523 participants resulted in an AUC of 0.940 with a sensitivity of 84.4% in 205 HCC cases (BCLC stage 0/A, 167) and a specificity of 90.3% in 318 controls (CHB/LC, 100; BHL, 102; HC, 116). When evaluated in an independent test set, the HepaAiQ model exhibited a sensitivity of 70.8% in 65 HCC patients at BCLC stage 0/A and a specificity of 89.5% in 124 patients with CHB/LC. Moreover, HepaAiQ model was assessed in paired pre- and postoperative plasma samples from 103 HCC patients and correlated with 2-year patient outcomes. Patients with high postoperative HepaAiQ score showed a higher recurrence risk (Hazard ratio, 3.33, p < .001). CONCLUSIONS: HepaAiQ, a noninvasive qMSP assay, was developed to accurately measure HCC-specific DMRs and shows great potential for the diagnosis, detection and prognosis of HCC, benefiting at-risk populations.

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies.

Objective: Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy. Methods: We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC. Results: We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines. Conclusion: The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

SOAT1 regulates cholesterol metabolism to induce EMT in hepatocellular carcinoma.

Cholesterol metabolism reprogramming is one of the significant characteristics of hepatocellular carcinoma (HCC). Cholesterol increases the risk of epithelial-mesenchymal transition (EMT) in cancer. Sterol O-acyltransferases 1 (SOAT1) maintains the cholesterol homeostasis. However, the exact mechanistic contribution of SOAT1 to EMT in HCC remains unclear. Here we demonstrated that SOAT1 positively related to poor prognosis of HCC, EMT markers and promoted cell migration and invasion in vitro, which was mediated by the increased cholesterol in plasmalemma and cholesterol esters accumulation. Furthermore, we reported that SOAT1 disrupted cholesterol metabolism homeostasis to accelerate tumorigenesis and development in HCC xenograft and NAFLD-HCC. Also, we detected that nootkatone, a sesquiterpene ketone, inhibited EMT by targeting SOAT1 in vitro and in vivo. Collectively, our finding indicated that SOAT1 promotes EMT and contributes to hepatocarcinogenesis by increasing cholesterol esterification, which is suppressed efficiently by nootkatone. This study demonstrated that SOAT1 is a potential biomarker and therapeutic target in NAFLD-HCC and SOAT1-targeting inhibitors are expected to be the potential new therapeutic treatment for HCC.

A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

AIMS: This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). METHODS: A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. RESULTS: A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). CONCLUSIONS: A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Ethanol responsive lnc171 promotes migration and invasion of HCC cells via mir-873-5p/ZEB1 axis.

BACKGROUNDS: Long nonconding RNAs (lncRNAs) have been found to be a vital regulatory factor in the development process of human cancer, and could regarded as diagnostic or prognostic biomarkers for human cancers. Here, we aim to confirm the expression and molecular mechanism of RP11-171K16.5 (lnc171) in hepatocellular carcinoma (HCC). METHODS: Screening of differentially expressed lncRNAs by RNA sequencing. Expression level of gene was studied by quantitative real-time PCR (qRT-PCR). The effects of lnc171, mir-873-5p, and ethanol on migration and invasion activity of cells were studied used transwell assay, and luciferase reporter assay was used to confirm the binding site. RESULTS: RNA sequencing showed that lnc171 was markedly up-regulated in HCC. siRNA-mediated knockdown of lnc171 repressed the migration and invasion ability of HCC cells. Bioinformatic analysis, dual luciferase reporter assay, and qRT-PCR indicated that lnc171 interacted with mir-873-5p in HCC cells, and Zin-finger E-box binding homeobox (ZEB1) was a downstream target gene of mir-873-5p. In addition, lnc171 could enhance migration and invasion ability of HCC cells by up-regulating ZEB1 via sponging mir-873-5p. More interestingly, ethanol stimulation could up-regulate the increase of lnc171, thereby regulating the expression of competing endogenous RNA (ceRNA) network factors which lnc171 participated in HCC cells. CONCLUSIONS: Our date demonstrates that lnc171 was a responsive factor of ethanol, and plays a vital role in development of HCC via binding of mir-873-5p.

Exploring the role of CDCA4 in liver hepatocellular carcinoma using bioinformatics analysis and experiments.

Liver hepatocellular carcinoma (LIHC) encompasses diverse therapeutic approaches, among which targeted therapy has gained significant prominence in recent years. The identification of numerous targets and the increasing clinical application of targeted drugs have greatly improved LIHC treatment. However, the precise role of CDCA4 (Cell Division Cycle Associated 4), as well as its underlying mechanisms and prognostic implications in LIHC, remains unclear. CDCA4 expression levels in LIHC were analyzed using multiple databases including the cancer genome atlas (TCGA), gene expression profiling interactive analysis (GEPIA), and ULCAN, as well as the datasets E_TABM_36, GSE144269, GSE14520, and GSE54236. The prognostic value of CDCA4 was then evaluated. Subsequently, the association between CDCA4 and immune cells was investigated. Enrichment analysis (GSEA) was utilized to investigate the functional roles and pathways linked to CDCA4. Additionally, the methylation patterns and drug sensitivity of CDCA4 were examined. A predictive model incorporating immune genes related to CDCA4 was developed. The TISCH dataset was used to investigate the single-cell expression patterns of CDCA4. Finally, validation of CDCA4 expression levels was conducted through RT-PCR, Western blotting, and immunohistochemistry. CDCA4 exhibited significant overexpression in LIHC and demonstrated significant correlations with clinical features. High expression of CDCA4 is associated with a poorer prognosis. Analysis of immune infiltration and enrichment revealed its association with the immune microenvironment. Furthermore, its expression is correlated with methylation and mutation patterns. CDCA4 is associated with 19 drugs. Prognostic models utilizing CDCA4 demonstrate favorable effectiveness. T cell subtypes were found to be associated with CDCA4 through single-cell analysis. The conclusive experiment provided evidence of significant upregulation of CDCA4 in LIHC. The high expression of CDCA4 in LIHC is associated with prognostic significance and is highly expressed in T cell subtypes, providing a new therapeutic target and potential therapeutic strategy for LIHC.