Identification of special key genes for alcohol-related hepatocellular carcinoma through bioinformatic analysis

Alcohol-associated liver cancer

Heavy alcohol intake induces a wide spectrum of liver diseases ranging from steatosis, steatohepatitis, cirrhosis, and HCC. Although alcohol consumption is a well-known risk factor for the development, morbidity, and mortality of HCC globally, alcohol-associated hepatocellular carcinoma (A-HCC) is poorly characterized compared to viral hepatitis-associated HCC. Most A-HCCs develop after alcohol-associated cirrhosis (AC), but the direct carcinogenesis from ethanol and its metabolites to A-HCC remains obscure. The differences between A-HCC and HCCs caused by other etiologies have not been well investigated in terms of clinical prognosis, genetic or epigenetic landscape, molecular mechanisms, and heterogeneity. Moreover, there is a huge gap between basic research and clinical practice due to the lack of preclinical models of A-HCC. In the current review, we discuss the pathogenesis, heterogeneity, preclinical approaches, epigenetic, and genetic profiles of A-HCC, and discuss the current insights into and the prospects for future research on A-HCC. The potential effect of alcohol on cholangiocarcinoma and liver metastasis is also discussed.

Gene Expression Aberrations in Alcohol-Associated Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer, ranking as the sixth most common cancer worldwide and the fourth leading cause of cancer-related deaths. Most HCC cases originate from cirrhotic livers, typically due to chronic liver diseases, such as hepatitis B (HBV) and hepatitis C (HCV) infections, and alcoholism. HCC cells often harbor numerous somatic mutations that are implicated in HCC development, but epigenetic factors, such as miRNA interference, can also affect HCC initiation and progress. miRNA-221 has been explored as a factor affecting HCC development in HCC of viral etiology, but little is known about its effects on gene expression in alcohol-associated HCC. This study aimed to explore potentially similar gene expression aberrations underlying viral and alcohol-induced HCC. We analyzed available transcriptome data from non-tumor hepatocytes and viral-induced HCC tissues. The most notable differences in gene expression associated with miRNA-221 between non-tumor hepatocytes and viral-induced HCC involved NTF-3 and MYBL1 genes. To assess these data in alcohol-induced HCC, we examined 111 tissue samples: tumor tissue and cirrhotic tissue samples from 37 HCC patients and 37 samples from non-tumor liver tissue using RT-Q PCR. We found no significant difference in NTF-3 expression, but MYBL1 expression was significantly lower in HCC tissue compared to non-tumor hepatocytes and cirrhotic tissue. Our findings highlight the importance of the MYBL1 gene in HCC development and emphasize the need for diverse approaches in evaluating tumor mechanisms.

Mimicking chronic alcohol effects through a controlled and sustained ethanol release device

Alcohol consumption, a pervasive societal issue, poses considerable health risks and socioeconomic consequences. Alcohol-induced hepatic disorders, such as fatty liver disease, alcoholic hepatitis, chronic hepatitis, liver fibrosis, and cirrhosis, underscore the need for comprehensive research. Existing challenges in mimicking chronic alcohol exposure in cellular systems, attributed to ethanol evaporation, necessitate innovative approaches. In this study, we developed a simple, reusable, and controllable device for examining the physiological reactions of hepatocytes to long-term alcohol exposure. Our approach involved a novel device designed to continuously release ethanol into the culture medium, maintaining a consistent ethanol concentration over several days. We evaluated device performance by examining gene expression patterns and cytokine secretion alterations during long-term exposure to ethanol. These patterns were correlated with those observed in patients with alcoholic hepatitis. Our results suggest that our ethanol-releasing device can be used as a valuable tool to study the mechanisms of chronic alcohol-mediated hepatic diseases at the cellular level. Our device offers a practical solution for studying chronic alcohol exposure, providing a reliable platform for cellular research. This innovative tool holds promise for advancing our understanding of the molecular processes involved in chronic alcohol-mediated hepatic diseases. Future research avenues should explore broader applications and potential implications for predicting and treating alcohol-related illnesses.

Signature construction and molecular subtype identification based on liver-specific genes for prediction of prognosis, immune activity, and anti-cancer drug sensitivity in hepatocellular carcinoma

BACKGROUND

Liver specific genes (LSGs) are crucial for hepatocyte differentiation and maintaining normal liver function. A deep understanding of LSGs and their heterogeneity in hepatocellular carcinoma (HCC) is necessary to provide clues for HCC diagnosis, prognosis, and treatment.

METHODS

The bulk and single-cell RNA-seq data of HCC were downloaded from TCGA, ICGC, and GEO databases. Through unsupervised cluster analysis, LSGs-based HCC subtypes were identified in TCGA-HCC samples. The prognostic effects of the subtypes were investigated with survival analyses. With GSVA and Wilcoxon test, the LSGs score, stemness score, aging score, immune score and stromal score of the samples were estimated and compared. The HCC subtype-specific genes were identified. The subtypes and their differences were validated in ICGC-HCC samples. LASSO regression analysis was used for key gene selection and risk model construction for HCC overall survival. The model performance was estimated and validated. The key genes were validated for their heterogeneities in HCC cell lines with quantitative real-time PCR and at single-cell level. Their dysregulations were investigated at protein level. Their correlations with HCC response to anti-cancer drugs were estimated in HCC cell lines.

RESULTS

We identified three LSGs-based HCC subtypes with different prognosis, tumor stemness, and aging level. The C1 subtype with low LSGs score and high immune score presented a poor survival, while the C2 subtype with high LSGs score and immune score indicated an enduring survival. Although no significant survival difference between C2 and C3 HCCs was shown, the C2 HCCs presented higher immune score and stroma score. The HCC subtypes and their differences were confirmed in ICGC-HCC dataset. A five-gene prognostic signature for HCC survival was constructed. Its good performance was shown in both the training and validation datasets. The five genes presented significant heterogeneities in different HCC cell lines and hepatocyte subclusters. Their dysregulations were confirmed at protein level. Furthermore, their significant associations with HCC sensitivities to anti-cancer drugs were shown.

CONCLUSIONS

LSGs-based HCC subtype classification and the five-gene risk model might provide useful clues not only for HCC stratification and risk prediction, but also for the development of more personalized therapies for effective HCC treatment.

Comprehensive scRNA-seq Analysis and Identification of CD8_+T Cell Related Gene Markers for Predicting Prognosis and Drug Resistance of Hepatocellular Carcinoma

BACKGROUND

Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis.

METHODS

Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy.

RESULTS

We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility.

CONCLUSION

To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated.

METHODS

Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified.

CONCLUSIONS

High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.

Characterization and clinical verification of immune-related genes in hepatocellular carcinoma to aid prognosis evaluation and immunotherapy

BACKGROUND

Immune-related genes (IRGs) have been confirmed to play an important role in tumorigenesis and tumor microenvironment formation in hepatocellular carcinoma (HCC). We investigated how IRGs regulates the HCC immunophenotype and thus affects the prognosis and response to immunotherapy.

METHODS

We investigated RNA expression of IRGs and developed an immune-related genes-based prognostic index (IRGPI) in HCC samples. Then, the influence of the IRGPI on the immune microenvironment was comprehensively analysed.

RESULTS

According to IRGPI, HCC patients are divided into two immune subtypes. A high IRGPI was characterized by an increased tumor mutation burden (TMB) and a poor prognosis. More CD8 + tumor infiltrating cells and expression of PD-L1 were observed in low IRGPI subtypes. Two immunotherapy cohorts confirmed patients with low IRGPI demonstrated significant therapeutic benefits. Multiplex immunofluorescence staining determined that there were more CD8 + T cells infiltrating into tumor microenvironment in IRGPI-low groups, and the survival time of these patients was longer.

CONCLUSIONS

This study demonstrated that the IRGPI serve as a predictive prognostic biomarker and potential indicator for immunotherapy.

Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis

One methodology extensively used to develop biomarkers is the precise detection of highly responsive genes that can distinguish cancer samples from healthy samples. The purpose of this study was to screen for potential hepatocellular carcinoma (HCC) biomarkers based on non-fusion integrative multi-platform meta-analysis method. The gene expression profiles of liver tissue samples from two microarray platforms were initially analyzed using a meta-analysis based on an empirical Bayesian method to robust discover differentially expressed genes in HCC and non-tumor tissues. Then, using the bioinformatics technique of weighted correlation network analysis, the highly associated prioritized Differentially Expressed Genes (DEGs) were clustered. Co-expression network and topological analysis were utilized to identify sub-clusters and confirm candidate genes. Next, a diagnostic model was developed and validated using a machine learning algorithm. To construct a prognostic model, the Cox proportional hazard regression analysis was applied and validated. We identified three genes as specific biomarkers for the diagnosis of HCC based on accuracy and feasibility. The diagnostic model's area under the curve was 0.931 with confidence interval of 0.923-0.952.•Non-fusion integrative multi-platform meta-analysis method.•Classification methods and biomarkers recognition via machine learning method.•Biomarker validation models.

CD5L-associated gene analyses highlight the dysregulations, prognostic effects, immune associations, and drug-sensitivity predicative potentials of LCAT and CDC20 in hepatocellular carcinoma

BACKGROUND

The dysregulation of CD5L has been reported in hepatocellular carcinoma (HCC). However, its functions in HCC were controversial. In this study, we aimed to identify CD5L-associated pathways and markers and explore their values in HCC diagnosis, prognosis and treatment.

METHODS

HCC datasets with gene expression profiles and clinical data in TCGA and ICGC were downloaded. The immune/stroma cell infiltrations were estimated with xCell. CD5L-associated pathways and CD5L-associated genes (CD5L-AGs) were identified with gene expression comparisons and gene set enrichment analysis (GSEA). Cox regression, Kaplan-Meier survival analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis were performed. The correlations of the key genes with immune/stroma infiltrations, immunoregulators, and anti-cancer drug sensitivities in HCC were investigated. At protein level, the key genes dysregulations, their correlations and prognostic values were validated in clinical proteomic tumor analysis consortium (CPTAC) database. Serum CD5L and LCAT activity in 50 HCC and 30 normal samples were evaluated and compared. The correlations of serum LCAT activity with alpha-fetoprotein (AFP), albumin (ALB) and high-density lipoprotein (HDL) in HCC were also investigated.

RESULTS

Through systemic analyses, 14 CD5L-associated biological pathways, 256 CD5L-AGs and 28 CD5L-associated prognostic and diagnostic genes (CD5L-APDGs) were identified. A risk model consisting of LCAT and CDC20 was constructed for HCC overall survival (OS), which could discriminate HCC OS status effectively in both the training and the validation sets. CD5L, LCAT and CDC20 were shown to be significantly correlated with immune/stroma cell infiltrations, immunoregulators and 31 anti-cancer drug sensitivities in HCC. At protein level, the dysregulations of CD5L, LCAT and CDC20 were confirmed. LCAT and CDC20 were shown to be significantly correlated with proliferation marker MKI67. In serum, no significance of CD5L was shown. However, the lower activity of LCAT in HCC serum was obvious, as well as its significant positive correlations ALB and HDL concentrations.

CONCLUSIONS

CD5L, LCAT and CDC20 were dysregulated in HCC both at mRNA and protein levels. The LCAT-CDC20 signature might be new predicator for HCC OS. The associations of the three genes with HCC microenvironment and anti-cancer drug sensitivities would provide new clues for HCC immunotherapy and chemotherapy.

Identification of a Prognostic Model Based on 2-Gene Signature and Analysis of Corresponding Tumor Microenvironment in Alcohol-Related Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors with the poor prognosis. Nowadays, alcohol is becoming a leading risk factor of HCC in many countries. In our study, we obtained the DEGs in alcohol-related HCC through two databases (TCGA and GEO). Subsequently, we performed enrichment analyses (GO and KEGG), constructed the PPI network and screened the 53 hub genes by Cytoscape. Two genes (BUB1B and CENPF) from hub genes was screened by LASSO and Cox regression analyses to construct the prognostic model. Then, we found that the high risk group had the worse prognosis and verified the clinical value of the risk score in alcohol-related HCC. Finally, we analyzed the tumor microenvironment between high and low risk groups through CIBERSORT and ESTIMATE. In summary, we constructed the two-gene prognostic model that could predict the poor prognosis in patients with alcohol-related HCC.

The Diagnostic Performance of lncRNAs from Blood Specimens in Patients with Hepatocellular Carcinoma: A Meta-Analysis

OBJECTIVE

Long noncoding RNAs (lncRNAs) are widely involved in the carcinogenesis and development of cancers. We conducted a meta-analysis to evaluate the diagnostic performance of lncRNAs in hepatocellular carcinoma (HCC).

METHODS

After the inclusion and exclusion process, relevant information was extracted. Heterogeneity between studies was evaluated, and data synthesis was conducted by employing a bivariate random-effects model.

RESULTS

In total, 20 eligible studies were enrolled. The pooled sensitivity and specificity were 0.86 (95% confidence interval [CI], 0.80-0.90) and 0.88 (95% CI, 0.82-0.92), respectively. The pooled positive likelihood ratio, pooled negative likelihood ratio, and pooled diagnostic odds ratio were 7.1 (95% CI, 4.9-10.2), 0.16 (95% CI, 0.11-0.23), and 44 (95% CI, 25-79), respectively. The results of the linear regression method and visual inspection of the Deeks funnel plot did not indicate significant publication bias.

CONCLUSION

Our meta-analysis suggested that lncRNAs have high diagnostic performance for HCC and have the potential for clinical application.

Chronic Alcohol Exposure of Cells Using Controlled Alcohol-Releasing Capillaries

Alcohol is one of the main causes of liver diseases such as fatty liver, alcoholic hepatitis, and chronic hepatitis with liver fibrosis or cirrhosis. To reproduce the conditions of alcohol-induced liver diseases and to identify the disease-causing mechanisms at the cellular level, several methods have been used to expose the cells to ethanol. As ethanol evaporates easily, it is difficult to mimic chronic alcohol exposure conditions at the cellular level. In this study, we developed a glass capillary system containing ethanol, which could steadily release ethanol from the polyethylene tubing and hydrogel portion at both sides of the capillary. The ethanol-containing capillary could release ethanol in the cell culture medium for up to 144 h, and the concentration of ethanol in the cell culture medium could be adjusted by controlling the number of capillaries. A long-term exposure to ethanol by the capillary system led to an increased toxicity of cells and altered the cellular physiologies, such as increasing the lipid accumulation and hepatic transaminase release in cells, as compared to the traditional direct ethanol addition method. Ethanol capillaries showed different gene expression patterns of lipid accumulation- or chronic alcoholism-related genes. Our results suggest that our ethanol-containing capillary system can be used as a valuable tool for studying the mechanism of chronic alcohol-mediated hepatic diseases at the cellular level.

Comprehensive Identification of Bridge Genes to Explain the Progression from Chronic Hepatitis B Virus Infection to Hepatocellular Carcinoma

Background

Hepatitis B virus infection co-occurs in 33% of individuals with hepatocellular carcinoma worldwide. However, the molecular link between hepatitis B virus and hepatocellular carcinoma is unknown. Thus, we aimed to elucidate molecular linkages underlying pathogenesis through in-depth data mining analysis.

Materials and Methods

Differentially expressed genes were identified from patients with chronic hepatitis B virus infection, hepatocellular carcinoma, or both. Gene set enrichment analysis revealed signaling pathways involving differentially expressed genes. Protein-protein interaction networks, protein crosstalk, and enrichment were analyzed to determine whether differentially expressed gene products might serve as a bridge from hepatitis B virus infection to hepatocellular carcinoma pathogenesis. Prognostic potential and transcriptional and post-transcriptional regulators of bridge genes were also examined.

Results

We identified vital bridge factors in hepatitis B virus infection-associated hepatocellular carcinoma. Differentially expressed genes were clustered into modules based on relative protein function. Signaling pathways associated with cancer, inflammation, immune system, and microenvironment showed significant crosstalk between modules. Thirty-two genes were dysregulated in hepatitis B virus infection-mediated hepatocellular carcinoma. CPEB3, RAB26, SLCO1B1, ST3GAL6 and XK had higher connectivity in the modular network, suggesting significant associations with survival. CDC20 and NUP107 were identified as driver genes as well as markers of poor prognosis.

Conclusion

Our results suggest that the sustained inflammatory environment created by hepatitis B virus infection is a risk factor for hepatocellular carcinoma. The identification of hepatitis B virus infection-related hepatocellular carcinoma bridge genes provides testable hypotheses about the pathogenesis of hepatocellular carcinoma.

Identification of genes associated with clinicopathological features of colorectal cancer

Objective

To identify genes associated with the clinicopathological features of colorectal cancer (CRC).

Methods

Gene expression profiles were downloaded and preprocessed by GEOquery and affy R packages, respectively. The limma package was applied to identify the differentially expressed genes (DEGs) in CRC. Gene Ontology and Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analyses for the DEGs were carried out using the clusterProfiler package. Protein–protein interaction (PPI) and weighted gene co-expression (WGC) networks were constructed using the STRING database and WGCNA package, respectively.

Results

A total of 523 DEGs (283 downregulated and 240 upregulated genes) in CRC tissues were identified. These DEGs were mainly enriched in 111 biological processes, 16 cellular components and 40 molecular functions, such as proteinaceous extracellular matrix, extracellular structure organization and chemokine-mediated signalling pathway. PPI and WGC networks showed that four upregulated genes (KIF2C, CDC45, CEP55 and DTL) were key genes. Subgroup analysis based on individual cancer stages and histological subtypes indicated that the expression of these key genes was upregulated in CRC stages I–IV, adenocarcinoma and mucinous adenocarcinoma.

Conclusions

The study provides new insights into understanding the pathogenesis of CRC. These identified genes may act as potential targets for CRC diagnosis and treatment.

Screening and Identification of Potential Biomarkers in Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is one of the most lethal cancers globally. Hepatitis B virus (HBV) infection might cause chronic hepatitis and cirrhosis, leading to HCC. To screen prognostic genes and therapeutic targets for HCC by bioinformatics analysis and determine the mechanisms underlying HBV-related HCC, three high-throughput RNA-seq based raw datasets, namely GSE25599, GSE77509, and GSE94660, were obtained from the Gene Expression Omnibus database, and one RNA-seq raw dataset was acquired from The Cancer Genome Atlas (TCGA). Overall, 103 genes were up-regulated and 127 were down-regulated. A protein–protein interaction (PPI) network was established using Cytoscape software, and 12 pivotal genes were selected as hub genes. The 230 differentially expressed genes and 12 hub genes were subjected to functional and pathway enrichment analyses, and the results suggested that cell cycle, nuclear division, mitotic nuclear division, oocyte meiosis, retinol metabolism, and p53 signaling-related pathways play important roles in HBV-related HCC progression. Further, among the 12 hub genes, kinesin family member 11 (KIF11), TPX2 microtubule nucleation factor (TPX2), kinesin family member 20A (KIF20A), and cyclin B2 (CCNB2) were identified as independent prognostic genes by survival analysis and univariate and multivariate Cox regression analysis. These four genes showed higher expression levels in HCC than in normal tissue samples, as identified upon analyses with Oncomine. In addition, in comparison with normal tissues, the expression levels of KIF11, TPX2, KIF20A, and CCNB2 were higher in HBV-related HCC than in HCV-related HCC tissues. In conclusion, our results suggest that KIF11, TPX2, KIF20A, and CCNB2 might be involved in the carcinogenesis and development of HBV-related HCC. They can thus be used as independent prognostic genes and novel biomarkers for the diagnosis of HBV-related HCC and development of pertinent therapeutic strategies.

Whole genome sequencing analysis identifies recurrent structural alterations in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is the predominant type of esophageal cancer in the Asian region, including Japan. A previous study reported mutational landscape of Japanese ESCCs by using exome sequencing. However, somatic structural alterations were yet to be explored. To provide a comprehensive mutational landscape, we performed whole genome sequencing (WGS) analysis of biopsy specimens from 20 ESCC patients in a Japanese population. WGS analysis identified non-silent coding mutations of TP53, ZNF750 and FAT1 in ESCC. We detected six mutational signatures in ESCC, one of which showed significant association with smoking status. Recurrent structural variations, many of which were chromosomal deletions, affected genes such as LRP1B, TTC28, CSMD1, PDE4D, SDK1 and WWOX in 25%–30% of tumors. Somatic copy number amplifications at 11q13.3 (CCND1), 3q26.33 (TP63/SOX2), and 8p11.23 (FGFR1) and deletions at 9p21.3 (CDKN2A) were identified. Overall, these multi-dimensional view of genomic alterations improve the understanding of the ESCC development at molecular level and provides future prognosis and therapeutic implications for ESCC in Japan.

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma

BACKGROUND

Recent genome-wide association studies (GWASs) have suggested several susceptibility loci of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) by statistical analysis at individual single-nucleotide polymorphisms (SNPs). However, these loci only explain a small fraction of HBV-related HCC heritability. In the present study, we aimed to identify additional susceptibility loci of HBV-related HCC using advanced knowledge-based analysis.

METHODS

We performed knowledge-based analysis (including gene- and gene-set-based association tests) on variant-level association p-values from two existing GWASs of HBV-related HCC. Five different types of gene-sets were collected for the association analysis. A number of SNPs within the gene prioritized by the knowledge-based association tests were selected to replicate genetic associations in an independent sample of 965 cases and 923 controls.

RESULTS

The gene-based association analysis detected four genes significantly or suggestively associated with HBV-related HCC risk: SLC39A8, GOLGA8M, SMIM31, and WHAMMP2. The gene-set-based association analysis prioritized two promising gene sets for HCC, cell cycle G1/S transition and NOTCH1 intracellular domain regulates transcription. Within the gene sets, three promising candidate genes (CDC45, NCOR1 and KAT2A) were further prioritized for HCC. Among genes of liver-specific expression, multiple genes previously implicated in HCC were also highlighted. However, probably due to small sample size, none of the genes prioritized by the knowledge-based association analyses were successfully replicated by variant-level association test in the independent sample.

CONCLUSIONS

This comprehensive knowledge-based association mining study suggested several promising genes and gene-sets associated with HBV-related HCC risks, which would facilitate follow-up functional studies on the pathogenic mechanism of HCC.

Aberrant expression of two miRNAs promotes proliferation, hepatitis B virus amplification, migration and invasion of hepatocellular carcinoma cells: evidence from bioinformatic analysis and experimental validation

Background

As key negative regulators of gene expression, microRNAs (miRNAs) play an important role in the onset and progression of hepatocellular carcinoma (HCC). This study aimed to identify the miRNAs involved in HCC carcinogenesis and their regulated genes.

Methods

The Gene Expression Omnibus (GEO) dataset (GSE108724) was chosen and explored to identify differentially expressed miRNAs using GEO2R. For the prediction of potential miRNA target genes, the miRTarBase was explored. Enrichment analysis of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed by the DAVID online tool. The hub genes were screened out using the CytoHubba plug-in ranked by degrees. The networks between miRNAs and hub genes were constructed by Cytoscape software. MiRNA mimics and negative control were transfected into HCC cell lines and their effects on proliferation, hepatitis B virus DNA (HBV-DNA) replication, TP53 expression, migration, and invasion were investigated. The following methods were employed: MTT assay, quantitative PCR (qPCR) assay, western blotting, wound healing assay, and transwell assay.

Results

A total of 50 differentially expressed miRNAs were identified, including 20 upregulated and 30 downregulated miRNAs, in HCC tumor tissues compared to matched adjacent tumor-free tissues. The top three upregulated (miR-221-3p, miR-222-3p, and miR-18-5p) and downregulated (miR-375, miR-214-3p and miR-378d) miRNAs, ranked by |log₂ fold change (log₂FC)|, were chosen and their potential target genes were predicted. Two gene sets, targeted by the upregulated and the downregulated miRNAs, were identified respectively. GO and KEGG pathway analysis showed that the predicted target genes of upregulated and downregulated miRNAs were mainly enriched in the cell cycle and cancer-related pathways. The top ten hub nodes of gene sets ranked by degrees were identified as hub genes. Analysis of miRNA-hub gene network showed that miR-221-3p and miR-375 modulated most of the hub genes, especially involving regulation of TP53. The q-PCR results showed that miR-221-3p and miR-375 were markedly upregulated and downregulated, respectively, in HCC cells and HCC clinical tissue samples compared to non-tumoral tissues. Furthermore, miR-221-3p overexpression significantly enhanced proliferation, HBV-DNA replication, as well as the migration and invasion of HCC cells, whereas miR-375 overexpression resulted in opposite effects. Western blotting analysis showed that the overexpression of miR-221-3p and miR-375 reduced and increased TP53 expression, respectively.

Conclusion

The present study revealed that miR-211-3p and miR-375 may exert vital effects on cell proliferation, HBV-DNA replication, cell migration, and invasion through the regulation of TP53 expression in HCC.

Promising key genes associated with tumor microenvironments and prognosis of hepatocellular carcinoma

BACKGROUND

Despite significant advances in multimodality treatments, hepatocellular carcinoma (HCC) remains one of the most common malignant tumors. Identification of novel prognostic biomarkers and molecular targets is urgently needed.

AIM

To identify potential key genes associated with tumor microenvironments and the prognosis of HCC.

METHODS

The infiltration levels of immune cells and stromal cells were calculated and quantified based on the ESTIMATE algorithm. Differentially expressed genes (DEGs) between high and low groups according to immune or stromal scores were screened using the gene expression profile of HCC patients in The Cancer Genome Atlas and were further linked to the prognosis of HCC. These genes were validated in four independent HCC cohorts. Survival-related key genes were identified by a LASSO Cox regression model.

RESULTS

HCC patients with a high immune/stromal score had better survival benefits than patients with a low score. A total of 899 DEGs were identified and found to be involved in immune responses and extracellular matrices, 147 of which were associated with overall survival. Subsequently, 52 of 147 survival-related DEGs were validated in additional cohorts. Finally, ten key genes (STSL2, TMC5, DOK5, RASGRP2, NLRC3, KLRB1, CD5L, CFHR3, ADH1C, and UGT2B15) were selected and used to construct a prognostic gene signature, which presented a good performance in predicting overall survival.

CONCLUSION

This study extracted a list of genes associated with tumor microenvironments and the prognosis of HCC, thereby providing several valuable directions for the prognostic prediction and molecular targeted therapy of HCC in the future.

Genetic Biomarkers For Hepatocellular Carcinoma In The Era Of Precision Medicine

Being one of the most lethal cancers that exhibit high levels of heterogeneity, hepatocellular carcinoma (HCC) is associated with diverse oncogenic pathways underpinned by varied driver genes. HCC can be induced by different etiological factors including virus infection, toxin exposure or metabolic disorders. Consequently, patients may display varied genetic profiles, and may respond differently to the treatments involving inhibition of target pathways. These DNA/RNA mutations, copy number variations, chromatin structural changes, aberrant expression of non-coding RNAs and epigenetic modifications were considered as biomarkers in the application of precision medication. To explore how genetic testing could contribute to early diagnosis, prognosis, treatment and postoperative monitoring of HCC, we conducted a systematic review of genetic markers associated with different pathologies. Moreover, we summarized on-going clinical trials for HCC treatment, including the trials for multiple kinase inhibitors and immune checkpoint blockade (ICB). The efficacy of ICB treatment in HCC is not as good as what was observed in lung cancer and melanoma, which might be due to the heterogeneity of the microenvironment of the liver.

Identification of crucial genes based on expression profiles of hepatocellular carcinomas by bioinformatics analysis

Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant cancers with no effective targets and treatments. However, the molecular pathogenesis of HCC remains largely uncertain. The aims of our study were to find crucial genes involved in HCC through multidimensional methods and revealed potential molecular mechanisms. Here, we reported the gene expression profile GSE121248 findings from 70 HCC and 37 adjacent normal tissues, all of which had chronic hepatitis B virus (HBV) infection, we were seeking to identify the dysregulated pathways, crucial genes and therapeutic targets implicated in HBV-associated HCC. We found 164 differentially expressed genes (DEGs) (92 downregulated genes and 72 upregulated genes). Gene ontology (GO) analysis of DEGs revealed significant functional enrichment of mitotic nuclear division, cell division, and the epoxygenase P450 pathway. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were mainly enriched in metabolism, cell cycle regulation and the p53 signaling pathway. The Mcode plugin was calculated to construct a module complex of DEGs, and the module was mainly enriched in cell cycle checkpoints, RHO GTPase effectors and cytochrome P450. Considering a weak contribution of each gene, gene set enrichment analysis (GSEA) was performed, revealing results consistent with those described above. Six crucial proteins were selected based on the degree of centrality, including NDC80, ESR1, ZWINT, NCAPG, ENO3 and CENPF. Real-time quantitative PCR analysis validated the six crucial genes had the same expression trend as predicted. Furthermore, the methylation data of The Cancer Genome Atlas (TCGA) with HCC showed that mRNA expression of crucial genes was negatively correlated with methylation levels of their promoter region. The overall survival reflected that high expression of NDC80, CENPF, ZWINT, and NCAPG significantly predicted poor prognosis, whereas ESR1 high expression exhibited a favorable prognosis. The identification of the crucial genes and pathways would contribute to the development of novel molecular targets and biomarker-driven treatments for HCC.