Hepatocellular Carcinoma Datasets Research Articles

Two differentially methylated region networks in nonalcoholic fatty liver disease, viral hepatitis, and hepatocellular carcinoma

BackgroundWe previously reported that two differentially methylated region (DMR) networks identified by DMR and co-methylation analyses are strongly correlated with the fibrosis stages of nonalcoholic fatty liver disease (NAFLD). In the current study, we examined these DMR networks in viral hepatitis and hepatocellular carcinoma (HCC).MethodsWe performed co-methylation analysis of DMRs using a normal dataset (GSE48325), two NAFLD datasets (JGAS000059 and GSE31803), and two HCC datasets (GSE89852 and GSE56588). The dataset GSE60753 was used for validation.ResultsOne DMR network was clearly observed in viral hepatitis and two HCC populations. Methylation levels of genes in this network were higher in viral hepatitis and cirrhosis, and lower in HCC. Fatty acid binding protein 1 (FABP1), serum/glucocorticoid regulated kinase 2 (SGK2), and hepatocyte nuclear factor 4 α (HNF4A) were potential hub genes in this network. Increased methylation levels of the FABP1 gene may be correlated with reduced protection of hepatocytes from oxidative metabolites in NAFLD and viral hepatitis. The decreased methylation levels of SGK2 may facilitate the growth and proliferation of HCC cells. Decreased methylation levels of HNF4A in HCC may be associated with tumorigenesis. The other DMR network was observed in NAFLD, but not in viral hepatitis or HCC. This second network included genes involved in transcriptional regulation, cytoskeleton organization, and cellular proliferation, which are specifically related to fibrosis and/or tumorigenesis in NAFLD.ConclusionsOur results suggest that one DMR network was associated with fibrosis and tumorigenesis in both NAFLD and viral hepatitis, while the other network was specifically associated with NAFLD progression. Furthermore, FABP1, SGK2, and HNF4A are potential candidate targets for the prevention and treatment of HCC.

NOL12 as an Oncogenic Biomarker Promotes Hepatocellular Carcinoma Growth and Metastasis.

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis worldwide. However, the pathogenesis of HCC remains poorly understood. In this study, we found that NOL12 was significantly overexpressed in independent HCC datasets from TCGA database. We confirmed that the expression level of NOL12 was upregulated in human HCC tissues and cell lines by RT-qPCR. High expression of NOL12 is associated with worse reduced overall survival (OS), high pathological grade, node metastasis, and advanced clinical stage in patients with HCC. Moreover, knockdown of NOL12 dramatically inhibits the proliferation and metastasis of HCC cells in vitro and in vivo. CIBERSORTx analysis revealed that twelve types of tumor-infiltrating immune cells (TICs) are correlated with NOL12 expression. The risk signature based on 8 NOL12-related genes is an independent prognostic factor for patients with HCC. The OS rate of patients in the low-risk score group was better than that in the high-risk score group. In addition, the total tumor mutation burden (TMB) in the high-risk score group increased significantly, and the risk scores could be used as an alternative indicator of immune checkpoint inhibitor (ICI) response. In conclusion, our findings indicated that NOL12 might be involved in the progression of HCC and can be used as a potential therapeutic target. Moreover, the NOL12-related risk signature may have predictive relevance with regard to ICI therapy.

ExpressVis: a biologist-oriented interactive web server for exploring multi-omics data.

In the era of life-omics, huge amounts of multi-omics data have been generated and widely used in biomedical research. It is challenging for biologists with limited programming skills to obtain biological insights from multi-omics data. Thus, a biologist-oriented platform containing visualization functions is needed to make complex omics data digestible. Here, we propose an easy-to-use, interactive web server named ExpressVis. In ExpressVis, users can prepare datasets; perform differential expression analysis, clustering analysis, and survival analysis; and integrate expression data with protein–protein interaction networks and pathway maps. These analyses are organized into six modules. Users can use each module independently or use several modules interactively. ExpressVis displays analysis results in interactive figures and tables, and provides comprehensive interactive operations in each figure and table, between figures or tables in each module, and among different modules. It is freely accessible at https://omicsmining.ncpsb.org.cn/ExpressVis and does not require login. To test the performance of ExpressVis for multi-omics studies of clinical cohorts, we re-analyzed a published hepatocellular carcinoma dataset and reproduced their main findings, suggesting that ExpressVis is convenient enough to analyze multi-omics data. Based on its complete analysis processes and unique interactive operations, ExpressVis provides an easy-to-use solution for exploring multi-omics data.

MiR-129-5p exerts Wnt signaling-dependent tumor-suppressive functions in hepatocellular carcinoma by directly targeting hepatoma-derived growth factor HDGF

BackgroundIn hepatocellular carcinoma (HCC), histone deacetylases (HDACs) are frequently overexpressed. This results in chromatin compaction and silencing of tumor-relevant genes and microRNAs. Modulation of microRNA expression is a potential treatment option for HCC. Therefore, we aimed to characterize the epigenetically regulated miR-129-5p regarding its functional effects and target genes to understand its relevance for HCC tumorigenesis.MethodsGlobal miRNA expression of HCC cell lines (HLE, HLF, Huh7, HepG2, Hep3B) and normal liver cell lines (THLE-2, THLE-3) was analyzed after HDAC inhibition by miRNA sequencing. An in vivo xenograft mouse model and in vitro assays were used to investigate tumor-relevant functional effects following miR-129-5p transfection of HCC cells. To validate hepatoma-derived growth factor (HDGF) as a direct target gene of miR-129-5p, luciferase reporter assays were performed. Survival data and HDGF expression were analyzed in public HCC datasets. After siRNA-mediated knockdown of HDGF, its cancer-related functions were examined.ResultsHDAC inhibition induced the expression of miR-129-5p. Transfection of miR-129-5p increased the apoptosis of HCC cells, decreased proliferation, migration and ERK signaling in vitro and inhibited tumor growth in vivo. Direct binding of miR-129-5p to the 3′UTR of HDGF via a noncanonical binding site was validated by luciferase reporter assays. HDGF knockdown reduced cell viability and migration and increased apoptosis in Wnt-inactive HCC cells. These in vitro results were in line with the analysis of public HCC datasets showing that HDGF overexpression correlated with a worse survival prognosis, primarily in Wnt-inactive HCCs.ConclusionsThis study provides detailed insights into the regulatory network of the tumor-suppressive, epigenetically regulated miR-129-5p in HCC. Our results reveal for the first time that the therapeutic application of mir-129-5p may have significant implications for the personalized treatment of patients with Wnt-inactive, advanced HCC by directly regulating HDGF. Therefore, miR-129-5p is a promising candidate for a microRNA replacement therapy to prevent HCC progression and tumor metastasis.

Establishment of a lncRNA-Based Prognostic Gene Signature Associated With Altered Immune Responses in HCC.

Hepatocellular carcinoma (HCC) is a common malignancy with higher mortality, and means are urgently needed to improve the prognosis. T cell exclusion (TCE) plays a pivotal role in immune evasion, and lncRNAs represent a large group of tumor development and progression modulators. Using the TCGA HCC dataset (n=374), we identified 2752 differentially expressed and 702 TCE-associated lncRNAs, of which 336 were in both groups. As identified using the univariate Cox regression analysis, those associated with overall survival (OS) were subjected to the LASSO-COX regression analysis to develop a prognosis signature. The model, which consisted of 11 lncRNAs and was named 11LNCPS for 11-lncRNA prognosis signature, was validated and performed better than two previous models. In addition to OS and TCE, higher 11LNCPS scores had a significant correlation with reduced infiltrations of CD8+ T cells and dendritic cells (DCs) and decreased infiltrations of Th1, Th2, and pro B cells. As expected, these infiltration alterations were significantly associated with worse OS in HCC. Analysis of published data indicates that HCCs with higher 11LNCPS scores were transcriptomically similar to those that responded better to PDL1 inhibitor. Of the 11LNCPS lncRNAs, LINC01134 and AC116025.2 seem more crucial, as their upregulations affected more immune cell types’ infiltrations and were significantly associated with TCE, worse OS, and compromised immune responses in HCC. LncRNAs in the 11LNCPS impacted many cancer-associated biological processes and signaling pathways, particularly those involved in immune function and metabolism. The 11LNCPS should be useful for predicting prognosis and immune responses in HCC.

Matrix metalloproteinase 12 (MMP12) as an adverse prognostic biomarker of vascular invasion in hepatic cell carcinoma.

Vascular invasion is closely associated with tumor recurrence and poor outcomes in hepatocellular carcinoma (HCC). In this study, we evaluated the potential prognostic value of matrix metalloproteinase-12 (MMP12) as a biomarker of vascular invasion in HCC patients. The Gene Expression Omnibus GSE77509 and TCGA Liver Hepatocellular Carcinoma datasets were analyzed to explore the relationships between genes, vascular invasion, and patient survival. The role of MMP12 in HCC was analyzed in terms of DNA methylation, immune cell infiltration, and patient survival, as well as in silico analysis. Overexpression of MMP12 was associated with poor prognosis in HCC patients with vascular invasion. MMP12 was identified as an independent predictor of overall survival (OS) (HR 2.543; 95% CI 1.224, 5.285; p = 0.012) and disease-free survival (DFS) (HR 2.034; 95% CI 1.160, 3.566; p = 0.013) in multivariate Cox analysis in HCC patients. MMP12 expression, vascular invasion, tumor status, and AJCC T stage were independent predictors of OS with a concordance index (C-index) of 0.713 (95% CI, 0.671, 0.756). MMP12 expression was related to hypomethylation status and positively correlated with tumor immune cell infiltration and the expression of immune cell-related biomarkers. Upregulation of MMP12 was associated with poor prognosis and vascular invasion in HCC. These data suggest that MMP12 may have potential as a therapeutic target and biomarker in HCC.

Integrating the Epigenome and Transcriptome of Hepatocellular Carcinoma to Identify Systematic Enhancer Aberrations and Establish an Aberrant Enhancer-Related Prognostic Signature.

Recently, emerging evidence has indicated that aberrant enhancers, especially super-enhancers, play pivotal roles in the transcriptional reprogramming of multiple cancers, including hepatocellular carcinoma (HCC). In this study, we performed integrative analyses of ChIP-seq, RNA-seq, and whole-genome bisulfite sequencing (WGBS) data to identify intergenic differentially expressed enhancers (DEEs) and genic differentially methylated enhancers (DMEs), along with their associated differentially expressed genes (DEE/DME-DEGs), both of which were also identified in independent cohorts and further confirmed by HiC data. Functional enrichment and prognostic model construction were conducted to explore the functions and clinical significance of the identified enhancer aberrations. We identified a total of 2,051 aberrant enhancer-associated DEGs (AE-DEGs), which were highly concurrent in multiple HCC datasets. The enrichment results indicated the significant overrepresentations of crucial biological processes and pathways implicated in cancer among these AE-DEGs. A six AE-DEG-based prognostic signature, whose ability to predict the overall survival of HCC was superior to that of both clinical phenotypes and previously published similar prognostic signatures, was established and validated in TCGA-LIHC and ICGC-LIRI cohorts, respectively. In summary, our integrative analysis depicted a landscape of aberrant enhancers and associated transcriptional dysregulation in HCC and established an aberrant enhancer-derived prognostic signature with excellent predictive accuracy, which might be beneficial for the future development of epigenetic therapy for HCC.

Metabolism-Associated Gene Signatures for FDG Avidity on PET/CT and Prognostic Validation in Hepatocellular Carcinoma.

IntroductionThe prognostic value of F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) in hepatocellular carcinoma (HCC) was established in previous reports. However, there is no evidence suggesting the prognostic value of transcriptomes associated with tumor FDG uptake in HCC. It was aimed to elucidate metabolic genes and functions associated with FDG uptake, followed by assessment of those prognostic value.MethodsSixty HCC patients with Edmondson–Steiner grade II were included. FDG PET/CT scans were performed before any treatment. RNA sequencing data were obtained from tumor and normal liver tissue. Associations between each metabolism-associated gene and tumor FDG uptake were investigated by Pearson correlation analyses. A novel score between glucose and lipid metabolism-associated gene expression was calculated. In The Cancer Genome Atlas Liver Hepatocellular Carcinoma dataset, the prognostic power of selected metabolism-associated genes and a novel score was evaluated for external validation.ResultsNine genes related to glycolysis and the HIF-1 signaling pathway showed positive correlations with tumor FDG uptake; 21 genes related to fatty acid metabolism and the PPAR signaling pathway demonstrated negative correlations. Seven potential biomarker genes, PFKFB4, ALDOA, EGLN3, EHHADH, GAPDH, HMGCS2, and ENO2 were identified. A metabolic gene expression balance score according to the dominance between glucose and lipid metabolism demonstrated good prognostic value in HCC.ConclusionsThe transcriptomic evidence of this study strongly supports the prognostic power of FDG PET/CT and indicates the potential usefulness of FDG PET/CT imaging biomarkers to select appropriate patients for metabolism-targeted therapy in HCC.

Overexpression of transcription factor 3 drives hepatocarcinoma development by enhancing cell proliferation via activating Wnt signaling pathway

BackgroundTranscription factor 3 (TCF3) plays pivotal roles in embryonic development, stem cell maintenance and carcinogenesis. However, its role in hepatocellular carcinoma (HCC) remains largely unknown. This study aimed to analyze the correlation between TCF3 expression and clinicopathological features of HCC, and further explore the underlying mechanism in HCC progression. MethodsThe expression of TCF3 was collected from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) HCC datasets, and further confirmed by immunostaining and Western blotting assays. The correlation between TCF3 expression and the clinicopathological features was evaluated. Bioinformatical analysis and in vitro experiments were conducted to explore the potential role of TCF3 in HCC development. ResultsBoth the mRNA and protein levels of TCF3 were significantly higher in HCC tumor tissues compared to tumor adjacent tissues (P < 0.001 and P < 0.01). Analysis based on TCGA datasets showed that TCF3 was positively correlated with tumor clinical stage and grade, and patients with high TCF3 expression had shorter overall survival (P = 0.012), disease-specific survival (P = 0.022) and progression-free survival (P = 0.013). Similarly, the immunostaining results revealed that the high expression of TCF3 was closely correlated with tumor size (P = 0.001) and TNM stage (P = 0.002), and TCF3 was an independent risk factor of HCC. In vitro study exhibited that TCF3 knockdown dramatically suppressed cancer cell proliferation, and the underlying mechanism might be that the silencing of TCF3 reduced the expression of critical regulating proteins towards cell cycle and proteins involved in Wnt signaling pathways. ConclusionsTCF3 expression is significantly elevated in HCC and positively associated with the tumor size and TNM stage, as well as poor prognosis of HCC patients. The mechanism might be that TCF3 promotes cancer cell proliferation via activating Wnt signaling pathway.

Long non‑coding RNA LINC00238 suppresses the malignant phenotype of liver cancer by sponging miR‑522.

Long non-coding RNAs can regulate the malignant tumor phenotype either as tumor suppressors or oncogenes. The present study investigated the underlying mechanism of LINC00238 in liver cancer. LINC00238 was identified as a downregulated molecule in The Cancer Genome Atlas liver hepatocellular carcinoma dataset through Gene Expression Profiling Interactive Analysis software. Through gain- and loss-of-function experiments, LINC00238 was confirmed as a tumor suppressor that could not only decrease cell viability, migration and invasion in vitro, but also tumorigenesis and tumor metastasis in vivo. By cytoplasmic and nuclear RNA isolation, LINC00238 was confirmed to be predominantly cytoplasmic. Mechanistically, RNA pull-down assays showed that LINC00238 sponged microRNA (miR)-522 and then reversed the inhibitory effects on two downstream targets, secreted frizzled related protein 2 and dickkopf1. Collectively, LINC00238 was identified as a tumor suppressor that acts via sponging miR-522 followed by silencing of downstream targets, suggesting that LINC00238 may have a key role in suppressing the malignant phenotype of liver cancer cells.

Identification and Verification of Ubiquitin D as a Gene Associated with Hepatitis C Virus-Induced Hepatocellular Carcinoma.

Accumulated studies have suggested that hepatitis C virus (HCV) infection is one of the leading causes for hepatocellular carcinoma (HCC). However, the mechanisms underlying the effect of HCV on the occurrence of HCC are still poorly understood. HCV infection datasets (GSE82177 and GSE17856) and HCC datasets (The Cancer Genome Atlas Liver Hepatocellular Carcinoma and GSE89377) were downloaded from Gene Expression Omnibus or TCGA for analysis. The common differentially expressed genes in the above four datasets were identified by R software. The expression of ubiquitin D (UBD) in HCV-infected HepG2 cells was detected by RT-qPCR and Western blot, respectively. The interaction between NS3 and p53 was detected by co-immunoprecipitation. The influence of UBD on the proliferation and migration ability of HepG2 cells was evaluated by CCK-8 and wound healing assay, respectively. UBD was upregulated in both HCV-infected samples and HCC samples. HCV NS3 interacted with p53 and inhibited its expression. HCV NS3-induced UBD promoted the proliferation and migration of HepG2 cells. Our results suggest that HCV NS3-induced UBD is positively correlated with the development of HCV-related HCC during HCV infection. Targeting UBD could be a potential strategy for preventing and treating HCV-induced HCC.

Ago-RIP Sequencing Identifies New MicroRNA-449a-5p Target Genes Increasing Sorafenib Efficacy in Hepatocellular Carcinoma.

BACKGROUND: Patients with hepatocellular carcinoma (HCC) have very limited treatment options. For the last fourteen years, the multi-tyrosine kinase inhibitor sorafenib has been used as standard-of-care therapeutic agent in advanced HCC. Unfortunately, drug resistance develops in many cases. Therefore, we aimed to find a way to mitigate drug resistance and to improve the sorafenib efficacy in HCC cells. MicroRNAs play a significant role in targeting genes involved in tumor control suggesting microRNA/sorafenib combination therapy as a promising treatment option in advanced HCC.METHODS: MiR-449a-5p target genes were identified by Ago-RIP sequencing and validated by luciferase reporter assays and expression analyses. Target gene expression and survival data were analyzed in public HCC datasets. Tumor-relevant functional effects of miR-449a-5p and its target genes as well as their impact on the effects of sorafenib were analyzed using in vitro assays. An indirect transwell co-culture system was used to survey anti-angiogenic effects of miR-449a-5p.RESULTS: PEA15, PPP1CA and TUFT1 were identified as direct target genes of miR-449a-5p. Overexpression of these genes correlated with a poor outcome of HCC patients. Transfection with miR-449a-5p and repression of miR-449a-5p target genes inhibited cell proliferation and angiogenesis, induced apoptosis and reduced AKT and ERK signaling in HLE and Huh7 cells. Importantly, miR-449a-5p potentiated the efficacy of sorafenib in HCC cells via downregulation of PEA15, PPP1CA and TUFT1.CONCLUSIONS: This study provides detailed insights into the targetome and regulatory network of miR-449a-5p. Our results demonstrate for the first time that targeting PEA15, PPP1CA and TUFT1 via miR-449a overexpression could have significant implications in counteracting sorafenib resistance suggesting miR-449a-5p as a promising candidate for a microRNA/sorafenib combination therapy.

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients.

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

CommPath: An R package for inference and analysis of pathway-mediated cell-cell communication chain from single-cell transcriptomics

Single-cell transcriptomics offers opportunities to investigate ligand-receptor (LR) interactions between heterogeneous cell populations within tissues. However, most existing tools for the inference of intercellular communication do not allow prioritization of functional LR associations that provoke certain biological responses in the receiver cells. In addition, current tools do not enable the identification of the impact on the downstream cell types of the receiver cells. We present CommPath, an open-source R package and webserver, to analyze and visualize the LR interactions and pathway-mediated intercellular communication chain with single-cell transcriptomic data. CommPath curates a comprehensive signaling pathway database to interpret the consequences of LR associations and therefore infers functional LR interactions. Furthermore, CommPath determines cell-cell communication chain by considering both the upstream and downstream cells of user-defined cell populations. Applying CommPath to human hepatocellular carcinoma dataset shows its ability to decipher complex LR interaction patterns and the associated intercellular communication chain, as well as their changes in disease versus homeostasis.

Transcriptomic identification of HBx-associated hub genes in hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies around the world. Among the risk factors involved in liver carcinogenesis, hepatitis B virus (HBV) X protein (HBx) is considered to be a key regulator in hepatocarcinogenesis. Whether HBx promotes or protects against HCC remains controversial, therefore exploring new HBx-associated genes is still important.MethodsHBx was overexpressed in HepG2, HepG2.2.15 and SMMC-7721 cell lines, primary mouse hepatocytes and livers of C57BL/6N mice. High-throughput RNA sequencing profiling of HepG2 cells with HBx overexpression and related differentially-expressed genes (DEGs), pathway enrichment analysis, protein-protein interaction networks (PPIs), overlapping analysis were conducted. In addition, Gene Expression Omnibus (GEO) and proteomic datasets of HBV-positive HCC datasets were used to verify the expression and prognosis of selected DEGs. Finally, we also evaluated the known oncogenic role of HBx by oncogenic array analysis.ResultsA total of 523 DEGs were obtained from HBx-overexpressing HepG2 cells. Twelve DEGs were identified and validated in cells transiently transfected with HBx and three datasets of HBV-positive HCC transcription profiles. In addition, using the Kaplan-Meier plotter database, the expression levels of the twelve different genes were further analyzed to predict patient outcomes.ConclusionAmong the 12 identified HBx-associated hub genes, HBV-positive HCC patients expressing ARG1 and TAT showed a good overall survival (OS) and relapse-free survival (RFS). Thus, ARG1 and TAT expression could be potential prognostic markers.

Comparison of parametric and semi-parametric models with randomly right-censored data by weighted estimators: Two applications in colon cancer and hepatocellular carcinoma datasets.

In this study, parametric and semi-parametric regression models are examined for random right censorship. The components of the aforementioned regression models are estimated with weights based on Cox and Kaplan-Meier estimates, which are semi-parametric and nonparametric methods used in survival analysis, respectively. The Tobit based on weights obtained from a Cox regression is handled as a parametric model instead of other parametric models requiring distribution assumptions such as exponential, Weibull, and gamma distributions. Also, the semi-parametric smoothing spline and the semi-parametric smoothing kernel estimators based on Kaplan-Meier weights are used. Therefore, estimates are obtained from two models with flexible approaches. To show the flexible shape of the models depending on the weights, Monte Carlo simulations are conducted, and all results are presented and discussed. Two empirical datasets are used to show the performance of the aforementioned estimators. Although three approaches gave similar results to each other, the semi-parametric approach was slightly superior to the parametric approach. The parametric approach method, on the other hand, yields good results in medium and large sample sizes and at a high censorship level. All other findings have been shared and interpreted.

Network-based prioritization of cancer biomarkers by phenotype-driven module detection and ranking

This paper describes an ensemble method with supervised module detection and further module prioritization for reliable network-based biomarker discovery. We design a module detection and ranking method called mRank to discover reliable network modules as cancer diagnostic biomarkers, with two procedures: (1) an iterative supervised module detection guided by phenotypic states in a specific network, (2) a block-based module ranking locally and globally via network topological centrality. We validate its effectiveness and efficiency by identifying hepatocellular carcinoma (HCC) network modules on a comprehensive gene regulatory network with specifying gene interactions by HCC RNA-seq data from the Cancer Genome Atlas (TCGA). These top-ranked modules by mRank get a mean AUC of 0.995 on TCGA HCC dataset with 371 tumor samples and 50 controls by cross-validation SVM. Based on the prior knowledge of cancer dysfunctions enriched in top-ranked modules, 69 genes are identified as HCC candidate biomarkers. They are further validated in independent cohorts with a classifier trained on TCGA HCC dataset. A mean AUC of 0.846 is achieved in distinguishing 976 disease samples from 827 controls. Moreover, some known HCC signatures such as AFP and SPP1 are also included in our identified biomarkers. mRank enables us to find more reliable network modules for cancer diagnosis. For a proof-of-concept study, we validate it in identifying HCC network biomarkers and it is generalizable to other cancers or complex disease. The overall results have demonstrated that mRank can find effective network biomarkers for cancer diagnosis which result in less false positives.

ANTXR1 as a potential prognostic biomarker for hepatitis B virus-related hepatocellular carcinoma identified by a weighted gene correlation network analysis.

With high incidence and mortality rates, hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors worldwide. Chronic hepatitis B virus (HBV) infection is a leading cause of HCC, especially for Asians and blacks. However, the molecular mechanisms underlying HBV-related HCC are unclear. This study sought to identify novel prognostic biomarkers and explore the potential pathogenesis of HBV-related HCC. The gene expression profiles and corresponding clinical information of HCC from The Cancer Genome Atlas Liver Hepatocellular Carcinoma data set were analyzed by a weighted gene co-expression network analysis. Correlations between the co-expression modules and clinical traits were calculated. Next, key modules associated with HBV infection were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were conducted for the genes in the key modules. The hub genes were identified based on the protein-protein interaction (PPI) network via the Cytoscape. Finally, an overall survival (OS) analysis was performed. The two modules (i.e., the brown and yellow modules) most relevant to HBV infection were constructed. A functional enrichment analysis revealed that the genes in the two modules were mainly enriched in HCC-related pathways, such as the phosphatidylinositol-3-kinase and protein kinase B signaling pathway, focal adhesion, human papillomavirus infection, the Rap1 signaling pathway, and the cyclic guanosine monophosphate-dependent protein kinase (cGMP-PKG) signaling pathway. Ten hub genes [i.e., COL3A1, ANTXR1, COL14A1, THBS2, ADAMTS2, AEBP1, PRELP, EMILIN1, DCN and PODN] in the brown module, and 10 hub genes [i.e., USP34, SEC24C, ZNF770, STAG1, TSTD2, PKD1P6, CCNK, GFT2I, NT5C2 and SMG6] in the yellow module were identified. Among the hub genes, ANTXR1 (Anthrax-toxin receptor 1) was significantly correlated with HBV-related HCC patients' OS. ANTXR1 represents a potential therapeutic target for HBV-related HCC. This study offers novel insights into the molecular mechanisms of HBV-induced tumorigenesis, which needs to be further validated by basic experiments and large-scale cohort studies.

Identification of the Immune Cell Infiltration Landscape in Hepatocellular Carcinoma to Predict Prognosis and Guide Immunotherapy.

Background: Globally, hepatocellular carcinoma (HCC) is the sixth most frequent malignancy with a high incidence and a poor prognosis. Immune cell infiltration (ICI) underlies both the carcinogenesis and immunogenicity of tumors. However, a comprehensive classification system based on the immune features for HCC remains unknown. Methods: The HCC dataset from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts was used in this study. The ICI patterns of 571 patients were characterized using two algorithms: the patterns were determined based on the ICI using the ConsensusClusterPlus package, and principal component analysis (PCA) established the ICI scores. Differences in the immune landscape, biological function, and somatic mutations across ICI scores were evaluated and compared, followed by a predictive efficacy evaluation of ICI scores for immunotherapy by the two algorithms and validation using an external immunotherapy cohort. Results: Based on the ICI profile of the HCC patients, three ICI patterns were identified, including three subtypes having different immunological features. Individual ICI scores were determined; the high ICI score subtype was characterized by enhanced activation of immune-related signaling pathways and a significantly high tumor mutation burden (TMB); concomitantly, diminished immunocompetence and enrichment of pathways associated with cell cycle and RNA degradation were found in the low ICI score subtype. Taken together, our results contribute to a better understanding of an active tumor and plausible reasons for its poor prognosis. Conclusion: The present study reveals that ICI scores may serve as valid prognostic biomarkers for immunotherapy in HCC.

A Minimal Subset of Seven Genes Associated with Tumor Hepatocyte Differentiation Predicts a Poor Prognosis in Human Hepatocellular Carcinoma.

Simple SummaryLiver cancer is one of the most commonly diagnosed cancers worldwide and the fourth leading cause of cancer-related deaths. Hepatocellular carcinoma (HCC) accounts for at least 80% of all malignant liver primary tumors. A better characterization of molecular mechanisms underlying HCC onset and progression may lead to discover new therapeutic targets and biomarkers. In this study, we performed an integrative transcriptomics analysis to evaluate the clinical relevance of genes associated with hepatocyte differentiation in human HCC. The HepaRG cell line model was used to define a gene expression signature reflecting the status of tumor hepatocyte differentiation. This signature was able to stratify HCC patients into clinically relevant molecular subtypes. Then, a minimal subset of seven differentiation-associated genes was identified to predict a poor prognosis in several cancer datasets.Hepatocellular carcinoma (HCC) is a deadly cancer worldwide as a result of a frequent late diagnosis which limits the therapeutic options. Tumor progression in HCC is closely correlated with the dedifferentiation of hepatocytes, the main parenchymal cells in the liver. Here, we hypothesized that the expression level of genes reflecting the differentiation status of tumor hepatocytes could be clinically relevant in defining subsets of patients with different clinical outcomes. To test this hypothesis, an integrative transcriptomics approach was used to stratify a cohort of 139 HCC patients based on a gene expression signature established in vitro in the HepaRG cell line using well-controlled culture conditions recapitulating tumor hepatocyte differentiation. The HepaRG model was first validated by identifying a robust gene expression signature associated with hepatocyte differentiation and liver metabolism. In addition, the signature was able to distinguish specific developmental stages in mice. More importantly, the signature identified a subset of human HCC associated with a poor prognosis and cancer stem cell features. By using an independent HCC dataset (TCGA consortium), a minimal subset of seven differentiation-related genes was shown to predict a reduced overall survival, not only in patients with HCC but also in other types of cancers (e.g., kidney, pancreas, skin). In conclusion, the study identified a minimal subset of seven genes reflecting the differentiation status of tumor hepatocytes and clinically relevant for predicting the prognosis of HCC patients.