Gene expression analysis of biopsy samples reveals critical limitations of transcriptome-based molecular classifications of hepatocellular carcinoma

Unveiling miRNA-Gene Regulatory Axes as Promising Biomarkers for Liver Cirrhosis and Hepatocellular Carcinoma

Liver cirrhosis, a severe scarring condition of the liver with the potential to progress to hepatocellular carcinoma (HCC), necessitates the development of reliable biomarkers for early detection due to the asymptomatic nature of its early stages. Recent discoveries in microRNAs (miRNAs) hold promise for a noninvasive test, with the potential to significantly improve patient outcomes. Building upon these promising findings, this study investigates gene expression data, identifying distinct sets of DEGs and DEMs using GEO2R. Subsequently, a gene-miRNA network was constructed using Cytoscape to explore potential interactions between DEMs and their target genes (DEGs). Boxplot analysis was carried out to identify and validate differences in gene expression between healthy and diseased tissues. This analysis revealed four significantly differentially expressed genes: CAV1, PEA15, EMP1, and ENAH. Notably, subsequent survival analysis demonstrated that EMP1 and ENAH significantly impact overall patient survival. Intriguingly, the constructed network identified several potential regulatory axes: hsa-miR-191-5p/ENAH, hsa-miR-3158-3p/ENAH, hsa-miR-371a-5p/ENAH, and hsa-miR-6753-5p/EMP1. Crucially, a direct comparison of DEGs and DEMs between liver cirrhosis and HCC pinpointed AGO3, NCOA3, and TNPO1, along with their regulatory elements, as potential key drivers of HCC development in cirrhotic patients, underscoring their importance as targets for early diagnostic and therapeutic strategies. Finally, immunohistochemical (IHC) analysis not only validates our findings but also reiterates the novelty of the identified genes. Overall, elucidating the role of these novel genes and regulatory elements could pave the way for an earlier and more accurate diagnosis of liver diseases.

Oncogenic role of PMEPA1 and its association with immune exhaustion and TGF-β activation in HCC

Background & Aims

Transforming growth factor β (TGF-β) plays an oncogenic role in advanced cancer by promoting cell proliferation, metastasis and immunosuppression. PMEPA1 (prostate transmembrane protein androgen induced 1) has been shown to promote TGF-β oncogenic effects in other tumour types. Thus, we aimed to explore the role of PMEPA1 in hepatocellular carcinoma (HCC).

Methods

We analysed 1,097 tumours from patients with HCC, including discovery (n = 228) and validation (n = 361) cohorts with genomic and clinicopathological data. PMEPA1 levels were assessed by qPCR (n = 228), gene expression data (n = 869) and at the single-cell level (n = 54). Genetically engineered mouse models overexpressing MYC+PMEPA1 compared to MYC were generated and molecular analyses were performed on the HCCs obtained.

Results

PMEPA1 was overexpressed in 18% of HCC samples (fold-change >2; n = 201/1,097), a feature associated with TGF-β signalling activation (p <0.05) and absence of gene body hypomethylation (p <0.01). HCCs showing both TGF-β signalling and high PMEPA1 levels (12% of cases) were linked to immune exhaustion, late TGF-β activation, aggressiveness and higher recurrence rates after resection, in contrast to HCCs with only TGF-β signalling (8%) or PMEPA1 overexpression (9%). Single-cell RNA sequencing analysis identified PMEPA1 expression in HCC and stromal cells. PMEPA1-expressing tumoural cells were predicted to interact with CD4+ regulatory T cells and CD4+ CXCL13+ and CD8+ exhausted T cells. In vivo, overexpression of MYC+PMEPA1 led to HCC development in ∼60% of mice and a decreased survival compared to mice overexpressing MYC alone (p = 0.014). MYC+PMEPA1 tumours were enriched in TGF-β signalling, paralleling our human data.

Conclusions

In human HCC, PMEPA1 upregulation is linked to TGF-β activation, immune exhaustion, and an aggressive phenotype. Overexpression of PMEPA1+MYC led to tumoural development in vivo, demonstrating the oncogenic role of PMEPA1 in HCC for the first time.

Impact and implications

PMEPA1 can enhance the tumour-promoting effects of TGF-β in cancer. In this study, we demonstrate that PMEPA1 is highly expressed in ∼18% of patients with hepatocellular carcinoma (HCC), a feature associated with poor prognosis, TGF-β activation and exhaustion of immune cells. Similarly, in mouse models, PMEPA1 overexpression promotes HCC development, which demonstrates its oncogenic role. The identification of PMEPA1 as oncogenic driver in HCC and its role in immune exhaustion and poor clinical outcomes enhances our understanding of HCC pathogenesis and opens new avenues for targeted therapeutic interventions.

ACSL6-activated IL-18R1-NF-κB promotes IL-18-mediated tumor immune evasion and tumor progression

Aberrant activation of IL-18 signaling regulates tumor immune evasion and progression. However, the underlying mechanism remains unclear. Here, we report that long-chain acyl-CoA synthase 6 (ACSL6) is highly expressed in liver cancer and correlated with poor prognosis. ACSL6 promotes tumor growth, metastasis, and immune evasion mediated by IL-18, independent of its metabolic enzyme activity. Mechanistically, upon IL-18 stimulation, ACSL6 is phosphorylated by ERK2 at S674 and recruits IL-18RAP to interact with IL-18R1, thereby reinforcing the IL-18R1-IL-18RAP heterodimer and triggering NF-κB-dependent gene expression to facilitate tumor development. Furthermore, the up-regulation of CXCL1 and CXCL5 by ACSL6 promotes tumor-associated neutrophil and tumor-associated macrophage recruitment, thereby inhibiting cytotoxic CD8+ T cell infiltration. Ablation or S674A mutation of ACSL6 potentiated anti-PD-1 therapeutic efficacy by increasing the effector activity of intertumoral CD8+ T cells. We revealed that ACSL6 is a potential adaptor that activates IL-18-NF-κB axis-mediated tumor immune evasion and provides valuable insights for developing effective immunotherapy strategies for cancer.

Exploration of the role of EMC3‑AS1 as a potential diagnostic and prognostic indicator in liver cancer

The aim of the present study was to evaluate the diagnostic and prognostic significance of the long non-coding RNA (lncRNA) endoplasmic reticulum membrane protein complex subunit 3 antisense RNA 1 (EMC3-AS1) in liver cancer, and its impact on the proliferative and invasive capabilities of liver cancer cells. EMC3-AS1 expression in liver cancer was assessed using data from The Cancer Genome Atlas and three Gene Expression Omnibus datasets, and validated in clinical liver cancer samples using reverse transcription-quantitative PCR. The prognostic and diagnostic potentials of this lncRNA were evaluated using Kaplan-Meier and receiver operating characteristic analyses, respectively. The infiltration of immune cells and differential expression of immune checkpoints (ICs) between high- and low-EMC3-AS1 expression groups were investigated. Therapeutic correlation analyses were also undertaken to assess the impact of EMC3-AS1 in the treatment of liver cancer. In addition, in vitro experiments were conducted using small interfering RNA to knock down the expression of EMC3-AS1 in HepG2, Sk-Hep-1 and Huh-7 cells, and evaluate the effect on cell proliferation, colony formation and migration. The results revealed a significant upregulation of EMC3-AS1 expression in liver cancer tissues compared with that in adjacent normal tissues, which was associated with an unfavorable prognosis and demonstrated diagnostic effectiveness for patients with liver cancer. Furthermore, patients with high EMC3-AS1 expression exhibited increased levels of IC markers in comparison with those with low EMC3-AS1 expression. In addition, EMC3-AS1 was indicated to have clinical significance in the prediction of the response to immunotherapy and chemotherapy. Notably, the in vitro experiments demonstrated that the knockdown of EMC3-AS1 significantly hindered cell proliferation, colony formation and migration. Consequently, it was concluded that EMC3-AS1 is upregulated in liver cancer and serves as a prognostic indicator for unfavorable outcomes in patients with liver cancer. Additionally, targeting EMC3-AS1 through knockdown interventions showed potential in mitigating the ability of liver cancer cells to proliferate and migrate, which highlights its dual role as a biomarker and therapeutic target for liver cancer.

Theranostic Fluorescent Probes

The ability to gain spatiotemporal information, and in some cases achieve spatiotemporal control, in the context of drug delivery makes theranostic fluorescent probes an attractive and intensely investigated research topic. This interest is reflected in the steep rise in publications on the topic that have appeared over the past decade. Theranostic fluorescent probes, in their various incarnations, generally comprise a fluorophore linked to a masked drug, in which the drug is released as the result of certain stimuli, with both intrinsic and extrinsic stimuli being reported. This release is then signaled by the emergence of a fluorescent signal. Importantly, the use of appropriate fluorophores has enabled not only this emerging fluorescence as a spatiotemporal marker for drug delivery but also has provided modalities useful in photodynamic, photothermal, and sonodynamic therapeutic applications. In this review we highlight recent work on theranostic fluorescent probes with a particular focus on probes that are activated in tumor microenvironments. We also summarize efforts to develop probes for other applications, such as neurodegenerative diseases and antibacterials. This review celebrates the diversity of designs reported to date, from discrete small-molecule systems to nanomaterials. Our aim is to provide insights into the potential clinical impact of this still-emerging research direction.

Identification of BMAL1-Regulated circadian genes in mouse liver and their potential association with hepatocellular carcinoma: Gys2 and Upp2 as promising candidates

Identification and functional analysis of key genes regulated by the circadian clock system will provide a comprehensive understanding of the underlying mechanisms through which circadian clock disruption impairs the health of living organisms. The initial phase involved bioinformatics analysis, drawing insights from three RNA-seq datasets (GSE184303, GSE114400, and GSE199061) derived from wild-type mouse liver tissues, which encompassed six distinct time points across a day. As expected, 536 overlapping genes exhibiting rhythmic expression patterns were identified. By intersecting these genes with differentially expressed genes (DEGs) originating from liver RNA-seq data at two representative time points (circadian time, CT: CT2 and CT14) in global Bmal1 knockout mice (Bmal1-/-), hepatocyte-specific Bmal1 knockout mice (L-Bmal1-/-), and their corresponding control groups, 80 genes potentially regulated by BMAL1 (referred to as BMAL1-regulated genes, BRGs) were identified. These genes were significantly enriched in glycolipid metabolism, immune response, and tumorigenesis pathways. Eight BRGs (Nr1d1, Cry1, Gys2, Homer2, Serpina6, Slc2a2, Nmrk1, and Upp2) were selected to validate their expression patterns in both control and L-Bmal1-/- mice livers over 24 h. Real-time quantitative polymerase chain reaction results demonstrated a comprehensive loss of rhythmic expression patterns in the eight selected BRGs in L-Bmal1-/- mice, in contrast to the discernible rhythmic patterns observed in the livers of control mice. Additionally, significant reductions in the expression levels of these selected BRGs, excluding Cry1, were also observed in L-Bmal1-/- mice livers. Chromatin immunoprecipitation (ChIP)-seq (GSE13505 and GSE39860) and JASPAR analyses validated the rhythmic binding of BMAL1 to the promoter and intron regions of these genes. Moreover, the progression of conditions, from basic steatosis to non-alcoholic fatty liver disease, and eventual malignancy, demonstrated a continuous gradual decline in Bmal1 transcripts in the human liver. Combining the aforementioned BRGs with DEGs derived from human liver cancer datasets identified Gys2 and Upp2 as potential node genes bridging the circadian clock system and hepatocellular carcinoma (HCC). In addition, CCK8 and wound healing assays demonstrated that the overexpression of human GYS2 and UPP2 proteins inhibited the proliferation and migration of HepG2 cells, accompanied by elevated expression of p53, a tumor suppressor protein. In summary, this study systematically identified rhythmic genes in the mouse liver, and a subset of circadian genes potentially regulated by BMAL1. Two circadian genes, Gys2 and Upp2, have been proposed and validated as potential candidates for advancing the prevention and treatment of HCC.

Comprehensive analysis of epigenetic and epitranscriptomic genes' expression in human NAFLD

Non-alcoholic fatty liver disease (NAFLD) is a multifactorial condition with a complex etiology. Its incidence is increasing globally in parallel with the obesity epidemic, and it is now considered the most common liver disease in Western countries. The precise mechanisms underlying the development and progression of NAFLD are complex and still poorly understood. The dysregulation of epigenetic and epitranscriptomic mechanisms is increasingly recognized to play pathogenic roles in multiple conditions, including chronic liver diseases. Here, we have performed a comprehensive analysis of the expression of epigenetic and epitranscriptomic genes in a total of 903 liver tissue samples corresponding to patients with normal liver, obese patients, and patients with non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH), advancing stages in NAFLD progression. We integrated ten transcriptomic datasets in an unbiased manner, enabling their robust analysis and comparison. We describe the complete landscape of epigenetic and epitranscriptomic genes' expression along the course of the disease. We identify signatures of genes significantly dysregulated in association with disease progression, particularly with liver fibrosis development. Most of these epigenetic and epitranscriptomic effectors have not been previously described in human NAFLD, and their altered expression may have pathogenic implications. We also performed a comprehensive analysis of the expression of enzymes involved in the metabolism of the substrates and cofactors of epigenetic and epitranscriptomic effectors. This study provides novel information on NAFLD pathogenesis and may also guide the identification of drug targets to treat this condition and its progression towards hepatocellular carcinoma.

GBAP1 functions as a tumor promotor in hepatocellular carcinoma via the PI3K/AKT pathway

Hepatocellular carcinoma (HCC) is common worldwide, and novel therapeutic targets and biomarkers are needed to improve outcomes. In this study, bioinformatics analyses combined with in vitro and in vivo assays were used to identify the potential therapeutic targets. Differentially expressed genes (DEG) in HCC were identified by the intersection between The Cancer Genome Atlas and International Cancer Genome Consortium data. The DEGs were evaluated by a gene set enrichment analysis as well as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. A protein interaction network, univariate Cox regression, and Lasso regression were used to screen out hub genes correlated with survival. Increased expression of the long noncoding RNA GBAP1 in HCC was confirmed in additional datasets and its biological function was evaluated in HCC cell lines and nude mice. Among 121 DEGs, GBAP1 and PRC1 were identified as hub genes with significant prognostic value. Overexpression of GBAP1 in HCC was confirmed in 21 paired clinical tissues and liver cancer or normal cell lines. The inhibition of GBAP1 expression reduced HCC cell proliferation and promoted apoptosis by inactivating the PI3K/AKT pathway in vitro and in vivo. Therefore, GBAP1 has a pro-oncogenic function in HCC and is a candidate prognostic biomarker and therapeutic target.

Analysis of the expression and prognostic significance of DDK complex in Hepatocarcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains one of the most common and lethal malignancies worldwide. Although DBF4-dependent kinase (DDK) complex composed of CDC7 kinase and its regulatory subunit DBF4 has been shown to be overexpressed in primary tumors and promotes tumor development, while its role and prognostic value in HCC remain largely unknown. In the present study, the expression of DBF4 and CDC7 and their relationship with clinical characteristics were comprehensively analyzed.

METHODS

The mRNA expression profiles of HCC and the corresponding clinical data of HCC patients were downloaded from TCGA and GEO databases, respectively. The differences in DBF4 and CDC7 expression in tumor tissues and adjacent normal tissues were analyzed. HCC-derived tissue microarray (TMA) was used to evaluate and score the expression of CDC7 by immunohistochemistry (IHC) staining. The Kaplan-Meier method and the Cox regression method were used to analyze the relationship between overall survival and clinical characteristics of the patients. Gene set enrichment analysis (GSEA) was used to analyze the pathway enrichment of DBF4 and CDC7.

RESULTS

DBF4 and CDC7 had similar expression patterns in HCC patients. Detailly, compared with adjacent tissues, both mRNA and protein of DBF4 and CDC7 were significantly higher in HCC, and their expression was positively correlated with AJCC_T stage, clinical stage and G stage (grade) of liver cancer patients, and higher DBF4 or CDC7 expression predicted a worse prognosis in HCC patients with shorter overall survival (OS), recurrence-free survival (RFS), disease-specific survival (DSS) and progress-free survival (PFS). Cox regression analysis suggested that both DBF4 and CDC7 were independent risk factors for the prognosis of HCC patients in TCGA dataset. GSEA suggested that both DBF4 and CDC7 were positively correlated with cell cycle and DNA replication. Finally, the prognostic value of CDC7 was furtherly confirmed by TMA-based IHC staining results.

CONCLUSIONS

Our study showed that DDK complex was significantly increased in HCC. Both DBF4 and CDC7 may be potential diagnostic and prognostic markers for HCC, and high expression of DDK members predicts a worse prognosis in patients with HCC, which may be associated with high tumor cell proliferation rate.

Systemic analysis identifying PVT1 / DUSP13 axis for microvascular invasion in hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is an independent detrimental risk factor for tumor recurrence and poor survival in hepatocellular carcinoma (HCC). Competitive endogenous RNA (ceRNA) networks play a pivotal role in the modulation of carcinogenesis and progression among diverse tumor types. However, whether the ceRNA mechanisms are engaged in promoting the MVI process in patients with HCC remains unknown.

METHODS

A ceRNA regulatory network was constructed based on RNA-seq data of patients with HCC from The Cancer Genome Atlas (TCGA) database. In total, 10 hub genes of the ceRNA network were identified using four algorithms: "MCC," "Degree," "Betweenness," and "Stress." Transcriptional expressions were verified by in situ hybridization using clinical samples. Interactions between ceRNA modules were validated by luciferase reporting assay. Logistic regression analysis, correlation analysis, enrichment analysis, promoter region analysis, methylation analysis, and immune infiltration analysis were performed to further investigate the molecular mechanisms and clinical transformation value.

RESULTS

The ceRNA regulatory network featuring a tumor invasion phenotype consisting of 3 long noncoding RNAs, 3 microRNAs, and 93 mRNAs was constructed using transcriptional data from the TCGA database. Systemic analysis and experimentally validation identified a ceRNA network (PVT1/miR-1258/DUSP13 axis) characterized by lipid regulatory potential, immune properties, and abnormal methylation states in patients with HCC and MVI. Meanwhile, 28 transcriptional factors were identified as potential promotors of PVT1 with 3 transcriptional factors MXD3, ZNF580, and KDM1A promising as therapeutic targets in patients with HCC and MVI. Furthermore, miR-1258 was an independent predictor for MVI in patients with HCC.

CONCLUSION

The PVT1/DUSP13 axis is significantly associated with MVI progression in HCC patients. This study provides new insight into mechanisms related to lipids, immune phenotypes, and abnormal epigenetics in oncology research.

PCLassoLog: A protein complex-based, group Lasso-logistic model for cancer classification and risk protein complex discovery

Risk gene identification has attracted much attention in the past two decades. Since most genes need to be translated into proteins and cooperate with other proteins to form protein complexes to carry out cellular functions, which significantly extends the functional diversity of individual proteins, revealing the molecular mechanism of cancer from a comprehensive perspective needs to shift from identifying individual risk genes toward identifying risk protein complexes. Here, we embed protein complexes into the regularized learning framework and propose a protein complex-based, group Lasso-logistic model (PCLassoLog) to discover risk protein complexes. Experiments on deep proteomic data of two cancer types show that PCLassoLog yields superior predictive performance on independent datasets. More importantly, PCLassoLog identifies risk protein complexes that not only contain individual risk proteins but also incorporate close partners that synergize with them. Furthermore, selection probabilities are calculated and two other protein complex-based models are proposed to complement PCLassoLog in identifying reliable risk protein complexes. Based on PCLassoLog, a pan-cancer analysis is performed to identify risk protein complexes in 12 cancer types. Finally, PCLassoLog is used to discover risk protein complexes associated with gene mutation. We implement all protein complex-based models as an R package PCLassoReg, which may serve as an effective tool to discover risk protein complexes in various contexts.

CD97 serves as a novel biomarker of immune cell infiltration in hepatocellular carcinoma

BACKGROUND

CD97 is the most widely expressed G protein-coupled receptor in the epidermal growth factor seven-span transmembrane family. It plays a vital role in cell adhesion, migration, and cell connection regulation. We explored the role of CD97 in hepatocellular carcinoma (HCC).

METHODS

We evaluated CD97 mRNA expression in HCC using TNMplot and the Gene Expression Omnibus database. The clinical prognostic significance of CD97 in HCC patients was evaluated by gene expression profiling interactive analysis, the Kaplan-Meier plotter, and the UALCAN database. The Tumor Immune Estimation Resource (TIMER) and CIBERSORT databases were used to analyze the relationships among CD97, genes positively related with CD97, and tumor-infiltrating immune cells.

RESULTS

CD97 was highly expressed in HCC tissues and was associated with an adverse prognosis. CD97 and genes positively related with CD97 were positively correlated with the abundance of tumor-infiltrating immune cells and strongly correlated with tumor-infiltrating macrophages (all r ≥ 0.513, P < 0.001). CD97 was positively correlated with M2 macrophage and tumor-associated macrophage markers (both r ≥ 0.464, P < 0.001). CD97 was found to be an immune-related gene in HCC and positively correlated with the TOX, PD-L1, PD-L2, CTLA4, and PD-1 immune checkpoint genes. CD97 copy number alterations affect the level of immune cell infiltration and mRNA expression.

CONCLUSIONS

CD97 can be used as a potential molecular marker of prognosis in HCC, which is associated with immune cell infiltration.

Borealin Promotes Tumor Growth and Metastasis by Activating the Wnt/β-Catenin Signaling Pathway in Hepatocellular Carcinoma

Background and Aims

Hepatocellular carcinoma (HCC) is a common malignant disease with high morbidity and mortality throughout the world. While Borealin is a putative oncogene that is dysregulated in multiple tumors, its exact role in HCC remains less investigated.

Methods

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) assays were employed to examine the relative amount of Borealin. Gene set enrichment analysis (GSEA) and other bioinformatic analyses were implemented to probe into the potential functions of Borealin. The biological roles and mechanisms of Borealin in the tumorigenesis and development of HCC were further evaluated using a battery of functional assays in vivo and in vitro.

Results

Borealin was enhanced in the HCC tissue samples and hepatoma cells when compared with the nontumor tissues and normal liver cells. Higher Borealin expression was positively linked with advanced pathological phenotypes and inferior overall survival. The overexpression of Borealin promoted the cells' abilities on proliferation, invasion and epithelial-mesenchymal transition (EMT) in vitro, facilitated tumor growth and lung metastasis in vivo, whereas the silencing of Borealin inhibited these capabilities in vitro. Furthermore, Borealin interacted with β-catenin and further activated the Wnt/β-catenin signaling pathway, which endowed HCC cells with highly aggressive and metastatic capabilities.

Conclusion

Borealin was identified as an oncogene that could promote HCC growth and metastasis by activating the WNT/β-catenin signaling pathway. These findings extended the understanding of Borealin in HCC tumorigenesis and development and highlighted the significance of Borealin in HCC diagnosis and treatment.

Acyl-CoA Binding Domain Containing 4 Polymorphism rs4986172 and Expression Can Serve as Overall Survival Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma Patients After Hepatectomy

Background

The aim of our study was to evaluate the potential of expression and single nucleotide polymorphism of Acyl-CoA binding domain containing 4 (ACBD4) gene as prognosis biomarkers in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after hepatectomy.

Methods

HBV-related HCC patients from the First Affiliated Hospital of Guangxi Medical University and GSE14520 were included in the current study, as well as The Cancer Genome Atlas (TCGA) HCC verification cohort. Prognostic analysis and multiple functional enrichment analysis methods were used to evaluate the prognostic value and potential biological functions of the ACBD4 gene in HBV-related HCC.

Results

We found that ACBD4 gene is highly expressed in normal liver tissues and markedly down-regulated in HBV-related HCC tissues. ACBD4 gene was significantly related to overall survival (OS) of HCC in TCGA and GSE14520 cohorts, and patients with low ACBD4 expression were markedly related to poor OS. Rs4986172 was observed as an OS biomarker after hepatectomy in the Guangxi HBV-related HCC cohort. The OS of rs4986172 GG genotype was worse than that of HCC patients with A allele (AA and AG genotypes). Multifunctional enrichment analysis suggested that ACBD4 gene is closely related to the metabolic, peroxisome proliferator-activated receptor and cytochrome P450 pathway. Through connectivity map, we also identified eight compounds that may be used as targeted therapeutic agents for ACBD4 gene in HBV-related HCC; these compounds were scopoletin, alfaxalone, bephenium hydroxynaphthoate, apramycin, 4,5-dianilinophthalimide, DL-thiorphan, aminohippuric acid and quinidine. Immune microenvironment analysis revealed that there were significant differences in immune scores of HBV-related HCC tumor tissues with different ACBD4 expression levels.

Conclusion

Our study reveals that ACBD4 expression and rs4986172 can be serve as biomarkers of OS in HBV-related HCC patients after hepatectomy.

Gene expression signature as a surrogate marker of microvascular invasion on routine hepatocellular carcinoma biopsies

BACKGROUND & AIMS

Microvascular invasion (MVI), a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma (HCC), is only detectable by microscopic examination of the surgical specimen. We aimed to define a transcriptomic signature associated with MVI in HCC than can be applied to formalin-fixed paraffin-embedded (FFPE) biopsies for use in clinical practice.

METHODS

To identify a gene expression signature related to MVI by using NanoString technology, we selected a set of 200 genes according to the literature and RNA-sequencing data obtained from a cohort of 150 frozen HCC samples previously published. We used 178 FFPE-archived HCC samples, including 109 surgical samples for the training set and 69 paired pre-operative biopsies for the validation set. In 14 cases of the training set, a paired biopsy was available and was also analyzed.

RESULTS

We identified a 6-gene signature (ROS1, UGT2B7, FAS, ANGPTL7, GMNN, MKI67) strongly associated with MVI in the training set of FFPE surgical HCC samples, with 82% accuracy (sensitivity 82%, specificity 81%, AUC 0.82). The NanoString gene expression was highly correlated in 14 paired surgical/biopsy HCC samples (mean R: 0.97). In the validation set of 69 FFPE HCC biopsies, the 6-gene NanoString signature predicted MVI with 74% accuracy (sensitivity 73%, specificity 76%, AUC 0.74). Moreover, on multivariate analysis, the MVI signature was associated with overall survival in both sets (hazard ratio 2.29; 95% CI 1.03-5.07; p = 0.041).

CONCLUSION

We defined a 6-gene signature that can accurately predict MVI in FFPE HCC biopsy samples, which is also associated with overall survival, although its survival impact must be confirmed by extensive study with further clinical data.

LAY SUMMARY

Microvascular invasion, a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma, is only detectable by microscopic examination of a surgical specimen. In this study, we defined a relevant surrogate signature of microvascular invasion in hepatocellular carcinoma that may be applied in clinical practice with routine tumor biopsy and integrated into the therapeutic strategy.

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.

A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system

Hepatocellular carcinoma (HCC) is a malignancy with a dismal survival rate. The novel autoantibodies panel may provide new insights for the diagnosis of HCC. Biomarkers screened by two methods (bioinformatics and the antigen‐antibody system) were taken as candidate tumor‐associated antigens (TAAs). Enzyme‐linked immunosorbent assay was used to detect the corresponding autoantibodies in 888 samples of verification and validation cohorts. The verification cohort was used to verify the autoantibodies. Samples in the validation cohort were randomly divided into a train set and a test set with the ratio of 6:4. A diagnostic model was established by support vector machines within the train set. The test set further verified the model. Eleven TAAs were selected (AAGAB, C17orf75, CDC37L1, DUSP6, EID3, PDIA2, RGS20, PCNA, TAF7L, TBC1D13, and ZIC2). The titer of six autoantibodies (PCNA, AAGAB, CDC37L1, TAF7L, DUSP6, and ZIC2) had a significant difference in any of the pairwise comparisons among the HCC, liver cirrhosis, and normal control groups. The titer of these autoantibodies had an increasing tendency. Finally, an optimum diagnostic model was constructed with the six autoantibodies. The AUCs were 0.826 in the train set and 0.773 in the test set. The area under the curve (AUC) of this panel for diagnosing early HCC was 0.889. The diagnostic ability of the panel reduced with the progress of HCC. The positive rate of the panel in diagnosing alpha‐fetoprotein (AFP)‐negative patients was 75.6%. For early HCC, the sensitivity of the combination of AFP with the panel was 90.9% and superior to 53.2% of AFP alone. The novel immunodiagnosis panel combining AFP may be a new approach for the diagnosis of HCC, especially for early‐HCC cases.

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.

METHODS

Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein-protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.

RESULTS

A total of 197 DEGs were identified. The protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.

CONCLUSION

Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.

Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

Objective

The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC).

Methods

Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes.

Results

We identified 11 hub genes (CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway.

Conclusions

In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

Prognostic and immunological potential of PPM1G in hepatocellular carcinoma

Liver hepatocellular carcinoma (LIHC) remains one of the most common causes of cancer death. Prior research suggested that the PPM1G gene is involved in LIHC. To explore the role of PPM1G in LIHC, we used several online databases. Expression profiling was performed via the Gene Expression Profiling Interactive Analysis (GEPIA), Hepatocellular Carcinoma Database (HCCDB), Oncomine and Human Protein Atlas (HPA) platforms. Mutation profiles were investigated via cBio Cancer Genomics Portal (cBioPortal). Survival analysis was performed via the Kaplan-Meier (KM) plotter and International Cancer Genome Consortium (ICGC) platforms. The biological function of PPM1G was analyzed via the Enrichr database. The influence of PPM1G expression in the tumor immune microenvironment was assessed via Tumor Immune Estimation Resource (TIMER). PPM1G expression was upregulated in various tumors, including LIHC. Overexpression of PPM1G was associated with poor prognosis in LIHC. PPM1G expression might be regulated by promoter methylation, copy number variations (CNVs) and kinases and correlate with immune infiltration. The gene ontology (GO) terms associated with high PPM1G expression were mRNA splicing and the cell cycle. The results suggest that PPM1G is correlated with the prognosis of LIHC patients and associated with the tumor immune microenvironment in LIHC.

Identification of Hub Genes and Their Correlation With Immune Infiltration Cells in Hepatocellular Carcinoma Based on GEO and TCGA Databases

Hepatocellular carcinoma (HCC) is a primary liver cancer with extremely high mortality in worldwide. HCC is hard to diagnose and has a poor prognosis due to the less understanding of the molecular pathological mechanisms and the regulation mechanism on immune cell infiltration during hepatocarcinogenesis. Herein, by performing multiple bioinformatics analysis methods, including the RobustRankAggreg (RRA) rank analysis, weighted gene co-expression network analysis (WGCNA), and a devolution algorithm (CIBERSORT), we first identified 14 hub genes (NDC80, DLGAP5, BUB1B, KIF20A, KIF2C, KIF11, NCAPG, NUSAP1, PBK, ASPM, FOXM1, TPX2, UBE2C, and PRC1) in HCC, whose expression levels were significantly up-regulated and negatively correlated with overall survival time. Moreover, we found that the expression of these hub genes was significantly positively correlated with immune infiltration cells, including regulatory T cells (Treg), T follicular helper (TFH) cells, macrophages M0, but negatively correlated with immune infiltration cells including monocytes. Among these hub genes, KIF2C and UBE2C showed the most significant correlation and were associated with immune cell infiltration in HCC, which was speculated as the potential prognostic biomarker for guiding immunotherapy.

Non-viral gene delivery of the oncotoxic protein NS1 for treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is related to increasing incidence rates and poor clinical outcomes due to lack of efficient treatment options and emerging resistance mechanisms. The aim of the present study is to exploit a non-viral gene therapy enabling the expression of the parvovirus-derived oncotoxic protein NS1 in HCC. This anticancer protein interacts with different cellular kinases mediating a multimodal host-cell death. Lipoplexes (LPX) designed to deliver a DNA expression plasmid encoding NS1 are characterized using a comprehensive set of in vitro assays. The mechanisms of cell death induction are assessed and phosphoinositide-dependent kinase 1 (PDK1) is identified as a potential predictive biomarker for a NS1-LPX-based gene therapy. In an HCC xenograft mouse model, NS1-LPX therapeutic approach results in a significant reduction in tumor growth and extended survival. Data provide convincing evidence for future studies using a targeted NS1 gene therapy for PDK1 overexpressing HCC.

A blood-based prognostic liver secretome signature and long-term hepatocellular carcinoma risk in advanced liver fibrosis

Background

Accurate non-invasive prediction of long-term hepatocellular carcinoma (HCC) risk in advanced liver fibrosis is urgently needed for cost-effective HCC screening; however, this currently remains an unmet need.

Methods

A serum-protein-based prognostic liver secretome signature (PLSec) was bioinformatically derived from previously validated hepatic transcriptome signatures and optimized in 79 patients with advanced liver fibrosis. We independently validated PLSec for HCC risk in 331 cirrhosis patients with mixed etiologies (validation set 1 [V1]) and thereafter developed a score with clinical prognostic variables. The score was then validated in two independent cohorts: validation set 2 (V2): 164 patients with advanced liver fibrosis due to hepatitis C virus (HCV) infection cured after direct-acting antiviral therapy; validation set 3 (V3): 146 patients with advanced liver fibrosis with successfully-treated HCC and cured HCV infection.

Findings

An 8-protein blood-based PLSec recapitulated transcriptome-based hepatic HCC risk status. In V1, PLSec was significantly associated with incident HCC risk (adjusted hazard ratio [aHR], 2.35; 95% confidence interval [CI], 1.30-4.23). A composite score with serum alpha-fetoprotein (PLSec-AFP) was defined in V1, and validated in V2 (adjusted odds ratio, 3.80 [95%CI, 1.66-8.66]) and V3 (aHR, 3.08 [95%CI, 1.78-5.31]; c-index, 0.74). PLSec-AFP outperformed AFP alone (Brier score, 0.165 vs. 0.186 in V2; 0.196 vs. 0.206 in V3, respectively).

Conclusions

The blood-based PLSec-AFP can accurately stratify patients with advanced liver fibrosis for long-term HCC risk and thereby guide risk-based tailored HCC screening.

Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

Background

Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers.

Methods

We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively.

Results

With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients.

Conclusions

In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08173-0.

EZH2 is a negative prognostic biomarker associated with immunosuppression in hepatocellular carcinoma

The enhancer of zeste homolog 2 (EZH2) plays a critical role in different components of anti-tumor immunity. However, the specific role of EZH2 in modulating MHC Class I antigen presentation and T cell infiltration have not been investigated in HCC. This study analyzed the expression and clinical significance of EZH2 in HCC. The EZH2 genetic alterations were identified using cBioPortal. The EZH2 mRNA and protein levels were found to be significantly higher in HCC than in adjacent normal liver tissues in multiple datasets from the GEO and TCGA databases. High expression of EZH2 was significantly correlated with poor overall survival, disease-specific survival, progression-free survival, and relapse-free survival in almost all patients with HCC. The gene set variance analysis (GSVA) showed that the expression of EZH2 is positively correlated with an immunosuppressive microenvironment and negatively correlated with major MHC class I antigen presentation molecules. Gene set enrichment analysis (GSEA) showed that high EZH2 expression is positively associated with the MYC and glycolysis signaling pathway and negatively associated with the interferon-gamma signaling pathway in HCC tissues. These findings demonstrate that EZH2 is a potential prognostic biomarker and therapeutic target in HCC.

PRELP has prognostic value and regulates cell proliferation and migration in hepatocellular carcinoma

Purpose: Hepatocellular carcinoma (HCC) is an aggressive and prevalent tumor threatening human health. A previous study suggested low PRELP (proline/arginine-rich end leucine-rich repeat protein) expression was associated with poor patient survival in pancreatic ductal adenocarcinoma (PDAC). However, the role of PRELP in HCC has not yet been illuminated. Methods: PRELP expression analyses were carried out using transcriptomic datasets from the Integrative Molecular Database of Hepatocellular Carcinoma (HCCDB). The correlations between PRELP expression and clinicopathological features, and prognostic analyses were performed with a tissue microarray (TMA) and immunohistochemistry (IHC). The endogenous expression and in vitro roles of PRELP were investigated in cultured HCC cell lines. The potential mechanisms were characterized by a Gene Set Enrichment Analysis (GSEA) and gene-gene correlation analyses. Results: We found that PRELP mRNA expression was dramatically decreased in HCCs in comparison with that in adjacent normal tissues (NTs) or hepatic cirrhosis. IHC staining showed that PRELP was down-regulated in HCCs, which mainly located in cytoplasm, and was also found in nuclei. The correlation analyses revealed that PRELP expression was relevant to later p-stages (p= 0.028) and tumor size (p= 0.001). The overall survival (OS) and relapse free survival (RFS) time was shorter in HCC patients with lower PRELP expression levels than that with higher PRELP expression levels. Overexpression of PRELP inhibited, while knockdown of PRELP promoted proliferation and migration of HCC cells. For potential mechanisms, PRELP may inhibit progression of HCCs by interacting with integrin family members and the extracellular microenvironment. Conclusion: Our findings demonstrated that overexpression of PRELP correlates with better patient survival and inhibits both cell proliferation and migration in HCC. Therefore, PRELP can serve as a potential prognostic biomarker and therapeutic target which deserves further investigation.

Identification of APEX2 as an oncogene in liver cancer

BACKGROUND

DNA damage is one of the critical contributors to the occurrence and development of some cancers. APEX1 and APEX2 are the most important molecules in the DNA damage, and APEX1 has been identified as a diagnostic and prognostic biomarker in liver hepatocellular carcinoma (LIHC). However, the expression of APEX2 and its functional mechanisms in LIHC are still unclear.

AIM

To examine the expression of APEX2 and the potential mechanism network in LIHC.

METHODS

We conducted a pan-cancer analysis of the expression of APEX1 and APEX2 using the interactive TIMER tool. GEO datasets, including GSE14520, GSE22058, and GSE64041, were used to compare the APEX2 expression level in tumor tissues and adjacent non-tumor tissues. Then, we calculated the 5-year survival rate according to the web-based Kaplan-Meier analysis. We included the TCGA liver cancer database in GSEA analysis based on the high and low APEX2 expression, showing the potential mechanisms of APEX2 in LIHC. After that, we conducted Pearson correlation analysis using GEPIA2. Next, we performed quantitative polymerase chain reaction (qPCR) assay to examine the APEX2 levels in normal liver cell line LO2 and several liver cancer cell lines, including HepG2, Huh7, SMMC7721, and HCCLM3. APEX2 in HCCLM3 cells was knocked down using small interfering RNA. The role of APEX2 in cell viability was confirmed using CCK-8. Dual-luciferase reporter assay was performed to examine the promoter activity of CCNB1 and MYC.

RESULTS

APEX1 and APEX2 are both highly expressed in the tumor tissues of BLCA, BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, READ, and STAD. APEX2 overexpression in LIHC was validated using GSE14520, GSE22058, and GSE64041 datasets. The survival analysis showed that LIHC patients with high expression of APEX2 had a lower overall survival rate, even in the AJCC T1 patients. High level of APEX2 could indicate a lower overall survival rate in patients with or without viral hepatitis. The GSEA analysis identified that kinetochore and spindle microtubules are the two main cellular components of APEX2 in GO Ontology. APEX2 was also positively associated with molecular function regulation of chromosome segregation and DNA replication. The results of KEGG analysis indicated that APEX2 expression was positively correlated with cell cycle pathway and pro-oncogenic MYC signaling. Pearson correlation analysis showed that APEX2 had a significant positive correlation with CCNB1 and MYC. APEX2 level was higher in liver cancer cell lines than in normal liver LO2 cells. Small interfering RNA could knock down the APEX2 expression in HCCLM3 cells. Knockdown of APEX2 resulted in a decrease in the viability of HCCLM3 cells as well as the expression and promoter activity of CCNB1 and MYC.

CONCLUSION

APEX2 is overexpressed in LIHC, and the higher APEX2 level is associated with a worse prognosis in overall survival. APEX2 is closely involved in the biological processes of chromosome segregation and DNA replication. APEX2 expression is positively correlated with the pro-oncogenic pathways. Knockdown of APEX2 could inhibit the cell viability and CCNB1 and MYC pathways, suggesting that APEX2 is an oncogene in LIHC, which could be a potential pharmaceutic target in the anti-tumor therapy.

Higher expression of cell division cycle-associated protein 5 predicts poorer survival outcomes in hepatocellular carcinoma

The upregulation of cell division cycle associated protein 5 (CDCA5) has been observed in various cancer types. However, the prognostic value of CDCA5 and its underlying mechanism contributing to tumorigenesis in hepatocellular carcinoma (HCC) remain poorly understood. We used tissue microarray (TMA) to evaluate the prognosis of 304 HCC samples based on their CDCA5 expression, and analyzed the genomic features correlated with CDCA5 by using dataset from The Cancer Genome Atlas (TCGA). Compared with adjacent normal tissues, increased expression of CDCA5 was found in HCC tissues. Moreover, higher expression of CDCA5 was associated with inferior OS and DFS outcomes in HCC patients. The enrichment plots showed that the gene signatures in cell cycle, DNA replication and p53 pathways were enriched in patients with higher CDCA5 expression. Meanwhile, statistically higher mutations burdens in TP53 could also be observed in CDCA5-high patients. Integrative analysis based on miRNAseq and methylation data demonstrated a potential association between CDCA5 expression and epigenetic changes. In conclusion, our study provided the evidence of CDCA5 as an oncogenic promoter in HCC and the potential function of CDCA5 in affecting tumor microenvironment.

CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis

Background

Hepatocellular carcinoma (HCC) is characterized by increased mortality and poor prognosis. We aimed to identify potential prognostic markers by weighted gene coexpression network analysis (WGCNA), to assist clinical outcome prediction and improve treatment decisions for HCC patients.

Methods

Prognosis-related gene modules were first established by WGCNA. Venn diagrams obtained intersection genes of module genes and differentially expressed genes. The Kaplan-Meier overall survival curves and disease-free survival curves of intersection genes were further analyzed on the Gene Expression Profiling Interactive Analysis website. Chi-square tests were performed to explore the associations between prognostic gene expressions and clinicopathological features.

Results

CCNB2, TOP2A, and ASPM were identified as both prognosis-related genes and differentially expressed genes. TOP2A (HR: 1.7, P = 0.003) and ASPM (HR: 1.8, P < 0.001) exhibited a significant difference between the high- and low-expression groups in the overall survival analysis, while CCNB2 (HR: 1.4, P = 0.052) was not statistically significant. CCNB2 (HR: 1.5, P = 0.006), TOP2A (HR: 1.7, P < 0.001), and ASPM (HR: 1.6, P = 0.003) were all statistically significant in the disease-free survival analysis. All three genes were significantly associated with race and fetoprotein values (P < 0.05). CCNB2 expression was associated with tumor stage (P = 0.01), and ASPM expression was associated with new tumor events (P = 0.03).

Conclusion

Overexpression of CCNB2, TOP2A, and ASPM are associated with poor prognosis, and these genes could serve as potential prognostic markers and therapeutic targets for HCC.

Identification of the possible therapeutic targets in the insulin-like growth factor 1 receptor pathway in a cohort of Egyptian hepatocellular carcinoma complicating chronic hepatitis C type 4

Background:

Molecular targeted drugs are the first line of treatment of advanced hepatocellular carcinoma (HCC) due to its chemo- and radioresistant nature. HCC has several well-documented etiologic factors that drive hepatocarcinogenesis through different molecular pathways. Currently, hepatitis C virus (HCV) is a leading cause of HCC. Therefore, we included a unified cohort of HCV genotype 4-related HCCs to study the expression levels of genes involved in the insulin-like growth factor 1 receptor (IGF1R) pathway, which is known to be involved in all aspects of cancer growth and progression.

Aim:

Determine the gene expression patterns of IGF1R pathway genes in a cohort of Egyptian HCV-related HCCs. Correlate them with different patient/tumor characteristics. Determine the activity status of involved pathways.

Methods:

Total ribonucleic acid (RNA) was extracted from 32 formalin-fixed paraffin-embedded tissues of human HCV-related HCCs and 6 healthy liver donors as controls. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) using RT² Profiler PCR Array for Human Insulin Signaling Pathway was done to determine significantly up- and downregulated genes with identification of most frequently coregulated genes, followed by correlation of gene expression with different patient/tumor characteristics. Finally, canonical pathway analysis was performed using the Ingenuity Pathway Analysis software.

Results:

Six genes – AEBP1, AKT2, C-FOS, PIK3R1, PRKCI, SHC1 – were significantly overexpressed. Thirteen genes – ADRB3, CEBPA, DUSP14, ERCC1, FRS3, IGF2, INS, IRS1, JUN, MTOR, PIK3R2, PPP1CA, RPS6KA1 – were significantly underexpressed. Several differentially expressed genes were related to different tumor/patient characteristics. Nitric oxide and reactive oxygen species production pathway was significantly activated in the present cohort, while the growth hormone signaling pathway was inactive.

Conclusions:

The gene expression patterns identified in this study may serve as possible therapeutic targets in HCV-related HCCs. The most frequently coregulated genes may serve to guide combined molecular targeted therapies. The IGF1R pathway showed evidence of inactivity in the present cohort of HCV-related HCCs, so targeting this pathway in therapy may not be effective.

Molecular Mechanism of Aflatoxin-Induced Hepatocellular Carcinoma Derived from a Bioinformatics Analysis

Exposure to aflatoxin is considered to be one of the causes of hepatocellular carcinoma (HCC). With the development of bioinformation, we sought to reveal the occurrence and development of aflatoxin-induced HCC through data research. We identified differentially expressed genes (DEGs) of datasets GSE127791 (Aflatoxin-treated pluripotent stem cell derived human hepatocytes vs. controls) and GSE64041 (liver carcinoma with unknown cause vs. non-cancerous tissue) by GEO2R to find the common DEGs. Gene ontology (GO) and KEGG path enrichment analysis were used to annotate the function of DEGs. Hub genes were screened from identified DEGs by protein-protein interaction (PPI) network analysis. The prognostic value of hub genes in cancer databases were evaluated. We obtained 132 common DEGs and 11 hub genes. According to cluster analysis and protein co-expression networks, we screened out the key genes, histidine-rich glycoprotein (HRG) and phosphoenolpyruvate carboxykinase 2 (PCK2). Oncomine database and survival curve analysis showed that the decline in HRG and PCK2 expression in the development of HCC indicated poor prognosis. We speculated that the decreased expression of HRG and PCK2 after aflatoxin exposure to hepatocyte may be related to aflatoxin induced hepatocyte injury and carcinogenesis. In addition, the decreased expression of HRG and PCK2 in the occurrence and development of HCC suggests a poor prognosis of HCC.

Metabolism-associated molecular classification of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a disease with unique management complexity because it displays high heterogeneity of molecular phenotypes. We herein aimed to characterize the molecular features of HCC by the development of a classification system that was based on the gene expression profile of metabolic genes. Integrative analysis was performed with a metadata set featuring 371 and 231 HCC human samples from the Cancer Genome Atlas and the International Cancer Genome Consortium, respectively. All samples were linked with clinical information. RNA sequencing data of 2752 previously characterized metabolism-related genes were used for non-negative matrix factorization clustering, and three subclasses of HCC (C1, C2, and C3) were identified. We then analyzed the metadata set for metabolic signatures, prognostic value, transcriptome features, immune infiltration, clinical characteristics, and drug sensitivity of subclasses, and compared the resulting subclasses with previously published classifications. Subclass C1 displayed high metabolic activity, low α-fetoprotein (AFP) expression, and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high expression of immune checkpoint genes, demonstrating drug sensitivity toward cytotoxic T-lymphocyte-associated protein-4 inhibitors and the receptor tyrosine kinase inhibitor cabozantinib. Subclass C3 displayed intermediate metabolic activity, high AFP expression level, and bad prognosis. Finally, a 90-gene classifier was generated to enable HCC classification. This study establishes a new HCC classification based on the gene expression profiles of metabolic genes, thereby furthering the understanding of the genetic diversity of human HCC.

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91–0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

BACKGROUND

Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC.

METHODS

We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice.

RESULTS

Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice.

CONCLUSION

This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

Data mining of the expression and regulatory role of BCAT1 in hepatocellular carcinoma

Branched chain amino acid transaminase 1 (BCAT1) catalyzes the production of glutamates and branched-chain α-ketoacids from branched chain amino acids, and a normal BCAT1 expression is associated with tumorigenesis. Sequencing data from public databases, including The Cancer Genome Atlas, was used to analyze BCAT1 expression and regulation networks for hepatocellular carcinoma (HCC). Expression and methylation were assessed using UALCAN analysis, and data from multiple datasets concerning the BCAT1 expression level and associated survival rates were further analyzed using HCCDB; interaction networks of biological function were constructed using GeneMANIA. LinkedOmics was used to indicate correlations between BCAT1 and any identified differentially expressed genes. Gene enrichment analysis of BCAT-associated genes was conducted using the Web-based Gene SeT AnaLysis Toolkit. The expression levels of BCAT1 were increased in patients with HCC and in most cases, the level of BCAT1 promoter methylation was reduced. Interaction network analysis suggested that BCAT1 was involved in ‘metabolism’, ‘carcinogenesis’ and the ‘immune response’ via numerous cancer-associated pathways. The present study revealed the expression patterns and potential function networks of BCAT1 in HCC, providing insights for future research into the role of BCAT1 in hepatocarcinogenesis. In addition, the study provided researchers with a way to analyze the genes of interest so they can continue their research in the right direction.

Downregulation of exosomal CLEC3B in hepatocellular carcinoma promotes metastasis and angiogenesis via AMPK and VEGF signals

Background

C-Type Lectin Domain Family 3 Member B (CLEC3B), is down-regulated in serum and tumor tissues in different cancers including hepatocellular carcinoma (HCC). However, the functions of CLEC3B in HCC remains elucidated. The aim of this study is to analyze the roles of CLEC3B in HCC.

Methods

The expression of genes was evaluated by immunohistochemistry, western blot, real-time PCR, enzyme-linked immunosorbent assays, and analysis on TCGA-LIHC database and gene expression omnibus. Transmission electron microscopy and immunofluorescence were applied to detect CLEC3B in exosomes. The function of exosomal CLEC3B in tumor progression were performed in vivo and in vitro.

Results

We determined that down-regulated CLEC3B in HCC indicated a poor prognosis. Exosomes derived from HCC with down-regulated CLEC3B promoted migration, invasion, epithelial–mesenchymal transition of both tumor cells and endothelial cells (ECs). Moreover, the downregulation CLEC3B in exosomes suppressed VEGF secretion in both HCC cells and ECs, and eventually inhibited angiogenesis. Mechanistically, CLEC3B-mediated VEGF expression in tumor cells and ECs depends on the activation of AMPK signal pathway.

Conclusion

This study demonstrates that CLEC3B acts as a novel independent prognostic factor, and CLEC3B in exosomes might be a potential therapeutic target for hepatocellular carcinoma.

Graphical abstract

Electronic supplementary material

The online version of this article (10.1186/s12964-019-0423-6) contains supplementary material, which is available to authorized users.

Pathological changes are associated with shifts in the employment of synonymous codons at the transcriptome level

BACKGROUND

The usage of different synonymous codons reflects the genome organization and has been connected to parameters such as mRNA abundance and protein folding. It is also been established that mutations targeting specific synonymous codons can trigger disease.

RESULTS

We performed a systematic meta-analysis of transcriptome results from 75 datasets representing 40 pathologies. We found that a subset of codons was preferentially employed in abundant transcripts, while other codons were preferentially found in low-abundance transcripts. By comparing control and pathological transcriptomes, we observed a shift in the employment of synonymous codons for every analyzed disease. For example, cancerous tissue employed preferentially A- or U-ending codons, shifting from G- or C-ending codons, which were preferred by control tissues. This analysis was able to discriminate patients and controls with high specificity and sensitivity.

CONCLUSIONS

Here we show that the employment of specific synonymous codons, quantified at the whole transcriptome level, changes profoundly in many diseases. We propose that the changes in codon employment offer a novel perspective for disease studies, and could be used to design new diagnostic tools.

Pseudogene RACGAP1P activates RACGAP1/Rho/ERK signalling axis as a competing endogenous RNA to promote hepatocellular carcinoma early recurrence

Accumulating evidence has indicated crucial roles for pseudogenes in human cancers. However, the roles played by pseudogenes in the pathogenesis of HCC, particularly HCC early recurrence, still incompletely elucidated. Herein, we identify a novel early recurrence related pseudogene RACGAP1P which was significantly upregulated in HCC and was associated with larger tumour size, advanced clinical stage, abnormal AFP level and shorter survival time. In vitro and in vivo experiments have shown that RACGAP1P is a prerequisite for the development of malignant characteristics of HCC cells, including cell growth and migration. Mechanistic investigations indicated that RACGAP1P elicits its oncogenic activity as a ceRNA to sequestrate miR-15-5p from its endogenous target RACGAP1, thereby leading to the upregulation of RACGAP1 and the activation of RhoA/ERK signalling. These results may provide new insights into the functional crosstalk of the pseudogene/miRNA/parent-gene genetic network during HCC early relapse and may contribute to improving the clinical intervention for this subset of HCC patients.

Identifying hepatocellular carcinoma-related hub genes by bioinformatics analysis and CYP2C8 is a potential prognostic biomarker

Hepatocellular carcinoma (HCC) is one type of the most common malignancies. However, the underlying molecular mechanisms involved in the development of HCC remain unknown. To identify the candidate genes in the progression of HCC, gene expression profiles GSE14520, GSE54236, GSE57957 and GSE64041 were downloaded from the Gene Expression Omnibus database (GEO). A total of 405 tumor and 399 para-carcinoma samples from patients with HCC were examined to identify the differentially expressed genes (DEGs), followed by function enrichment analyses including Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. A total of 78 DEGs were screened, including 62 downregulated genes and 16 upregulated genes. Subsequently, the protein-protein interaction network (PPI) was constructed using the Search Tool for Retrieval of Interacting Genes (STRING) database. The module analysis and Hub genes validation were performed using Cytoscape software. Hierarchical clustering of hub genes was evaluated using UCSC Cancer Genomics Browser. Survival analyses of Hub genes were performed using Kaplan Meier Plotter database. Genes specifically expressed in the liver were analyzed using GENEVESTIGATOR database. CYP2C8 was identified as one of the most promising molecules among all the candidate genes. The expression profile of CYP2C8 in HCC was analyzed using ONCOMINE and UALCAN database. The expression levels of CYP2C8 in HCC samples and hepatoma cells were verified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry analysis. In summary, DEGs and hub genes were identified in the present study, which provides novel insight on the development of HCC. CYP2C8 was downregulated in HCC and could be a potential prognostic biomarker.

Screening and function analysis of hub genes and pathways in hepatocellular carcinoma via bioinformatics approaches

BACKGROUND

Liver carcinoma is a major cause of cancer-related death worldwide. Up to date, the mechanisms of liver cancerigenesis and development have not been fully understood. Multi-genes and pathways were involved in the tumorigenesis of liver cancer.

OBJECTIVE

The aim of the present study was to screen key genes and pathways in liver cancerigenesis and development by using bioinformatics methods.

METHODS

A dataset GSE64041 were retrieved from GEO database and the differentially expressed genes (DEGs) were screened out. Then the DEG functions were annotated by gene ontology (GO) and pathway enrichment analysis, respectively. The hub genes were further selected by protein-protein interaction (PPI) analysis. Afterwards, the mRNA and protein expressions as well as the prognostic values of the hub genes were assessed.

RESULTS

As a result, 208 up-regulated and 82 down-regulated genes were screened out. These DEGs were mainly enriched in cell cycle and metabolism-related pathways. Through PPI analysis, TOP2A, PRDM10, CDK1, AURKA, BUB1, PLK1, CDKN3, NCAPG, BUB1B and CCNA2 were selected as hub genes, which were all over-expressed in liver cancers relative to those in normal tissues, respectively. Among them, PLK1 and CCNA2 were suggested to be prognostic factors for liver carcinoma.

CONCLUSION

In conclusion, the present study identified several hub genes, and cell cycle and metabolism-related pathways that may play critical roles in the tumorigenesis of liver cancer. Future validation laboratory experiments are required to confirm the results.

A qualitative signature for early diagnosis of hepatocellular carcinoma based on relative expression orderings

BACKGROUND & AIMS

Currently, using biopsy specimens to confirm suspicious liver lesions of early hepatocellular carcinoma are not entirely reliable because of insufficient sampling amount and inaccurate sampling location. It is necessary to develop a signature to aid early hepatocellular carcinoma diagnosis using biopsy specimens even when the sampling location is inaccurate.

METHODS

Based on the within-sample relative expression orderings of gene pairs, we identified a simple qualitative signature to distinguish both hepatocellular carcinoma and adjacent non-tumour tissues from cirrhosis tissues of non-hepatocellular carcinoma patients.

RESULTS

A signature consisting of 19 gene pairs was identified in the training data sets and validated in 2 large collections of samples from biopsy and surgical resection specimens. For biopsy specimens, 95.7% of 141 hepatocellular carcinoma tissues and all (100%) of 108 cirrhosis tissues of non-hepatocellular carcinoma patients were correctly classified. Especially, all (100%) of 60 hepatocellular carcinoma adjacent normal tissues and 77.5% of 80 hepatocellular carcinoma adjacent cirrhosis tissues were classified to hepatocellular carcinoma. For surgical resection specimens, 99.7% of 733 hepatocellular carcinoma specimens were correctly classified to hepatocellular carcinoma, while 96.1% of 254 hepatocellular carcinoma adjacent cirrhosis tissues and 95.9% of 538 hepatocellular carcinoma adjacent normal tissues were classified to hepatocellular carcinoma. In contrast, 17.0% of 47 cirrhosis from non-hepatocellular carcinoma patients waiting for liver transplantation were classified to hepatocellular carcinoma, indicating that some patients with long-lasting cirrhosis could have already gained hepatocellular carcinoma characteristics.

CONCLUSIONS

The signature can distinguish both hepatocellular carcinoma tissues and tumour-adjacent tissues from cirrhosis tissues of non-hepatocellular carcinoma patients even using inaccurately sampled biopsy specimens, which can aid early diagnosis of hepatocellular carcinoma.

DNA topoisomerase 1 and 2A function as oncogenes in liver cancer and may be direct targets of nitidine chloride

The aim of the present study was to determine the role of topoisomerase 1 (TOP1) and topoisomerase 2A (TOP2A) in liver cancer (LC), and to investigate the inhibitory effect of nitidine chloride (NC) on these two topoisomerases. Immunohistochemistry (IHC) staining and microarray or RNA sequencing data mining showed markedly higher expression of TOP1 and TOP2A at the protein and mRNA levels in LC tissues compared with that in control non-tumor tissues. The prognostic values of TOP1 and TOP2A expression were also estimated based on data from The Cancer Genome Atlas. The elevated expression levels of TOP1 and TOP2A were closely associated with poorer overall survival and disease-free survival rates. When patients with LC were divided into high- and low-risk groups according to their prognostic index, TOP1 and TOP2A were highly expressed in the high-risk group. Bioinformatics analyses conducted on the co-expressed genes of TOP1 and TOP2A revealed that the topoisomerases were involved in several key cancer-related pathways, including the 'p53 pathway', 'pathway in cancer' and 'apoptosis signaling pathway'. Reverse transcription-quantitative polymerase chain reaction and IHC performed on triplicate tumor tissue samples from LC xenografts in control or NC-treated nude mice showed that NC treatment markedly reduced the protein and mRNA expression of TOP1 and TOP2A in LC tissues. Molecular docking studies further confirmed the direct binding of NC to TOP1 and TOP2A. In conclusion, the present findings indicate that TOP1 and TOP2A are oncogenes in LC and could serve as potential biomarkers for the prediction of the prognosis of patients with LC and for identification of high-risk cases, thereby optimizing individual treatment management. More importantly, the findings support TOP1 and TOP2A as potential drug targets of NC for the treatment of LC.

Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets

Multiple studies suggested using different miRNAs as biomarkers for prognosis of hepatocellular carcinoma (HCC). We aimed to assemble a miRNA expression database from independent datasets to enable an independent validation of previously published prognostic biomarkers of HCC. A miRNA expression database was established by searching the TCGA (RNA-seq) and GEO (microarray) repositories to identify miRNA datasets with available expression and clinical data. A PubMed search was performed to identify prognostic miRNAs for HCC. We performed a uni- and multivariate Cox regression analysis to validate the prognostic significance of these miRNAs. The Limma R package was applied to compare the expression of miRNAs between tumor and normal tissues. We uncovered 214 publications containing 223 miRNAs identified as potential prognostic biomarkers for HCC. In the survival analysis, the expression levels of 55 and 84 miRNAs were significantly correlated with overall survival in RNA-seq and gene chip datasets, respectively. The most significant miRNAs were hsa-miR-149, hsa-miR-139, and hsa-miR-3677 in the RNA-seq and hsa-miR-146b-3p, hsa-miR-584, and hsa-miR-31 in the microarray dataset. Of the 223 miRNAs studied, the expression was significantly altered in 102 miRNAs in tumors compared to normal liver tissues. In summary, we set up an integrated miRNA expression database and validated prognostic miRNAs in HCC.

Transcriptomic Profiling of Obesity-Related Nonalcoholic Steatohepatitis Reveals a Core Set of Fibrosis-Specific Genes

Nonalcoholic steatohepatitis (NASH) is strongly associated with obesity and type 2 diabetes. The molecular factors underlying the development of inflammation and severe fibrosis in NASH remain largely unknown. The purpose of this study was to identify gene expression patterns related to obesity-related NASH inflammation and fibrosis. We performed sequencing-based mRNA profiling analysis of liver samples from individuals with normal histology (n = 24), lobular inflammation (n = 53), or bridging fibrosis, incomplete cirrhosis, or cirrhosis (n = 65). Hepatic expression of a subset of mRNAs was validated using an orthogonal method, analyzed in a hepatic stellate cell line, and used to identify transcriptional patterns shared by other forms of cirrhosis. We observed evidence for differential levels of 3820 and 2980 transcripts in lobular inflammation and advanced fibrosis, respectively, compared with normal histology (false discovery rate ≤0.05), including 176 genes specific to fibrosis. Functional enrichment analysis of these genes revealed participation in pathways involving cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and extracellular matrix-receptor interaction. We identified 34 differentially expressed transcripts in comparisons of lobular inflammation and fibrosis, a proportion of which were also upregulated during activation of hepatic stellate cells. A set of 16 genes from a previous independent study of NASH bridging fibrosis/cirrhosis were replicated, several of which have also been associated with advanced fibrosis/cirrhosis due to hepatitis viruses or alcohol in human patients. Dysregulated mRNA expression is associated with inflammation and fibrosis in NASH. Advanced NASH fibrosis is characterized by distinct set of molecular changes that are shared with other causes of cirrhosis.

Key signaling pathways, genes and transcription factors associated with hepatocellular carcinoma

The purpose of the present study was to investigate the underlying molecular mechanism of hepatocellular carcinoma (HCC) using bioinformatics approaches. The microarray dataset GSE64041 was downloaded from the Gene Expression Omnibus database, which included 60 tumor liver samples and 60 matched control samples. Differentially expressed genes (DEGs) between HCC and control groups were identified. Then functional enrichment analyses, protein‑protein interaction (PPI) network, sub‑network and integrated transcription factor (TF)‑microRNA (miRNA)‑target network analyses were performed for these DEGs. A total of 378 DEGs were obtained, including 101 upregulated and 277 downregulated DEGs. In addition, functional enrichment analysis for DEGs in the sub‑network revealed 'cell division' and 'cell cycle' as key Gene Ontology (GO) terms and pathways. Topoisomerase (DNA) IIα (TOP2A) and integrin subunit α2 (ITGA2) were hub nodes in the PPI network. TOP2A, cyclin dependent kinase 1 (CDK1) and polo like kinase 1 (PLK1) were revealed to be hub nodes in the sub‑network. Finally, 4 TFs including forkhead box M1 (FOXM1), E2F transcription factor 4 (E2F4), SIN3 transcription regulator family member A (SIN3A) and transcription factor 7 like 1 (TCF7L1) were obtained through integrated network analysis. TOP2A, ITGA2, PLK1 and CDK1 may be key genes involved in HCC development. 'Cell division' and 'cell cycle' were indicated to act as key GO terms and Kyoto Encyclopedia of Genes and Genomes pathways in HCC. In addition, FOXM1, TCF7L1, E2F4 and SIN3A were revealed to be key TFs associated with HCC.

Identification of molecular target genes and key pathways in hepatocellular carcinoma by bioinformatics analysis

Background and aim

Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and is increasing incidence worldwide. The aim of this study was to identify the key genes and microRNAs in HCC and explore their potential mechanisms.

Methods

The gene expression profiles of GSE76427, GSE64041, GSE57957, and the microRNA dataset GSE67882 were downloaded from the Gene Expression Omnibus database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs) and miRNAs (DEMs). The gene ontology and the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed for DEGs using the Database for Annotation, Visualization, and Integrated Discovery. A protein–protein interaction (PPI) network of the DEGs was constructed by Search Tool for the Retrieval of Interacting Genes and visualized by Cytoscape. Moreover, miRecords was used to predict the target genes of DEMs.

Results

In total, 106 DEGs were screened out in HCC, consisting of 89 upregulated genes and 17 downregulated genes, which were mainly enriched in biological processes associated with oxidation–reduction process. Besides, the Kyoto Encyclopedia of Genes and Genomes pathways including chemical carcinogenesis, drug metabolism-cytochrome P450, tryptophan metabolism, and retinol metabolism were involved. A PPI network was constructed consisting of 105 nodes and 66 edges. A significant module including nine hub genes, ASPM, AURKA, CCNB2, CDKN3, MELK, NCAPG, NUSAP1, PRC1, and TOP2A, was detected from the PPI network by Molecular Complex Detection. The enriched functions were mainly associated with the mitotic cell cycle process, cell division, and mitotic cell cycle. In addition, a total of 21 DEMs were identified, including 9 upregulated and 12 downregulated miRNAs. Interestingly, ZBTB41 was the potential target of seven miRNAs. Finally, the nine hub genes and three miRNA-target genes expression levels were validated by reverse transcription-polymerase chain reaction. The relative expression levels of nine genes (ASPM, AURKA, CDKN3, MELK, NCAPG, PRC1, TOP2A, ZBTB41, and ZNF148) were significantly upregulated in cancer tissues.

Conclusion

This study identified the key genes and potential molecular mechanisms underlying the development of HCC, which could provide new insight for HCC interventional strategies.

Aldo-Keto Reductases as Early Biomarkers of Hepatocellular Carcinoma: A Comparison Between Animal Models and Human HCC

BACKGROUND

The intrinsic heterogeneity of hepatocellular carcinoma (HCC) represents a great challenge for its molecular classification and for detecting predictive biomarkers. Aldo-keto reductase (Akr) family members have shown differential expression in human HCC, while AKR1B10 overexpression is considered a biomarker; AKR7A3 expression is frequently reduced in HCC.

AIMS

To investigate the time-course expression of Akr members in the experimental hepatocarcinogenesis.

METHODS

Using DNA-microarray data, we analyzed the time-course gene expression profile from nodules to tumors (4-17 months) of 17 Akr members induced by the resistant hepatocyte carcinogenesis model in the rat.

RESULTS

The expression of six members (Akr1c19, Akr1b10, Akr7a3, Akr1b1, Akr1cl1, and Akr1b8) was increased, comparable to that of Ggt and Gstp1, two well-known liver cancer markers. In particular, Akr7a3 and Akr1b10 expression also showed a time-dependent increment at mRNA and protein levels in a second hepatocarcinogenesis model induced with diethylnitrosamine. We confirmed that aldo-keto reductases 7A3 and 1B10 were co-expressed in nine biopsies of human HCC, independently from the presence of glypican-3 and cytokeratin-19, two well-known HCC biomarkers. Because it has been suggested that expression of Akr members is regulated through NRF2 activity at the antioxidant response element (ARE) sequences, we searched and identified at least two ARE sites in Akr1b1, Akr1b10, and Akr7a3 from rat and human gene sequences. Moreover, we observed higher NRF2 nuclear translocation in tumors as compared with non-tumor tissues.

CONCLUSIONS

Our results demonstrate that Akr7a3 mRNA and protein levels are consistently co-expressed along with Akr1b10, in both experimental liver carcinogenesis and some human HCC samples. These results highlight the presence of AKR7A3 and AKR1B10 from early stages of the experimental HCC and introduce them as a potential application for early diagnosis, staging, and prognosis in human cancer.

The protein histidine phosphatase LHPP is a tumour suppressor

Histidine phosphorylation, the so-called hidden phosphoproteome, is a poorly characterized post-translational modification of proteins. Here we describe a role of histidine phosphorylation in tumorigenesis. Proteomic analysis of 12 tumours from an mTOR-driven hepatocellular carcinoma mouse model revealed that NME1 and NME2, the only known mammalian histidine kinases, were upregulated. Conversely, expression of the putative histidine phosphatase LHPP was downregulated specifically in the tumours. We demonstrate that LHPP is indeed a protein histidine phosphatase. Consistent with these observations, global histidine phosphorylation was significantly upregulated in the liver tumours. Sustained, hepatic expression of LHPP in the hepatocellular carcinoma mouse model reduced tumour burden and prevented the loss of liver function. Finally, in patients with hepatocellular carcinoma, low expression of LHPP correlated with increased tumour severity and reduced overall survival. Thus, LHPP is a protein histidine phosphatase and tumour suppressor, suggesting that deregulated histidine phosphorylation is oncogenic.