TCGA whole-transcriptome sequencing data reveals significantly dysregulated genes and signaling pathways in hepatocellular carcinoma

Effects of essential oil of Origanum onites and its major component carvacrol on the expression of toxicity pathway genes in HepG2 cells

BACKGROUND

Origanum species have been used in various commercial constructions as a remedy against burns and wounds, agriculture, alcoholic drinks, fragrance, and flavoring substances of food products. The essential oil of Origanum onites L. (EOOO) and its component carvacrol (CV) possesses a wide range of biological activities including anti-cancer activity.

PURPOSE

The purpose of this study was to investigate the growth inhibitory activity of the essential oil and its major component CV and then hepatotoxicity pathway-related genes in HepG2 cells.

METHODS

The effects of the EOOO and CV on cell growth and mRNA expressions of 84 hepatotoxicity pathway-related genes were investigated in HepG2, using trypan blue exclusion/ bromodeoxyuridine (BrdU) incorporation tests and real-time-polymerase chain reaction (RT-PCR) array, respectively.

RESULTS

The EOOO and CV inhibited cell growth with IC50 values of 0.08 µg/mL and 45 µg/mL, respectively, after 24 h. Real-time, reverse-transcription-polymerase chain reaction (RT2-PCR) array analysis revealed that expressions of 32 genes out of 84 were changed at least 2-fold or more in the EOOO-treated cells. Among them, expression levels of 17 genes were elevated, while expression levels of 15 genes were diminished. Furthermore, after exposure of cells to 45 µg/mL of CV, the expression of 8 genes was increased while the other 8 genes were decreased. Both the EOOO and carvacrol affected the expression of 48 genes of HepG2 cells which are involved in the hepatotoxicity pathway, indicating their hepatoprotective and possible anti-hepatocarcinogenic effects.

CONCLUSION

The present study demonstrates that the essential oil of Origanum onites and carvacrol can be used in various applications such as anticancer or herbal drugs, since its non-hepatotoxicity.

Identification of the hub genes linked to zearalenone-induced hepatotoxicity in broiler chickens

Zearalenone (ZEN) is a mycotoxin found in food and feed that impairs the function of multiple organs, especially the liver. However, the specific mechanisms through which ZEN induces liver damage in broiler chickens are not well understood. Therefore, this study aimed to identify the key genes linked to the hepatotoxicity induced by ZEN exposure in broiler chickens. Gene expression data from ZEN-treated and control chicken embryo primary hepatocytes (CEPHs) were used to implement differential expression analysis. Totally, 436 differentially expressed genes (DEGs) were detected, in which 223 and 213 genes were up- and down-regulated in ZEN-treated CEPHs, respectively. Gene ontology analysis suggested that these DEGs were involved in various biological processes, including chromosome segregation, mitotic cytokinesis, mitotic cell cycle, cell division, and mitotic spindle organization. Pathway analysis showed that the DEGs were associated with p53, FoxO, ubiquitin-mediated proteolysis, cell cycle, and mismatch repair signaling pathways. Furthermore, the hub genes, including BRCA1, CDC45, CDCA3, CDKN3, CENPE, CENPF, CENPI, CENPM, CENPU, and CEP55, potentially contributed to ZEN-induced hepatotoxicity. In conclusion, our study provides the valuable insight into the mechanism underlying ZEN-induced hepatotoxicity in broiler chickens.

Investigation of Functional Synergism of CENPF and FOXM1 Identifies POLD1 as Downstream Target in Hepatocellular Carcinoma

Background

Lines of evidence implicate CENPF and FOXM1 may have novel co-operative roles in driving hepatocellular carcinoma (HCC).

Objective

We investigated the clinicopathological correlation, functional characterization, molecular mechanism and translational significance of CENPF and FOXM1.

Methods

We carried out integrative studies investigating functional synergism of CENPF and FOXM1 in HCC and its metastasis. Human HCC samples, HCC cell lines and mouse model were used in the studies. Stable knockdown, q-PCR, Western blotting, whole-transcriptomic sequencing (RNA-seq), as well as cell and mouse assays were performed.

Results

Upon clinicopathological correlation, we found that co-overexpression of CENPF and FOXM1 in human HCCs was associated with more aggressive tumor behavior including presence of venous invasion, tumor microsatellite formation, and absence of tumor encapsulation. Moreover, co-silencing FOXM1 and CENPF using shRNA approach in HCC cell lines resulted in significantly reduced cell proliferation. Furthermore, our RNA-seq and differential gene expression analysis delineated that CENPF and FOXM1 co-regulated a specific set of target genes in various metabolic processes and oncogenic signaling pathways. Among them, POLD1, which encodes the catalytic subunit of DNA polymerase δ, was ranked as the top downstream target co-regulated by CENPF and FOXM1. POLD1 expression was positively correlated with that of FOXM1 and CENPF in HCCs. In addition, POLD1 expression was significantly upregulated in HCC tumors. Functionally, in vivo orthotopic injection model showed that stable knockdown of POLD1 in HCC cells suppressed tumor incidence and tumorigenicity and had a trend of diminished lung metastasis.

Conclusion

Taken together, our data suggest that CENPF and FOXM1 could synergistically support hepatocarcinogenesis via the regulation of POLD1. CENPF and FOXM1 may represent new vulnerabilities to novel drug-based therapy in HCC.

CRHBP is degraded via autophagy and exerts anti-hepatocellular carcinoma effects by reducing cyclin B2 expression and dissociating cyclin B2-CDK1 complex

Autophagy is the predominant self-eating catabolic pathway activated in response to nutrient starvation and hypoxia within the microenvironment of varied malignancies, including hepatocellular carcinoma (HCC). SQSTM1/p62 links its cargos to autophagosomes for degradation, and reportedly acts as a contributor for hepatocarcinogenesis. Five GEO gene microarrays identified corticotropin releasing hormone (CRH) binding protein (CRHBP) as a significantly downregulated gene in HCC (log2 Fold change < -3 and p < 0.001), and an earlier human interactome study indicated that CRHBP may interact with p62. This study aimed to explore (1) the role of CRHBP in HCC development, and (2) whether p62-mediated autophagy was responsible for low CRHBP expression within HCC tissue. Following functional experiments first revealed an anti-proliferative, anti-metastatic, and anti-angiogenic role of CRHBP in HCC cells (Huh-7, Li-7 and HCCLM3) and xenografts. CRHBP negatively regulated cyclin B2 expression, and dissociated cyclin B2-CDK1 complex in HCC cells, thereby leading to cell cycle arrest at G2 phase. To simulate HCC microenvironment in vitro, Huh-7 cells were incubated in Earle's Balanced Salt Solution (nutrient starvation) or exposed to 1% O₂ (hypoxic exposure). In addition to activating autophagy, nutrient starvation and hypoxic exposure also induced CRHBP degradation. Interestingly, CRHBP was demonstrated as a novel cargo targeted by p62 for degradation in autophagosomes. Blocking autophagy with 3-MA, chloroquine or siSQSTM1 prevented CRHBP degradation in HCC cells. Collectively, our study uncovers a role for CRHBP in retarding HCC development, reducing cyclin B2 expression and impairing cyclin B2-CDK1 interaction. CRHBP downregulation in HCC may attribute to p62-mediated autophagy.

A Novel Four-Gene Signature as a Potential Prognostic Biomarker for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor with high incidence and mortality rates. However, a reliable prognostic signature has not yet been confirmed. Thus, the purpose of the present study was to develop a biomarker with high specificity and sensitivity for the diagnosis and prognosis of patients with HCC. The mRNA expression profiles of HCC were obtained from the GSE19665, GSE41804, and TCGA databases. Subsequently, 193 differentially expressed genes (DEGs) were identified from the intersection of the data from the three datasets. Bioinformatics analysis showed that the identified DEGs are related to the cell cycle, oocyte meiosis, and p53 signaling pathway, among other factors, in cancers. A protein-protein interaction (PPI) and a functional analysis were performed to investigate the biological function of the DEGs and obtain the candidate genes using the MCODE of Cytoscape. The candidate genes were introduced into the TCGA database for survival analysis, and the four candidate genes that were hub genes and meaningful for survival were retained for further verification. We validated the gene and protein expression and determined the prognosis of our patient cohort. In addition, we evaluated the biological functions regulating tumor cell proliferation and metastasis in vitro. According to the ROC curve analysis of gene expression in clinical samples, it was found that the four genes can be used to predict the diagnosis. A survival analysis based on data from the TCGA database and clinical samples showed that the four genes may be used as biomarkers for providing prognoses for patients. The cell functional experiments revealed that these four genes were related to tumor proliferation, migration, and invasion. In conclusion, the genes identified in the present study could be used as markers to diagnose and predict the prognosis of patients with HCC and guide targeted therapy.

The phosphorylated retinoid X receptor-α promotes diethylnitrosamine-induced hepatocarcinogenesis in mice through the activation of β-catenin signaling pathway

Previous studies have shown that phosphorylation of the retinoid X receptor-α (RXRα) is associated with the development of hepatocellular carcinoma (HCC). However, these findings were revealed using HCC cell lines that express phosphorylated-RXRα (p-RXRα) proteins; therefore, it remains unclear whether p-RXRα affects hepatocarcinogenesis in vivo. Therefore, to investigate the biological function of p-RXRα in vivo, we developed a doxycycline-inducible ES cell line and transgenic mouse, both of which overexpress the phosphomimetic mutant form of RXRα, T82D/S260D, in a doxycycline-dependent manner. We found that the development of liver tumors, especially high-grade adenoma and HCC, was enhanced in diethylnitrosamine (DEN)-induced T82D/S260D-inducible mice. Moreover, the increased incidence of liver tumors in the transgenic mice was attributable to the promotion of cell cycle progression. Interestingly, the expression of β-catenin protein and its target gene cyclin D1 was elevated in the liver tumors of DEN-treated T82D/S260D-inducible mice, concurrent with increased cytoplasmic and nuclear β-catenin protein expression, indicating its stabilization and transcriptional activation. These results indicate that p-RXRα promotes DEN-induced hepatocarcinogenesis in mice through the activation of the β-catenin signaling pathway, suggesting that p-RXRα may serve as a possible therapeutic target for HCC.

Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers. The discovery of specific genes severing as biomarkers is of paramount significance for cancer diagnosis and prognosis. The high-throughput omics data generated by the cancer genome atlas (TCGA) consortium provides a valuable resource for the discovery of HCC biomarker genes. Numerous methods have been proposed to select cancer biomarkers. However, these methods have not investigated the robustness of identification with different feature selection techniques.

METHODS

We use six different recursive feature elimination methods to select the gene signiatures of HCC from TCGA liver cancer data. The genes shared in the six selected subsets are proposed as robust biomarkers. Akaike information criterion (AIC) is employed to explain the optimization process of feature selection, which provides a statistical interpretation for the feature selection in machine learning methods. And we use several methods to validate the screened biomarkers.

RESULTS

In this paper, we propose a robust method for discovering biomarker genes for HCC from gene expression data. Specifically, we implement recursive feature elimination cross-validation (RFE-CV) methods based on six different classication algorithms. The overlaps in the discovered gene sets via different methods are referred as the identified biomarkers. We give an interpretation of the feature selection process based on machine learning using AIC in statistics. Furthermore, the features selected by the backward logistic stepwise regression via AIC minimum theory are completely contained in the identified biomarkers. Through the classification results, the superiority of interpretable robust biomarker discovery method is verified.

CONCLUSIONS

It is found that overlaps among gene subsets contain different quantitative features selected by the RFE-CV of 6 classifiers. The AIC values in the model selection provide a theoretical foundation for the feature selection process of biomarker discovery via machine learning. What's more, genes containing in more optimally selected subsets make better biological sense and implication. The quality of feature selection is improved by the intersections of biomarkers selected from different classifiers. This is a general method suitable for screening biomarkers of complex diseases from high-throughput data.

TOP2A and CENPF are synergistic master regulators activated in cervical cancer

Background

Identification of master regulators (MRs) using transcriptome data in cervical cancer (CC) could help us to develop biomarkers and find novel drug targets to fight this disease.

Methods

We performed differential expression (DE) analyses of public microarray and RNA-seq transcriptome data of CC and normal cervical tissues (N). Virtual Inference of Protein activity by Enriched Regulon analysis (VIPER) was used to convert the DE outcomes to differential activity (DA) signature for MRs. Synergy analysis was conducted to study synergistic effect of MR-pairs. TCGA and microarray data were used to test the association of expression of a MR and a clinical feature or a molecular feature (e.g. somatic mutations). Various bioinformatic tools/websites (DAVID, GEPIA2, Oncomine, cBioPortal) were used to analyze the expression of the top MRs and their regulons.

Results

Ten DE and 10 DA signatures were generated for CC. Two MRs, DNA topoisomerase II alpha (TOP2A) and centromere protein F (CENPF) were found to be up-regulated, activated and synergistic in CC compared to N across the 10 datasets. The two MRs activate a common set of genes (regulons) with functions in cell cycle, chromosome, DNA damage etc. Higher expression of CENPF was associated with metastasis. High expression of both MRs is associated with somatic mutation of a set of genes including tumor suppressors (TP53, MSH2, RB1) and genes involved in cancer pathways, cell cycle, DNA damage and repair. The magnitude of up-regulation and the absolute expression level of both MRs in CC are significantly higher compared to many other cancer types.

Conclusion

TOP2A and CENPF are a synergistic pair of MRs that are overexpressed and activated in CC. Their high expression is correlated with some prognosis features (e.g. metastasis) and molecular features (e.g. somatic mutations) and distinctly high in CC vs. many other cancer types. They may be good biomarkers and anticancer drug targets for CC.

Weighted Gene Coexpression Network Analysis Reveals the Dynamic Transcriptome Regulation and Prognostic Biomarkers of Hepatocellular Carcinoma

This study was aimed at revealing the dynamic regulation of mRNAs, long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) in hepatocellular carcinoma (HCC) and to identify HCC biomarkers capable of predicting prognosis. Differentially expressed mRNAs (DEmRNAs), lncRNAs, and miRNAs were acquired by comparing expression profiles of HCC with normal samples, using an expression data set from The Cancer Genome Atlas. Altered biological functions and pathways in HCC were analyzed by subjecting DEmRNAs to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Gene modules significantly associated with disease status were identified by weighted gene coexpression network analysis. An lncRNA-mRNA and an miRNA-mRNA coexpression network were constructed for genes in disease-related modules, followed by the identification of prognostic biomarkers using Kaplan-Meier survival analysis. Differential expression and association with the prognosis of 4 miRNAs were verified in independent data sets. A total of 1220 differentially expressed genes were identified between HCC and normal samples. Differentially expressed mRNAs were significantly enriched in functions and pathways related to “plasma membrane structure,” “sensory perception,” “metabolism,” and “cell proliferation.” Two disease-associated gene modules were identified. Among genes in lncRNA-mRNA and miRNA-mRNA coexpression networks, 9 DEmRNAs and 7 DEmiRNAs were identified to be potential prognostic biomarkers. MIMAT0000102, MIMAT0003882, and MIMAT0004677 were successfully validated in independent data sets. Our results may advance our understanding of molecular mechanisms underlying HCC. The biomarkers may contribute to diagnosis in future clinical practice.

The G Protein-Coupled Estrogen Receptor (GPER) Expression Correlates with Pro-Metastatic Pathways in ER-Negative Breast Cancer: A Bioinformatics Analysis

The G protein-coupled estrogen receptor (GPER, formerly known as GPR30) is a seven-transmembrane receptor that mediates estrogen signals in both normal and malignant cells. In particular, GPER has been involved in the activation of diverse signaling pathways toward transcriptional and biological responses that characterize the progression of breast cancer (BC). In this context, a correlation between GPER expression and worse clinical-pathological features of BC has been suggested, although controversial data have also been reported. In order to better assess the biological significance of GPER in the aggressive estrogen receptor (ER)-negative BC, we performed a bioinformatics analysis using the information provided by The Invasive Breast Cancer Cohort of The Cancer Genome Atlas (TCGA) project and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets. Gene expression correlation and the statistical analysis were carried out with R studio base functions and the tidyverse package. Pathway enrichment analysis was evaluated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway on the Database for Annotation, Visualization and Integrated Discovery (DAVID) website, whereas gene set enrichment analysis (GSEA) was performed with the R package phenoTest. The survival analysis was determined with the R package survivALL. Analyzing the expression data of more than 2500 primary BC, we ascertained that GPER levels are associated with pro-migratory and metastatic genes belonging to cell adhesion molecules (CAMs), extracellular matrix (ECM)-receptor interaction, and focal adhesion (FA) signaling pathways. Thereafter, evaluating the disease-free interval (DFI) in ER-negative BC patients, we found that the subjects expressing high GPER levels exhibited a shorter DFI in respect to those exhibiting low GPER levels. Overall, our results may pave the way to further dissect the network triggered by GPER in the breast malignancies lacking ER toward a better assessment of its prognostic significance and the action elicited in mediating the aggressive features of the aforementioned BC subtype.

The role of the CDCA gene family in ovarian cancer

Background

Ovarian cancer is a frequently-occurring reproductive system malignancy in females, which leads to an annual of over 100 thousand deaths worldwide.

Methods

The electronic databases, including GEPIA, ONCOMINE, Metascape, and Kaplan-Meier Plotter, were used to examine both survival and transcriptional data regarding the cell division cycle associated (CDCA) gene family among ovarian cancer patients.

Results

All CDCA genes expression levels were up-regulated in ovarian cancer tissues relative to those in non-carcinoma ovarian counterparts. Besides, CDCA5/7 expression levels were related to the late tumor stage. In addition, the Kaplan-Meier Plotter database was employed to carry out survival analysis, which suggested that ovarian cancer patients with increased CDCA2/3/5/7 expression levels had poor overall survival (OS) (P<0.05). Moreover, ovarian cancer patients that had up-regulated mRNA expression levels of CDCA2/5/8 had markedly reduced progression-free survival (PFS) (P<0.05); and up-regulated CDCA4 expression showed remarkable association with reduced post-progression survival (PPS) (P<0.05). Additionally, the following processes were affected by CDCA genes alterations, including R-HAS-2500257: resolution of sister chromatid cohesion; GO:0051301: cell division; CORUM: 1118: Chromosomal passenger complex (CPC, including CDCA8, INCENP, AURKB and BIRC5); CORUM: 127: NDC80 kinetochore complex; M129: PID PLK1 pathway; and GO: 0007080: mitotic metaphase plate congression, all of which were subjected to marked regulation since the alterations affected CDCA genes.

Conclusions

Up-regulated CDCA gene expression in ovarian cancer tissues probably played a crucial part in the occurrence of ovarian cancer. The up-regulated CDCA2/3/5/7 expression levels were used as the potential prognostic markers to improve the poor ovarian cancer survival and prognostic accuracy. Moreover, CDCA genes probably exerted their functions in tumorigenesis through the PLK1 pathway.

Integrative module analysis of HCC gene expression landscapes

Despite hepatocellular carcinoma (HCC) being a common cancer globally, its initiation and progression are not well understood. The present study was designed to investigate the hub genes and biological processes of HCC, which change substantially during its progression. Three gene expression profiles of 480 patients with HCC were obtained from the Gene Expression Omnibus database. Subsequent to performing functional annotations and constructing protein-protein interaction (PPI) networks, 657 differentially expressed genes were identified, which were subsequently used to screen candidate hub genes. PPI networks were modularized using the weighted gene correlation network analysis algorithm, the topological overlapping matrix and the hierarchical cluster tree, which were utilized via STRING. Clinical data obtained from The Cancer Genome Atlas were then analyzed to validate the experiments performed using six hub genes. Additionally, a transcription factor and microRNA-mRNA network were constructed to determine the potential regulatory mechanisms of six hub genes. The results revealed that the oxidation-reduction process and cell cycle associated processes were markedly involved in HCC progression. Six highly expressed genes, including cyclin B2, cell division cycle 20, mitotic arrest deficient 2 like 1, minichromosome maintenance complex component 2, centromere protein F and BUB mitotic checkpoint serine/threonine kinase B, were confirmed as hub genes and validated via experiments associated with cell division. These hub genes are necessary for confirmatory experiments and may be used in clinical gene therapy as biomarkers or drug targets.

Screening and verification of microRNA promoter methylation sites in hepatocellular carcinoma

The promoter methylation mode of microribonucleic acid (miRNA) plays a crucial role in the process of hepatocellular carcinoma (HCC). Therefore, the primary purpose of this study was to screen and verify the miRNA methylation sites associated with the overall survival (OS) and clinical characteristics of HCC patients. Methylation-related data were from the Cancer Genome Atlas (TCGA). R software was utilized to screen the methylation sites. The least absolute shrinkage and selection operator algorithm was utilized to develop the miRNA promoter methylation models. Then, methylation-specific polymerase chain reaction was performed with 146 HCC tissues to verify the accuracy of the vascular infiltration-related model. Additionally, we verified the functions of vascular infiltration-related miRNA by utilizing cells transfected with miR-199a-3p mimic. The model for predicting OS of HCC patients contained eight methylation sites. The Kaplan-Meier analysis suggested that the model could divide HCC patients into high- and low-risk groups (P < .0001). COX regression analysis suggested that the model (P < .001; 95% CI, 1.264-2.709) and T category (P < .001; 95% CI, 1.472-3.119) were independent risk factors for affecting OS of HCC patients. The model for predicting vascular infiltration, pathological grade, and clinical stage contained 7, 10, and 9 methylation sites respectively, with their area under the receiver operating characteristic curve (AUC) values 0.667, 0.745, and 0.725, respectively. The functional analysis suggested that miRNA methylation is involved in various biological processes such as WNT, MAPK, and mTOR signaling pathways. The accuracy of the vascular infiltration-related model was consistent with our previous bioinformatics assay. And upregulation of miR-199a-3p decreased migration and invasion abilities. The screened miRNA promoter methylation sites can be served as biomarkers for judging OS, vascular infiltration, pathology grade, and clinical stage. It can also provide new targets for improving the treatment and prognosis of HCC patients.

Definition of a novel vascular invasion-associated multi-gene signature for predicting survival in patients with hepatocellular carcinoma

The aim of the present study was to identify a vascular invasion-associated gene signature for predicting prognosis in patients with hepatocellular carcinoma (HCC). Using RNA-sequencing data of 292 HCC samples from The Cancer Genome Atlas (TCGA), the present study screened differentially expressed genes (DEGs) between patients with and without vascular invasion. Feature genes were selected from the DEGs by support vector machine (SVM)-based recursive feature elimination (RFE-SVM) algorithm to build a classifier. A multi-gene signature was selected by L1 penalized (LASSO) Cox proportional hazards (PH) regression model from the feature genes selected by the RFE-SVM to develop a prognostic scoring model. TCGA set was defined as the training set and was divided by the gene signature into a high-risk group and a low-risk group. Involvement of the DEGs between the two risk groups in pathways was also investigated. The presence and absence of vascular invasion between patients of training set was 175 DEGs. A classification model of 42 genes performed well in differentiating patients with and without vascular invasion on the training set and the validation set. A 14-gene prognostic model was built that could divide the training set or the validation set into two risk groups with significantly different survival outcomes. A total of 762 DEGs in the two risk groups of the training set were revealed to be significantly associated with a number of signaling pathways. The present study provided a 42-gene classifier for predicting vascular invasion, and identified a vascular invasion-associated 14-gene signature for predicting prognosis in patients with HCC. Several genes and pathways in HCC development are characterized and may be potential therapeutic targets for this type of cancer.

Overexpression of CDCA5, KIF4A, TPX2, and FOXM1 Coregulated Cell Cycle and Promoted Hepatocellular Carcinoma Development

This study aimed to identify key functional modules and genes in functional module involved in hepatocellular carcinoma (HCC) development. The microarray data set GSE54236 was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between HCC, and normal samples were identified by Limma. DAVID was used to identify the gene ontology terms these genes enriched. The co-expression network was constructed based on Pearson correlation coefficient between gene expression values, and the functional modules these DEGs obviously enriched in were recognized through GraphWeb. Then, based on the genes related to the development of HCC, the DEGs interacting with HCC-associated genes were spotted. Finally, survival analysis and real-time quantitative polymerase chain reaction were performed. Totally, 427 upregulated (e.g., cell division cycle associated 5 [CDCA5], kinesin family member 4A [KIF4A], TPX2 microtubule nucleation factor [TPX2]) and 313 downregulated (e.g., metallothionein 1E [MT1E]) DEGs were identified in HCC. Besides, CDCA5, KIF4A, and TPX2 had interacting relationship and played important roles in HCC development by interrelating with HCC-related gene, forkhead box M1 (FOXM1). Furthermore, CDCA5, KIF4A, TPX2, and FOXM1 obviously enriched in cell cycle-related functional module, whereas MT1E enriched in mineral absorption module in Kyoto Encyclopedia of Genes and Genomes. CDCA5, KIF4A, and TPX2 expression were increased in HCC cells, and their high expressions were related to poor prognosis. Overexpression of CDCA5, KIF4A, TPX2, and FOXM1 coregulated cell cycle and thereby promoted the development of HCC. The finding provided potential targets for the study and treatment of HCC.

SUCO as a Promising Diagnostic Biomarker of Hepatocellular Carcinoma: Integrated Analysis and Experimental Validation

Background

Hepatocellular carcinoma (HCC) is not frequently diagnosed until the late stage due to its concealed symptoms. Therefore, the identification of biomarkers that have effective diagnostic performance and act as potential key therapeutic targets for HCC becomes urgent.

Material/Methods

Comprehensive analysis of accumulated data downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases was used to obtain more reliable potential diagnostic biomarkers of HCC and to explore related molecular mechanisms. Meta-analysis and summary receiver operating characteristic (SROC) curve analysis were performed to evaluate the differential expression of SUCO gene in HCC and identify the capability of SUCO in distinguishing HCC-tissues from normal liver-tissues.

Results

SUCO was found to be upregulated in HCC-tissues and exhibited a favorable value in diagnosing HCC. Bioinformatics analysis showed that SUCO might play important roles in HCC progression, and was significantly related to cell cycle, cell metabolism, and proliferation.

Conclusions

This study was the first to demonstrate that SUCO was overexpressed in HCC-tissues, and that high expression of SUCO was significantly related to poor overall survival in HCC patients. SUCO might be a potential diagnostic biomarker for HCC patients, which promotes the tumorigenesis and progression of HCC.

Transcriptomic analysis to affirm the regulatory role of long non-coding RNA in horn cancer of Indian zebu cattle breed Kankrej (Bos indicus)

Long non-coding RNA (lncRNA) was previously considered as a non-functional transcript, which now established as part of regulatory elements of biological events such as chromosome structure, remodeling, and regulation of gene expression. The study presented here showed the role of lncRNA through differential expression analysis on cancer-related coding genes in horn squamous cell carcinoma of Indian zebu cattle. A total of 10,360 candidate lncRNAs were identified and further analyzed for its coding potential ability using three tools (CPC, CPAT, and PLEK) that provide 8862 common lncRNAs. Pfam analysis of these common lncRNAs gave 8612 potential candidates for lncRNA differential expression analysis. Differential expression analysis showed a total of 59 significantly differentially expressed genes and 19 lncRNAs. Pearson's correlation analysis was used to identify co-expressed mRNA-lncRNAs to established relation of the regulatory role of lncRNAs in horn cancer. We established a positive relation of seven upregulated (XLOC_000016, XLOC_002198, XLOC_002851, XLOC_ 007383, XLOC_010701, XLOC_010272, and XLOC_011517) and one downregulated (XLOC_011302) lncRNAs with eleven genes that are related to keratin family protein, keratin-associated protein family, cornifelin, corneodesmosin, serpin family protein, and metallothionein that have well-established role in squamous cell carcinoma through cellular communication, cell growth, cell invasion, and cell migration. These biological events were found to be related to the MAPK pathway of cell cycle regulation indicating the role of lncRNAs in manipulating cell cycle regulation during horn squamous cell carcinomas that will be useful in identifying molecular portraits related to the development of horn cancer.

Novel significant stage-specific differentially expressed genes in hepatocellular carcinoma

BACKGROUND

Liver cancer is among top deadly cancers worldwide with a very poor prognosis, and the liver is a vulnerable site for metastases of other cancers. Early diagnosis is crucial for treatment of the predominant liver cancers, namely hepatocellular carcinoma (HCC). Here we developed a novel computational framework for the stage-specific analysis of HCC.

METHODS

Using publicly available clinical and RNA-Seq data of cancer samples and controls and the AJCC staging system, we performed a linear modelling analysis of gene expression across all stages and found significant genome-wide changes in the log fold-change of gene expression in cancer samples relative to control. To identify genes that were stage-specific controlling for confounding differential expression in other stages, we developed a set of six pairwise contrasts between the stages and enforced a p-value threshold (< 0.05) for each such contrast. Genes were specific for a stage if they passed all the significance filters for that stage. The monotonicity of gene expression with cancer progression was analyzed with a linear model using the cancer stage as a numeric variable.

RESULTS

Our analysis yielded two stage-I specific genes (CA9, WNT7B), two stage-II specific genes (APOBEC3B, FAM186A), ten stage-III specific genes including DLG5, PARI, NCAPG2, GNMT and XRCC2, and 35 stage-IV specific genes including GABRD, PGAM2, PECAM1 and CXCR2P1. Overexpression of DLG5 was found to be tumor-promoting contrary to the cancer literature on this gene. Further, GABRD was found to be signifincantly monotonically upregulated across stages. Our work has revealed 1977 genes with significant monotonic patterns of expression across cancer stages. NDUFA4L2, CRHBP and PIGU were top genes with monotonic changes of expression across cancer stages that could represent promising targets for therapy. Comparison with gene signatures from the BCLC staging system identified two genes, HSP90AB1 and ARHGAP42. Gene set enrichment analysis indicated overrepresented pathways specific to each stage, notably viral infection pathways in HCC initiation.

CONCLUSIONS

Our study identified novel significant stage-specific differentially expressed genes which could enhance our understanding of the molecular determinants of hepatocellular carcinoma progression. Our findings could serve as biomarkers that potentially underpin diagnosis as well as pinpoint therapeutic targets.

Dissecting the expression landscape of cytochromes P450 in hepatocellular carcinoma: towards novel molecular biomarkers

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths around the world. Recent advances in genomic technologies have allowed the identification of various molecular signatures in HCC tissues. For instance, differential gene expression levels of various cytochrome P450 genes (CYP450) have been reported in studies performed on limited numbers of HCC tissue samples, or focused on a small subset on CYP450s. In the present study, we monitored the expression landscape of all the members of the CYP450 family (57 genes) in more than 200 HCC tissues using RNA-Seq data from The Cancer Genome Atlas. Using stringent statistical filters and data from paired tissues, we identified significantly dysregulated CYP450 genes in HCC. Moreover, the expression level of selected CYP450s was validated by qPCR on cDNA samples from an independent cohort. Threshold values (sensitivity and specificity) based on dysregulated gene expression were also determined to allow for confident identification of HCC tissues. Finally, a global look at expression levels of the 57 members of the CYP450 family across ten different cancer types revealed specific expression signatures. Overall, this study provides useful information on the transcriptomic landscape of CYP450 genes in HCC and on new potential HCC biomarkers.

Bioinformatics analysis to identify the key genes affecting the progression and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer, which has poor outcome. The present study aimed to investigate the key genes implicated in the progression and prognosis of HCC. The RNA-sequencing data of HCC was extracted from The Cancer Genome Atlas (TCGA) database. Using the R package (DESeq), the differentially expressed genes (DEGs) were analyzed. Based on the Cluepedia plug-in in Cytoscape software, enrichment analysis for the protein-coding genes amongst the DEGs was conducted. Subsequently, protein–protein interaction (PPI) network was built by Cytoscape software. Using survival package, the genes that could distinguish the survival differences of the HCC samples were explored. Moreover, quantitative real-time reverse transcription-PCR (qRT-PCR) experiments were used to detect the expression of key genes. There were 2193 DEGs in HCC samples. For the protein-coding genes amongst the DEGs, multiple functional terms and pathways were enriched. In the PPI network, cyclin-dependent kinase 1 (CDK1), polo-like kinase 1 (PLK1), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), serum amyloid A1 (SAA1), and lysophosphatidic acid receptor 3 (LPAR3) were hub nodes. CDK1 interacting with PLK1 and FOS, and LPAR3 interacting with FOS and SAA1 were found in the PPI network. Amongst the 40 network modules, 4 modules were with scores not less than 10. Survival analysis showed that anterior gradient 2 (AGR2) and RLN3 could differentiate the high- and low-risk groups, which were confirmed by qRT-PCR. CDK1, PLK1, FOS, SAA1, and LPAR3 might be key genes affecting the progression of HCC. Besides, AGR2 and RLN3 might be implicated in the prognosis of HCC.

Comprehensive analysis of potential prognostic genes for the construction of a competing endogenous RNA regulatory network in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an extremely common malignant tumor with worldwide prevalence. The aim of this study was to identify potential prognostic genes and construct a competing endogenous RNA (ceRNA) regulatory network to explore the mechanisms underlying the development of HCC.

METHODS

Integrated analysis was used to identify potential prognostic genes in HCC with R software based on the GSE14520, GSE17548, GSE19665, GSE29721, GSE60502, and the Cancer Genome Atlas databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enrichment analyses were performed to explore the molecular mechanisms of potential prognostic genes. Differentially expressed miRNAs (DEMs) and lncRNAs (DELs) were screened based on the Cancer Genome Atlas database. An lncRNA-miRNA-mRNA ceRNA regulatory network was constructed based on information about interactions derived from the miRcode, TargetScan, miRTarBase, and miRDB databases.

RESULTS

A total of 152 potential prognostic genes were screened that were differentially expressed in HCC tissue and significantly associated with overall survival of HCC patients. There were 13 key potential prognostic genes in the ceRNA regulatory network: eleven upregulated genes (CCNB1, CEP55, CHEK1, EZH2, KPNA2, LRRC1, PBK, RRM2, SLC7A11, SUCO, and ZWINT) and two downregulated genes (ACSL1 and CDC37L1) whose expression might be regulated by eight DEMs and 61 DELs. Kaplan-Meier curve analysis showed that nine DELs (AL163952.1, AL359878.1, AP002478.1, C2orf48, C10orf91, CLLU1, CLRN1-AS1, ERVMER61-1, and WARS2-IT1) in the ceRNA regulatory network were significantly associated with HCC-patient prognoses.

CONCLUSION

This study identified potential prognostic genes and constructed an lncRNA- miRNA-mRNA ceRNA regulatory network of HCC, which not only has important clinical significance for early diagnoses but also provides effective targets for HCC treatments and could provide new insights for HCC-interventional strategies.

APOBEC3B promotes hepatocarcinogenesis and metastasis through novel deaminase-independent activity

Cytidine deaminase APOBEC3B (A3B) is known to play important roles in creating de novo genomic C-to-T mutations in cancers and contribute to induction of genomic instability. Our study evaluated the roles of A3B in the progression and metastasis of human hepatocellular carcinoma (HCC). Using whole-transcriptome and whole-exome sequencing, and quantitative PCR, we found that A3B was overexpressed in human HCCs and A3B expression was significantly correlated with the proportion of genomic C-to-A and G-to-T mutations. Upon clinicopathological correlation, higher A3B expression was associated with more aggressive tumor behavior. Wild-type A3B (wt-A3B) overexpression in HCC cells promoted cell proliferation, and cell migratory and invasive abilities in vitro, and tumorigenicity and metastasis in vivo. On the other hand, knockdown of A3B suppressed cell proliferation, migratory, and invasive abilities of HCC cells with high endogenous A3B level. However, to our surprise, overexpression of A3B deaminase-dead double mutant (E68A/E255Q) led to similar results as wt-A3B in HCC. Furthermore, overexpression of wt-A3B and mutant A3B both enhanced cell cycle progression in HCC cells. Altogether, our data demonstrated a novel deaminase-independent role of A3B in contributing to HCC tumorigenesis and metastasis.

Deregulation of the Genes that Are Involved in Drug Absorption, Distribution, Metabolism, and Excretion in Hepatocellular Carcinoma

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are called ADME genes. Currently, 298 genes that encode phase I and II drug metabolizing enzymes, transporters, and modifiers are designated as ADME genes by the PharmaADME Consortium. ADME genes are highly expressed in the liver and their levels can be influenced by liver diseases such as hepatocellular carcinoma (HCC). In this study, we obtained RNA-sequencing and microRNA (miRNA)-sequencing data from 371 HCC patients via The Cancer Genome Atlas liver hepatocellular carcinoma project and performed ADME gene-targeted differential gene expression analysis and expression correlation analysis. Two hundred thirty-three of the 298 ADME genes (78%) were expressed in HCC. Of these genes, almost one-quarter (58 genes) were significantly downregulated, while only 6% (15) were upregulated in HCC relative to healthy liver. Moreover, one-half (14/28) of the core ADME genes (CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2E1, CYP3A4, NAT1, NAT2, UGT2B7, SLC22A1, SLCO1B1, and SLCO1B3) were downregulated. In addition, about one-half of the core ADME genes were positively correlated with each other and were also positively (AHR, ARNT, HNF4A, PXR, CAR, PPARA, and RXRA) or negatively (PPARD and PPARG) correlated with transcription factors known as ADME modifiers. Finally, we show that most miRNAs known to regulate core ADME genes are upregulated in HCC. Collectively, these data reveal 1) an extensive transcription factor-mediated ADME coexpression network in the liver that efficiently coordinates the metabolism and elimination of endogenous and exogenous compounds; and 2) a widespread deregulation of this network in HCC, most likely due to deregulation of both transcriptional and post-transcriptional (miRNA) pathways.

Current Challenges and Opportunities in Treating Glioblastoma

Glioblastoma multiforme (GBM), the most common and aggressive primary brain tumor, has a high mortality rate despite extensive efforts to develop new treatments. GBM exhibits both intra- and intertumor heterogeneity, lending to resistance and eventual tumor recurrence. Large-scale genomic and proteomic analysis of GBM tumors has uncovered potential drug targets. Effective and “druggable” targets must be validated to embark on a robust medicinal chemistry campaign culminating in the discovery of clinical candidates. Here, we review recent developments in GBM drug discovery and delivery. To identify GBM drug targets, we performed extensive bioinformatics analysis using data from The Cancer Genome Atlas project. We discovered 20 genes, BOC, CLEC4GP1, ELOVL6, EREG, ESR2, FDCSP, FURIN, FUT8-AS1, GZMB, IRX3, LITAF, NDEL1, NKX3-1, PODNL1, PTPRN, QSOX1, SEMA4F, TH, VEGFC, and C20orf166AS1 that are overexpressed in a subpopulation of GBM patients and correlate with poor survival outcomes. Importantly, nine of these genes exhibit higher expression in GBM versus low-grade glioma and may be involved in disease progression. In this review, we discuss these proteins in the context of GBM disease progression. We also conducted computational multi-parameter optimization to assess the blood-brain barrier (BBB) permeability of small molecules in clinical trials for GBM treatment. Drug delivery in the context of GBM is particularly challenging because the BBB hinders small molecule transport. Therefore, we discuss novel drug delivery methods, including nanoparticles and prodrugs. Given the aggressive nature of GBM and the complexity of targeting the central nervous system, effective treatment options are a major unmet medical need. Identification and validation of biomarkers and drug targets associated with GBM disease progression present an exciting opportunity to improve treatment of this devastating disease.

Identification of long noncoding RNAs biomarkers in patients with hepatitis B virus-associated hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common and aggressive cancer worldwide and chronic infection of hepatitis B virus (HBV) serve as one of leading causes of HCC.

OBJECTIVE

This study aimed to identify the novel long noncoding RNAs (lncRNAs) biomarkers for HBV-associated HCC.

METHODS

The lncRNA and mRNA expression profiles of HCC patients with HBV infection were downloaded from The Cancer Genome Atlas. The differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between HCC and adjacent tissues were identified. The optimal diagnostic and prognostic lncRNA biomarkers for HCC were identified by using feature selection procedure and classification model. Functional annotation of DEmRNAs co-expressed with these lncRNAs biomarkers were performed. Receiver operating characteristic (ROC) curve and survival analysis of these lncRNAs biomarkers were performed. qRT-PCR validation was performed.

RESULTS

A total of 82 DElncRNAs and 805 DEmRNAs between HBV-associated HCC and normal tissues were identified. CAPN10-AS1, LINC01093, RP5-890E16.2, FENDRR and C17orf82 were selected as optimal diagnostic and prognostic lncRNA biomarkers for HBV-associated HCC that were co-expressed with 105, 86, 70, 30 and 1 DEmRNAs, respectively. Based on the DEmRNAs co-expressed with these five lncRNAs biomarkers, Jak-STAT signaling pathway and retinol metabolism were two significantly enriched pathways. The result in qRT-PCR validation were consistent with our analysis based on TCGA, generally.

CONCLUSIONS

This study identified five potential lncRNAs biomarkers for HBV-associated HCC with great diagnostic and prognostic value and provided clues for their functions in HBV-associated HCC.

Identification of key genes and pathways by bioinformatics analysis with TCGA RNA sequencing data in hepatocellular carcinoma

Improved insight into the molecular characteristics of hepatocellular carcinoma (HCC) is required to predict prognosis and to develop a new rationale for targeted therapeutic strategy. Bioinformatics methods, including functional enrichment and network analysis combined with survival analysis, are required to process a large volume of data to obtain further information on differentially expressed genes (DEGs). The RNA sequencing data related to HCC in The Cancer Genome Atlas (TCGA) database were analyzed to screen DEGs, which were separately submitted to perform gene enrichment analysis to identify gene sets and signaling pathways, and to construct a protein-protein interaction (PPI) network. Subsequently, hub genes were selected by the core level in the network, and the top hub genes were focused on gene expression analysis and survival analysis. A total of 610 DEGs were identified, including 444 upregulated and 166 downregulated genes. The upregulated DEGs were significantly enriched in the Gene Ontology analysis (GO): Cell division and in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway: Cell cycle, whereas the downregulated DEGs were enriched in GO: Negative regulation of growth and in the KEGG pathway: Retinol metabolism, with significant differences. Cyclin-dependent kinase (CDK)1 was selected as the top hub gene by the PPI network, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Survival analysis revealed a significantly negative correlation between CDK1 expression level and overall survival in the TCGA group (P<0.01) and the GEO group (P<0.01). Therefore, high-throughput TCGA data analysis appears to be an effective method for screening tumor molecular markers, and high expression of CDK1 is a prognostic factor for HCC.

Dysfunction of Sister Chromatids Separation Promotes Progression of Hepatocellular Carcinoma According to Analysis of Gene Expression Profiling

Despite studying the various molecular mechanisms of hepatocellular carcinoma (HCC), effective drugs and biomarkers in HCC therapy are still scarce. The present study was designed to investigate dysregulated pathways, novel biomarkers and therapeutic targets for HCC. The gene expression dataset of GSE14520, which included 362 tumor and their paired non-tumor tissues of HCC, was extracted for processing by the Robust multi-array average (RMA) algorithm in the R environment. SAM methods were leveraged to identify differentially expressed genes (DEGs). Functional analysis of DEGs was performed using DAVID. The GeneMania and Cytohubba were used to construct the PPI network. To avoid individual bias, GSEA and survival analysis were employed to verify the results. The results of these analyses indicated that separation of sister chromatids was the most aberrant phase in the progression of HCC, and the most frequently involved genes, EZH2, GINS1, TPX2, CENPF, and BUB1B, require further study to be used as drug targets or biomarkers in diagnosis and treatment of HCC.

Precision oncology in liver cancer

With the widespread adoption of molecular profiling in clinical oncology practice, many physicians are faced with making therapeutic decisions based upon isolated genomic alterations. For example, epidermal growth factor receptor tyrosine kinase inhibitors (TKIs) are effective in EGFR-mutant non-small cell lung cancers (NSCLC) while anti-EGFR monoclonal antibodies are ineffective in Ras-mutant colorectal cancers. The matching of mutations with drugs aimed at their respective gene products represents the current state of "precision" oncology. Despite the great expectations of this approach, only a fraction of cancers responds to 'targeted' interventions, and many early responders will ultimately develop resistance to these agents. The underwhelming success of mutation-driven therapies across all cancer types is not due to an inability to detect genetic changes in tumors; rather a deficit in functional insight into the genomic alterations that give rise to each cancer. The Achilles heel of precision oncology thus remains the lack of a robust functional understanding of an individual cancer genome that then allows prediction of the best therapy and resultant outcome for that patient. Current practice focuses on one 'actionable' mutation at a time, while solid cancers typically possess many mutations that involve different cellular sub-populations within a tumor. No method or platform currently exists to guide the interpretation of these complex data, nor to accurately predict response to treatment. This problem is particularly germane to primary liver cancers (PLC), for which only a handful of targeted therapies have been introduced. Here, we will review strategies aimed at overcoming some of these challenges in precision oncology, using liver cancer as an example.

Development Of A Three-Gene Prognostic Signature For Hepatitis B Virus Associated Hepatocellular Carcinoma Based On Integrated Transcriptomic Analysis

Integration of public genome-wide gene expression data together with Cox regression analysis is a powerful weapon to identify new prognostic gene signatures for cancer diagnosis and prognosis. Hepatitis B virus (HBV) is a major cause of hepatocellular carcinoma (HCC), however, it remains largely unknown about the specific gene prognostic signature of HBV-associated HCC. Using Robust Rank Aggreg (RRA) method to integrate seven whole genome expression datasets, we identified 82 up-regulated genes and 577 down-regulated genes in HBV-associated HCC patients. Combination of several enrichment analysis, univariate and multivariate Cox proportional hazards regression analysis, we revealed that a three-gene (SPP2, CDC37L1, and ECHDC2) prognostic signature could act as an independent prognostic indicator for HBV-associated HCC in both the discovery cohort and the internal testing cohort. Gene set enrichment analysis showed that the high-risk group with lower expression levels of the three genes was enriched in bladder cancer and cell cycle pathway, whereas the low-risk group with higher expression levels of the three genes was enriched in drug metabolism-cytochrome P450, PPAR signaling pathway, fatty acid and histidine metabolisms. This indicates that patients of HBV-associated HCC with higher expression of these three genes may preserve relatively good hepatic cellular metabolism and function, which may also protect HCC patients from persistent drug toxicity in response to various medication. Our findings suggest a three-gene prognostic model that serves as a specific prognostic signature for HBV-associated HCC.

Transcriptome profiling identifies a recurrent CRYL1-IFT88 chimeric transcript in hepatocellular carcinoma

We performed transcriptome sequencing for hepatocellular carcinoma (HCC) and adjacent non-tumorous tissues to investigate the molecular basis of HCC. Nine HCC patients were recruited and differentially expressed genes (DEGs) were identified. Candidate fusion transcripts were also identified. A total of 1943 DEGs were detected, including 690 up-regulated and 1253 down-regulated genes, and enriched in ten pathways including cell cycle, DNA replication, p53, complement and coagulation cascades, etc. Seven candidate fusion genes were detected and CRYL1-IFT88 was successfully validated in the discovery sequencing sample and another 5 tumor samples with the recurrent rate of about 9.52% (6/63). The full length of CRYL1-IFT88 was obtained by 3′ and 5′ RACE. The function of the fusion transcript is closed to CRYL1 because it contained most of domain of CRYL1. According to the bioinformatics analysis, IFT88, reported as a tumor suppressor, might be seriously depressed in the tumor cell with this fusion because the transcript structure of IFT88 was totally changed. The function depression of IFT88 caused by gene fusion CRYL1-IFT88 might be associated with tumorigenesis or development of HCC.

Datasets describing the growth and molecular features of hepatocellular carcinoma patient-derived xenograft cells grown in a three-dimensional macroporous hydrogel

This data article presents datasets associated with the research article entitled “Generation of matched patient-derived xenograft in vitro–in vivo models using 3D macroporous hydrogels for the study of liver cancer” (Fong et al., 2018) [1]. A three-dimensional macroporous sponge system was used to generate in vitro counterparts to various hepatocellular carcinoma patient-derived xenograft (HCC-PDX) lines. This article describes the viability, proliferative capacity and molecular features (genomic and transcriptomic profiles) of the cultured HCC-PDX cells. The sequencing datasets are made publicly available to enable critical or further analyzes.

Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and it remains a challenge to understand the genetic mechanisms underlying hepatocarcinogenesis. A global gene network of differential expression profiles in HCC has yet to be fully characterized. In the present study, we performed transcriptome sequencing (mRNA and lncRNA) in liver cancer and cirrhotic tissues of nine HCC patients. We identified differentially expressed genes (DEGs) and constructed a weighted gene co-expression network for the DEGs. In total, 755 DEGs (747 mRNA and eight lncRNA) were identified, and several co-expression modules were significantly associated with HCC clinical traits, including tumor location, tumor grade, and the α-fetoprotein (AFP) level. Of note, we identified 15 hub genes in the module associated with AFP level, and three (SPX, AFP and ADGRE1) of four hub genes were validated in an independent HCC cohort (n=78). Identification of hub genes for HCC clinical traits has implications for further understanding of the molecular genetic basis of HCC.

Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples

Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches. However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation. In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples. Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples. On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time. The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples. Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples. The signature was validated in four datasets of FFPE samples and eight datasets of FF samples. In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA.

Cripto-1 contributes to stemness in hepatocellular carcinoma by stabilizing Dishevelled-3 and activating Wnt/β-catenin pathway

Identification and characterization of functional molecular targets conferring stemness properties in hepatocellular carcinoma (HCC) offers crucial insights to overcome the major hurdles of tumor recurrence, metastasis and chemoresistance in clinical management. In the current study, we investigated the significance of Cripto-1 in contributing to HCC stemness. Cripto-1 was upregulated in the sorafenib-resistant clones derived from HCC cell lines and patient-derived xenograft that we previously developed, suggesting an association between Cripto-1 and stemness. By in vitro experiments, Cripto-1 fostered cell proliferation, migration, and invasion. It also enhanced self-renewal ability and conferred chemoresistance of HCC cells. Consistently, silencing of Cripto-1 suppressed in vivo tumorigenicity on serial transplantation. On the downstream signaling mechanism, expression of major components of Wnt/β-catenin pathway β-catenin, AXIN2, and C-MYC, accompanied by β-catenin activity was reduced upon Cripto-1 knockdown. The suppressive effects on stemness properties with Cripto-1 knockdown in vitro and in vivo were partially rescued by forced expression of constitutively active β-catenin. Further elucidation revealed the binding of Cripto-1 to Frizzled-7 (FZD7), low-density lipoprotein receptor-related protein 6 (LRP6) and Dishevelled-3 (DVL3) of the Wnt/β-catenin pathway and stabilized DVL3 protein. Analyses with clinical samples validated Cripto-1 overexpression in HCC tissues, as well as a positive correlation between Cripto-1 and AXIN2 expressions. High Cripto-1 level in tumor was associated with poorer disease-free survival of HCC patients. Taken together, Cripto-1 binds to FZD7/LRP6 and DVL3, stabilizes DVL3 expression and activates the Wnt/β-catenin signaling cascade to confer stemness in HCC. Our study findings substantiated the role of Cripto-1 in determining stemness phenotypes of HCC and mechanistically in modulating the Wnt/β-catenin signaling cascade, one of the most frequently deregulated pathways in liver cancer.

Distinct expression of CDCA3, CDCA5, and CDCA8 leads to shorter relapse free survival in breast cancer patient

Breast cancer is a dangerous disease that results in high mortality rates for cancer patients. Many methods have been developed for the treatment and prevention of this disease. Determining the expression patterns of certain target genes in specific subtypes of breast cancer is important for developing new therapies for breast cancer. In the present study, we performed a holistic approach to screening the mRNA expression of six members of the cell division cycle-associated gene family (CDCA) with a focus on breast cancer using the Oncomine and The Cancer Cell Line Encyclopedia (CCLE) databases. Furthermore, Gene Expression-Based Outcome for Breast Cancer Online (GOBO) was also used to deeply mine the expression of each CDCA gene in clinical breast cancer tissue and breast cancer cell lines. Finally, the mRNA expression of the CDCA genes as related to breast cancer patient survival were analyzed using a Kaplan-Meier plot. CDCA3, CDCA5, and CDCA8 mRNA expression levels were significantly higher than the control sample in both clinical tumor sample and cancer cell lines. These highly expressed genes in the tumors of breast cancer patients dramatically reduced patient survival. The interaction network of CDCA3, CDCA5, and CDCA8 with their co-expressed genes also revealed that CDCA3 expression was highly correlated with cell cycle related genes such as CCNB2, CDC20, CDKN3, and CCNB1. CDCA5 expression was correlated with BUB1 and TRIP13, while CDCA8 expression was correlated with BUB1 and CCNB1. Altogether, these findings suggested CDCA3, CDCA5, and CDCA8 could have a high potency as targeted breast cancer therapies.

Generation of matched patient-derived xenograft in vitro-in vivo models using 3D macroporous hydrogels for the study of liver cancer

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide, often manifesting at the advanced stage when cure is no longer possible. The discrepancy between preclinical findings and clinical outcome in HCC is well-recognized. So far, sorafenib is the only targeted therapy approved as first-line therapy for patients with advanced HCC. There is an urgent need for improved preclinical models for the development of HCC-targeted therapies. Patient-derived xenograft (PDX) tumor models have been shown to closely recapitulate human tumor biology and predict patient drug response. However, the use of PDX models is currently limited by high costs and low throughput. In this study, we engineered in vitro conditions conducive for the culture of HCC-PDX organoids derived from a panel of 14 different HCC-PDX lines through the use of a three-dimensional macroporous cellulosic sponge system. To validate the in vitro HCC-PDX models, both in vivo and in vitro HCC-PDX models were subjected to whole exome sequencing and RNA-sequencing. Correlative studies indicate strong concordance in genomic and transcriptomic profiles as well as intra-tumoral heterogeneity between each matched in vitro-in vivo HCC-PDX pairs. Furthermore, we demonstrate the feasibility of using these in vitro HCC-PDX models for drug testing, paving the way for more efficient preclinical studies in HCC drug development.

Virtual Sorting Has a Distinctive Advantage in Identification of Anticorrelated Genes and Further Negative Regulators of Immune Cell Subpopulations

Immune cells are highly plastic in both gene expression and cell phenotype. We have established a method of gene expressional plasticity and virtual sorting to evaluate immune cell subpopulations and their characteristic genes in human CD4⁺ T cells. In this study, we continued to investigate the informatics mechanism on the effectiveness of virtual sorting. We found that virtual sorting had an overall positive correlation to the Pearson correlation in the identification of positively correlated genes. However, owing to nonlinear biological anticorrelation, virtual sorting showed a distinctive advantage for anticorrelated genes, suggesting an important role in the identification of negative regulators. In addition, based on virtual sorting results, we identified two basic gene sets among highly plastic genes, i.e., highly plastic cell cycle-associated molecules and highly plastic immune and defense response-associated molecules. Genes within each set tended to be positively connected, but genes between two sets were often anticorrelated. Further analysis revealed preferential transcription factor binding motifs existed between highly plastic cell cycle-associated molecules and highly plastic immune and defense response-associated molecules. Our results strongly suggested predetermined regulation, which was called an immune cell internal phenotype, should exist and could be mined by virtual sorting analysis. This provided efficient functional clues to study immune cell phenotypes and their regulation. Moreover, the current substantial virtual sorting results in both CD4⁺ T cells and B cells provide a useful resource for big-data-driven experimental studies and knowledge discoveries.

The CCCTC-binding factor (CTCF)-forkhead box protein M1 axis regulates tumour growth and metastasis in hepatocellular carcinoma

CCCTC‐binding factor (CTCF) is a DNA‐binding protein that interacts with a large number of highly divergent target sequences throughout the genome. It is implicated in a variety of functions, including chromatin organization and transcriptional control. The functional role of CTCF in tumour pathogenesis remains elusive. We showed that CTCF is frequently upregulated in a subset of primary hepatocellular carcinomas (HCCs) as compared with non‐tumoural liver. Overexpression of CTCF was associated with shorter disease‐free survival of patients. Short hairpin RNA (shRNA)‐mediated suppression of CTCF inhibited cell proliferation, motility and invasiveness in HCC cell lines; these effects were correlated with prominent reductions in the expression of telomerase reverse transcriptase (TERT), the shelterin complex member telomerase repeat‐binding factor 1, and forkhead box protein M1 (FOXM1). In contrast, upregulation of CTCF was positively correlated with FOXM1 and TERT expression in clinical HCC biopsies. Depletion of CTCF resulted in reduced motility and invasiveness in HCC cells that could be reversed by ectopic expression of FOXM1, suggesting that FOXM1 is one of the important downstream effectors of CTCF in HCC. Reporter gene analysis suggested that depletion of CTCF is associated with reduced FOXM1 and TERT promoter activity. Chromatin immunoprecipitation (ChIP)–polymerase chain reaction (PCR) analysis further revealed occupancy of the FOXM1 promoter by CTCF in vivo. Importantly, depletion of CTCF by shRNA significantly inhibited tumour progression and metastasis in HCC mouse models. Our work uncovered a novel functional role of CTCF in HCC pathogenesis, which suggests that targeting CTCF could be further explored as a potential therapeutic strategy for HCC. © 2017 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

The protective value of miR-204-5p for prognosis and its potential gene network in various malignancies: a comprehensive exploration based on RNA-seq high-throughput data and bioinformatics

Purpose

The prognostic role of miR-204-5p (previous ID: miR-204) is varied and inconclusive in diverse types of malignant neoplasm. Therefore, the purposes of the study comprehensively explore the overall prognostic role of miR-204-5p based on high-throughput microRNA sequencing data, and to investigate the potential role of miR-204-5p via bioinformatics approaches.

Materials and Methods

The data of microRNA sequencing and survival were downloaded from The Cancer Genome Atlas (TCGA), and the prognostic value of miR-204-5p was analyzed by using Kaplan-Meier and univariate cox regressions. Then a meta-analysis was conducted with all TCGA data and relevant studies collected from literature. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. The prospective molecular mechanism of miR-204-5p was also assessed at a functional level with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-to-protein interactions (PPI) network.

Results

From TCGA data, the prognostic value of miR-204-5p obviously varied among 20 types of cancers. The pooled HR was 0.928 (95% CI: 0.774-1.113, P = 0.386, 6203 cases of malignancies). For the meta-analysis based on 15 studies from literature, the pooled HR was 0.420 (95% CI: 0.306-0.576, P < 0.001, 1783 cases of malignancies) for overall survival (OS). Furthermore, the combined HR from both TCGA and literature was 0.708 (95% CI: 0.600-0.834, P < 0.001, 7986 cases of malignancies). Subgroup analyses revealed that miR-204-5p could act as a prognostic marker in cancers of respiratory system and digestive system. Functional analysis was conducted on genes predicted as targets (n = 2057) after the overlay genes from six out of twelve software were extracted. Two significant KEGG pathways were enriched (hsa04360: Axon guidance and hsa04722: Neurotrophin signaling pathway). PPI network revealed some hub genes/proteins (CDC42, SOS1, PIK3R1, MAPK1, PLCG1, ESR1, MAPK11, and AR).

Conclusions

The current study demonstrates that over-expression of miR-204-5p could be a protective factor for a certain group of cancers. Clinically, the low miR-204-5p level could gain a predictive value for a poor survival in cancers of respiratory system and digestive system. The detailed molecular mechanisms of miR-204-5p remain to be verified.

FoxM1: Repurposing an oncogene as a biomarker

The past few decades have witnessed a tremendous progress in understanding the biology of cancer, which has led to more comprehensive approaches for global gene expression profiling and genome-wide analysis. This has helped to determine more sophisticated prognostic and predictive signature markers for the prompt diagnosis and precise screening of cancer patients. In the search for novel biomarkers, there has been increased interest in FoxM1, an extensively studied transcription factor that encompasses most of the hallmarks of malignancy. Considering the attractive potential of this multifarious oncogene, FoxM1 has emerged as an important molecule implicated in initiation, development and progression of cancer. Bolstered with the skill to maneuver the proliferation signals, FoxM1 bestows resistance to contemporary anti-cancer therapy as well. This review sheds light on the large body of literature that has accumulated in recent years that implies that FoxM1 neoplastic functions can be used as a novel predictive, prognostic and therapeutic marker for different cancers. This assessment also highlights the key features of FoxM1 that can be effectively harnessed to establish FoxM1 as a strong biomarker in diagnosis and treatment of cancer.

Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma

Background/Aims

The integration of multiple profiling data and the construction of a transcriptional regulatory network may provide additional insights into the molecular mechanisms of hepatocellular carcinoma (HCC). The present study was conducted to investigate the deregulation of genes and the transcriptional regulatory network in HCC.

Methods

An integrated analysis of HCC gene expression datasets was performed in Gene Expression Omnibus. Functional annotation of the differentially expression genes (DEGs) was conducted. Furthermore, transcription factors (TFs) were identified, and a global transcriptional regulatory network was constructed.

Results

An integrated analysis of eight eligible gene expression profiles of HCC led to 1,835 DEGs. Consistent with the fact that the cell cycle is closely related to various tumors, the functional annotation revealed that genes involved in the cell cycle were significantly enriched. A transcriptional regulatory network was constructed using the 62 TFs, which consisted of 872 TF-target interactions between 56 TFs and 672 DEGs in the context of HCC. The top 10 TFs covering the most downstream DEGs were ZNF354C, NFATC2, ARID3A, BRCA1, ZNF263, FOXD1, GATA3, FOXO3, FOXL1, and NR4A2. This network will appeal to future investigators focusing on the development of HCC.

Conclusions

The transcriptional regulatory network can provide additional information that is valuable in understanding the underlying molecular mechanism in hepatic tumorigenesis.

Secretory Stanniocalcin 1 promotes metastasis of hepatocellular carcinoma through activation of JNK signaling pathway

The hypoxic microenvironment is well-characterized in hepatocellular carcinoma (HCC). Delineation of hypoxia-responsive events is an integral part to understand the pathogenesis of HCC. We studied the functional role and clinical relevance of Stanniocalcin 1 (STC1), a hypoxia-induced molecular target, in HCC. In our clinical cohort, STC1 transcript was up-regulated in HCC tumor tissues. Moreover, STC1 protein was detected in the sera of HCC patients. A higher serum STC1 level was correlated with larger tumor size and poorer 5-year disease-free survival. Functionally, recombinant STC1 protein (rhSTC1) promoted cell migration and cell invasion in vitro; and the effect was abolished by co-treatment of anti-STC1 neutralizing antibody. By in vivo mouse model, silencing of STC1 in HCC cells downregulated secretory STC1 level and suppressed lung metastasis. Furthermore, we found that rhSTC1 activated the JNK pathway, as evidenced by altered expression of the key molecular targets pJNK and p-c-Jun. The functional effects conferred by rhSTC1 were abrogated by co-treatment of JNK inhibitor. In summary, secretory STC1 enhances metastatic potential of HCC via JNK signaling. It potentially serves as a prognostic serum biomarker and a therapeutic target for HCC.

Identification and functional analysis of differentially expressed genes in poorly differentiated hepatocellular carcinoma using RNA-seq

Poorly differentiated (PD) hepatocellular carcinoma (HCC) has a worse prognosis compared to moderately differentiated (MD) and well differentiated (WD) HCC. We aimed to identify differentially expressed genes (DEGs) to explore the mechanism of PD HCC. Transcriptome sequencing was performed on tumor and adjacent non-tumorous tissues of PD, MD and WD HCC patients (3 for each group). DEGs were thus identified and functionally analyzed. Further RT-PCR was performed to validate DEGs specific for PD HCC in 47 pairs of samples (15 for PD, 18 for MD, 14 for WD). A total of 681 PD DEGs were detected, including 368 up-regulated and 313 down-regulated genes. Less DEGs were found for MD and especially for WD HCC. Through bioinformatics analysis, PD HCC DEGs were enriched in liver tissue and liver cancer cells, and in biological process and pathway including metabolism, cell cycle, translation and blood coagulation. Potential drugs and genetic perturbations were found to reverse the cancer condition. The RT-PCR results showed consistency with RNA-seq in the validation of 4 DEGs specific for PD HCC. This study detected and validated DEGs of PD HCC, which provides useful information on molecular mechanism of PD HCC for development of new biomarkers, therapeutic targets and drugs.

Enhancing identification of cancer types via lowly-expressed microRNAs

The primary function of microRNAs (miRNAs) is to maintain cell homeostasis. In cancerous tissues miRNAs' expression undergo drastic alterations. In this study, we use miRNA expression profiles from The Cancer Genome Atlas of 24 cancer types and 3 healthy tissues, collected from >8500 samples. We seek to classify the cancer's origin and tissue identification using the expression from 1046 reported miRNAs. Despite an apparent uniform appearance of miRNAs among cancerous samples, we recover indispensable information from lowly expressed miRNAs regarding the cancer/tissue types. Multiclass support vector machine classification yields an average recall of 58% in identifying the correct tissue and tumor types. Data discretization had led to substantial improvement, reaching an average recall of 91% (95% median). We propose a straightforward protocol as a crucial step in classifying tumors of unknown primary origin. Our counter-intuitive conclusion is that in almost all cancer types, highly expressing miRNAs mask the significant signal that lower expressed miRNAs provide.

From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

Background

Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.

Methods

Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.

Results

A list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.

Discussion

The study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

iTRAQ-Based Membrane Proteomics Reveals Plasma Membrane Proteins Change During HepaRG Cell Differentiation

HepaRG cell, a stabilized bipotent liver progenitor cell line, exhibits hepatocyte functions only after differentiation. However, the mechanism of transition from nondifferentiated to differentiated states, accompanied by proliferation migration and differentiation, remains poorly understood, particularly those proteins residing in the plasma membrane. In this study, the membrane protein expression change of HepaRG cell during differentiation were systematically analyzed using an iTRAQ labeled quantitative membrane proteomics approach. A total of 70 membrane proteins were identified to be differentially expressed among 849 quantified membrane proteins. Function and disease clustering analysis proved that 11 of these proteins are involved in proliferation, migration, and differentiation. Two key factors (MMP-14 and OCLN) were validated by qRT-PCR and Western blot. Blockade of MMP-14 further demonstrated its important function during tumor cell migration. The data sets have been uploaded to ProteomeXchange with the identifier PXD004752.

Molecular Pathogenesis of Hepatocellular Carcinoma

The pathogenesis of hepatocellular carcinoma (HCC) is a multistep process involving the progressive accumulation of molecular alterations pinpointing different molecular and cellular events. The next-generation sequencing technology is facilitating the global and systematic evaluation of molecular landscapes in HCC. There is emerging evidence supporting the importance of cancer metabolism and tumor microenvironment in providing a favorable and supportive niche to expedite HCC development. Moreover, recent studies have identified distinct surface markers of cancer stem cell (CSC) in HCC, and they also put forward the profound involvement of altered signaling pathways and epigenetic modifications in CSCs, in addition to the concomitant drug resistance and metastasis. Taken together, multiple key genetic and non-genetic factors, as well as liver CSCs, result in the development and progression of HCC.

Microarray-based identification of genes associated with cancer progression and prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths. The average survival and 5-year survival rates of HCC patients still remains poor. Thus, there is an urgent need to better understand the mechanisms of cancer progression in HCC and to identify useful biomarkers to predict prognosis.

METHODS

Public data portals including Oncomine, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) profiles were used to retrieve the HCC-related microarrays and to identify potential genes contributed to cancer progression. Bioinformatics analyses including pathway enrichment, protein/gene interaction and text mining were used to explain the potential roles of the identified genes in HCC. Quantitative real-time polymerase chain reaction analysis and Western blotting were used to measure the expression of the targets. The data were analysed by SPSS 20.0 software.

RESULTS

We identified 80 genes that were significantly dysregulated in HCC according to four independent microarrays covering 386 cases of HCC and 327 normal liver tissues. Twenty genes were consistently and stably dysregulated in the four microarrays by at least 2-fold and detection of gene expression by RT-qPCR and western blotting showed consistent expression profiles in 11 HCC tissues compared with corresponding paracancerous tissues. Eleven of these 20 genes were associated with disease-free survival (DFS) or overall survival (OS) in a cohort of 157 HCC patients, and eight genes were associated with tumour pathologic PT, tumour stage or vital status. Potential roles of those 20 genes in regulation of HCC progression were predicted, primarily in association with metastasis. INTS8 was specifically correlated with most clinical characteristics including DFS, OS, stage, metastasis, invasiveness, diagnosis, and age.

CONCLUSION

The significantly dysregulated genes identified in this study were associated with cancer progression and prognosis in HCC, and might be potential therapeutic targets for HCC treatment or potential biomarkers for diagnosis and prognosis.

Bioinformatics Analysis Reveals Distinct Molecular Characteristics of Hepatitis B-Related Hepatocellular Carcinomas from Very Early to Advanced Barcelona Clinic Liver Cancer Stages

Hepatocellular carcinoma (HCC)is the fifth most common malignancy associated with high mortality. One of the risk factors for HCC is chronic hepatitis B virus (HBV) infection. The treatment strategy for the disease is dependent on the stage of HCC, and the Barcelona clinic liver cancer (BCLC) staging system is used in most HCC cases. However, the molecular characteristics of HBV-related HCC in different BCLC stages are still unknown. Using GSE14520 microarray data from HBV-related HCC cases with BCLC stages from 0 (very early stage) to C (advanced stage) in the gene expression omnibus (GEO) database, differentially expressed genes (DEGs), including common DEGs and unique DEGs in different BCLC stages, were identified. These DEGs were located on different chromosomes. The molecular functions and biology pathways of DEGs were identified by gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the interactome networks of DEGs were constructed using the NetVenn online tool. The results revealed that both common DEGs and stage-specific DEGs were associated with various molecular functions and were involved in special biological pathways. In addition, several hub genes were found in the interactome networks of DEGs. The identified DEGs and hub genes promote our understanding of the molecular mechanisms underlying the development of HBV-related HCC through the different BCLC stages, and might be used as staging biomarkers or molecular targets for the treatment of HCC with HBV infection.