Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

Integrated bioinformatic analysis reveals YWHAB as a novel diagnostic biomarker for idiopathic pulmonary arterial hypertension

Idiopathic pulmonary arterial hypertension (IPAH) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for IPAH have not been identified. The aim of this study was to explore the potential diagnostic biomarkers and possible regulatory targets of IPAH. We performed a weighted gene coexpression network analysis and calculated module-trait correlations based on a public microarray data set (GSE703) and six modules were found to be related to IPAH. Two modules which have the strongest correlation with IPAH were further analyzed and the top 10 hub genes in the two modules were identified. Furthermore, we validated the data by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent sample set originated from our study center. Overall, the qRT-PCR results were consistent with most of the results of the microarray analysis. Intriguingly, the highest change was found for YWHAB, a gene encodes a protein belonging to the 14-3-3 family of proteins, members of which mediate signal transduction by binding to phosphoserine-containing proteins. Thus, YWHAB was subsequently selected for validation. In congruent with the gene expression analysis, plasma 14-3-3β concentrations were significantly increased in patients with IPAH compared with healthy controls, and 14-3-3β expression was also positively correlated with mean pulmonary artery pressure ( R ² = 0.8783; p < 0.001). Taken together, using weighted gene coexpression analysis, YWHAB was identified and validated in association with IPAH progression, which might serve as a biomarker and/or therapeutic target for IPAH.     

Microarray-based gene expression analysis of hepatocellular carcinoma

Microarray studies have successfully shed light on various aspects of the molecular mechanisms behind the development of hepatocellular carcinoma (HCC), such as the identification of novel molecular subgroups and the genetic profiles associated with metastasis and venous invasion. These experiments, mainly comprising genome wide profiling, potentially represent the basis of novel targeted therapeutic strategies in HCC. In response, we summarize the multiple reported expression profiles in HCC associated with HCC development, novel subgroups, venous invasion and metastasis.     

Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency.Methods: Lasso regression analysis followed by univariate Cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset {"type":"entrez-geo","attrs":{"text":"GSE14520","term_id":"14520"}}GSE14520. R package ‘clusterProfiler’ was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by single-sample GSEA (ssGSEA) of R package ‘GSVA’.Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective by combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition.Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.     

Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Genome-scale CRISPRa screening identifies MTX1 as a contributor for sorafenib resistance in hepatocellular carcinoma by augmenting autophagy

Sorafenib is the standard first-line drug for the treatment of advanced hepatocellular carcinoma (HCC), however, its therapeutic efficacy is not satisfactory due to primary or secondary resistance of HCC cells. In the present study, we identified Metaxin 1 (MTX1) as a new regulator of sorafenib resistance in HCC through genome-scale CRISPR activation (CRISPRa) screening. We found that MTX1 was frequently upregulated in HCC tissues and overexpression of MTX1 promoted HCC cell proliferation in vitro and in vivo. As well, MTX1 overexpression increased cell growth rate and decreased cell apoptosis upon sorafenib treatment. Consistently, the resistance induced by MTX1 was also observed in subcutaneous xenograft tumor model. Clinically, high expression of MTX1 was closely related with poor outcomes in HCC patients who received sorafenib treatment. Mechanistically, overexpression of MTX1 could promote HCC cell autophagy via interacting with and inhibiting CDGSH iron sulfur domain 1 (CISD1), an autophagy negative regulator. Taken together, our findings suggest that MTX1 is upregulated in HCC and contributes to sorafenib resistance via a possible mechanism involving CISD1 mediated autophagy.     

FXYD6: a novel therapeutic target toward hepatocellular carcinoma

FXYD6, FXYD domain containing ion transport regulator 6, has been reported to affect the activity of Na⁺/K⁺-ATPase and be associated with mental diseases. Here, we demonstrate that FXYD6 is up-regulated in hepatocellular carcinoma (HCC) and enhances the migration and proliferation of HCC cells. Up-regulation of FXYD6 not only positively correlates with the increase of Na⁺/K⁺-ATPase but also coordinates with the activation of its downstream Src-ERK signaling pathway. More importantly, blocking FXYD6 by its functional antibody significantly inhibits the growth potential of the xenografted HCC tumors in mice, indicating that FXYD6 represents a potential therapeutic target toward HCC. Altogether, our results establish a critical role of FXYD6 in HCC progression and suggest that the therapy targeting FXYD6 can benefit the clinical treatment toward HCC patients.     

Prognostic evaluation and immune infiltration analysis of five bioinformatic selected genes in hepatocellular carcinoma

Despite the development in hepatocellular carcinoma (HCC) treatment in recent years, the therapeutic outcome of HCC remains unfavourable. This study examines the prognosis of HCC from a genetic level using clinical databases and single-cell data to identify genes with a high prognostic value. Three up-regulated genes (UBE2S, PTTG1, and CDC20) and two down-regulated genes (SOCS2 and DNASE1L3) in HCC tissues were identified. Various analyses confirmed its correlation with tumour stage (p < 0.01) and patient survival time (log-rank p < 0.001). Immune analysis, single-cell analysis, and gene set enrichment analysis (GSEA) were employed to provide insight on how they affect cancer progression, and we observed a close relation between these genes and tumour immune infiltration. Eventually, we constructed a risk score system that risk score = (0.0465) × UBE2S + (0.1851) × CDC20 + (−0.0461) × DNASE1L3 + (−0.2279) × SOCS2 (5-year area under curve = 0.706). The risk score system may serve as an effective novel prognostic system for HCC patients. This study might provide novel ideas for prognostic or therapeutic biomarkers for HCC.     

A prognostic model for the combined analysis of gene expression profiling in hepatocellular carcinoma

Microarray techniques using cDNA array and comparative genomic hybridization (CGH) have been developed for several discovery applications. They are frequently applied for the prediction and diagnosis of cancer in recent years. Many studies have shown that integrating genomic data from different sources may increase the reliability of gene expression analysis results in understanding cancer progression. Therefore, developing a good prognostic model dealing simultaneously with different types of dataset is important. The challenge with these types of data is high background noise. We describe an analytical two-stage framework with a multi-parallel data analysis method named wavelet-based generalized singular value decomposition and shaving method (WGSVD-shaving). This method is proposed for de-noising and dimension-reduction during early stage prognosis modeling. We also applied a supervised gene clustering technique with penalized logistic regression with Cox-model on an integrated data. We show the accuracy of the method using a simulated dataset with a case study on Hepatocelluar Carcinoma (HCC) cDNA and CGH data. The method shows improved results from GSVD-shaving and has application in the discovery of candidate genes associated with cancer.     

Construction of a competing endogenous RNA network to analyse glucose-6-phosphate dehydrogenase dysregulation in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumour with high rates of morbidity and mortality worldwide. Therefore, it is of great significance to find new molecular markers for HCC diagnosis and treatment. G6PD is known to be dysregulated in a variety of tumours. In addition, the ceRNA network plays a crucial role in the occurrence and development of HCC. However, the mechanism by which the ceRNA network regulates G6PD in HCC remains unclear. We used TCGA-LIHC data to analyse the possibility of using G6PD as an independent prognostic marker. Univariate Cox proportional hazards regression, multivariate Cox proportional hazards regression, and receiver operating characteristic curve analysis were used to analyse the influence of G6PD overexpression on the prognosis of HCC patients. We also analysed the biological function of G6PD, its effect on the immune microenvironment, and drug sensitivity. Finally, we constructed a ceRNA network of lncRNAs/miR-122-5p/G6PD to explore the regulatory mechanism of G6PD. G6PD was highly expressed in HCC, was related to pathological stage and poor prognosis, and could be used as an independent prognostic indicator of HCC. The expression of G6PD was closely related to the immune microenvironment of HCC. In addition, the expression of G6PD in HCC could be regulated by the ceRNA network. Therefore, G6PD can be used as an immunotherapy target to improve the survival and prognosis of HCC patients, and the ceRNA regulatory network of G6PD has potential diagnostic and therapeutic value for HCC.     

Identification of LZAP as a new candidate tumor suppressor in hepatocellular carcinoma

Background

LZAP was isolated as a binding protein of the Cdk5 activator p35. LZAP has been highly conserved during evolution and has been shown to function as a tumor suppressor in various cancers. This study aimed to investigate LZAP expression and its prognostic value in hepatocellular carcinoma (HCC). Meanwhile, the function of LZAP in hepatocarcinogenesis was further investigated in cell culture models and mouse models.

Methods

Real-time quantitative PCR, western blot and immunohistochemistry were used to explore LZAP expression in HCC cell lines and primary HCC clinical specimens. The functions of LZAP in the proliferation, colony formation, cell cycle, migration, invasion and apoptosis of HCC cell lines were also analyzed by infecting cells with an adenovirus containing full-length LZAP. The effect of LZAP on tumorigenicity in nude mice was also investigated.

Results

LZAP expression was significantly decreased in the tumor tissues and HCC cell lines. Clinicopathological analysis showed that LZAP expression was significantly correlated with tumor size, histopathological classification and serum α-fetoprotein (AFP). The Kaplan–Meier survival curves revealed that decreasing LZAP expression was associated with poor prognosis in HCC patients. LZAP expression was an independent prognostic marker of overall HCC patient survival in a multivariate analysis. The re-introduction of LZAP expression in the HepG2 and sk-Hep1 HCC cell lines significantly inhibited proliferation and colony formation in the HCC cells and induced G1 phase arrest and apoptosis of the HCC cells in vitro. Restoring LZAP expression in the HCC cell lines also inhibited migration and invasion. In addition, experiments with a mouse model revealed that LZAP overexpression could suppress HCC tumorigenicity in vivo.

Conclusions

Our data suggest that LZAP may play an important role in HCC progression and could be a potential molecular therapy target for HCC.     

Identification of circulating protein biomarkers in patients with hepatocellular carcinoma concomitantly infected with chronic hepatitis C virus

Context: The incidence rate of hepatocellular carcinoma (HCC) is higher in developing countries, and most cases are associated with chronic hepatitis C virus (HCV) infection.

Objective: To evaluate the circulating proteins as liver biomarkers for the identification of HCC associated with HCV infection in Egyptian patients using LC-MS/MS analysis.

Methods: Blood sera were collected from 31 HCC patients and the fractionated proteins were subjected to LC-MS/MS analysis. Protein candidates were validated by enzyme-linked immunosorbent assay (ELISA).

Results: Thirty-three proteins were significantly identified in the sera of HCC patients with persistent HCV infection. These proteins are involved in several biological processes including acute phase response, complement activation, hemostasis process and lipid metabolism. The level of lectin galactoside-binding soluble 3 binding protein (LGALS3BP), Kininogen-1 (KNG1), serum amyloid A2 (SAA2) and paraoxonase 1 (PON1) and alpha-fetoprtoein (AFP) were elevated in serum.

Conclusion: In HCC patients with chronic HCV infection, we identified a group of differentially expressed circulating proteins involved in regulating different cellular mechanisms.