Identification of hub genes and potential drugs in hepatocellular carcinoma through integrated bioinformatics analysis

Abstract BackgroundHepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven-genes and potential drugs in HCC. MethodsThree mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, GO terms analysis and KEGG enrichment analysis, construction of protein–protein interaction network. The expression levels of hub genes were validated based on TCGA, GEPIA and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of the hub genes were further conducted by Kaplan-Meier plotter and the GEPIA. DGIdb database was performed to search the candidate drugs for HCC. ResultsFinally, 197 DEGs were identified. The PPI network was constructed using STRING software. Then ten genes were selected and considered as the hub genes. The ten genes were all closely related to the survival of HCC patients. DGIdb database predicted 39 small molecules as the possible drugs for treating HCC. ConclusionsOur study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in future.

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Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-019-53471-0 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Chengzhang Li ◽

Jiucheng Xu

Keyword(s):

Hepatocellular Carcinoma ◽

Random Forest ◽

Interaction Network ◽

Enrichment Analysis ◽

Maximal Clique ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Fisher Score ◽

Protein Protein Interaction ◽

Kaplan Meier

AbstractThis study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

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Expression and prognostic roles of PRDXs gene family in hepatocellular carcinoma

Journal of Translational Medicine ◽

10.1186/s12967-021-02792-8 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Mingxing Xu ◽

Jianliang Xu ◽

Dun Zhu ◽

Rishun Su ◽

Baoding Zhuang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Acid Metabolism ◽

Family Members ◽

Interaction Network ◽

Genetic Alterations ◽

Database Analysis ◽

Kaplan Meier ◽

Canonical Pathways ◽

Human Protein Atlas ◽

Kaplan Meier Plotter

Abstract Background As the fourth leading cause of cancer-related death in the world, the therapeutic effect and 5-year overall survival of hepatocellular carcinoma (HCC) are not optimistic. Previous researches indicated that the disorder of PRDXs was related to the occurrence and development of cancers. Methods In this study, PRDXs were found in various tumor cell lines by CCLE database analysis. The analysis results of UALCAN, HCCDB and Human Protein Atlas databases showed the expression of PRDXs mRNA and protein in HCC tissues was dysregulated. Besides, UALCAN was used to assess the correlations between PRDXs mRNA as well as methylation levels and clinical characterization. Results High expression of PRDX1 or low expression of PRDX2/3 suggested poor prognosis for HCC patients which was demonstrated by Kaplan–Meier Plotter. The genetic alterations and biological interaction network of PRDXs in HCC samples were obtained from c-Bioportal. In addition, LinkedOmics was employed to analyze PRDXs related differentially expressed genes, and on this basis, enrichment of KEGG pathway and miRNAs targets of PRDXs were conducted. The results indicated that these genes were involved in several canonical pathways and certain amino acid metabolism, some of which may effect on the progression of HCC. Conclusions In conclusion, the disordered expression of some PRDX family members was associated with the prognosis of HCC patients, suggesting that these PRDX family members may become new molecular targets for the treatment and prognosis prediction of HCC.

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Three novel circRNAs upregulated in tissue and plasma from hepatocellular carcinoma patients and their regulatory network

Cancer Cell International ◽

10.1186/s12935-021-01762-w ◽

2021 ◽

Vol 21 (1) ◽

Cited By ~ 1

Author(s):

Lianghai Wang ◽

Lisha Zhou ◽

Jun Hou ◽

Jin Meng ◽

Ke Lin ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regulatory Network ◽

Expression Profiles ◽

Interaction Network ◽

Enrichment Analysis ◽

Circular Rnas ◽

Pathway Enrichment Analysis ◽

Plasma Samples ◽

Hub Genes ◽

Protein Protein Interaction

Abstract Background The regulatory roles of circular RNAs (circRNAs) in tumorigenesis have attracted increasing attention. However, novel circRNAs with the potential to be used as serum/plasma biomarkers and their regulatory mechanism in the pathogenesis of hepatocellular carcinoma (HCC) remain explored. Methods CircRNA expression profiles of tumor tissues and plasma samples from HCC patients were compiled and jointly analyzed. CircRNA–miRNA–mRNA interactions were predicted by bioinformatics tools. The expression of interacting miRNAs and mRNA was verified in independent datasets. Survival analysis and pathway enrichment analysis were conducted on hub genes. Results We identified three significantly up-regulated circRNAs (hsa_circ_0009910, hsa_circ_0049783, and hsa_circ_0089172) both in HCC tissues and plasma samples. Two of them were validated to be indeed circular and could be excreted from hepatoma cells. We further revealed four miRNAs (hsa-miR-455-5p, hsa-miR-615-3p, hsa-miR-18a-3p, hsa-miR-4524a-3p) that targeting circRNAs and expressed in human HCC samples, and 95 mRNAs targeted by miRNAs and significantly up-regulated in two HCC cohorts. A protein-protein interaction network revealed 19 hub genes, 12 of them (MCM6, CCNB1, CDC20, NDC80, ZWINT, ASPM, CENPU, MCM3, MCM5, ECT2, CDC7, and DLGAP5) were associated with reduced survival in two HCC cohorts. KEGG, Reactome, and Wikipathway enrichment analysis indicated that the hub genes mainly functioned in DNA replication and cell cycle. Conclusions Our study uncovers three novel deregulated circRNAs in tumor and plasma from HCC patients and provides an insight into the pathogenesis from the circRNA–miRNA–mRNA regulatory network.

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Exploring the Molecular Mechanism of the Drug-Treated Breast Cancer Based on Gene Expression Microarray

Biomolecules ◽

10.3390/biom9070282 ◽

2019 ◽

Vol 9 (7) ◽

pp. 282

Author(s):

Alshabi ◽

BasavarajVastrad ◽

Shaikh ◽

Vastrad

Keyword(s):

Target Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Network Module ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Go Enrichment ◽

Human Protein Atlas ◽

Go Enrichment Analysis

: Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential expressed genes (DEGs) between estradiol-treated BRCA sample and tamoxifen-treated BRCA sample were identified by limma package. The pathway and gene ontology (GO) enrichment analysis, construction of protein-protein interaction (PPI) network, module analysis, construction of target genes—miRNA interaction network and target genes-transcription factor (TF) interaction network were performed using bioinformatics tools. The expression, prognostic values, and mutation of hub genes were validated by SurvExpress database, cBioPortal, and human protein atlas (HPA) database. A total of 856 genes (421 up-regulated genes and 435 down-regulated genes) were identified in T47D (overexpressing Split Ends (SPEN) + estradiol) samples compared to T47D (overexpressing Split Ends (SPEN) + tamoxifen) samples. Pathway and GO enrichment analysis revealed that the DEGs were mainly enriched in response to lysine degradation II (pipecolate pathway), cholesterol biosynthesis pathway, cell cycle pathway, and response to cytokine pathway. DEGs (MCM2, TCF4, OLR1, HSPA5, MAP1LC3B, SQSTM1, NEU1, HIST1H1B, RAD51, RFC3, MCM10, ISG15, TNFRSF10B, GBP2, IGFBP5, SOD2, DHF and MT1H) , which were significantly up- and down-regulated in estradiol and tamoxifen-treated BRCA samples, were selected as hub genes according to the results of protein-protein interaction (PPI) network, module analysis, target genes—miRNA interaction network and target genes-TF interaction network analysis. The SurvExpress database, cBioPortal, and Human Protein Atlas (HPA) database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. A comprehensive bioinformatics analysis was performed, and potential therapeutic applications of estradiol and tamoxifen were predicted in BRCA samples. The data may unravel the future molecular mechanisms of BRCA.

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Transcriptomic identification of HBx-associated hub genes in hepatocellular carcinoma

PeerJ ◽

10.7717/peerj.12697 ◽

2021 ◽

Vol 9 ◽

pp. e12697

Author(s):

Zhengzhong Ni ◽

Jun Lu ◽

Weiyi Huang ◽

Hanif Khan ◽

Xuejun Wu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Hepg2 Cells ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Transcription Profiles ◽

Kaplan Meier ◽

Primary Mouse

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

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Up-regulated RFC2 Predicts Unfavorably Progression in Hepatocellular Carcinoma

10.21203/rs.3.rs-72856/v1 ◽

2020 ◽

Author(s):

Zaixiong Ji ◽

Jiaqi Li ◽

Jianbo Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cancer ◽

Tumor Grade ◽

Replication Factor C ◽

Ppi Network ◽

Protein Protein Interaction ◽

Kaplan Meier ◽

Human Protein Atlas ◽

Factor C ◽

Kaplan Meier Plotter

Abstract Background: Replication factor C (RFC) is closely related to tumor progression and metastasis. However, the functional significance of RFC2 in hepatocellular carcinoma remains unclear.Materials and methods: In order to solve this problem, the expression of RFC2 in liver cancer patients was analyzed through ONCOMINE, UALCAN, human protein atlas. Survival analysis was conducted using Kaplan-Meier plotter and GEPIA. GO and KEGG enrichment analyses were carried out. The protein-protein interaction (PPI) network was performed through Metascape.Result: The transcription and protein level of RFC2 in HCC were overexpressed, which was significantly related to the clinical individual cancer stage and pathological tumor grade of HCC patients. In addition, in patients with liver cancer, higher RFC2 expression was found to be significantly correlated with shorter OS and DFS. Furthermore, the function of RFC2 in liver cancer was DNA replication, and its main mechanism was the phase transition of the cell cycle.Conclusion: RFC2 might promote the development of liver cancer. It could also be used as a novel biomarker for the prognosis of liver cancer.

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Identification of Potential Biomarkers in Hepatocellular Carcinoma: A Network-Based Approach

10.21203/rs.3.rs-91501/v1 ◽

2020 ◽

Author(s):

Mehrdad Ameri ◽

Haniye Salimi ◽

Sedigheh Eskandari ◽

Navid Nezafat

Keyword(s):

Hepatocellular Carcinoma ◽

Protein Interaction ◽

Microarray Data ◽

Protein Interaction Network ◽

Interaction Network ◽

Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network ◽

Potential Biomarkers

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of death worldwide. Identification of potential therapeutic and diagnostic biomarkers can be helpful to screen cancer progress. This study implemented with the aim of discovering potential biomarkers for HCC within a network-based approach integrated with microarray data. Methods: Through downloading a gene expression profile GSE62232 differentially expressed genes (DEGs) were identified. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for DEGs were performed utilizing enrichr server. Following reconstruction of protein-protein interaction network of DEGs with STRING, network visualization, analyses, and clustering into structural modules carried out using Cytoscape. Considering degree centrality, 15 hub genes were selected as early biomarker candidates for final validation. In order to validate hub genes, GEPIA server was used to perform overall survival (OS) and disease-free survival (DFS). Results: In our approach 1996 DEGs were identified including 995 up-regulated genes and 1001 down-regulated genes. KEGG pathway enrichment analysis shown that DEGs are associated with Chemical carcinogenesis, and Cell cycle. GO term enrichment analysis indicated the relation of DEGs with epoxygenase P450 pathway, arachidonic acid monooxygenase activity, and secretory granule lumen. Following analysis of protein-protein interaction network of DEGs top three structural modules and 15 early hub genes were selected. Validation of hub genes performed using GEPIA. Consequently, CDK1, CCNB1, CCNA2, CDC20, AURKA, MAD2L1, TOP2A, KIF11, BUB1B, TYMS, EZH2, and BUB1 were considered as our final proposed biomarkers. Conclusion: using an integrated network-based approach with microarray data our results revealed 12 final candidates with potential to considered as biomarkers in hepatocellular carcinoma.

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

Journal of International Medical Research ◽

10.1177/0300060520910019 ◽

2020 ◽

Vol 48 (7) ◽

pp. 030006052091001

Author(s):

Ziqi Meng ◽

Jiarui Wu ◽

Xinkui Liu ◽

Wei Zhou ◽

Mengwei Ni ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Molecular Therapy ◽

Integrated Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Interactive Analysis ◽

Kaplan Meier

Objective The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC). Methods Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes. Results We identified 11 hub genes ( CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway. Conclusions In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

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Expression and Prognostic Roles of PRDXs Gene Family in Hepatocellular Carcinoma

10.21203/rs.3.rs-37315/v1 ◽

2020 ◽

Author(s):

Mingxing Xu ◽

Jianliang Xu ◽

Dun Zhu ◽

Rishun Su ◽

Baoding Zhuang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Acid Metabolism ◽

Interaction Network ◽

Public Database ◽

Kaplan Meier ◽

Online Tools ◽

Canonical Pathways ◽

Human Protein Atlas ◽

Kaplan Meier Plotter ◽

Analyze Data

Abstract Background: As the second leading cause of cancer-related death in the world, the therapeutic effect and 5-year overall survival of hepatocellular carcinoma (HCC) are not optimistic. Previous researches indicated that the disorder of PRDXs was related to the occurrence and development of cancer. Methods: In this study,analyzing through CCLE, UALCAN, HCCDB and Human Protein Atlas database databases, PRDXs not only existedin various tumor cell lines, but alsohad maladjusted expression on mRNA and protein levelsin HCC tissues. Besides, the correlations between mRNA as well as methylation levels of PRDXs and clinical characterization wereevaluated using UALCAN. Results: Kaplan-Meier Plotter demonstrated that high expression of PRDX1 or low expression of PRDX2/3 suggested poor prognosis for HCC patients. The mutation frequency, type and biological interaction network of PRDXs were obtained from c-Bioportal. By LinkedOmics,PRDXs related differential expressions geneswere obtained, which were involved in several canonical pathways and certain amino acid metabolism, as well as miRNAs targeting PRDXs, some of which may effect on the progression of HCC. Conclusions: In conclusion, using online tools to analyze data based on several public database, it was found that disordered expression of PRDXs was correlated with the prognosis of HCC patients, suggesting that PRDXs may be new molecular targets for HCC therapy and prognosis prediction.

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Expression and prognostic roles PRDXs gene family in hepatocellular carcinoma

10.21203/rs.3.rs-18264/v1 ◽

2020 ◽

Author(s):

Mingxing Xu ◽

Jianliang Xu ◽

Dun Zhu ◽

Rishun Su ◽

Baoding Zhuang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Acid Metabolism ◽

Interaction Network ◽

Tumor Cell Lines ◽

Protein Levels ◽

Kaplan Meier ◽

Online Tools ◽

Canonical Pathways ◽

Human Protein Atlas ◽

Kaplan Meier Plotter

Abstract Background: As the second leading cause of cancer-related death in the world, the therapeutic effect and 5-year overall survival of hepatocellular carcinoma (HCC) are not optimistic. Previous researches indicated that the disorder of PRDXs was related to the occurrence and development of cancer.Methods: In this study, analyzing through CCLE, UALCAN, HCCDB and Human Protein Atlas database databases, PRDXs not only existed in various tumor cell lines, but also had maladjusted expression on mRNA and protein levels in HCC tissues. Besides, the correlations between mRNA as well as methylation levels of PRDXs and clinical characterization were evaluated using UALCAN.Results: Kaplan-Meier Plotter demonstrated that high expression of PRDX1 or low expression of PRDX2/3 suggested poor prognosis for HCC patients. The mutation frequency, type and biological interaction network of PRDXs were obtained from c-Bioportal. By LinkedOmics, PRDXs related differential expressions genes were obtained, which were involved in several canonical pathways and certain amino acid metabolism, as well as miRNAs targeting PRDXs, some of which may effect on the progression of HCC.Conclusions: In conclusion, the disordered expression of PRDXs was correlated with prognosis of HCC patients analyzing by online tools, suggesting that PRDXs could be new molecular targets for HCC treatment and prognosis prediction.

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