A Bioinformatics-Based Screening and Analysis of Key Genes in Hepatocellular Carcinoma

Abstract Hepatocellular carcinoma (HCC) is considered as the leading killer disease in the world. So far most of the diagnosis of HCC is mainly established on imaging and biopsy. As sequencing technology is developing quite fast, and it has already been widely applied in the medical area, such as cancer diagnosis. In this article, GSE121248, GSE76427 and GSE60502 datasets were chosen to analyze and screen key genes which could affect the development of liver cancer through the bioinformatics method. The results showed up regulated genes mainly reside in cell division, nucleus, protein binding pathway, and down regulated genes are mostly located in the Oxidation-reduction process, Extracellular region, Heme binding, Metabolic pathway. Secondly, hub gene analysis indicated there were twelve critical hub genes found: RFC4, RACGAP1, CCNB2, CDC20, UBE2C, PTTG1, AURKA, PRC1, NCAPG, CDKN3, TOP2A, KIF20A, AURKA and CDKN3. By applying bioinformatic measures , the genes associated with hepatocellular carcinoma can be efficiently analyzed, that would provide invaluable information for translational studies.

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Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

Frontiers in Genetics ◽

10.3389/fgene.2021.571231 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhuolin Li ◽

Yao Lin ◽

Bizhen Cheng ◽

Qiaoxin Zhang ◽

Yingmu Cai

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Reduction Process ◽

The Cancer Genome Atlas ◽

Ppi Network ◽

Hub Genes ◽

Gene Interaction Network ◽

Poor Overall Survival ◽

Primary Liver Tumor ◽

Key Genes

BackgroundHepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.MethodsThe bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.ResultsWe ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.ConclusionThe present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

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Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

International Journal of Genomics ◽

10.1155/2019/3518378 ◽

2019 ◽

Vol 2019 ◽

pp. 1-21 ◽

Cited By ~ 1

Author(s):

Meng Wang ◽

Licheng Wang ◽

Shusheng Wu ◽

Dongsheng Zhou ◽

Xianming Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Expression Profiles ◽

Cyclin B1 ◽

Ppi Network ◽

Hub Genes ◽

Altered Expression ◽

Value Analysis ◽

Kaplan Meier ◽

Key Genes

Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P<0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.

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Screening and Identification of Significant Genes in Hepatocellular Carcinoma via Bioinformatical Analysis and Validation in Clinical Tissue

10.21203/rs.3.rs-99437/v1 ◽

2020 ◽

Author(s):

jun guo zhou ◽

Dongsheng Wang ◽

Xiaorong Zhong ◽

Wei Ying ◽

Yanchao Feng ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Prognostic Factors ◽

Function Analysis ◽

Hub Genes ◽

P53 Signaling ◽

Immunohistochemistry Staining ◽

Bioinformatical Analysis ◽

Screening And Identification ◽

Key Genes

Abstract Background: To screen out significant genes associated with occurrence and development of hepatocellular carcinoma (HCC) via bioinformatical analysis and validation using clinical specimens. Methods: Gene expression chips were obtained from GEO database, differentially expressed genes (DEGs) between HCC and para-cancerous tissues were identified by GEO2R and Venn diagrams. What’s more, Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were carried out by DAVID. The protein-protein interaction (PPI) network and module analysis of DEGs were performed by STRING and Cytoscape to get hub genes. Subsequently, the influence of hub genes on overall survival and the expression levels were determined with Ualcan and GEPIA, and found the pathway via re-analysis of DAVID. Besides, immunohistochemistry staining were performed to verify the key genes, and follow-up results including prognoses and the clinicopathological features were statistically analyzed. Results: 49 up-regulated DEGs and 122 down-regulated DEGs were selected. There were to tally 33 Hub genes were screened out, while 28 of them were related to prognosis and high expression in HCC. Furthermore, CDK1 and RRM2 were significantly enriched in p53 signaling pathway. Meanwhile, CDK1, RRM2 were highly expressed in HCC tissues by immunohistochemistry staining. Additionally, CDK1 and RRM2 were negatively correlated with overall survival. Tumor size and AFP were significant prognostic factors, while CDK1 and RRM2 were independent prognostic factors. Conclusion: This study confirmed CDK1 and RRM2 as the key genes in HCC which could be potential biological target for diagnosis and treatment.

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Integrated Analysis of the lncRNA/circRNA-miRNA-mRNA Expression Profiles Reveals Novel Insights Into Potential Mechanisms in Response to Root-knot Nematodes in Peanut

10.21203/rs.3.rs-968829/v1 ◽

2021 ◽

Author(s):

Ping Xu ◽

Hui Li ◽

Xiaohua Wang ◽

Ge Zhao ◽

Xiaofei Lu ◽

...

Keyword(s):

Expression Profiles ◽

Reduction Process ◽

Synthesis Process ◽

Integrated Analysis ◽

Pathway Enrichment Analysis ◽

Root Knot Nematode ◽

Root Knot Nematodes ◽

Oxidation Reduction ◽

The World

Abstract BackgroundThe high content of oil and protein makes peanut the main oil and edible crop in the world. Root-knot nematode forms root-knot by infecting peanut roots, which lead to poor development of peanut roots and seriously restricts the yield of peanut in the world. With the release of peanut genome, a large number of genetic loci controlling peanut root-knot nematode have been detected, but the molecular mechanism of root-knot nematode is still unclear. ResultsThe whole transcriptome RNA-seq was used to reveal the divergent response to root-knot nematode stress in peanut roots. A total of 430 mRNAs, 111 miRNAs, 4453 lncRNAs and 123 circRNAs were identified differential expression between infected and no-infected peanut, respectively. To understand the potential mechanisms in response to root-knot nematodes in peanut roots, the expression profiles of lncRNA/circRNA-miRNA-mRNA network were constructed. A total of 10 lncRNAs, 4 circRNAs, 5 miRNAs and 13 mRNAs can regularly the expression of mRNA during root-knot nematodes stress by forming competing endogenous RNA and participate in oxidation-reduction process and other various biological metabolism pathways in peanut. The results gained will reveal the role of ceRNAs of peanut in response to root-knot nematodes.ConclusionThe GO classification and KEGG pathway enrichment analysis of core regulatory networks revealing the ceRNAs participate in oxidation-reduction, peroxidase activity, lignin synthesis in xylem and flavonoid synthesis process. Overall, those results could gain the knowledge of the role of no-coding RNAs in response to root-knot nematodes.

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Identifying SLC27A5 as a potential prognostic marker of hepatocellular carcinoma by weighted gene co-expression network analysis and in vitro assays

Cancer Cell International ◽

10.1186/s12935-021-01871-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fan Zhang ◽

Mengjuan Xue ◽

Xin Jiang ◽

Huiyuan Yu ◽

Yixuan Qiu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Survival Analysis ◽

Network Analysis ◽

Prognostic Marker ◽

In Vitro Assays ◽

Hub Genes ◽

Hub Gene ◽

Tumor Tissues ◽

Potential Biomarker

Abstract Background The incidence and mortality rates of hepatocellular carcinoma are among the highest of all cancers all over the world. However the survival rates are relatively low due to lack of effective treatments. Efforts to elucidate the mechanisms of HCC and to find novel prognostic markers and therapeutic targets are ongoing. Here we tried to identify prognostic genes of HCC through co-expression network analysis. Methods We conducted weighted gene co-expression network analysis with a microarray dataset GSE14520 of HCC from Gene Expression Omnibus database and identified a hub module associated with HCC prognosis. Function enrichment analysis of the hub module was performed. Clinical information was analyzed to select candidate hub genes. The expression profiles and survival analysis of the selected genes were performed using additional datasets (GSE45267 and TCGA-LIHC) and the hub gene was identified. GSEA and in vitro experiments were conducted to further verify the function of the hub gene. Results Genes in the hub module were mostly involved in the metabolism pathway. Four genes (SLC27A5, SLC10A1, PCK2 and FMO4) from the module were identified as candidate hub genes according to correlation analysis with prognostic indicators. All these genes were significantly down-regulated in tumor tissues compared with non-tumor tissues in additional datasets. After survival analysis and network construction, SLC27A5 was selected as a prognostic marker. GSEA analysis and in vitro assays suggested that SLC27A5 downregulation promoted tumor cell migration via enhancing epithelial-mesenchymal transition. Conclusion SLC27A5 is a potential biomarker of HCC and SLC27A5 downregulation promoted HCC progression by enhancing EMT.

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Network Analysis for the Identification of Hub Genes and Related Molecules as Potential Biomarkers Associated With the Differentiation of Bone Marrow-derived Stem Cells Into Hepatocytes

10.21203/rs.3.rs-1237260/v1 ◽

2022 ◽

Author(s):

Taeho Kwon ◽

Ying-Hao Han ◽

Xin-Mei He ◽

Ying-Ying Mao ◽

Xuan-Chen Liu ◽

...

Keyword(s):

Stem Cells ◽

Bone Marrow ◽

Microarray Data ◽

Liver Diseases ◽

Reduction Process ◽

Organ Donor ◽

Gene Expression Omnibus ◽

Gene Microarray ◽

Hub Genes ◽

Oxidation Reduction

Abstract The incidence of liver diseases has been increasing steadily. However, it has some shortcomings, such as high cost and organ donor scarcity. The application of stem cell research has brought new ideas for the treatment of liver diseases. Therefore, it is particularly important to clarify the molecular and regulatory mechanisms of differentiation of bone marrow-derived stem cells (BMSCs) into liver cells. Herein, we screened differentially expressed genes between hepatocytes and untreated BMSCs to identify the genes responsible for the differentiation of BMSCs into hepatocytes. GSE30419 gene microarray data of BMSCs and GSE72088 gene microarray data of primary hepatocytes were obtained from the Gene Expression Omnibus database. Transcriptome Analysis Console software showed that 1896 genes were upregulated and 2506 were downregulated in hepatocytes as compared with BMSCs. Hub genes were analyzed using the STRING, revealing that two hub genes, Cat and Cyp2e1, play a pivotal role in oxidation-reduction process. The results indicate that the lncRNA-miRNA-mRNA interaction chain may play an important role in the differentiation of BMSCs into hepatocytes, which provides a new therapeutic target for liver disease treatment.

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Screening key genes and signaling pathways in COVID-19 infection and its associated complications by integrated bioinformatics analysis

10.1101/2021.09.24.461631 ◽

2021 ◽

Author(s):

Basavaraj Mallikarjunayya Vastrad ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Immune System ◽

Gene Regulatory Network ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Hub Gene ◽

Gene Regulatory ◽

Key Genes

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2)/ coronavirus disease 2019 (COVID-19) infection is the leading cause of respiratory tract infection associated mortality worldwide. The aim of the current investigation was to identify the differentially expressed genes (DEGs) and enriched pathways in COVID-19 infection and its associated complications by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid next-generation sequencing (NGS) data of 93 COVID 19 samples and 100 non COVID 19 samples (GSE156063) were obtained from the Gene Expression Omnibus database. Gene ontology (GO) and REACTOME pathway enrichment analysis was conducted to identify the biological role of DEGs. In addition, a protein-protein interaction network, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network and receiver operating characteristic curve (ROC) analysis were used to identify the key genes. A total of 738 DEGs were identified, including 415 up regulated genes and 323 down regulated genes. Most of the DEGs were significantly enriched in immune system process, cell communication, immune system and signaling by NTRK1 (TRKA). Through PPI, modules, miRNA-hub gene regulatory network, TF-hub gene regulatory network analysis, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were selected as hub genes, which were expressed in COVID-19 samples relative to those in non COVID-19 samples, respectively. Among them, ESR1, UBD, FYN, STAT1, ISG15, EGR1, ARRB2, UBE2D1, PRKDC and FOS were suggested to be diagonstic factors for COVID-19. The findings from this bioinformatics analysis study identified molecular mechanisms and the key hub genes that might contribute to COVID-19 infection and its associated complications.

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Identification and Analysis of the Key Genes associated with the Development from Liver Cirrhosis to Hepatocellular Carcinoma Based on Bioinformatics Analysis

10.21203/rs.2.24361/v1 ◽

2020 ◽

Author(s):

Tianyu Li ◽

Fang Ding ◽

Huimin Yan

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cirrhosis ◽

Poor Prognosis ◽

Cancer Genomics ◽

Hierarchical Cluster ◽

Expression Level ◽

Ppi Network ◽

Hub Genes ◽

Monooxygenase Activity ◽

Key Genes

Abstract Background: Hepatocellular carcinoma (HCC) is the most frequent primary liver tumor, and one of the most common malignant cancer with poor prognosis. Liver cirrhosis is the major risk factor for HCC. The aim of this study was to identify potential key genes associated with the development from liver cirrhosis to HCC and explore their potential mechanisms. Methods: Four microarray datasets GSE17548, GSE63898, GSE25097 and GSE89377 were downloaded from the Gene Expression Omnibus database. A protein-protein interaction (PPI) network was constructed using the STRING database, and potential hub genes were screened using MCODE plug-in in Cytoscape software. The Oncomine database was used to verify the expression of differential genes in cirrhosis and HCC. In order to further verify those hub genes, the hierarchical cluster between normal and HCC tissues was constructed using the UCSC Cancer Genomics Browser. The UALCAN database was used to verify the difference of hub genes in normal and HCC tissues and in different tumor grades. Finally, the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC.Results: A total of 360 DEGs, including 280 downregulated and 80 upregulated genes, were identified. Gene ontology enrichment (GO) analysis showed that these DEGs were mainly enriched in monooxygenase activity, cofactor binding, and oxidoreductase activity (acting on CH-OH group of donors). The mainly enriched pathways were complement and coagulation cascades, prion diseases, and arachidonic acid metabolism. By extracting key modules from the PPI network, 16 hub genes were screened out. In the hierarchical cluster of hub genes between normal and HCC tissues, the results showed that the expression level of 16 hub genes in HCC tissues was significantly higher than that in normal tissues. In addition, expression level of the hub genes was significantly associated with the tumor grades. The survival analysis showed that six hub DEGs, including KIF20A,HMMR, RRM2, TPX2, TTK and UBE2C, were closely associated with the poor prognosis of HCC.Conclusion: Our study discovered six novel potential genes associated with the development from liver cirrhosis to HCC. These key genes may be used as prognostic biomarkers and molecular therapeutic targets for HCC.

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Hub Genes and Key Pathway Identification in Colorectal Cancer Based on Bioinformatic Analysis

BioMed Research International ◽

10.1155/2019/1545680 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13

Author(s):

Jian Lv ◽

Lili Li

Keyword(s):

Colorectal Cancer ◽

Molecular Mechanisms ◽

Malignant Tumors ◽

Disease Free Survival ◽

Bioinformatic Analysis ◽

Receptor Interaction ◽

Estrogen Metabolism ◽

Hub Genes ◽

Extracellular Region ◽

Key Genes

Colorectal cancer (CRC) is one of the most common malignant tumors. The aim of the present study was to identify key genes and pathways to improve the understanding of the mechanism of CRC. GSE87211, including 203 CRC samples and 160 control samples, was screened to identify differentially expressed genes (DEGs). In total, 853 DEGs were obtained, including 363 upregulated genes and 490 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs were performed to obtain enrichment datasets. GO analysis showed that DEGs were significantly enriched in the extracellular region, cell-cell signaling, hormone activity, and cytokine activity. KEGG pathway analysis revealed that the DEGs were mainly enriched in the cytokine-cytokine receptor interaction, drug metabolism, androgen and estrogen metabolism, and neuroactive ligand-receptor interaction. The Protein-Protein Interaction (PPI) network of DEGs was constructed by using Search Tool for the Retrieval of Interacting Genes (STRING). The app MCODE plugged in Cytoscape was used to explore the key modules involved in disease development. 43 key genes involved in the top two modules were identified. Six hub genes (CXCL2, CXCL3, PTGDR2, GRP, CXCL11, and AGTR1) were statistically associated with patient overall survival or disease-free survival. The functions of six hub genes were mainly related to the hormone and chemokine activities. In conclusion, the present study may help understand the molecular mechanisms of CRC development.

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Identification of potential biomarkers and pivotal biological pathways for prostate cancer using bioinformatics analysis methods

PeerJ ◽

10.7717/peerj.7872 ◽

2019 ◽

Vol 7 ◽

pp. e7872 ◽

Cited By ~ 5

Author(s):

Zihao He ◽

Xiaolu Duan ◽

Guohua Zeng

Keyword(s):

Prostate Cancer ◽

Rna Polymerase Ii ◽

Kegg Pathway ◽

Reduction Process ◽

Biological Pathways ◽

The Cancer Genome Atlas ◽

Regulation Of Transcription ◽

Ppi Network ◽

Hub Genes ◽

Key Genes

Background Prostate cancer (PCa) is a common urinary malignancy, whose molecular mechanism has not been fully elucidated. We aimed to screen for key genes and biological pathways related to PCa using bioinformatics method. Methods Differentially expressed genes (DEGs) were filtered out from the GSE103512 dataset and subjected to the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein–protein interactions (PPI) network was constructed, following by the identification of hub genes. The results of former studies were compared with ours. The relative expression levels of hub genes were examined in The Cancer Genome Atlas (TCGA) and Oncomine public databases. The University of California Santa Cruz Xena online tools were used to study whether the expression of hub genes was correlated with the survival of PCa patients from TCGA cohorts. Results Totally, 252 (186 upregulated and 66 downregulated) DEGs were identified. GO analysis enriched mainly in “oxidation-reduction process” and “positive regulation of transcription from RNA polymerase II promoter”; KEGG pathway analysis enriched mostly in “metabolic pathways” and “protein digestion and absorption.” Kallikrein-related peptidase 3, cadherin 1 (CDH1), Kallikrein-related peptidase 2 (KLK2), forkhead box A1 (FOXA1), and epithelial cell adhesion molecule (EPCAM) were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant gene expression omnibus datasets. All hub genes were validated by both TCGA and Oncomine except KLK2. Two additional top DEGs (ABCC4 and SLPI) were found to be associated with the prognosis of PCa patients. Conclusions This study excavated the key genes and pathways in PCa, which might be biomarkers for diagnosis, prognosis, and potential therapeutic targets.

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