Identification of Key Pathways and Genes in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

Abstract Purpose: The purpose of this study is to identify novel molecular markers and potential molecular targets for NPC based on bioinformatics analysis.Methods: We used bioinformatics to analyze one miRNA and two mRNA expression microarray datasets from the Gene Expression Omnibus database. The study included nasopharyngeal tissue samples from 57 patients with NPC and 32 patients without NPC. Fifty-one screened differentially expressed genes (DEGs) were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analyses, and a protein-protein interaction (PPI) network was constructed. Results: The GO analysis results showed that the DEGs were mainly related to cell cycle checkpoints, cell division, and DNA synthesis during DNA repair. The KEGG analysis results suggested that the DEGs were mainly associated with extracellular matrix receptor interactions. In the PPI network, we identified RAD51AP1, MAD2L1, SPP1, CCNE2, CNTNAP2, and MELK as hub genes, clustered a key module, and identified eight key transcription factors: TFII-I, Pax-5, STAT4, GR-alpha, YY1, C/EBPβ, GRβ, and TFIID. Conclusion: The hub genes and signaling pathways identified above may play an important role in NPC development and provide ideas for the selection of valuable prognostic markers and the development of new molecular-targeted drugs.

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Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

BioMed Research International ◽

10.1155/2020/4505720 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Weishuang Xue ◽

Jinwei Li ◽

Kailei Fu ◽

Weiyu Teng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Peripheral Blood ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

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The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

Journal of Biomaterials and Tissue Engineering ◽

10.1166/jbt.2022.2834 ◽

2022 ◽

Vol 12 (3) ◽

pp. 523-532

Author(s):

Xin Yan ◽

Chunfeng Liang ◽

Xinghuan Liang ◽

Li Li ◽

Zhenxing Huang ◽

...

Keyword(s):

Cell Cycle ◽

Gene Ontology ◽

Protein Interaction ◽

Adrenocortical Carcinoma ◽

Gene Expression Omnibus ◽

Heparin Binding ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

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An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

Scientific Reports ◽

10.1038/s41598-020-76024-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Sepideh Dashti ◽

Mohammad Taheri ◽

Soudeh Ghafouri-Fard

Keyword(s):

Breast Cancer ◽

Clinical Importance ◽

Interaction Network ◽

R Package ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ncbi Gene Expression Omnibus ◽

Kaplan Meier ◽

Microarray Datasets

Abstract Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein–protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA–mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan–Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan–Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA–lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

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Bioinformatics Analysis of Potential Key Genes in Trastuzumab-Resistant Gastric Cancer

Disease Markers ◽

10.1155/2019/1372571 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Guangda Yang ◽

Liumeng Jian ◽

Xiangan Lin ◽

Aiyu Zhu ◽

Guohua Wen

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Prognostic Value ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Trastuzumab Resistance ◽

Hub Genes ◽

Her2 Positive ◽

Protein Protein Interaction ◽

Stem Cell Pluripotency

Background. This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods. The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results. A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions. Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.

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Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

Iranian Journal of Public Health ◽

10.18502/ijph.v50i11.7578 ◽

2021 ◽

Author(s):

Congcong Wang ◽

Jianping Guo ◽

Xiaoyang Zhao ◽

Jia Jia ◽

Wenting Xu ◽

...

Keyword(s):

Bioinformatics Analysis ◽

Expression Profiles ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Analysis Tool ◽

Hub Genes ◽

Protein Protein Interaction ◽

Kegg Analysis ◽

Prognostic Assessment ◽

Scheme Selection

Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis. Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA. Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1. Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.

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Identification of Pulpitis-Related Potential Biomarkers Using Bioinformatics Approach

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/1808361 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Bingchang Xin ◽

Yuxiang Lin ◽

He Tian ◽

Jia Song ◽

Liwei Zhang ◽

...

Keyword(s):

Inflammatory Reaction ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Hub Genes ◽

Pulp Tissue ◽

Network Nodes ◽

Protein Protein Interaction ◽

Pathogen Elimination ◽

Potential Biomarkers ◽

Geo Database

Inflammatory reaction of pulp tissue plays a role in the pathogen elimination and tissue repair. The evaluation of severity of pulpitis can serve an instructive function in therapeutic scheme. However, there are many limitations in the traditional evaluation methods for the severity of pulpitis. Based on the Gene Expression Omnibus (GEO) database, our study discovered 843 differentially expressed genes (DEGs) related to pulpitis. Afterwards, we constructed a protein-protein interaction (PPI) network of DEGs and used MCODE plugin to determine the key functional subset. Meanwhile, genes in the key functional subset were subjected to GO and KEGG enrichment analyses. The result showed that genes were mainly enriched in inflammatory reaction-related functions. Next, we screened out intersections of PPI network nodes and pulpitis-related genes. Then, 20 genes were obtained as seed genes. In the PPI network, 50 genes that had the highest correlation with seed genes were screened out using random walk with restart (RWR). Furthermore, 4 pulpitis-related hub genes were obtained from the intersection of the top 50 genes and genes in the key functional subset. Finally, GeneMANIA was utilized to predict genes coexpressed with hub genes, and expression levels of the 4 hub genes in normal and pulpitis groups were analyzed based on GEO data. The result demonstrated that the 4 hub genes were mainly coexpressed with chemokine-related genes and were remarkably upregulated in the pulpitis group. In short, we eventually determined 4 potential biomarkers of pulpitis.

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Identification of the Key Genes and Pathways in Esophageal Carcinoma

Gastroenterology Research and Practice ◽

10.1155/2016/2968106 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Peng Su ◽

Shiwang Wen ◽

Yuefeng Zhang ◽

Yong Li ◽

Yanzhao Xu ◽

...

Keyword(s):

Cell Cycle ◽

Esophageal Carcinoma ◽

Gene Expression Omnibus ◽

Expression Level ◽

Ppi Network ◽

Protein Protein Interaction ◽

Go Enrichment ◽

Key Genes ◽

Microarray Datasets ◽

Upregulated Genes

Objective. Esophageal carcinoma (EC) is a frequently common malignancy of gastrointestinal cancer in the world. This study aims to screen key genes and pathways in EC and elucidate the mechanism of it.Methods. 5 microarray datasets of EC were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network construction were performed to obtain the biological roles of DEGs in EC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression level of DEGs in EC.Results. A total of 1955 genes were filtered as DEGs in EC. The upregulated genes were significantly enriched in cell cycle and the downregulated genes significantly enriched in Endocytosis. PPI network displayed CDK4 and CCT3 were hub proteins in the network. The expression level of 8 dysregulated DEGs including CDK4, CCT3, THSD4, SIM2, MYBL2, CENPF, CDCA3, and CDKN3 was validated in EC compared to adjacent nontumor tissues and the results were matched with the microarray analysis.Conclusion. The significantly DEGs including CDK4, CCT3, THSD4, and SIM2 may play key roles in tumorigenesis and development of EC involved in cell cycle and Endocytosis.

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Identification of key genes in allergic rhinitis by bioinformatics analysis

Journal of International Medical Research ◽

10.1177/03000605211029521 ◽

2021 ◽

Vol 49 (7) ◽

pp. 030006052110295

Author(s):

Yunfei Zhang ◽

Yue Huang ◽

Wen-xia Chen ◽

Zheng-min Xu

Keyword(s):

Allergic Rhinitis ◽

Antigen Processing ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Validation Dataset ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Pathway Analyses ◽

Antigen Processing And Presentation

Objective This study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods. Methods The dataset GSE19187 was used to screen differentially expressed genes (DEGs) between samples from patients with AR and healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied for the DEGs. Subsequently, a protein–protein interaction (PPI) network was constructed to identify hub genes. GSE44037 and GSE43523 datasets were screened to validate critical genes. Results A total of 156 DEGs were identified. GO analysis verified that the DEGs were enriched in antigen processing and presentation, the immune response, and antigen binding. KEGG analysis demonstrated that the DEGs were enriched in Staphylococcus aureus infection, rheumatoid arthritis, and allograft rejection. PPI network and module analysis predicted seven hub genes, of which six ( CD44, HLA-DPA1, HLA-DRB1, HLA-DRB5, MUC5B, and CD274) were identified in the validation dataset. Conclusions Our findings suggest that hub genes play important roles in the development of AR.

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Analysis of Potential Hub Genes and Related Transcription Factors in Rosacea Patients using Bioinformatics Analysis

10.21203/rs.3.rs-824062/v1 ◽

2021 ◽

Author(s):

Xin Wang ◽

Wenfang Dong ◽

Huan Wang ◽

Jianjun You ◽

Ruobing Zheng ◽

...

Keyword(s):

Transcription Factors ◽

Regulatory Network ◽

Bioinformatics Analysis ◽

Biological Functions ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks ◽

String Databases ◽

Cell Signaling Pathways

Abstract Objective The aim of this study is to discover the adipocyte genes and pathways involved in rosacea using bioinformatics analysis.Methods The GSE65914 gene expression profile was obtained. The GEO2R tool was used to screen out differentially expressed genes (DEGs). It was further analyzed with Gene Ontology (GO) to explore functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore cell signaling pathways. Protein-protein interaction (PPI) networks among the DEGs were found by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs were also constructed.Results A total of 254 DEGs, including 72 up-regulated genes and 182 down-regulated genes, were obtained in rosacea samples. The biological functions of DEGs are mainly involved in the inflammatory response and chemokine activity. A PPI network consisting of 217 nodes and 710 edges was constructed using STRING, and ten hub genes were identified with Cytoscape software. Some transcriptional factors were also found to interact with these hub DEGs.Conclusion In this study, we obtained ten hub genes, including CXCL8, CCR5, CXCR4, CXCL10, MMP9, CD2, CCL19, CXCL9, CCL5, CD3D, which play an essential role in the pathology of rosacea, and these genes may provide a basis for the screening of treatment biomarkers for rosacea in the future.

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Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis

Biochemical Genetics ◽

10.1007/s10528-021-10144-w ◽

2021 ◽

Author(s):

Ji-Chun Chen ◽

Tian-Ao Xie ◽

Zhen-Zong Lin ◽

Yi-Qing Li ◽

Yu-Fei Xie ◽

...

Keyword(s):

Gene Expression ◽

Digestive Tract ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Hub Genes ◽

Data Set ◽

Alcoholic Fatty Liver ◽

Protein Protein Interaction ◽

Human Digestive Tract ◽

Gene Expression Profile Data

AbstractCOVID-19 is a serious infectious disease that has recently swept the world, and research on its causative virus, SARS-CoV-2, remains insufficient. Therefore, this study uses bioinformatics analysis techniques to explore the human digestive tract diseases that may be caused by SARS-CoV-2 infection. The gene expression profile data set, numbered GSE149312, is from the Gene Expression Omnibus (GEO) database and is divided into a 24-h group and a 60-h group. R software is used to analyze and screen out differentially expressed genes (DEGs) and then gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses are performed. In KEGG, the pathway of non-alcoholic fatty liver disease exists in both the 24-h group and 60-h group. STRING is used to establish a protein–protein interaction (PPI) network, and Cytoscape is then used to visualize the PPI and define the top 12 genes of the node as the hub genes. Through verification, nine statistically significant hub genes are identified: AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1. In conclusion, the results of this study can provide a certain direction and basis for follow-up studies of SARS-CoV-2 infection of the human digestive tract and provide new insights for the prevention and treatment of diseases caused by SARS-CoV-2.

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