scholarly journals Gene network in pulmonary tuberculosis based on bioinformatic analysis

2020 ◽  
Author(s):  
Lili Li ◽  
Jian Lv ◽  
Yuan He ◽  
Zhihua Wang
Keyword(s):  
Pulmonary Tuberculosis ◽  
Chemokine Receptors ◽  
Gene Network ◽  
Kegg Pathway ◽  
Bioinformatic Analysis ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Causal Genes

Abstract Background: Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. Methods: This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. Results: Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. Conclusions: Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

scholarly journals Gene network in pulmonary tuberculosis based on bioinformatic analysis

2020 ◽  
Author(s):  
Lili Li ◽  
Jian Lv ◽  
Yuan He ◽  
Zhihua Wang
Keyword(s):  
Pulmonary Tuberculosis ◽  
Chemokine Receptors ◽  
Gene Network ◽  
Kegg Pathway ◽  
Bioinformatic Analysis ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Causal Genes

Abstract Background: Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. Methods: This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. Results: Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. Conclusions: Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

scholarly journals Weighted gene co-expression network analysis to define pivotal modules and genes in diabetic heart failure

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Weiwei Liang ◽  
FangFang Sun
Keyword(s):  
Heart Failure ◽  
Kegg Pathway ◽  
Diabetic Heart ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Light Yellow ◽  
Go Enrichment ◽  
Key Genes ◽  
Functional Analyses

Abstract This research was carried out to reveal specific hub genes involved in diabetic heart failure, as well as remarkable pathways that hub genes locate. The GSE26887 dataset from the GEO website was downloaded. The gene co-expression network was generated and central modules were analyzed to identify key genes using the WGCNA method. Functional analyses were conducted on genes of the clinical interest modules via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment, associated with protein–protein interaction (PPI) network construction in a sequence. Centrality parameters of the PPI network were determined using the CentiScape plugin in Cytoscape. Key genes, defined as genes in the ≥95% percentile of the degree distribution of significantly perturbed networks, were identified. Twenty gene co-expression modules were detected by WGCNA analysis. The module marked in light yellow exhibited the most significant association with diabetes (P=0.08). Genes involved in this module were primarily located in immune response, plasma membrane and receptor binding, as shown by the GO analysis. These genes were primarily assembled in endocytosis and phagosomes for KEGG pathway enrichment. Three key genes, STK39, HLA-DPB1 and RAB5C, which may be key genes for diabetic heart failure, were identified. To our knowledge, our study is the first to have constructed the co-expression network involved in diabetic heart failure using the WGCNA method. The results of the present study have provided better understanding the molecular mechanism of diabetic heart failure.

scholarly journals Construction of a Novel Ferroptosis-Related Gene Signature of Atherosclerosis

2022 ◽  
Vol 9 ◽  
Author(s):  
Tucheng Huang ◽  
Kangjie Wang ◽  
Yuewei Li ◽  
Yanchen Ye ◽  
Yangxin Chen ◽  
...  
Keyword(s):  
Regulatory Mechanism ◽  
Kegg Pathway ◽  
Gene Signature ◽  
Chronic Inflammatory Disease ◽  
Ppi Network ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Causes Of Mortality

Atheroclerosis refers to a chronic inflammatory disease featured by the accumulation of fibrofatty lesions in the intima of arteries. Cardiovasular events associated with atherosclerosis remain the major causes of mortality worldwide. Recent studies have indicated that ferroptosis, a novel programmed cell death, might participate in the process of atherosclerosis. However, the ferroptosis landscape is still not clear. In this study, 59 genes associated with ferroptosis were ultimately identified in atherosclerosis in the intima. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for functional annotation. Through the construction of protein–protein interaction (PPI) network, five hub genes (TP53, MAPK1, STAT3, HMOX1, and PTGS2) were then validated histologically. The competing endogenous RNA (ceRNA) network of hub genes was ultimately constructed to explore the regulatory mechanism between lncRNAs, miRNAs, and hub genes. The findings provide more insights into the ferroptosis landscape and, potentially, the therapeutic targets of atherosclerosis.

scholarly journals Cell Adhesion-Related Molecules Play a Key Role in Renal Cancer Progression by Multinetwork Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Anbang Wang ◽  
Ming Chen ◽  
Hui Wang ◽  
Jinming Huang ◽  
Yi Bao ◽  
...  
Keyword(s):  
Cell Adhesion ◽  
Cancer Progression ◽  
Kegg Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Genetic Characteristics ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Upregulated Genes

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The study aimed to identify genetic characteristics and reveal the underlying mechanisms in RCC. GSE53757, GSE46699, and TCGA KIRC database (n = 897) were analyzed to screen differentially expressed genes (DEGs) in RCC. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by the analysis of the protein-protein interaction (PPI) network of the DEGs by Cytoscape software. In all, 834 DEGs were identified in RCC, including 416 upregulated genes and 418 downregulated genes. The top 10 hub genes, VEGFA, EGFR, EGF, CD44, CD86, FN1, ITGAM, ITGB2, TLR2, and PTPRC, were identified from the PPI network according to the core degree. The following subnetwork revealed that these significant modules were enriched in positive regulation of response to external stimulus, regulation of leukocyte-mediated immunity, and regulation of exocytosis. The expressions of these hub genes were also validated using qRT-PCR and IHC in Changzheng RCC database (n = 160). We especially found that half of the top ten hub genes were cell adhesion-related molecules, which were associated with RCC progression and poor prognosis. In conclusion, these hub genes, particularly cell adhesion-related molecules, could be used as prognostic biomarkers and potential therapeutic targets for RCC.

scholarly journals Identification of Key Candidate Proteins and Pathways Associated with Temozolomide Resistance in Glioblastoma Based on Subcellular Proteomics and Bioinformatical Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Guo-zhong Yi ◽  
Wei Xiang ◽  
Wen-yan Feng ◽  
Zi-yang Chen ◽  
Yao-min Li ◽  
...  
Keyword(s):  
Focal Adhesion ◽  
Kegg Pathway ◽  
Bioinformatic Analysis ◽  
Differentially Expressed Proteins ◽  
Ppi Network ◽  
Network Construction ◽  
Protein Protein Interaction ◽  
Kegg Pathway Analysis ◽  
Tcga Dataset ◽  
U87 Cells

TMZ resistance remains one of the main reasons why treatment of glioblastoma (GBM) fails. In order to investigate the underlying proteins and pathways associated with TMZ resistance, we conducted a cytoplasmic proteome research of U87 cells treated with TMZ for 1 week, followed by differentially expressed proteins (DEPs) screening, KEGG pathway analysis, protein–protein interaction (PPI) network construction, and validation of key candidate proteins in TCGA dataset. A total of 161 DEPs including 65 upregulated proteins and 96 downregulated proteins were identified. Upregulated DEPs were mainly related to regulation in actin cytoskeleton, focal adhesion, and phagosome and PI3K-AKT signaling pathways which were consistent with our previous studies. Further, the most significant module consisted of 28 downregulated proteins that were filtered from the PPI network, and 9 proteins (DHX9, HNRNPR, RPL3, HNRNPA3, SF1, DDX5, EIF5B, BTF3, and RPL8) among them were identified as the key candidate proteins, which were significantly associated with prognosis of GBM patients and mainly involved in ribosome and spliceosome pathway. Taking the above into consideration, we firstly identified candidate proteins and pathways associated with TMZ resistance in GBM using proteomics and bioinformatic analysis, and these proteins could be potential biomarkers for prevention or prediction of TMZ resistance in the future.

scholarly journals Identification of key genes in allergic rhinitis by bioinformatics analysis

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.

scholarly journals Identification of Potential Biomarkers in PBMC of Systemic Lupus Erythematosus: Results from Bioinformatic Analysis

2021 ◽  
Author(s):  
Yan Sun ◽  
Chen-chen Wang ◽  
Fu-quan Wang ◽  
Rui Chen ◽  
Chun-lin Yao ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Bioinformatic Analysis ◽  
Organ Damage ◽  
Specific Gene ◽  
Ppi Network ◽  
Systemic Lupus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Potential Biomarkers

Abstract BackgroundThe discovery of biomarkers has become an attractive field in studying autoimmune diseases. For example, in the study of systemic lupus erythematosus (SLE), various biomarkers such as genes and miRNAs have been identified for the diagnosis of SLE and its organ involvement. ResultsThe expression data of gene microarray GSE50772 was downloaded from the GEO, and 257 differentially expressed genes (DEGs) were obtained by using limma plug-in for R software. The tissue-specific gene expression analyses were performed in BioGPS database. Then, a protein-protein interaction (PPI) network was constructed with STRING and visualized in Cytoscape. Whereafter, top twenty hub genes derived from the PPI network, could basically differentiate the SLE samples from the non-SLE samples, were ascertained through CytoHubba. What is noticeable is that the five novel hub genes ( ORM1, SLPI, OLFM4, TCN1 and CRISP3) and a related miRNA (hsa-let-7e-5p) may be considered as candidate biomarkers of SLE. ConclusionsFive genes (ORM1, SLPI, OLFM4, TCN1 and CRISP3) and a miRNA(hsa-let-7e-5p) in this discovery-driven study may become potential biomarkers for diagnosing SLE and assessing its organ damage, and they also will provide valuable information on the pathogenesis of SLE.

scholarly journals The Identification of Core Gene Expression Signature in Hepatocellular Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ning Li ◽  
Ling Li ◽  
Yongshun Chen
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Financial Burden ◽  
Gene Expression Signature ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Hepatocellular carcinoma (HCC) is one of the most common malignancies, which causes serious financial burden worldwide. This study aims to investigate the potential mechanisms contributing to HCC and identify core biomarkers. The HCC gene expression profile GSE41804 was picked out to analyze the differentially expressed genes (DEGs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out using DAVID. We constructed a protein-protein interaction (PPI) network to visualize interactions of the DEGs. The survival analysis of these hub genes was conducted to evaluate their potential effects on HCC. In this analysis, 503 DEGs were captured (360 downregulated genes and 143 upregulated genes). Meanwhile, 15 hub genes were identified. GO analysis showed that the DEGs were mainly enriched in oxidative stress, cell cycle, and extracellular structure. KEGG analysis suggested the DEGs were enriched in the absorption, metabolism, and cell cycle pathway. PPI network disclosed that the top3 modules were mainly enriched in cell cycle, oxidative stress, and liver detoxification. In conclusion, our analysis uncovered that the alterations of oxidative stress and cell cycle are two major signatures of HCC. TOP2A, CCNB1, and KIF4A might promote the development of HCC, especially in proliferation and differentiation, which could be novel biomarkers and targets for diagnosis and treatment of HCC.

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

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.

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

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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