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Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis.

ABSTRACT: The study aimed to identify the potential biomarkers in pulmonary tuberculosis (TB) and TB latent infection based on bioinformatics analysis.The microarray data of GSE57736 were downloaded from Gene Expression Omnibus database. A total of 7 pulmonary TB and 8 latent infection samples were used to identify the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed by Cytoscape software. Then network-based neighborhood scoring analysis was performed to identify the important genes. Furthermore, the functional enrichment analysis, correlation analysis and logistic regression analysis for the identified important genes were performed.A total of 1084 DEGs were identified, including 565 down- and 519 up-regulated genes. The PPI network was constructed with 446 nodes and 768 edges. Down-regulated genes RIC8 guanine nucleotide exchange factor A (RIC8A), basic leucine zipper transcription factor, ATF-like (BATF) and microtubule associated monooxygenase, calponin LIM domain containing 1 (MICAL1) and up-regulated genes ATPase, Na+/K+ transporting, alpha 4 polypeptide (ATP1A4), histone cluster 1, H3c (HIST1H3C), histone cluster 2, H3d (HIST2H3D), histone cluster 1, H3e (HIST1H3E) and tyrosine kinase 2 (TYK2) were selected as important genes in network-based neighborhood scoring analysis. The functional enrichment analysis results showed that these important DEGs were mainly enriched in regulation of osteoblast differentiation and nucleoside triphosphate biosynthetic process. The gene pairs RIC8A-ATP1A4, HIST1H3C-HIST2H3D, HIST1H3E-BATF and MICAL1-TYK2 were identified with high positive correlations. Besides, these genes were selected as significant feature genes in logistic regression analysis.The genes such as RIC8A, ATP1A4, HIST1H3C, HIST2H3D, HIST1H3E, BATF, MICAL1 and TYK2 may be potential biomarkers in pulmonary TB or TB latent infection.

SUBMITTER: Qin XB

PROVIDER: S-EPMC5031349 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis.

Qin Xue-Bing XB Zhang Wei-Jue WJ Zou Lin L Huang Pei-Jia PJ Sun Bao-Jun BJ

BMC infectious diseases 20160921 1

<h4>Background</h4>The study aimed to identify the potential biomarkers in pulmonary tuberculosis (TB) and TB latent infection based on bioinformatics analysis.<h4>Methods</h4>The microarray data of GSE57736 were downloaded from Gene Expression Omnibus database. A total of 7 pulmonary TB and 8 latent infection samples were used to identify the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed by Cytoscape software. Then network-based neighborhoo ...[more]

PMID: 27655333

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Project description:AbstractTuberculosis (TB) is one of the leading causes of childhood morbidity and death globally. Lack of rapid, effective non-sputum diagnosis and prediction methods for TB in children are some of the challenges currently faced. In recent years, blood transcriptional profiling has provided a fresh perspective on the diagnosis and predicting the progression of tuberculosis. Meanwhile, combined with bioinformatics analysis can help to identify the differentially expressed genes (DEGs) and functional pathways involved in the different clinical stages of TB. Therefore, this study investigated potential diagnostic markers for use in distinguishing between latent tuberculosis infection (LTBI) and active TB using children's blood transcriptome data.From the Gene Expression Omnibus database, we downloaded two gene expression profile datasets (GSE39939 and GSE39940) of whole blood-derived RNA sequencing samples, reflecting transcriptional signatures between latent and active tuberculosis in children. GEO2R tool was used to screen for DEGs in LTBI and active TB in children. Database for Annotation, Visualization and Integrated Discovery tools were used to perform Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape analyzed the protein-protein interaction network and the top 15 hub genes respectively. Receiver operating characteristics curve was used to estimate the diagnostic value of the hub genes.A total of 265 DEGs were identified, including 79 upregulated and 186 downregulated DEGs. Further, 15 core genes were picked and enrichment analysis revealed that they were highly correlated with neutrophil activation and degranulation, neutrophil-mediated immunity and in defense response. Among them TLR2, FPR2, MMP9, MPO, CEACAM8, ELANE, FCGR1A, SELP, ARG1, GNG10, HP, LCN2, LTF, ADCY3 had significant discriminatory power between LTBI and active TB, with area under the curves of 0.84, 0.84, 0.84, 0.80, 0.87, 0.78, 0.88, 0.84, 0.86, 0.82, 0.85, 0.85, 0.79, and 0.88 respectively.Our research provided several genes with high potential to be candidate gene markers for developing non-sputum diagnostic tools for childhood Tuberculosis.

Project description:Pulmonary tuberculosis (TB) is a chronic infectious disease that is caused by respiratory infections, principally Mycobacterium tuberculosis. Increasingly, studies have shown that circular (circ)RNAs play regulatory roles in different diseases through different mechanisms. However, their roles and potential regulatory mechanisms in pulmonary TB remain unclear. In this study, we analyzed circRNA sequencing data from adjacent normal and diseased tissues from pulmonary TB patients and analyzed the differentially expressed genes. We then constructed machine learning models and used single-factor analysis to identify hub circRNAs. We downloaded the pulmonary TB micro (mi)RNA (GSE29190) and mRNA (GSE83456) gene expression datasets from the Gene Expression Omnibus database and performed differential expression analysis to determine the differentially expressed miRNAs and mRNAs. We also constructed a circRNA-miRNA-mRNA interaction network using Cytoscape. Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were used to predict the biological functions of the identified RNAs and determine hub genes. Then, the STRING database and cytoHubba were used to construct protein-protein interaction networks. The results showed 125 differentially expressed circRNAs in the adjacent normal and diseased tissues of pulmonary TB patients. Among them, we identified three hub genes associated with the development of pulmonary TB: hsa_circ_0007919 (upregulated), hsa_circ_0002419 (downregulated), and hsa_circ_0005521 (downregulated). Through further screening, we determined 16 mRNAs of potential downstream genes for hsa-miR-409-5p and hsa_circ_0005521 and established an interaction network. This network may have important roles in the occurrence and development of pulmonary TB. We constructed a model with 100% prediction accuracy by machine learning and single-factor analysis. We constructed a protein-protein interaction network among the top 50 hub mRNAs, with FBXW7 scoring the highest and SOCS3 the second highest. These results may provide a new reference for the identification of candidate markers for the early diagnosis and treatment of pulmonary TB.

Project description:ObjectiveThe aim of this study was to search for key genes in ankylosing spondylitis (AS) through comprehensive bioinformatics analysis, thus providing some theoretical support for future diagnosis and treatment of AS and further research.MethodsGene expression profiles were collected from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/ ) by searching for the term "ankylosing spondylitis". Ultimately, two microarray datasets (GSE73754 and GSE11886) were downloaded from the GEO database. A bioinformatic approach was used to screen differentially expressed genes and perform functional enrichment analysis to obtain biological functions and signalling pathways associated with the disease. Weighted correlation network analysis (WGCNA) was used to further obtain key genes. Immune infiltration analysis was performed using the CIBERSORT algorithm to conduct a correlation analysis of key genes with immune cells. The GWAS data of AS were analysed to identify the pathogenic regions of key genes in AS. Finally, potential therapeutic agents for AS were predicted using these key genes.ResultsA total of 7 potential biomarkers were identified: DYSF, BASP1, PYGL, SPI1, C5AR1, ANPEP and SORL1. ROC curves showed good prediction for each gene. T cell, CD4 naïve cell, and neutrophil levels were significantly higher in the disease group than in the paired normal group, and key gene expression was strongly correlated with immune cells. CMap results showed that the expression profiles of ibuprofen, forskolin, bongkrek-acid, and cimaterol showed the most significant negative correlation with the expression profiles of disease perturbations, suggesting that these drugs may play a role in AS treatment.ConclusionThe potential biomarkers of AS screened in this study are closely related to the level of immune cell infiltration and play an important role in the immune microenvironment. This may provide help in the clinical diagnosis and treatment of AS and provide new ideas for further research.

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Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis.

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Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysis.

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