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Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

ABSTRACT: Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The aim of the present study was to screen critical differentially expressed genes (DEGs) involved in asthma in children. Gene expression in different tissues was compared between asthmatic children and healthy control subjects in order to identify DEGs associated with asthma. Protein?protein interaction (PPI) networks were constructed for the DEGs and weighted gene co?expression network analysis methods were used to further determine the functional modules associated with DEGs in different tissue samples. In addition, the gene co?expression network was constructed. Gene Ontology function analysis and pathway analysis were conducted to identify critical DEGs. The results identified numerous DEGs from the different tissue samples, including 1,662 DEGs from nasal?epithelium tissue samples, 572 DEGs from peripheral blood (PB) samples and 146 DEGs from PB mononuclear cells samples. In the PPI network, F?box only protein 6 (FBXO6), histone deacetylase 1 (HDAC1) and amyloid ? precursor protein (APP) were hub genes and served an important role in the process of asthma. In addition, proliferating cell nuclear antigen (PCNA), integrin ??4 (ITGA4), catenin ??1 (CTNNA1), nuclear factor??B1 (NF??B1) and mechanistic target of rapamycin (MTOR) may be critical DEGs involved in the progression of asthma in children. These results suggested that FBXO6, HDAC1 and APP may interact with PCNA, ITGA4, CTNNA1, NF??B1 and mTOR in the progression of asthma in children.

SUBMITTER: Wang C

PROVIDER: S-EPMC6072229 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

Wang Chunyan C Li Hengtao H Cao Lanfang L Wang Genzai G

Molecular medicine reports 20180622 2

Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The aim of the present study was to screen critical differentially expressed genes (DEGs) involved in asthma in children. Gene expression in different tissues was compared between asthmatic children and healthy control subjects in order to identify DEGs associated with asthma. Protein‑protein interaction (PPI) networks were constructed for the DEGs an ...[more]

PMID: 29956778

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Project description:Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates.

Dataset Information

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

Publications

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

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