Biomarkers associated with disease severity in allergic and nonallergic asthma

Baos, Selene; Calzada, David; Cremades, Lucía; Sastre, Joaquı́n; Quiralte, J.; Florido, Fernando; Lahoz, Carlos; Cárdaba, Blanca

doi:10.1016/j.molimm.2016.12.012

Cited by 21 publications

(46 citation statements)

References 40 publications

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“…36,37 The hub gene CD44, an adhesion molecule, has been associated with asthma and plays a key role in mediating the adhesion of mast cells to bronchial smooth muscle. 41 The yellow hub gene was CFTR which encodes the cystic fibrosis transmembrane conductance regulator; mutation on the copies of the CFTR gene has been known to lead to cystic fibrosis. The protein encoded by this gene is a tyrosine phosphatase receptor that is involved in autoimmune disorders.…”

Section: Modulementioning

confidence: 99%

Identification of surrogate prognostic biomarkers for allergic asthma in nasal epithelial brushing samples by WGCNA

Liu

Fang

et al. 2018

J of Cellular Biochemistry

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Background Allergic asthma is a lower respiratory tract disease of Th2 inflammation with multiple molecular mechanisms. The upper and lower airways can be unified by the concept of a united airway and, as such, gene expression studies of upper epithelial cells may provide effective surrogate biomarkers for the prognostic study of allergic asthma. Objective To identify surrogate biomarkers in upper airway epithelial cells for the prognostic study of allergic asthma. Methods Nasal epithelial cell gene expression in 40 asthmatic and 17 healthy control subjects were analyzed by weighted gene coexpression network analysis (WGCNA) to identify gene network modules and profiles in allergic asthma. Functional enrichment analysis was performed on the coexpression genes in certain highlighted modules. Results A total of 13 coexpression modules were constructed by WGCNA from 2804 genes in nasal epithelial brushing samples of the 40 asthmatic and 17 healthy subjects. The number of genes in these modules ranged from 1086 (Turquoise module) to 45 (Salmon). Eight coexpression modules were found to be significantly correlated (P < 0.05) with two clinic traits, namely disease status, and severity. Four modules were positively correlated ( P < 0.05) with the traits and these, therefore, contained genes that are mostly overexpressed in asthma. Contrastingly, the four other modules were found to be negatively correlated with the clinic traits. Functional enrichment analysis of the positively correlated modules showed that one (Magenta) was mainly enriched in mast cell activation and degranulation; another (Pink) was largely involved in immune cell response; the third (Yellow) was predominantly enriched in transmembrane signal pathways; and the last (Blue) was mainly enriched in substructure components of the cells. The hub genes in the modules were KIT, KITLG, GATA2, CD44, PTPRC, and CFTR, and these were confirmed as having significantly higher expression in the nasal epithelial cells. Combining the six hub genes enabled a relatively high capacity for discrimination between asthmatics and healthy subjects with an area under the receiver operating characteristic (ROC) curve of 0.924. Conclusions Our findings provide a framework of coexpression gene modules from nasal epithelial brushing samples that could be used for the prognostic study of allergic asthma.

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Section: Modulementioning

confidence: 99%

Identification of surrogate prognostic biomarkers for allergic asthma in nasal epithelial brushing samples by WGCNA

Liu

Fang

et al. 2018

J of Cellular Biochemistry

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“…Against this backdrop, our research team has recently defined a group of genes that was differentially expressed in peripheral samples from nonallergic asthmatic patients (low or non-Th2 inflammation) and mainly associated with disease severity ( 21 , 22 ). The current study assessing gene and protein biomarkers is a follow up of our previous study ( 21 , 22 ). Here, we explore the relevance of the gene and protein expression of these potential biomarkers according to sensitivity and specificity analysis [receiver operating characteristic (ROC) curves].…”

Section: Introductionmentioning

confidence: 99%

Nonallergic Asthma and Its Severity: Biomarkers for Its Discrimination in Peripheral Samples

et al. 2018

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Asthma is a complex and heterogeneous respiratory disorder characterized by chronic airway inflammation. It has generally been associated with allergic mechanisms related to type 2 airway inflammation. Nevertheless, between 10 and 33% of asthmatic individuals have nonallergic asthma (NA). Several targeted treatments are in clinical development for patients with Th2 immune response, but few biomarkers are been defined for low or non-Th2-mediated inflammation asthma. We have recently defined by gene expression a set of genes as potential biomarkers of NA, mainly associated with disease severity: IL10, MSR1, PHLDA1, SERPINB2, CHI3L1, IL8, and PI3. Here, we analyzed their protein expression and specificity using sera and isolated peripheral blood mononuclear cells (PBMCs). First, protein quantification was carried out using ELISA (in sera) or Western blot (proteins extracted from PBMCs by Trizol procedure), depending on the biomarker in 30 healthy controls (C) subjects and 30 NA patients. A receiver operating characteristic curve analysis was performed by using the R program to study the specificity and sensitivity of the candidate biomarkers at a gene- and protein expression level. Four kinds of comparisons were performed: total NA group vs C group, severe NA patients vs C, moderate–mild NA patients vs C, and severe NA patients vs moderate–mild NA patients. We found that all the single genes showed good sensitivity vs specificity for some phenotypic discrimination, with CHI3L1 and PI3 exhibiting the best results for C vs NA: CHI3L1 area under the curve (AUC) (CI 95%): 0.95 (0.84–1.00) and PI3 AUC: 0.99 (0.98–1.00); C vs severe NA: PI3 AUC: 1 (0.99–1.00); and C vs moderate–mild NA: CHI3L1 AUC: 1 (0.99–1.00) and PI3 AUC: 0.99 (0.96–1.00). However, the results for discriminating asthma disease and severity with protein expression were better when two or three biomarkers were combined. In conclusion, individual genes and combinations of proteins have been evaluated as reliable biomarkers for classifying NA subjects and their severity. These new panels could be good diagnostic tests.

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“…Asthma is a complex, chronic respiratory disease with a wide clinical spectrum, with contributions from several environmental and genetic factors [20, 21]. Recent studies have shown that various gene polymorphisms influence the onset and progression of asthma [22, 23].…”

Section: Discussionmentioning

confidence: 99%

A Role of the ABCC4 Gene Polymorphism in Airway Inflammation of Asthmatics

Palikhe

Udval

Trinh

et al. 2017

Mediators of Inflammation

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The ATP-binding cassette subfamily C member 4 gene encodes a transmembrane protein involved in the export of proinflammatory molecules, including leukotriene, prostaglandin, and sphingosine-1-phosphate across the plasma membrane. Those metabolites play important roles in asthma. We investigated the potential associations between ABCC4 gene polymorphisms and asthma phenotype. In total, 270 asthma patients and 120 normal healthy controls were enrolled for a genetic association study. Two polymorphisms (−1508A>G and −642C>G) in the ABCC4 promoter were genotyped. The functional variability of the promoter polymorphisms was analyzed by luciferase reporter assay. Inflammatory cytokine levels were measured by enzyme-linked immunosorbent assay. Serum and urinary eicosanoid metabolites, sphingosine-1-phosphate, were evaluated by quadrupole time-of-flight mass spectrometry. Asthma patients carrying the G allele at −1508A>G had significantly higher serum levels of periostin, myeloperoxidase, and urinary levels of 15-hydroxyeicosatetraenoic acid and sphingosine-1-phosphate (P = 0.016, P = 0.027, P = 0.032, and P = 0.010, resp.) compared with noncarrier asthma patients. Luciferase activity was significantly enhanced in human epithelial A549 cells harboring a construct containing the −1508G allele (P < 0.01 for each) compared with a construct containing the −1508A allele. A functional polymorphism in the ABCC4 promoter, −1508A>G, may increase extracellular 15-hydroxyeicosatetraenoic acid, sphingosine-1-phosphate, and periostin levels, contributing to airway inflammation in asthmatics.

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Biomarkers associated with disease severity in allergic and nonallergic asthma

Cited by 21 publications

References 40 publications

Identification of surrogate prognostic biomarkers for allergic asthma in nasal epithelial brushing samples by WGCNA

Identification of surrogate prognostic biomarkers for allergic asthma in nasal epithelial brushing samples by WGCNA

Nonallergic Asthma and Its Severity: Biomarkers for Its Discrimination in Peripheral Samples

A Role of the ABCC4 Gene Polymorphism in Airway Inflammation of Asthmatics

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