Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis

Cao, Yujin; Wu, Yi; Li, Lin; Yang, Lin; Peng, Xin; Chen, Lina

doi:10.1186/s12920-022-01241-9

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…A recent study explored the relationship between COPD and blood eosinophil count and airway epithelial transcriptome, showing that CST1 was the only gene positively associated with blood eosinophil count in asthma and COPD patients ( 68 ). CST1 is highly expressed in the airway epithelium of Th2-high asthma patients, and is one of the biomarkers to distinguish Th2-high from Th2-low asthma ( 69 ). These findings indicate that CST1 plays a key role in the pathogenesis of asthma and CRSwNP, and is a key biomarker linking CRSwNP and asthma.…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps and asthma comorbidity using bioinformatics approaches

et al. 2022

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Patients with chronic rhinosinusitis with nasal polyps (CRSwNP) and asthma comorbidity (ACRSwNP) present severe symptoms and are more likely to relapse. However, the pathogenesis of ACRSwNP is not fully understood. The aim of this study was to explore the underlying pathogenesis of ACRSwNP using bioinformatics approaches. ACRSwNP-related differentially expressed genes (DEGs) were identified by the analysis of the GSE23552 dataset. The clusterProfiler R package was used to carry out functional and pathway enrichment analysis. A protein–protein interaction (PPI) network was built using the STRING database to explore key genes in the pathogenesis of ACRSwNP. The bioinformatics analysis results were verified through qRT-PCR. The Connectivity Map (CMap) database was used to predict potential drugs for the treatment of ACRSwNP. A total of 36 DEGs were identified, which were mainly enriched in terms of regulation of immune response and detection sensory perception of taste. Thirteen hub genes including AZGP1, AQP9, GAPT, PIP, and PRR4 were identified as potential hub genes in ACRSwNP from the PPI network. Analysis of the GSE41861 dataset showed that upregulation of CST1 in nasal mucosa was associated with asthma. qRT-PCR detection confirmed the bioinformatics analysis results. Tacrolimus and spaglumic acid were identified as potential drugs for the treatment of ACRSwNP from the CMap database. The findings of this study provide insights into the pathogenesis of ACRSwNP and may provide a basis for the discovery of effective therapeutic modalities for ACRSwNP.

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Section: Discussionmentioning

confidence: 99%

Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps and asthma comorbidity using bioinformatics approaches

et al. 2022

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“…LASSO identified potential DCEGs includes CPA3, SERPINB2, CHCHD5, EMC6, RPUSD3, POSTN, SEC14L1 and UPK1B in AECs dataset and these DCEGs were also persistently upregulated in multiple tissue/cell type datasets from asthmatic subjects. The previous study reported that elevated expression of CPA3 gene was observed in asthmatic subjects compared to controls and CPA3 gene correlated with sputum mast cells, asthma and rhinitis 42 . Recent study reported that the expression level of SERPINB2 gene was increased in airway epithelial cells of asthmatic and in atopic asthmatic subjects compared controls 43 .…”

Section: Discussionmentioning

confidence: 93%

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Dessie

Gautam

Ding

et al. 2023

Sci Rep

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Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.

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“…CEACAM5 is an IL-13-regulated epithelial gene that is upregulated in severe asthma and associated with increased asthma exacerbations 44 – 46 . It has also been previously identified as a hub gene in a limited microarray-based analysis in 42 cases and 28 controls 47 . Carboxypeptidase A3 (CPA3) is a mast cell (MC) protease and there is a well-known switching of MCs expressing tryptase only (MC T ) to MCs expressing tryptase, chymase and CPA3 (MC TC ) with a dominant expansion of the latter in airways of asthma cases and more specifically in the context of severe asthma 48 .…”

Section: Discussionmentioning

confidence: 97%

Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations

Szczesny,

Boorgula,

Chavan

et al. 2024

Nat Commun

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Asthma has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omic signatures of asthma focusing on populations of African ancestry. RNASeq and DNA methylation data are generated from nasal epithelium including cases (current asthma, N = 253) and controls (never-asthma, N = 283) from 7 different geographic sites to identify differentially expressed genes (DEGs) and gene networks. We identify 389 DEGs; the top DEG, FN1, was downregulated in cases (q = 3.26 × 10−9) and encodes fibronectin which plays a role in wound healing. The top three gene expression modules implicate networks related to immune response (CEACAM5; p = 9.62 × 10−16 and CPA3; p = 2.39 × 10−14) and wound healing (FN1; p = 7.63 × 10−9). Multi-omic analysis identifies FKBP5, a co-chaperone of glucocorticoid receptor signaling known to be involved in drug response in asthma, where the association between nasal epithelium gene expression is likely regulated by methylation and is associated with increased use of inhaled corticosteroids. This work reveals molecular dysregulation on three axes – increased Th2 inflammation, decreased capacity for wound healing, and impaired drug response – that may play a critical role in asthma within the African Diaspora.

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Identifying key genes and functionally enriched pathways in Th2-high asthma by weighted gene co-expression network analysis

Cited by 10 publications

References 31 publications

Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps and asthma comorbidity using bioinformatics approaches

Identification of key genes and pathways in chronic rhinosinusitis with nasal polyps and asthma comorbidity using bioinformatics approaches

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations

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