Identification of Potential Key Genes in the Pathogenesis of Chronic Obstructive Pulmonary Disease Through Bioinformatics Analysis

Guan, Qingzhou; Tian, Yange; Zhang, Zhenzhen; Zhang, Lanxi; Zhao, Peng; Li, Jiansheng

doi:10.3389/fgene.2021.754569

Cited by 9 publications

(10 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, MCC has proved to be a more accurate method for predicting important targets [18], MNC can be applied to discover some unrecognized hubs from previous dataset [19], Degree can be used to predict key proteins, proteins with a high Degree were more likely to be key proteins [20], EPC was used to explore protein interaction networks [21], Closeness was a topology analysis method based on shortest path [22]. In the previous research, MCC, MNC, Degree, EPC, and closeness algorithms were more commonly used [23,24]. In this study, the above five algorithms were used to screen out the top 10 hub genes.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate aberrant differentially methylated/expressed genes in asthma

Wang¹,

Wang²,

Dai³

et al. 2022

Allergy Asthma Clin Immunol

View full text Add to dashboard Cite

Background Asthma is an important non-communicable disease worldwide. DNA methylation is associated with the occurrence and development of asthma. We are aimed at assuring differential expressed genes (DEGs) modified by aberrantly methylated genes (DMGs) and pathways related to asthma by integrating bioinformatics analysis. Methods One mRNA dataset (GSE64913) and one gene methylation dataset (GSE137716) were selected from the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed using GeneCodies 4.0 database. All gene expression matrices were analyzed by Gene set enrichment analysis (GSEA) software. STRING was applied to construct a protein-protein interaction (PPI) network to find the hub genes. Then, electronic validation was performed to verify the hub genes, followed by the evaluation of diagnostic value. Eventually, quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to detect the expression of hub genes. Results In total, 14 hypomethylated/high-expression genes and 10 hypermethylated/low-expression genes were obtained in asthma. Among them, 10 hub genes were identified in the PPI network. Functional analysis demonstrated that the differentially methylated/expressed genes were primarily associated with the lung development, cytosol and protein binding. Notably, HLA-DOA was enriched in asthma. FKBP5, WNT5A, TM4SF1, PDK4, EPAS1 and GMPR had potential diagnostic value for asthma. Conclusion The project explored the pathogenesis of asthma, which may provide a research basis for the prediction and the drug development of asthma.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of candidate aberrant differentially methylated/expressed genes in asthma

Wang¹,

Wang²,

Dai³

et al. 2022

Allergy Asthma Clin Immunol

View full text Add to dashboard Cite

show abstract

“…Five online miRNA databases (namely miRDIP, the Encyclopedia of RNA Interactomes [ENCORI], TargetScan, DIANA-micro T, and miRWalk) (accessed on 19 October 2021) were used to predict the target miRNAs of genes of interest with the default parameters [ 20 , 32 ]. In this study, if a gene (such as gene A) could be targeted by a certain miRNA in at least four of the five databases, then it was defined as the target miRNA of gene A.…”

Section: Methodsmentioning

confidence: 99%

“…In recent years, high-throughput genetic testing technology has become increasingly advanced, and many transcriptomics features have become more common in disease research [ 16 – 18 ]. In addition, competitive endogenous RNA (ceRNA) regulatory networks have been found to be involved in the transcription and regulation of various disease-related genes in a variety of studies [ 19 , 20 ]. By combining high-throughput microarray/RNA-sequencing data and bioinformatics analysis algorithms, pathogenic genes can be identified, providing some guidance for pathogenesis research and the clinical treatment of these diseases.…”

Section: Introductionmentioning

confidence: 99%

Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs

Zhang,

Guan,

Tian

et al. 2023

BMC Pulm Med

Self Cite

View full text Add to dashboard Cite

Objective The pathogenesis of idiopathic pulmonary fibrosis (IPF) remains unclear. We sought to identify IPF-related genes that may participate in the pathogenesis and predict potential targeted traditional Chinese medicines (TCMs). Methods Using IPF gene-expression data, Wilcoxon rank-sum tests were performed to identify differentially expressed genes (DEGs). Protein–protein interaction (PPI) networks, hub genes, and competitive endogenous RNA (ceRNA) networks were constructed or identified by Cytoscape. Quantitative polymerase chain reaction (qPCR) experiments in TGF-β1-induced human fetal lung (HFL) fibroblast cells and a pulmonary fibrosis mouse model verified gene reliability. The SymMap database predicted potential TCMs targeting IPF. The reliability of TCMs was verified in TGF-β1-induced MRC-5 cells. Materials Multiple gene-expression profile data of normal lung and IPF tissues were downloaded from the Gene Expression Omnibus database. HFL fibroblast cells and MRC-5 cells were purchased from Wuhan Procell Life Science and Technology Co., Ltd. (Wuhan, China). C57BL/12 mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). Results In datasets GSE134692 and GSE15197, DEGs were identified using Wilcoxon rank-sum tests (both p < 0.05). Among them, 1885 DEGs were commonly identified, and 87% (1640 genes) had identical dysregulation directions (binomial test, p < 1.00E-16). A PPI network with 1623 nodes and 8159 edges was constructed, and 18 hub genes were identified using the Analyze Network plugin in Cytoscape. Of 18 genes, CAV1, PECAM1, BMP4, VEGFA, FYN, SPP1, and COL1A1 were further validated in the GeneCards database and independent dataset GSE24206. ceRNA networks of VEGFA, SPP1, and COL1A1 were constructed. The genes were verified by qPCR in samples of TGF-β1-induced HFL fibroblast cells and pulmonary fibrosis mice. Finally, Sea Buckthorn and Gnaphalium Affine were predicted as potential TCMs for IPF. The TCMs were verified by qPCR in TGF-β1-induced MRC-5 cells. Conclusion This analysis strategy may be useful for elucidating novel mechanisms underlying IPF at the transcriptome level. The identified hub genes may play key roles in IPF pathogenesis and therapy.

show abstract

“…To identify the genes associated with aging in COPD patients, two microarray datasets (GSE76925 and GSE47460) were downloaded from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) [50,51]. Young COPD patients were defined as ≤ 50 years old, and aged COPD patients were defined as ≥ 70 years old.…”

Section: Microarray Data Acquisition and Processmentioning

confidence: 99%

Identification and validation of genetic signature associated with aging in chronic obstructive pulmonary disease

Chen

Zhan

Chen

et al. 2022

Aging

View full text Add to dashboard Cite

Aging plays an essential role in the development for chronic obstructive pulmonary disease (COPD). The aim of this study was to identify and validate the potential aging-related genes of COPD through bioinformatics analysis and experimental validation. Firstly, we compared the gene expression profiles of aged and young COPD patients using two datasets (GSE76925 and GSE47460) from Gene Expression Omnibus (GEO), and identified 244 aging-related different expressed genes (DEGs), with 132 up-regulated and 112 downregulated. Then, by analyzing the data for cigarette smoke-induced COPD mouse model (GSE125521), a total of 783 DEGs were identified between aged and young COPD mice, with 402 genes increased and 381 genes decreased. Additionally, functional enrichment analysis revealed that these DEGs were actively involved in COPD-related biological processes and function pathways. Meanwhile, six genes were identified as the core aging-related genes in COPD after combining the human DEGs and mouse DEGs. Eventually, five out of six core genes were validated to be up-regulated in the lung tissues collected from aged COPD patients than young COPD patients, namely NKG7, CKLF, LRP4, GDPD3 and CXCL9. Thereinto, the expressions of NKG7 and CKLF were negatively associated with lung function. These results may expand the understanding for aging in COPD.

show abstract

Identification of Potential Key Genes in the Pathogenesis of Chronic Obstructive Pulmonary Disease Through Bioinformatics Analysis

Cited by 9 publications

References 58 publications

Identification of candidate aberrant differentially methylated/expressed genes in asthma

Identification of candidate aberrant differentially methylated/expressed genes in asthma

Integrated bioinformatics analysis for the identification of idiopathic pulmonary fibrosis–related genes and potential therapeutic drugs

Identification and validation of genetic signature associated with aging in chronic obstructive pulmonary disease

Contact Info

Product

Resources

About