Open Access

Integrative analysis of the contribution of mRNAs and long non‑coding RNAs to the pathogenesis of asthma

  • Authors:
    • Xiaochuang Liu
    • Yanyan Zhang
    • Hui Jiang
    • Nannan Jiang
    • Jiarong Gao
  • View Affiliations

  • Published online on: July 19, 2019     https://doi.org/10.3892/mmr.2019.10511
  • Pages: 2617-2624
  • Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Asthma, a common but poorly controlled disease, is one of the most serious health problems worldwide; however, the mechanisms underlying the development of asthma remain unknown. Long non‑coding RNAs (lncRNAs) and mRNAs serve important roles in the initiation and progression of various diseases. The present study aimed to investigate the role of differentially expressed lncRNAs and mRNAs associated with asthma. Differentially expressed lncRNAs and mRNAs were screened between the expression data of 62 patients with asthma and 43 healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the biological functions and pathways associated with the lncRNAs and mRNAs identified. Protein‑protein interaction (PPI) networks were subsequently generated. In addition, lncRNA‑mRNA weighted co‑expression networks were obtained. In total, 159 differentially expressed lncRNAs and 1,261 mRNAs were identified. GO and KEGG analyses revealed that differentially expressed mRNAs regulated asthma by participating in the ‘vascular endothelial (VEGF) signaling pathway’, ‘oxidative phosphorylation’, ‘Fc ε RI signaling pathway’, ‘amino sugar and nucleotide sugar metabolism’, ‘histidine metabolism’, ‘β‑alanine metabolism’ and ‘extracellular matrix‑receptor interaction’ (P<0.05). Furthermore, protein kinase B 1 had the highest connectivity degree in the PPI network, and was significantly enriched in the ‘VEGF signaling pathway’ and ‘Fc ε RI signaling pathway’. A total of 8 lncRNAs in the lncRNA‑mRNA co‑expression network were reported to interact with 52 differentially expressed genes, which were enriched in asthma‑associated GO and KEGG pathways. The results obtained in the present study may provide insight into the profile of differentially expressed lncRNAs associated with asthma. The identification of a cluster of dysregulated lncRNAs and mRNAs may serve as a potential therapeutic strategy to reverse the progression of asthma.

Introduction

Asthma is a chronic inflammatory disease of the airways, characterized by airway hyperresponsiveness, chronic lung inflammation and airway remodeling (1,2). Asthma affects an estimated 358 million people worldwide, leading to a mortality rate of 0.4 million in 2015. Inhaled corticosteroids are the main method of pharmaceutical treatment; however, their effects are not satisfactory. Smoking and occupational asthmagens are the primary risk factors for asthma (3). Asthma affects people of all ages and there are no effective clinical treatments due to the complex pathophysiology of the condition (4). Therefore, understanding the molecular basis underlying the pathophysiology of asthma, and the screening of molecular markers for the development of therapeutic targets remains critical.

Long non-coding RNAs (lncRNAs) are RNA transcripts >200 nucleotides in length (5). The various functions of lncRNAs have been reported with advances in sequencing and bioinformatics analysis (6). Previous studies have suggested that lncRNAs regulate gene expression by interacting with DNA, RNA or proteins (5,6). For example, the lncRNA MAR1 acts as a sponge for miR-487b, promoting skeletal muscle differentiation and regeneration (7). Thus, these transcripts may be considered as potential biomarkers and therapeutic targets (812); however, the expression profiles of lncRNAs and mRNAs in asthma remain unclear.

In the present study, microarray data was downloaded from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database for the analysis of differentially expressed mRNA and lncRNA profiles in asthma. In addition, the database was used to investigate asthma-associated mRNAs and lncRNAs in airway epithelial brushings. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network and weighted correlation network analyses (WGCNAs) were applied to investigate the role of the differentially expressed lncRNAs and mRNAs in asthma. To the best of our knowledge, the present study is the first to comprehensively analyze mRNAs and lncRNAs in asthma. The findings may provide in-depth molecular insight into the pathophysiology of this condition.

Materials and methods

Tissue samples and data acquisition

The gene expression data of the GSE67472 dataset was downloaded from the NCBI GEO (www.ncbi.nlm.nih.gov/geo). The GEO is the largest database for high-throughput molecular data, primarily gene expression data (13), and contains links to ~20,000 published studies comprising 800,000 samples derived from >1,600 organisms (14). Following screening, the GSE67472 dataset with a large sample size was selected for analysis to ensure the stability and reliability of the data (15). Analysis was conducted via the GPL1355 platform of Affymetrix Human Genome U133 Plus 2.0 (Thermo Fisher Scientific, Inc.). The GSE67472 dataset containing the data of airway epithelial gene expression in 62 patients with asthma and 43 healthy control samples from airway epithelial brushings was included in the microarray analysis (16).

GeneChip probe re-annotation

Numerous lncRNAs were identified via the Affymetrix microarray according to the lncRNA classification pipeline developed in a previous study (17). The latest version of NetAffx Annotation File (release 36; HG-U133_Plus_2 Annotations; CSV format; 36 MB; accessed on 7th December 2016) was obtained from Affymetrix (Thermo Fisher Scientific, Inc.). This annotation file was mapped to the identifications (IDs) of the HG-U133_Plus_2 probe sets. For the probe sets from the RefSeq database (RefSeq Release 89; www.ncbi.nlm.nih.gov/refseq/), probes labeled with protein mixed (NP) were removed, but those with an ID beginning with non-coding RNAs (NR) were included. For the probe sets from the Ensembl database (ensemble 96; www.ensembl.org/index.html), the online software BioMart (GRCh38.p11; http://asia.ensembl.org/biomart/martview/f0b2ccb5ee23510bf3f1e71d87ba7122) was applied to convert Affymetrix microarray IDs to Ensembl IDs with the corresponding gene type. Probe sets from NONCODE were retained. Furthermore, genes annotated as ‘lincRNA’, ‘sense_intronic’, ‘processed_transcript’, ‘antisense’, ‘sense_overlapping’, ‘3prime_overlapping_ncRNA’ or ‘misc_RNA’ were retained. Finally, probe set IDs annotated as ‘microRNA’ or ‘snoRNAs’, and other small RNAs were removed.

Data preprocessing

Affymetrix Expression Console (v1.4; http://www.affymetrix.com/support/technical/byproduct.affx?product=expressionconsole) with Ro-bust microarray was applied to normalize the raw files. Limma package (http://bioinf.wehi.edu.au/limma) in R (version 3.5.1; http://cran.r-project.org/) was used to identify differentially expressed lncRNAs and mRNAs among the asthma and control groups via a t-test (18). Fold change (FC)>1.2 and P<0.05 were set as the criteria for differential expression (19). Hierarchical clustering was conducted in R scripts.

GO and pathway enrichment analysis

GO (geneontology.org) analysis, frequently used in functional enrichment studies of large-scale genes (20), and KEGG (www.genome.jp/kegg) enrichment analysis were performed to investigate the biological pathways that involve differentially expressed mRNAs. In the present study, clusterProfiler (v3.12.0; guangchuangyu.github.io/software/clusterProfiler) and Database for Annotation, Visualization and Integrated Discovery tools (v6.8; http://david.ncifcrf.gov/) were used to analyze the functional enrichment conditions for dysregulated mRNAs (2123). The false discovery rate (FDR) was calculated to correct the P-value and FDR<0.05 was selected as the threshold for a statistically significant difference.

PPI network construction

Protein interactions were analyzed using the online Search Tool for the Retrieval of Interacting Genes (v 10.5; string-db.org) tool and a combined score of >0.7 was used as the cut-off criterion (2426). PPI networks were subsequently generated using Cytoscape software (v3.2.8; cytoscapeweb.cytoscape.org) (27,28).

Construction of the lncRNA-mRNA WGCNA

The WGCNA package (29) in R was used to construct the co-expression network of lncRNAs and mRNAs as follows: i) Network construction, the weighted co-expression network of lncRNAs and mRNAs was specified in its adjacency matrix (amn), which encodes the network connection strength between nodes m and n. In order to calculate the amn, the default approach was employed, which defines the co-expression similarity (Smn) as the absolute value of the correlation coefficient between the nodes of m and n, and Smn=|cor (am, an)|. The weighted adjacency amn between two genes is proportional to their similarity on a logarithmic scale with absolute value of the correlation to a power β≥0.8, log (amn)=β × log (Smn). Adjacency functions were obtained by using the approximate scale-free topology criterion (30). The amn was converted into a topology matrix; and ii) module detection: The Dynamic Tree Cut and Static Tree Cut methods were applied to detect modules with ≥30 lncRNAs/genes (31). The cluster dendrogram was visualization using the WGCNA package.

Results

Identification of differentially expressed lncRNAs and mRNAs

Based on the screening criteria, FC>1.2 and P<0.05, differentially expressed lncRNAs and mRNAs in the GSE67472 dataset were identified. A total of 159 differentially expressed lncRNAs and 1,261 mRNAs were identified. In total, 48 lncRNAs were found to be upregulated and 111 lncRNAs were found to be downregulated while 744 mRNAs were found to be upregulated and 517 mRNAs were found to be downregulated. Hierarchical clustering was conducted to evaluate the altered expression of lncRNAs and mRNAs in the samples (Fig. 1).

Functional annotation and pathway analysis for differentially expressed mRNAs

GO and KEGG pathway enrichment analyses were performed to determine the functions of the identified differentially expressed mRNAs. GO analysis revealed that the top 30 terms of the 2,184 and 1,605 GO terms were enriched in the upregulated (Fig. 2A) and downregulated (Fig. 2C) genes, respectively. In the pathway analysis, the top 30 of the 245 and 219 pathways were enriched in the upregulated (Fig. 2B) and downregulated (Fig. 2D) genes, respectively.

PPI network

The PPI network of genes with significantly altered expression (FC>1.2 and P<0.05) was delineated using the STRING database. The PPI network contained 421 nodes and 1,232 edges (Fig. 3). Genes with the top 15 highest degrees are presented in Table I. Genes with a high degree may be potential targets for clinical treatment (32).

Table I.

Top 15 genes with the connectivity in the protein-protein interaction network.

Table I.

Top 15 genes with the connectivity in the protein-protein interaction network.

GeneNode degreeFold change (asthma/control)P-valueTrend
AKT1551.24 3.13×10−4Up
ACACB491.27 1.71×10−3Down
DNAJC10371.36 2.09×10−2Up
FOS351.37 6.04×10−3Down
GART351.43 3.91×10−4Down
LRGUK331.25 3.20×10−3Down
INSR311.44 1.00×10−5Down
KIT281.62 2.25×10−7Up
ENO2261.22 1.75×10−3Down
HSPA5251.33 1.33×10−2Up
CD44211.62 4.30×10−5Up
DYNC2H1211.33 4.00×10−5Down
EGR1211.31 4.19×10−2Down
HDAC9211.66 1.59×10−7Up
HPGDS211.48 2.00×10−5Up

[i] Up, upregulated; down, downregulated.

Construction of the lncRNA-mRNA WGCNA

The lncRNA-mRNA co-expression network was established to investigate the association between differentially expressed lncRNAs and mRNAs. In the present study, a cluster dendrogram was obtained using the WGCNA package in R (Fig. 4A); two weighted co-expression subnetworks were identified. lncRNA-mRNA weighted co-expression networks were generated based on genes with the top 30 degrees in the two modules, which comprised 8 lncRNAs and 52 mRNAs. The two modules included a number of lncRNA-mRNA co-expression interactions, which indicated the mRNAs that could be regulated by certain lncRNAs. The lncRNAs in the blue and turquoise modules are listed in Table II. As presented in Fig. 4B and C, the round and arrow-shaped nodes represented mRNAs and lncRNAs, respectively; red indicated upregulation, while green indicated downregulation expression.

Table II.

lncRNAs in the blue module and the turquoise module.

Table II.

lncRNAs in the blue module and the turquoise module.

lncRNAModuleTrendP-valueFold change
AC124067.4BlueDown 1.99×10−21.31
ZNF667-AS1BlueDown 4.80×10−21.24
AC005906.2BlueDown 2.28×10−21.26
AL357568.2BlueDown 6.92×10−41.22
AC130650.2TurquoiseDown 9.64×10−71.31
STX18-AS1TurquoiseDown 8.91×10−41.24
LINC02363TurquoiseDown 1.34×10−21.22
LINC02145TurquoiseDown 2.00×10−61.37

[i] lncRNA, long non-coding RNA; down, downregulated.

Functional annotation and pathway analysis for the differentially expressed mRNAs in the blue and turquoise modules

GO analysis revealed a total of 756 and 1,882 enriched terms in the blue and the turquoise modules, respectively. The top 30 terms are presented in Fig. 5A and C. KEGG analysis revealed that 116 and 164 pathways were enriched in the two modules, respectively. The top 30 pathways are presented in Fig. 5B and D.

Discussion

Asthma is a common health issue, which poses an economic and social burden to patients (2); however, the pathogenesis of this condition remains poorly understood. To investigate the pathogenesis of asthma and to identify potential biomarkers for clinical treatment in the present study, lncRNA and mRNA expression data in the GSE67472 dataset were downloaded. A total of 159 and 1,261 dysregulated lncRNAs and mRNAs, respectively, were identified in bronchial mucosa samples obtained from patients with asthma compared with samples obtained from normal controls.

Genes and their protein products are the basic components of cells, and can be assembled into functional modules. Gene co-expression networks are used to investigate the associations between gene transcripts (33). WGCNA is a biological application for screening clusters (modules) of highly associated genes and demonstrates the link between genes across microarray samples. This analytic tool has been utilized in numerous studies (12,18,29,34). Using WGCNA and bioinformatics analysis, the major biological functions of asthma-associated lncRNAs, and the potential underlying molecular mechanisms in which these lncRNAs may be involved, were investigated in the present study. WGCNA analysis identified 8 key lncRNAs and 52 genes in two modules; genes in the blue module were enriched in the GOs of several metabolic and catabolic processes. Previous studies have indicated that lncRNAs are involved in asthma (35,36). ZNF667 antisense RNA 1 (ZNF667-AS1) inhibits the inflammatory response of spinal cord injury by suppressing the Janus kinase-STAT pathway (3741). The present study observed that the level of lncRNA ZNF667-AS1 in asthma was downregulated in comparison with healthy controls. Therefore, lncRNA ZNF667-AS1 may play a role in the pathogenesis of asthma.

Airway remodeling plays an important role in the progressive worsening of asthma and is irreversible (1,2). Vascular endothelial growth factor (42,43), oxidative stress (44,45), the Fc ε RI signaling pathway (46,47), and the dysregulation of amino acids, sugar and nucleotide metabolism (48,49) have been implicated in asthma. Functional gene analysis of the 159 lncRNAs and 1,261 mRNAs identified in the present study demonstrated that the aforementioned pathways are involved in the pathogenesis of asthma. In order to investigate the role of the 8 key lncRNAs and 52 genes in two modules, functional analysis using GO and KEGG was conducted. Certain pathways, including ‘Toll-like receptors’ (50,51), ‘activation of PPAR signaling pathway’ (52) and ‘eosinophil apoptosis’ (53), are important for elucidating the mechanisms underlying the pathogenesis of asthma. Genes in the turquoise module were enriched in the following GO terms: ‘respiratory chain’, ‘regulation of T cell differentiation in thymus’ and ‘cilium movement involved in cell motility’. KEGG analysis revealed that genes in the turquoise module were enriched in the ‘Fc ε RI signaling pathway’ and ‘ECM-receptor interaction’. These results indicated that genes in the two modules may serve key roles in the initiation and development of asthma.

In the present study, upregulated protein kinase B (Akt) exhibited the highest degree in the PPI network and was significantly enriched in the ‘VEGF signaling pathway’ and ‘Fc ε RI signaling pathway’. Akt is also involved in the phosphoinositide 3-kinase/Akt signaling pathway, which served a key role in lung inflammation and airway remodeling in a rat model of ovalbumin (OVA)-induced asthma (54). The expression of phosphorylated-Akt was increased in the lung tissues of rats with OVA-induced asthma compared with controls.

lncRNAs regulate the transcription and expression of mRNAs, and participate in the initiation and development of various diseases (7,55). Therefore, the lncRNAs identified in the present study and their interacting genes may serve important roles in the onset and development of asthma. There are some limitations to the present study. The data was only extracted from one dataset that was not confirmed in independent studies. Additionally, cell, animal and clinical experiments are required to corroborate the results obtained in the present study as the mRNA expression level may not always be consistent with the protein level (56). Furthermore, the interactions between the identified lncRNA and mRNA requires experimental verification. Such future research may elucidate the mechanisms underlying lncRNAs and mRNAs in the development of asthma.

Acknowledgements

The authors would like to thank Mr Qiang Fan (Ao Ji Bio-tech Co., Ltd.) for assisting with the data analysis.

Funding

The present study was supported by the National Natural Science Foundation of China (grant no. 81804043).

Availability of data and materials

The datasets analyzed during the present study are available from the Gene Expression Omnibus repository, www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE67472.

Authors' contributions

JG conceived and designed the study. XL and YZ acquired, analyzed and interpreted data and wrote the manuscript. NJ and HJ analyzed data and critically revised the manuscript. JG generally supervised the research group and gave final approval. All authors have read and approved the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Abreu SC, Lopes-pacheco M, da Silva AL, Xisto DG, de Oliveira TB, Kitoko JZ, de Castro LL, Amorim NR, Martins V, Silva LHA, et al: Eicosapentaenoic acid enhances the effects of mesenchymal stromal cell therapy in experimental allergic asthma. Front Immunol. 9:11472018. View Article : Google Scholar : PubMed/NCBI

2 

Mejias SG and Ramphul K: Prevalence and associated risk factors of bronchial asthma in children in santo domingo, dominican republic. Cureus. 10:e22112018.PubMed/NCBI

3 

Soriano JB, Abajobir AA, Abate KH, Abera SF, Agrawal A, Ahmed MB, Aichour AN, Aichour I, Aichour MT, Alam K, et al: Global, regional, and national deaths, prevalence, disability-adjusted life years, and years lived with disability for chronic obstructive pulmonary disease and asthma, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet Respir Med. 5:691–706. 2017. View Article : Google Scholar : PubMed/NCBI

4 

de Castro LL, Xisto DG, Kitoko JZ, Cruz FF, Olsen PC, Redondo PAG, Ferreira TPT, Weiss DJ, Martins MA, Morales MM and Rocco PRM: Human adipose tissue mesenchymal stromal cells and their extracellular vesicles act differentially on lung mechanics and inflammation in experimental allergic asthma. Stem Cell Res Ther. 8:151–162. 2017. View Article : Google Scholar : PubMed/NCBI

5 

Ponting CP, Oliver PL and Reik W: Evolution and functions of long noncoding RNAs. Cell. 136:629–641. 2009. View Article : Google Scholar : PubMed/NCBI

6 

Mercer TR, Dinger ME and Mattick JS: Long non-coding RNAs: Insights into functions. Nat Rev Genet. 10:155–159. 2009. View Article : Google Scholar : PubMed/NCBI

7 

Zhang ZK, Li J, Guan D, Liang C, Zhuo Z, Liu J, Lu A, Zhang G and Zhang BT: A newly identified lncRNA MAR1 acts as a miR-487b sponge to promote skeletal muscle differentiation and regeneration. J Cachexia Sarcopenia Muscle. 9:613–626. 2018. View Article : Google Scholar : PubMed/NCBI

8 

Li XQ, Ren ZX, Li K, Huang JJ, Huang ZT, Zhou TR, Cao HY, Zhang FX and Tan B: Key Anti-fibrosis associated long noncoding RNAs identified in human hepatic stellate cell via transcriptome sequencing analysis. Int J Mol Sci. 19:E6752018. View Article : Google Scholar : PubMed/NCBI

9 

Yan B, Liu JY, Yao J, Li XM, Wang XQ, Li YJ, Tao ZF, Song YC, Chen Q and Jiang Q: lncRNA-MIAT regulates microvascular dysfunction by functioning as a competing endogenous RNA. Circ Res. 116:1143–1156. 2015. View Article : Google Scholar : PubMed/NCBI

10 

Wang K, Liu CY, Zhou LY, Wang JX, Wang M, Zhao B, Zhao WK, Xu SJ, Fan LH, Zhang XJ, et al: APF lncRNA regulates autophagy and myocardial infarction by targeting miR-188-3p. Nat Commun. 6:67792015. View Article : Google Scholar : PubMed/NCBI

11 

Xing Z, Park PK, Lin C and Yang L: LncRNA BCAR4 wires up signaling transduction in breast cancer. RNA Biol. 12:681–689. 2015. View Article : Google Scholar : PubMed/NCBI

12 

Gao JR, Qin XJ, Jiang H, Gao YC, Guo MF and Jiang NN: Potential role of lncRNAs in contributing to pathogenesis of chronic glomerulonephritis based on microarray data. Gene. 643:46–54. 2018. View Article : Google Scholar : PubMed/NCBI

13 

Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res 41 (Database). D991–D995. 2013.

14 

Sharma M, Batra J, Mabalirajan U, Sharma S, Nagarkatti R, Aich J, Sharma SK, Niphadkar PV and Ghosh B: A genetic variation in inositol polyphosphate 4 phosphatase a enhances susceptibility to asthma. Am J Respir Crit Care Med. 177:712–719. 2008. View Article : Google Scholar : PubMed/NCBI

15 

Christenson SA, Steiling K, van den Berge M, Hijazi K, Hiemstra PS, Postma DS, Lenburg ME, Spira A and Woodruff PG: Asthma-COPD overlap. Clinical relevance of genomic signatures of type 2 inflammation in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 191:758–766. 2015. View Article : Google Scholar : PubMed/NCBI

16 

Christenson SA, Steiling K, van den Berge M, Hijazi K, Hiemstra PS, Postma DS, Lenburg ME, Spira A and Woodruff PG: Asthma-COPD Overlap: Clinical Relevance of Genomic Signatures of Type 2 Inflammation in COPD. Am J Respir Crit Care Med. 191:758–766. 2015. View Article : Google Scholar : PubMed/NCBI

17 

Zhang XQ, Sun S, Pu JK, Tsang AC, Lee D, Man VO, Lui WM, Wong ST and Leung GK: Long non-coding RNA expression profiles predict clinical phenotypes in glioma. Neurobiol Dis. 48:1–8. 2012. View Article : Google Scholar : PubMed/NCBI

18 

Yu C, Ni HJ, Zhao YC, Chen K, Li M, Li C, Zhu XD and Fu Q: Potential role of lncRNAs in contributing to pathogenesis of intervertebral disc degeneration based on microarray data. Med Sci Monit. 21:3449–3458. 2015. View Article : Google Scholar : PubMed/NCBI

19 

Luchessi AD, Silbiger VN, Hirata RD, Lima-Neto LG, Cavichioli D, Iñiguez A, Bravo M, Bastos G, Sousa AG, Brión M, et al: Pharmacogenomics of anti-platelet therapy focused on peripheral blood cells of coronary arterial disease patients. Clin Chim Acta. 425:9–17. 2013. View Article : Google Scholar : PubMed/NCBI

20 

Xing ZH, Chu C, Chen L and Kong XY: The use of Gene Ontology terms and KEGG pathways for analysis and prediction of oncogenes. Biochim Biophys Acta. 1860:2725–2734. 2016. View Article : Google Scholar : PubMed/NCBI

21 

Maere S, Heymans K and Kuiper M: BiNGO: A Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks. Bioinformatics. 21:3448–3449. 2005. View Article : Google Scholar : PubMed/NCBI

22 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

23 

Huang DW, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI

24 

von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M, Jouffre N, Huynen MA and Bork P: STRING: Known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res. 33:D433–D437. 2005. View Article : Google Scholar : PubMed/NCBI

25 

Cuesta-Astroz Y, Santos A, Oliveira G and Jensen LJ: Analysis of predicted host-parasite interactomes reveals commonalities and specificities related to parasitic lifestyle and tissues tropism. Front Immunol. 10:2122019. View Article : Google Scholar : PubMed/NCBI

26 

Wang Y, Ruan Z, Yu S, Tian T, Liang X, Jing L, Li W, Wang X, Xiang L, Claret FX, et al: A four-methylated mRNA signature-based risk score system predicts survival in patients with hepatocellular carcinoma. Aging (Albany NY). 11:160–173. 2019. View Article : Google Scholar : PubMed/NCBI

27 

Safari-Alighiarloo N, Taghizadeh M, Tabatabaei SM, Shahsavari S, Namaki S, Khodakarim S and Rezaei-Tavirani M: Identification of new key genes for type 1 diabetes through construction and analysis of protein–protein interaction networks based on blood and pancreatic islet transcriptomes. J Diabetes. 9:764–777. 2017. View Article : Google Scholar : PubMed/NCBI

28 

Le DH and Pham VH: HGPEC: A Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network. Bmc Syst Biol. 11:612017. View Article : Google Scholar : PubMed/NCBI

29 

Langfelder P and Horvath S: WGCNA: An R package for weighted correlation network analysis. Bmc Bioinformatics. 9:5592008. View Article : Google Scholar : PubMed/NCBI

30 

Zhang B and Horvath S: A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 4:Article 17. 2005. View Article : Google Scholar : PubMed/NCBI

31 

Dudoit S and Fridlyand J: A prediction-based resampling method for estimating the number of clusters in a dataset. Genome Biol. 3:RESEARCH00362002. View Article : Google Scholar : PubMed/NCBI

32 

Hsin KY, Ghosh S and Kitano H: Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One. 8:e839222013. View Article : Google Scholar : PubMed/NCBI

33 

Langfelder P and Horvath S: Eigengene networks for studying the relationships between co-expression modules. Bmc Syst Biol. 1:542007. View Article : Google Scholar : PubMed/NCBI

34 

Zhao W, Langfelder P, Fuller T, Dong J, Li A and Hovarth S: Weighted gene coexpression network analysis: State of the art. J Biopharm Stat. 20:281–300. 2010. View Article : Google Scholar : PubMed/NCBI

35 

Wang SY, Fan XL, Yu QN, Deng MX, Sun YQ, Gao WX, Li CL, Shi JB and Fu QL: The lncRNAs involved in mouse airway allergic inflammation following induced pluripotent stem cell-mesenchymal stem cell treatment. Stem Cell Res Ther. 8:22017. View Article : Google Scholar : PubMed/NCBI

36 

Zhang XY, Zhang LX, Tian CJ, Tang XY, Zhao LM, Guo YL, Cheng DJ, Chen XL, Ma LJ and Chen ZC: LncRNAs BCYRN1 promoted the proliferation and migration of rat airway smooth muscle cells in asthma via upregulating the expression of transient receptor potential 1. Am J Transl Res. 8:3409–3418. 2016.PubMed/NCBI

37 

Vrba L, Garbe JC, Stampfer MR and Futscher BW: A lincRNA connected to cell mortality and epigenetically-silenced in most common human cancers. Epigenetics. 10:1074–1083. 2015. View Article : Google Scholar : PubMed/NCBI

38 

Meng W, Cui W, Zhao L, Chi W, Cao H and Wang B: Aberrant methylation and downregulation of ZNF667-AS1 and ZNF667 promote the malignant progression of laryngeal squamous cell carcinoma. J Biomed Sci. 26:132019. View Article : Google Scholar : PubMed/NCBI

39 

Li JW, Kuang Y, Chen L and Wang JF: LncRNA ZNF667-AS1 inhibits inflammatory response and promotes recovery of spinal cord injury via suppressing JAK-STAT pathway. Eur Rev Med Pharmacol Sci. 22:7614–7620. 2018.PubMed/NCBI

40 

Zhao LP, Li RH, Han DM, Zhang XQ, Nian GX, Wu MX, Feng Y, Zhang L and Sun ZG: Independent prognostic Factor of low-expressed LncRNA ZNF667-AS1 for cervical cancer and inhibitory function on the proliferation of cervical cancer. Eur Rev Med Pharmacol Sci. 21:5353–5360. 2017.PubMed/NCBI

41 

Vrba L and Futscher BW: Epigenetic silencing of lncRNA MORT in 16 TCGA cancer types. F1000Res. 7:2112018. View Article : Google Scholar : PubMed/NCBI

42 

Smith R: Is VEGF a potential therapeutic target in asthma? Pneumologia. 63(194): 197–199. 2014.

43 

Na HJ, Hwang JY, Lee KS, Choi YK, Choe J, Kim JY, Moon HE, Kim KW, Koh GY, Lee H, et al: TRAIL negatively regulates VEGF-induced angiogenesis via caspase-8-mediated enzymatic and non-enzymatic functions. Angiogenesis. 17:179–194. 2014. View Article : Google Scholar : PubMed/NCBI

44 

Lan N, Luo G, Yang X, Cheng Y, Zhang Y, Wang X, Wang X, Xie T, Li G, Liu Z and Zhong N: 25-Hydroxyvitamin D3-deficiency enhances oxidative stress and corticosteroid resistance in severe asthma exacerbation. PLoS One. 9:e1115992014. View Article : Google Scholar : PubMed/NCBI

45 

Chung KF and Marwick JA: Molecular mechanisms of oxidative stress in airways and lungs with reference to asthma and chronic obstructive pulmonary disease. Ann N Y Acad Sci. 1203:85–91. 2010. View Article : Google Scholar : PubMed/NCBI

46 

Wu LC: Immunoglobulin E receptor signaling and asthma. J Biol Chem. 286:32891–32897. 2011. View Article : Google Scholar : PubMed/NCBI

47 

Gounni AS, Wellemans V, Yang J, Bellesort F, Kassiri K, Gangloff S, Guenounou M, Halayko AJ, Hamid Q and Lamkhioued B: Human airway smooth muscle cells express the high affinity receptor for IgE (Fc epsilon RI): A critical role of Fc epsilon RI in human airway smooth muscle cell function. J Immunol. 175:2613–2621. 2005. View Article : Google Scholar : PubMed/NCBI

48 

Ho WE, Xu YJ, Xu F, Cheng C, Peh HY, Tannenbaum SR, Wong WS and Ong CN: Metabolomics reveals altered metabolic pathways in experimental asthma. Am J Resp Cell Mol. 48:204–211. 2013. View Article : Google Scholar

49 

Xu W, Comhair SAA, Janocha AJ, Lara A, Mavrakis LA, Bennett CD, Kalhan SC and Erzurum SC: Arginine metabolic endotypes related to asthma severity. PLoS One. 12:e01830662017. View Article : Google Scholar : PubMed/NCBI

50 

Xiao HT, Liao Z, Chen L and Tong RS: A promising approach for asthma treatment by multiwayly modulating toll-like receptors. Eur Rev Med Pharmacol Sci. 16:2088–2091. 2012.PubMed/NCBI

51 

Bezemer GF, Sagar S, ven Bergenhenegouwen J, Georgiou NA, Garssen J, Kraneveld AD and Folkerts G: Dual role of Toll-like receptors in asthma and chronic obstructive pulmonary disease. Pharmcol Rev. 64:337–358. 2012. View Article : Google Scholar

52 

Xu J, Zhu YT, Wang GZ, Han D, Wu YY, Zhang DX, Liu Y, Zhang YH, Xie XM, Li SJ, et al: The PPARγ agonist, rosglitazone, attenuates airway inflammation and remodeling via heme oxygenase-1 in murine model of asthma. Acta Pharmacol Sin. 36:171–178. 2015. View Article : Google Scholar : PubMed/NCBI

53 

Ilmarinen P and Kankaanranta H: Eosinophil apoptosis as a therapeutic target in allergic asthma. Basic Clin Pharmacol Toxicol. 114:109–117. 2014. View Article : Google Scholar : PubMed/NCBI

54 

Lin HY, Xu L, Xie SS, Yu F, Hu HY, Song XL and Wang CH: Mesenchymal stem cells suppress lung inflammation and airway remodeling in chronic asthma rat model via PI3K/Akt signaling pathway. Int J Clin Exp Pathol. 8:8958–8967. 2015.PubMed/NCBI

55 

Kung JT, Colognori D and Lee JT: Long noncoding RNAs: Past, present, and future. Genetics. 193:651–669. 2013. View Article : Google Scholar : PubMed/NCBI

56 

Huang KL, Li S, Mertins P, Cao S, Gunawardena HP, Ruggles KV, Mani DR, Clauser KR, Tanioka M, Usary J, et al: Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat Commun. 8:148642017. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

September 2019
Volume 20 Issue 3

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Liu, X., Zhang, Y., Jiang, H., Jiang, N., & Gao, J. (2019). Integrative analysis of the contribution of mRNAs and long non‑coding RNAs to the pathogenesis of asthma. Molecular Medicine Reports, 20, 2617-2624. https://doi.org/10.3892/mmr.2019.10511
MLA
Liu, X., Zhang, Y., Jiang, H., Jiang, N., Gao, J."Integrative analysis of the contribution of mRNAs and long non‑coding RNAs to the pathogenesis of asthma". Molecular Medicine Reports 20.3 (2019): 2617-2624.
Chicago
Liu, X., Zhang, Y., Jiang, H., Jiang, N., Gao, J."Integrative analysis of the contribution of mRNAs and long non‑coding RNAs to the pathogenesis of asthma". Molecular Medicine Reports 20, no. 3 (2019): 2617-2624. https://doi.org/10.3892/mmr.2019.10511