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Identifying miRNA-mRNA Networks Associated With COPD Phenotypes

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation and symptoms such as shortness of breath. Although many studies have demonstrated dysregulated microRNA (miRNA) and gene (mRNA) expression in the pathogenesis of COPD, how miRNAs and mRNAs systematically interact and contribute to COPD development is still not clear. To gain a deeper understanding of the gene regulatory network underlying COPD pathogenesis, we used Sparse Multiple Canonical Correlation Network (SmCCNet) to integrate whole blood miRNA and RNA-sequencing data from 404 participants in the COPDGene study to identify novel miRNA–mRNA networks associated with COPD-related phenotypes including lung function and emphysema. We hypothesized that phenotype-directed interpretable miRNA–mRNA networks from SmCCNet would assist in the discovery of novel biomarkers that traditional single biomarker discovery methods (such as differential expression) might fail to discover. Additionally, we investigated whether adjusting -omics and clinical phenotypes data for covariates prior to integration would increase the statistical power for network identification. Our study demonstrated that partial covariate adjustment for age, sex, race, and CT scanner model (in the quantitative emphysema networks) improved network identification when compared with no covariate adjustment. However, further adjustment for current smoking status and relative white blood cell (WBC) proportions sometimes weakened the power for identifying lung function and emphysema networks, a phenomenon which may be due to the correlation of smoking status and WBC counts with the COPD-related phenotypes. With partial covariate adjustment, we found six miRNA–mRNA networks associated with COPD-related phenotypes. One network consists of 2 miRNAs and 28 mRNAs which had a 0.33 correlation (p = 5.40E-12) to forced expiratory volume in 1 s (FEV1) percent predicted. We also found a network of 5 miRNAs and 81 mRNAs that had a 0.45 correlation (p = 8.80E-22) to percent emphysema. The miRNA–mRNA networks associated with COPD traits provide a systems view of COPD pathogenesis and complements biomarker identification with individual miRNA or mRNA expression data.

SUBMITTER: Zhuang Y

PROVIDER: S-EPMC8581181 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIdentifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.ResultsIn this paper, we propose a method, weighted similarity network fusion (WSNF), to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs), transcription factors (TFs) and messenger RNAs (mRNAs) and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients) and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some miRNA-TF-mRNA sub-networks vary across different identified subtypes. In addition, pathway enrichment analyses show that the top pathways involving the most differentially expressed genes in each of the identified subtypes are different. The results would provide valuable information for understanding the mechanisms characterising different cancer subtypes and assist the design of treatment therapies. All datasets and the R scripts to reproduce the results are available online at the website: http://nugget.unisa.edu.au/Thuc/cancersubtypes/.

Project description:H1N1 influenza virus is a major factor in seasonal influenza outbreaks. After the body is infected with the influenza virus, the expression of certain mRNAs, including miRNAs, could be affected. However, the association between these mRNAs and miRNAs remains unclear. This study is aimed at identifying differentially expressed genes (DEGs) and miRNAs (DEmiRs) caused by H1N1 influenza virus infection and constructing a miRNA-mRNA regulatory network. Nine GSE datasets were downloaded from the Gene Expression Omnibus database, of which seven were mRNA data and two were miRNA data. The limma package in R language package was used to analyze array data, and edgeR package was used to analyze high-throughput sequencing data. At the same time, the genes related to H1N1 infection were further screened by WGCNA analysis. DEGs were subjected to Gene Ontology and KEGG pathway enrichment analyses by DAVID database, while the STRING database predicted the protein-protein interaction (PPI) network. The correspondence between miRNA and target mRNA was analyzed by the miRWalk database. Cytoscape software was used to output PPI results, identify hub genes, and construct a miRNA-mRNA regulatory network. 114 DEGs and 37 candidate DEmiRs were identified for subsequent analysis. These DEGs were significantly enriched in response to the virus, cytokine activity, and symbiont-containing vacuole membrane. According to KEGG analysis, DEGs were enriched in PD-L1 expression and PD-1 checkpoint pathway. The key point Cd274 (PD-L1) was highly expressed in the H1N1-infected group. Finally, a potential miRNA-mRNA regulatory network (containing 8 candidate DEmiRs and 69 candidate DEGs) and a PPI network were constructed. After that, three hub genes were identified: Ifit3, Stat2, and Irf7. These hub genes and Cd274 were validated by another independent high-throughput dataset and were highly expressed pattern. This study will help researchers gain insights into the intrinsic effects of H1N1 influenza virus infection on the host and suggest a novel association of H1N1 virus with the host immune system.

Project description:BackgroundAccumulating evidence supports the importance of noncoding RNAs and exosomes in coronary heart disease (CHD). However, exosomal-associated competing endogenous RNA- (ceRNA-) mediated regulatory mechanisms in CHD are largely unexplored. The present study aimed to explore exosomal-associated ceRNA networks in CHD.MethodsData from 6 CHD patients and 32 normal controls were downloaded from the ExoRBase database. CHD and normal controls were compared by screening differentially expressed mRNAs (DEMs), lncRNAs (DELs), and circRNAs (DECs) in serum exosomes. MicroRNAs (miRNAs) targeting DEMs were predicted using the Targetscan and miRanda databases, and miRNAs targeted by DELs and DECs were predicted using the miRcode and starBase databases, respectively. The biological functions and related signaling pathways of DEMs were analyzed using the David and KOBAS databases. Subsequently, a protein-protein interaction (PPI) network was established to screen out on which hub genes enrichment analyses should be performed, and a ceRNA network (lncRNA/circRNA-miRNA-mRNA) was constructed to elucidate ceRNA axes in CHD.ResultsA total of 312 DEMs, 43 DELs, and 85 DECs were identified between CHD patients and normal controls. Functional enrichment analysis showed that DEMs were significantly enriched in "chromatin silencing at rDNA," "telomere organization," and "negative regulation of gene expression, epigenetic." PPI network analysis showed that 25 hub DEMs were closely related to CHD, of which ubiquitin C (UBC) was the most important. Hub genes were mainly enriched in "cellular protein metabolic process" functions. The exosomal-associated ceRNA regulatory network incorporated 48 DEMs, 73 predicted miRNAs, 10 DELs, and 15 DECs. The LncRNA/circRNA-miRNA-mRNA interaction axes (RPL7AP11/hsa-miR-17-5p/UBC and RPL7AP11/hsa-miR-20b-5p/UBC) were obtained from the network.ConclusionsOur findings provide a novel perspective on the potential role of exosomal-associated ceRNA network regulation of the pathogenesis of CHD.

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Identifying miRNA-mRNA Networks Associated With COPD Phenotypes

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