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Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis.

ABSTRACT: Chronic obstructive pulmonary disease (COPD) is a multifactorial and heterogeneous disease that creates public health challenges worldwide. The underlying molecular mechanisms of COPD are not entirely clear. In this study, we aimed to identify the critical genes and potential molecular mechanisms of COPD by bioinformatic analysis. The gene expression profiles of lung tissues of COPD cases and healthy control subjects were obtained from the Gene Expression Omnibus. Differentially expressed genes were analyzed by integration with annotations from Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, followed by construction of a protein-protein interaction network and weighted gene coexpression analysis. We identified 139 differentially expressed genes associated with the progression of COPD, among which 14 Hub genes were identified and found to be enriched in certain categories, including immune and inflammatory response, response to lipopolysaccharide and receptor for advanced glycation end products binding; in addition, these Hub genes are involved in multiple signaling pathways, particularly hematopoietic cell lineage and cytokine-cytokine receptor interaction. The 14 Hub genes were positively or negatively associated with COPD by wgcna analysis. The genes CX3CR1, PTGS2, FPR1, FPR2, S100A12, EGR1, CD163, S100A8 and S100A9 were identified to mediate inflammation and injury of the lung, and play critical roles in the pathogenesis of COPD. These findings improve our understanding of the underlying molecular mechanisms of COPD.

SUBMITTER: Huang X

PROVIDER: S-EPMC6823288 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis.

Huang Xinwei X Li Yunwei Y Guo Xiaoran X Zhu Zongxin Z Kong Xiangyang X Yu Fubing F Wang Qiang Q

FEBS open bio 20190929 11

Chronic obstructive pulmonary disease (COPD) is a multifactorial and heterogeneous disease that creates public health challenges worldwide. The underlying molecular mechanisms of COPD are not entirely clear. In this study, we aimed to identify the critical genes and potential molecular mechanisms of COPD by bioinformatic analysis. The gene expression profiles of lung tissues of COPD cases and healthy control subjects were obtained from the Gene Expression Omnibus. Differentially expressed genes ...[more]

PMID: 31419078

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Project description:Psoriasis is an immune‑mediated cutaneous disorder with a high incidence and prevalence. Patients with psoriasis may experience irritation, pain and psychological problems. The cause and underlying molecular etiology of psoriasis remains unknown. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of psoriasis, the gene expression profiles of 175 pairs of lesional and corresponding non‑lesional skin samples were downloaded from 5 data sets in the Gene Expression Omnibus (GEO) database. Integrated differentially expressed genes (DEGs) were obtained with the use of R software. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using the DAVID online analysis tool. The protein‑protein interaction (PPI) network was constructed on the STRING platform and hub genes were calculated with the use of Cytoscape software. Finally, GEO2R was used to determine the expression of the hub genes in scalp psoriasis. A total of 373 genes from the 5 data sets were identified as DEGs, including 277 upregulated and 96 downregulated genes. GO analysis revealed that immune responses and epidermal differentiation/development were the most enriched terms in biological processes, extracellular space/matrix was the most enriched term in cellular components, and endopeptidase inhibitor activity was the most enriched term in molecular functions. In the KEGG pathway enrichment, DEGs were mainly enriched in the metabolic and viral infection‑associated pathways. A total of 17 hub genes were calculated, including CSK2, CDC45, MCM10, SPC25, NDC80, NUF2, AURKA, CENPE, RRM2, DLGP5, HMMR, TTK, IFIT1, RSAD2, IFI6, IFI27 and ISG20, among which interferon‑α‑inducible genes were revealed to display a similar expression pattern as that obtained in scalp psoriasis. This comprehensive bioinformatic re‑analysis of GEO data provides new insights on the molecular pathogenesis of psoriasis and the identification of potential therapeutic targets for the treatment of psoriasis.

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Project description:ObjectiveWound healing is a complex process involving the coordinated interaction of various genes and molecularpathways. The study aimed to uncover novel therapeutic targets, biomarkers and candidate genes for drug developmentto improve successful wound repair interventions.Materials and Methods: This study is a network-meta analysis study. Nine wound healing microarray datasets obtainedfrom the Gene Expression Omnibus (GEO) database were used for this study. Differentially expressed genes (DEGs)were described using the Limma package and shared genes were used as input for weighted gene co-expressionnetwork analysis. The Gene Ontology analysis was performed using the EnrichR web server, and construction of aprotein-protein interaction (PPI) network was achieved by the STRING and Cytoscape.Results: A total of 424 DEGs were determined. A co-expression network was constructed using 7692 shared genesbetween nine data sets, resulting in the identification of seven modules. Among these modules, those with the top 20genes of up and down-regulation were selected. The top down-regulated genes, including TJP1, SEC61A1, PLEK,ATP5B, PDIA6, PIK3R1, SRGN, SDC2, and RBBP7, and the top up-regulated genes including RPS27A, EEF1A1,HNRNPA1, CTNNB1, POLR2A, CFL1, CSNk1E, HSPD1, FN1, and AURKB, which can potentially serve as therapeutictargets were identified. The KEGG pathway analysis found that the majority of the genes are enriched in the "Wntsignaling pathway".Conclusion: In our study of nine wound healing microarray datasets, we identified DEGs and co-expressed modulesusing WGCNA. These genes are involved in important cellular processes such as transcription, translation, and posttranslationalmodifications. We found nine down-regulated genes and ten up-regulated genes, which could serve aspotential therapeutic targets for further experimental validation. Targeting pathways related to protein synthesis and celladhesion and migration may enhance wound healing, but additional experimental validation is needed to confirm theeffectiveness and safety of targeted interventions.

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Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis.

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Identification of differentially expressed genes and signaling pathways in chronic obstructive pulmonary disease via bioinformatic analysis.

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