Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrotic interstitial pneumonia with progressive worsening of dyspnea and lung function. The etiology of IPF is unknown, and the pathogenesis remains unclear. Our study aimed to investigate the key genes of the peripheral blood mononuclear cell in IPF by bioinformatics analysis. Our study used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE28042 to identify differentially expressed genes (DEGs) between IPF patients and healthy controls. We performed the Gene Ontology (GO) and pathway enrichment analyses of genes for annotation, visualization, and integrated discovery. The STRING database constructed Protein-protein interaction (PPI) network analysis, and hub genes were identified by the CytoHubba plugin. Moreover, we used the receiver operating characteristic (ROC) curve to assess the diagnostic value of the hub genes. In total, 28 upregulated and 44 downregulated genes were identified in the differential expression analysis. The protein-protein interaction network (PPI) was established with 69 nodes and 68 edges. The top 10 hub genes were JUN, FOS, STAT3, SOCS3, JUNB, DUSP1, IL4, FCER1A, MS4A2, and CPA3. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the important module containing hub genes contained Fc epsilon RI signaling pathway, TNF signaling pathway, Jak-STAT signaling pathway, and MAPK signaling pathway. Additionally, the identified hub genes show high functional similarity and diagnostic value in IPF. Our study used bioinformatics analysis to provide new insight into the mechanisms underlying IPF. However, more experiments are needed to explore the relationships between the top 10 hub genes and IPF in the future.
| Published in | Computational Biology and Bioinformatics (Volume 8, Issue 2) |
| DOI | 10.11648/j.cbb.20200802.17 |
| Page(s) | 77-89 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2020. Published by Science Publishing Group |
Bioinformatic Analysis, Genes, Peripheral Blood Mononuclear Cell, Idiopathic Pulmonary Fibrosis
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APA Style
Lijun Liu, Daxia Cai, Yi Wu. (2020). Bioinformatics Analysis Identifies Potential Key Genes of Peripheral Blood Mononuclear Cell in Idiopathic Pulmonary Fibrosis. Computational Biology and Bioinformatics, 8(2), 77-89. https://doi.org/10.11648/j.cbb.20200802.17
ACS Style
Lijun Liu; Daxia Cai; Yi Wu. Bioinformatics Analysis Identifies Potential Key Genes of Peripheral Blood Mononuclear Cell in Idiopathic Pulmonary Fibrosis. Comput. Biol. Bioinform. 2020, 8(2), 77-89. doi: 10.11648/j.cbb.20200802.17
AMA Style
Lijun Liu, Daxia Cai, Yi Wu. Bioinformatics Analysis Identifies Potential Key Genes of Peripheral Blood Mononuclear Cell in Idiopathic Pulmonary Fibrosis. Comput Biol Bioinform. 2020;8(2):77-89. doi: 10.11648/j.cbb.20200802.17
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Download@article{10.11648/j.cbb.20200802.17,
author = {Lijun Liu and Daxia Cai and Yi Wu},
title = {Bioinformatics Analysis Identifies Potential Key Genes of Peripheral Blood Mononuclear Cell in Idiopathic Pulmonary Fibrosis},
journal = {Computational Biology and Bioinformatics},
volume = {8},
number = {2},
pages = {77-89},
doi = {10.11648/j.cbb.20200802.17},
url = {https://doi.org/10.11648/j.cbb.20200802.17},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20200802.17},
abstract = {Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrotic interstitial pneumonia with progressive worsening of dyspnea and lung function. The etiology of IPF is unknown, and the pathogenesis remains unclear. Our study aimed to investigate the key genes of the peripheral blood mononuclear cell in IPF by bioinformatics analysis. Our study used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE28042 to identify differentially expressed genes (DEGs) between IPF patients and healthy controls. We performed the Gene Ontology (GO) and pathway enrichment analyses of genes for annotation, visualization, and integrated discovery. The STRING database constructed Protein-protein interaction (PPI) network analysis, and hub genes were identified by the CytoHubba plugin. Moreover, we used the receiver operating characteristic (ROC) curve to assess the diagnostic value of the hub genes. In total, 28 upregulated and 44 downregulated genes were identified in the differential expression analysis. The protein-protein interaction network (PPI) was established with 69 nodes and 68 edges. The top 10 hub genes were JUN, FOS, STAT3, SOCS3, JUNB, DUSP1, IL4, FCER1A, MS4A2, and CPA3. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the important module containing hub genes contained Fc epsilon RI signaling pathway, TNF signaling pathway, Jak-STAT signaling pathway, and MAPK signaling pathway. Additionally, the identified hub genes show high functional similarity and diagnostic value in IPF. Our study used bioinformatics analysis to provide new insight into the mechanisms underlying IPF. However, more experiments are needed to explore the relationships between the top 10 hub genes and IPF in the future.},
year = {2020}
}
TY - JOUR T1 - Bioinformatics Analysis Identifies Potential Key Genes of Peripheral Blood Mononuclear Cell in Idiopathic Pulmonary Fibrosis AU - Lijun Liu AU - Daxia Cai AU - Yi Wu Y1 - 2020/11/27 PY - 2020 N1 - https://doi.org/10.11648/j.cbb.20200802.17 DO - 10.11648/j.cbb.20200802.17 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 77 EP - 89 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20200802.17 AB - Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrotic interstitial pneumonia with progressive worsening of dyspnea and lung function. The etiology of IPF is unknown, and the pathogenesis remains unclear. Our study aimed to investigate the key genes of the peripheral blood mononuclear cell in IPF by bioinformatics analysis. Our study used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE28042 to identify differentially expressed genes (DEGs) between IPF patients and healthy controls. We performed the Gene Ontology (GO) and pathway enrichment analyses of genes for annotation, visualization, and integrated discovery. The STRING database constructed Protein-protein interaction (PPI) network analysis, and hub genes were identified by the CytoHubba plugin. Moreover, we used the receiver operating characteristic (ROC) curve to assess the diagnostic value of the hub genes. In total, 28 upregulated and 44 downregulated genes were identified in the differential expression analysis. The protein-protein interaction network (PPI) was established with 69 nodes and 68 edges. The top 10 hub genes were JUN, FOS, STAT3, SOCS3, JUNB, DUSP1, IL4, FCER1A, MS4A2, and CPA3. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the important module containing hub genes contained Fc epsilon RI signaling pathway, TNF signaling pathway, Jak-STAT signaling pathway, and MAPK signaling pathway. Additionally, the identified hub genes show high functional similarity and diagnostic value in IPF. Our study used bioinformatics analysis to provide new insight into the mechanisms underlying IPF. However, more experiments are needed to explore the relationships between the top 10 hub genes and IPF in the future. VL - 8 IS - 2 ER -
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