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.
| DOI | 10.11648/j.cbb.20200802.17 |
| Published in | Computational Biology and Bioinformatics (Volume 8, Issue 2, December 2020) |
| 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), 2024. Published by Science Publishing Group |
Bioinformatic Analysis, Genes, Peripheral Blood Mononuclear Cell, Idiopathic Pulmonary Fibrosis
| [1] | Raghu G, Collard HR, Egan JJ, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med, 2011, 183 (6): 788-824. |
| [2] | Lederer DJ, Martinez FJ. Idiopathic Pulmonary Fibrosis. N Engl J Med, 2018, 378 (19): 1811-1823. |
| [3] | Richeldi L, Collard HR, Jones MG. Idiopathic pulmonary fibrosis. Lancet, 2017, 389 (10082): 1941-1952. |
| [4] | Hutchinson J, Fogarty A, Hubbard R, et al. Global incidence and mortality of idiopathic pulmonary fibrosis: a systematic review. Eur Respir J, 2015, 46 (3): 795-806. |
| [5] | American Thoracic Society/European Respiratory Society International Multidisciplinary Consensus Classification of the Idiopathic Interstitial Pneumonias. This joint statement of the American Thoracic Society (ATS), and the European Respiratory Society (ERS) was adopted by the ATS board of directors, June 2001 and by the ERS Executive Committee, June 2001. Am J Respir Crit Care Med, 2002, 165 (2): 277-304. |
| [6] | Sgalla G, Iovene B, Calvello M, et al. Idiopathic pulmonary fibrosis: pathogenesis and management. Respir Res, 2018, 19 (1): 32. |
| [7] | Wolters PJ, Collard HR, Jones KD. Pathogenesis of idiopathic pulmonary fibrosis. Annu Rev Pathol, 2014, 9: 157-79. |
| [8] | Wolters PJ, Blackwell TS, Eickelberg O, et al. Time for a change: is idiopathic pulmonary fibrosis still idiopathic and only fibrotic? Lancet Respir Med, 2018, 6 (2): 154-160. |
| [9] | Qiu CC, Su QS, Zhu SY, et al. Identification of Potential Biomarkers and Biological Pathways in Juvenile Dermatomyositis Based on miRNA-mRNA Network. Biomed Res Int, 2019, 2019: 7814287. |
| [10] | Zhang G, Xu S, Zhang Z, et al. Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis. Front Psychiatry, 2020, 11: 192. |
| [11] | Yi XH, Zhang B, Fu YR, et al. STAT1 and its related molecules as potential biomarkers in Mycobacterium tuberculosis infection. J Cell Mol Med, 2020, 24 (5): 2866-2878. |
| [12] | Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature genetics, 2000, 25 (1): 25-29. |
| [13] | Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research, 2000, 28 (1): 27-30. |
| [14] | Eferl R, Wagner EF. AP-1: a double-edged sword in tumorigenesis. Nat Rev Cancer, 2003, 3 (11): 859-68. |
| [15] | Shaulian E. AP-1 — The Jun proteins: Oncogenes or tumor suppressors in disguise? Cellular Signalling, 2010, 22 (6): 894-899. |
| [16] | Gazon H, Barbeau B, Mesnard JM, et al. Hijacking of the AP-1 Signaling Pathway during Development of ATL. Front Microbiol, 2017, 8: 2686. |
| [17] | Jiang X, Xie H, Dou Y, et al. Expression and function of FRA1 protein in tumors. Mol Biol Rep, 2020, 47 (1): 737-752. |
| [18] | Klymenko O, Huehn M, Wilhelm J, et al. Regulation and role of the ER stress transcription factor CHOP in alveolar epithelial type-II cells. J Mol Med (Berl), 2019, 97 (7): 973-990. |
| [19] | Wernig G, Chen SY, Cui L, et al. Unifying mechanism for different fibrotic diseases. Proc Natl Acad Sci U S A, 2017, 114 (18): 4757-4762. |
| [20] | Chang H, Liu Y, Xue M, et al. Synergistic action of master transcription factors controls epithelial-to-mesenchymal transition. Nucleic Acids Res, 2016, 44 (6): 2514-27. |
| [21] | Salton F, Volpe MC, Confalonieri M. Epithelial-Mesenchymal Transition in the Pathogenesis of Idiopathic Pulmonary Fibrosis. Medicina (Kaunas), 2019, 55 (4). |
| [22] | Knight D, Mutsaers SE, Prêle CM. STAT3 in tissue fibrosis: is there a role in the lung? Pulm Pharmacol Ther, 2011, 24 (2): 193-8. |
| [23] | Waters DW, Blokland KEC, Pathinayake PS, et al. Fibroblast senescence in the pathology of idiopathic pulmonary fibrosis. Am J Physiol Lung Cell Mol Physiol, 2018, 315 (2): L162-l172. |
| [24] | Milara J, Hernandez G, Ballester B, et al. The JAK2 pathway is activated in idiopathic pulmonary fibrosis. Respir Res, 2018, 19 (1): 24. |
| [25] | Pechkovsky DV, Prêle CM, Wong J, et al. STAT3-mediated signaling dysregulates lung fibroblast-myofibroblast activation and differentiation in UIP/IPF. Am J Pathol, 2012, 180 (4): 1398-412. |
| [26] | Yoshimura A, Naka T, Kubo M. SOCS proteins, cytokine signalling and immune regulation. Nat Rev Immunol, 2007, 7 (6): 454-65. |
| [27] | Rottenberg ME, Carow B. SOCS3 and STAT3, major controllers of the outcome of infection with Mycobacterium tuberculosis. Semin Immunol, 2014, 26 (6): 518-32. |
| [28] | O'Donoghue RJ, Knight DA, Richards CD, et al. Genetic partitioning of interleukin-6 signalling in mice dissociates Stat3 from Smad3-mediated lung fibrosis. EMBO Mol Med, 2012, 4 (9): 939-51. |
| [29] | Akram KM, Lomas NJ, Forsyth NR, et al. Alveolar epithelial cells in idiopathic pulmonary fibrosis display upregulation of TRAIL, DR4 and DR5 expression with simultaneous preferential over-expression of pro-apoptotic marker p53. Int J Clin Exp Pathol, 2014, 7 (2): 552-64. |
| [30] | Shochet GE, Brook E, Bardenstein-Wald B, et al. TGF-β pathway activation by idiopathic pulmonary fibrosis (IPF) fibroblast derived soluble factors is mediated by IL-6 trans-signaling. Respir Res, 2020, 21 (1): 56. |
| [31] | Moosavi SM, Prabhala P, Ammit AJ. Role and regulation of MKP-1 in airway inflammation. Respir Res, 2017, 18 (1): 154. |
| [32] | Keyse SM. Dual-specificity MAP kinase phosphatases (MKPs) and cancer. Cancer Metastasis Rev, 2008, 27 (2): 253-61. |
| [33] | Redente EF, Black BP, Aguilar MA, et al. DUSP1 inhibition impairs fibrosis development by altering macrophage programming. American Journal of Respiratory and Critical Care Medicine, 2017, 195. |
| [34] | Goda C, Balli D, Black M, et al. Loss of FOXM1 in macrophages promotes pulmonary fibrosis by activating p38 MAPK signaling pathway. PLoS Genetics, 2020, 16 (4). |
| [35] | Tsoutsou PG, Gourgoulianis KI, Petinaki E, et al. Cytokine levels in the sera of patients with idiopathic pulmonary fibrosis. Respir Med, 2006, 100 (5): 938-45. |
| [36] | Huaux F, Liu T, McGarry B, et al. Dual roles of IL-4 in lung injury and fibrosis. J Immunol, 2003, 170 (4): 2083-92. |
| [37] | Kraft S, Kinet JP. New developments in FcepsilonRI regulation, function and inhibition. Nat Rev Immunol, 2007, 7 (5): 365-78. |
| [38] | Potaczek DP, Michel S, Sharma V, et al. Different FCER1A polymorphisms influence IgE levels in asthmatics and non-asthmatics. Pediatr Allergy Immunol, 2013, 24 (5): 441-9. |
| [39] | Wu LC, Zarrin AA. The production and regulation of IgE by the immune system. Nat Rev Immunol, 2014, 14 (4): 247-59. |
| [40] | Ferreira MA, Zhao ZZ, Thomsen SF, et al. Association and interaction analyses of eight genes under asthma linkage peaks. Allergy, 2009, 64 (11): 1623-8. |
| [41] | Pejler G. The emerging role of mast cell proteases in asthma. Eur Respir J, 2019, 54 (4). |
| [42] | Ly D, Zhu CQ, Cabanero M, et al. Role for High-Affinity IgE Receptor in Prognosis of Lung Adenocarcinoma Patients. Cancer Immunol Res, 2017, 5 (9): 821-829. |
| [43] | Visser S, Hou J, Bezemer K, et al. Prediction of response to pemetrexed in non-small-cell lung cancer with immunohistochemical phenotyping based on gene expression profiles. BMC Cancer, 2019, 19 (1): 440. |
| [44] | Akobeng AK. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr, 2007, 96 (5): 644-7. |
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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