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Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension
Pulmonary arterial hypertension (PAH) is a chronic cardiopulmonary syndrome with high pulmonary vascular load and eventually causing RV heart failure even death. However, the mechanism of pulmonary hypertension remains unclear. The purpose of this research is to detect the underlying key genes and p...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203517/ https://www.ncbi.nlm.nih.gov/pubmed/35710932 http://dx.doi.org/10.1038/s41598-022-14307-6 |
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author | Tang, Shengxin Liu, Yue Liu, Bin |
author_facet | Tang, Shengxin Liu, Yue Liu, Bin |
author_sort | Tang, Shengxin |
collection | PubMed |
description | Pulmonary arterial hypertension (PAH) is a chronic cardiopulmonary syndrome with high pulmonary vascular load and eventually causing RV heart failure even death. However, the mechanism of pulmonary hypertension remains unclear. The purpose of this research is to detect the underlying key genes and potential mechanism of PAH using several bioinformatic methods. The microarrays GSE22356, GSE131793 and GSE168905 were acquired from the GEO. Subsequently, a host of bioinformatics techniques such as DAVID, STRING, R language and Cytoscape were utilized to investigate DEGs between PAH and healthy controls and conduct GO annotation, KEGG enrichment analysis and PPI network construction etc. Additionally, we predicted the transcription factors regulating DEGs through iRegulon plugin of Cytoscape and CIBERSORT was used to conduct immune infiltration analysis. One thousand two hundred and seventy-seven DEGs (403 up-regulated and 874 down-regulated) were identified from peripheral blood samples of 32 PAH patients and 29 controls, among which SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B were detected as hub genes. The functional enrichment changes of DEGs were mainly enriched in protein binding, extracellular exosome, extracellular space, extracellular region and integral component of plasma membrane. The hub genes are chiefly enriched at extracellular exosome, hemoglobin complex, blood microparticle, oxygen transporter activity. Among TF-DEGs network, 42 target DEGs and 6 TFs were predicted with an NES > 4 (TEAD4, TGIF2LY, GATA5, GATA1, GATA2, FOS). Immune infiltration analysis showed that monocytes occupied the largest proportion of immune cells. The trend analysis results of infiltration immune cells illustrated that PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. We detected SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B as the most significant markers of PAH. The PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. These identified genes and these immune cells probably have precise regulatory relationships in the development of PAH. |
format | Online Article Text |
id | pubmed-9203517 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92035172022-06-18 Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension Tang, Shengxin Liu, Yue Liu, Bin Sci Rep Article Pulmonary arterial hypertension (PAH) is a chronic cardiopulmonary syndrome with high pulmonary vascular load and eventually causing RV heart failure even death. However, the mechanism of pulmonary hypertension remains unclear. The purpose of this research is to detect the underlying key genes and potential mechanism of PAH using several bioinformatic methods. The microarrays GSE22356, GSE131793 and GSE168905 were acquired from the GEO. Subsequently, a host of bioinformatics techniques such as DAVID, STRING, R language and Cytoscape were utilized to investigate DEGs between PAH and healthy controls and conduct GO annotation, KEGG enrichment analysis and PPI network construction etc. Additionally, we predicted the transcription factors regulating DEGs through iRegulon plugin of Cytoscape and CIBERSORT was used to conduct immune infiltration analysis. One thousand two hundred and seventy-seven DEGs (403 up-regulated and 874 down-regulated) were identified from peripheral blood samples of 32 PAH patients and 29 controls, among which SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B were detected as hub genes. The functional enrichment changes of DEGs were mainly enriched in protein binding, extracellular exosome, extracellular space, extracellular region and integral component of plasma membrane. The hub genes are chiefly enriched at extracellular exosome, hemoglobin complex, blood microparticle, oxygen transporter activity. Among TF-DEGs network, 42 target DEGs and 6 TFs were predicted with an NES > 4 (TEAD4, TGIF2LY, GATA5, GATA1, GATA2, FOS). Immune infiltration analysis showed that monocytes occupied the largest proportion of immune cells. The trend analysis results of infiltration immune cells illustrated that PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. We detected SLC4A1, AHSP, ALAS2, CA1, HBD, SNCA, HBM, SELENBP1, SERPINE1 and ITGA2B as the most significant markers of PAH. The PAH patients had higher infiltration of NK cell activation, monocyte, T cell CD4 memory activation, and mast cell than healthy controls and lower infiltration of T cell CD4 naive. These identified genes and these immune cells probably have precise regulatory relationships in the development of PAH. Nature Publishing Group UK 2022-06-16 /pmc/articles/PMC9203517/ /pubmed/35710932 http://dx.doi.org/10.1038/s41598-022-14307-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tang, Shengxin Liu, Yue Liu, Bin Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension |
title | Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension |
title_full | Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension |
title_fullStr | Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension |
title_full_unstemmed | Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension |
title_short | Integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension |
title_sort | integrated bioinformatics analysis reveals marker genes and immune infiltration for pulmonary arterial hypertension |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203517/ https://www.ncbi.nlm.nih.gov/pubmed/35710932 http://dx.doi.org/10.1038/s41598-022-14307-6 |
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