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Analysis of m(7)G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension
BACKGROUND: M(7)G methylation modification plays an important role in cardiovascular disease development. Dysregulation of the immune microenvironment is closely related to the pathogenesis of PAH. However, it is unclear whether m(7)G methylation is involved in the progress of PAH by affecting the i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762157/ https://www.ncbi.nlm.nih.gov/pubmed/36544768 http://dx.doi.org/10.3389/fimmu.2022.1014509 |
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author | Wang, Desheng Mo, Yanfei Zhang, Dongfang Bai, Yang |
author_facet | Wang, Desheng Mo, Yanfei Zhang, Dongfang Bai, Yang |
author_sort | Wang, Desheng |
collection | PubMed |
description | BACKGROUND: M(7)G methylation modification plays an important role in cardiovascular disease development. Dysregulation of the immune microenvironment is closely related to the pathogenesis of PAH. However, it is unclear whether m(7)G methylation is involved in the progress of PAH by affecting the immune microenvironment. METHODS: The gene expression profile of PAH was obtained from the GEO database, and the m(7)G regulatory factors were analyzed for differences. Machine learning algorithms were used to screen characteristic genes, including the least absolute shrinkage and selection operator, random forest, and support vector machine recursive feature elimination analysis. Constructed a nomogram model, and receiver operating characteristic was used to evaluate the diagnosis of disease characteristic genes value. Next, we used an unsupervised clustering method to perform consistent clustering analysis on m(7)G differential genes. Used the ssGSEA algorithm to estimate the relationship between the m(7)G regulator in PAH and immune cell infiltration and analyze the correlation with disease-characteristic genes. Finally, the listed drugs were evaluated through the screened signature genes. RESULTS: We identified 15 kinds of m(7)G differential genes. CYFIP1, EIF4E, and IFIT5 were identified as signature genes by the machine learning algorithm. Meanwhile, two m(7)G molecular subtypes were identified by consensus clustering (cluster A/B). In addition, immune cell infiltration analysis showed that activated CD4 T cells, regulatory T cells, and type 2 T helper cells were upregulated in m(7)G cluster B, CD56 dim natural killer cells, MDSC, and monocyte were upregulated in the m(7)G cluster A. It might be helpful to select Calpain inhibitor I and Everolimus for the treatment of PAH. CONCLUSION: Our study identified CYFIP1, EIF4E, and IFIT5 as novel diagnostic biomarkers in PAH. Furthermore, their association with immune cell infiltration may facilitate the development of immune therapy in PAH. |
format | Online Article Text |
id | pubmed-9762157 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97621572022-12-20 Analysis of m(7)G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension Wang, Desheng Mo, Yanfei Zhang, Dongfang Bai, Yang Front Immunol Immunology BACKGROUND: M(7)G methylation modification plays an important role in cardiovascular disease development. Dysregulation of the immune microenvironment is closely related to the pathogenesis of PAH. However, it is unclear whether m(7)G methylation is involved in the progress of PAH by affecting the immune microenvironment. METHODS: The gene expression profile of PAH was obtained from the GEO database, and the m(7)G regulatory factors were analyzed for differences. Machine learning algorithms were used to screen characteristic genes, including the least absolute shrinkage and selection operator, random forest, and support vector machine recursive feature elimination analysis. Constructed a nomogram model, and receiver operating characteristic was used to evaluate the diagnosis of disease characteristic genes value. Next, we used an unsupervised clustering method to perform consistent clustering analysis on m(7)G differential genes. Used the ssGSEA algorithm to estimate the relationship between the m(7)G regulator in PAH and immune cell infiltration and analyze the correlation with disease-characteristic genes. Finally, the listed drugs were evaluated through the screened signature genes. RESULTS: We identified 15 kinds of m(7)G differential genes. CYFIP1, EIF4E, and IFIT5 were identified as signature genes by the machine learning algorithm. Meanwhile, two m(7)G molecular subtypes were identified by consensus clustering (cluster A/B). In addition, immune cell infiltration analysis showed that activated CD4 T cells, regulatory T cells, and type 2 T helper cells were upregulated in m(7)G cluster B, CD56 dim natural killer cells, MDSC, and monocyte were upregulated in the m(7)G cluster A. It might be helpful to select Calpain inhibitor I and Everolimus for the treatment of PAH. CONCLUSION: Our study identified CYFIP1, EIF4E, and IFIT5 as novel diagnostic biomarkers in PAH. Furthermore, their association with immune cell infiltration may facilitate the development of immune therapy in PAH. Frontiers Media S.A. 2022-12-05 /pmc/articles/PMC9762157/ /pubmed/36544768 http://dx.doi.org/10.3389/fimmu.2022.1014509 Text en Copyright © 2022 Wang, Mo, Zhang and Bai https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Wang, Desheng Mo, Yanfei Zhang, Dongfang Bai, Yang Analysis of m(7)G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension |
title | Analysis of m(7)G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension |
title_full | Analysis of m(7)G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension |
title_fullStr | Analysis of m(7)G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension |
title_full_unstemmed | Analysis of m(7)G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension |
title_short | Analysis of m(7)G methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension |
title_sort | analysis of m(7)g methylation modification patterns and pulmonary vascular immune microenvironment in pulmonary arterial hypertension |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762157/ https://www.ncbi.nlm.nih.gov/pubmed/36544768 http://dx.doi.org/10.3389/fimmu.2022.1014509 |
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