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Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis
Background: Thymoma-associated myasthenia gravis (TAMG) is a well-described subtype of Myasthenia gravis (MG). Nevertheless, the detailed proteins and bioprocess differentiating TAMG from TAMG (−) thymoma have remained unclear. Methods: The proteomics and metabolomics were carried out on serum sampl...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Impact Journals
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320536/ https://www.ncbi.nlm.nih.gov/pubmed/35802752 http://dx.doi.org/10.18632/aging.204156 |
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author | Tong, Tong Zhang, Jing Jia, Li Liang, Ping Wang, Na |
author_facet | Tong, Tong Zhang, Jing Jia, Li Liang, Ping Wang, Na |
author_sort | Tong, Tong |
collection | PubMed |
description | Background: Thymoma-associated myasthenia gravis (TAMG) is a well-described subtype of Myasthenia gravis (MG). Nevertheless, the detailed proteins and bioprocess differentiating TAMG from TAMG (−) thymoma have remained unclear. Methods: The proteomics and metabolomics were carried out on serum samples from thymoma group (n = 60, TNMG), TAMG (+) thymoma group (n = 70, TAMG (+)), and TAMG (−) thymomas group (n = 62, TAMG (−)), and controls (n = 159). groups. Proteomics and metabolomics analyses, including weighted gene co-expression network analysis (WGCNA), was conducted to detect the hub proteins and metabolomics processes that could differentiate TAMG (+) from TAMG (−) thymomas. MetaboAnalyst was used to examine the integration of proteomic and metabolomic analysis to differentiate TAMG (+) from TAMG (−) thymomas. Results: The of module–trait correlation of WGCNA analysis identified KRT1, GSN, COL6A1, KRT10, FOLR2, KRT9, KRT2, TPI1, ARF3, LYZ, ADIPOQ, SEMA4B, IGKV1-27, MASP2, IGF2R was associated with TAMG (+) thymomas. In addition, organismal systems-immune system and metabolism-biosynthesis of other secondary metabolites were closely related to the mechanism of TAMG (+) pathogenesis. Conclusion: Our integrated proteomics and metabolomics analysis supply a systems-level view of proteome changes in TAMG (+), TAMG (−) thymomas and exposes disease-associated protein network alterations involved in. |
format | Online Article Text |
id | pubmed-9320536 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Impact Journals |
record_format | MEDLINE/PubMed |
spelling | pubmed-93205362022-07-27 Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis Tong, Tong Zhang, Jing Jia, Li Liang, Ping Wang, Na Aging (Albany NY) Research Paper Background: Thymoma-associated myasthenia gravis (TAMG) is a well-described subtype of Myasthenia gravis (MG). Nevertheless, the detailed proteins and bioprocess differentiating TAMG from TAMG (−) thymoma have remained unclear. Methods: The proteomics and metabolomics were carried out on serum samples from thymoma group (n = 60, TNMG), TAMG (+) thymoma group (n = 70, TAMG (+)), and TAMG (−) thymomas group (n = 62, TAMG (−)), and controls (n = 159). groups. Proteomics and metabolomics analyses, including weighted gene co-expression network analysis (WGCNA), was conducted to detect the hub proteins and metabolomics processes that could differentiate TAMG (+) from TAMG (−) thymomas. MetaboAnalyst was used to examine the integration of proteomic and metabolomic analysis to differentiate TAMG (+) from TAMG (−) thymomas. Results: The of module–trait correlation of WGCNA analysis identified KRT1, GSN, COL6A1, KRT10, FOLR2, KRT9, KRT2, TPI1, ARF3, LYZ, ADIPOQ, SEMA4B, IGKV1-27, MASP2, IGF2R was associated with TAMG (+) thymomas. In addition, organismal systems-immune system and metabolism-biosynthesis of other secondary metabolites were closely related to the mechanism of TAMG (+) pathogenesis. Conclusion: Our integrated proteomics and metabolomics analysis supply a systems-level view of proteome changes in TAMG (+), TAMG (−) thymomas and exposes disease-associated protein network alterations involved in. Impact Journals 2022-07-08 /pmc/articles/PMC9320536/ /pubmed/35802752 http://dx.doi.org/10.18632/aging.204156 Text en Copyright: © 2022 Tong et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Paper Tong, Tong Zhang, Jing Jia, Li Liang, Ping Wang, Na Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis |
title | Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis |
title_full | Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis |
title_fullStr | Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis |
title_full_unstemmed | Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis |
title_short | Integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis |
title_sort | integrated proteomics and metabolomics analysis reveals hubs protein and network alterations in myasthenia gravis |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9320536/ https://www.ncbi.nlm.nih.gov/pubmed/35802752 http://dx.doi.org/10.18632/aging.204156 |
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