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Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics

OBJECTIVE: Here, we aimed to identify protein biomarkers that could rapidly and accurately diagnose multiple sclerosis (MS) using a highly sensitive proteomic immunoassay. METHODS: Tandem mass tag (TMT) quantitative proteomic analysis was performed to determine the differentially expressed proteins...

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Autores principales: Shi, Yijun, Ding, Yaowei, Li, Guoge, Wang, Lijuan, Osman, Rasha Alsamani, Sun, Jialu, Qian, Lingye, Zheng, Guanghui, Zhang, Guojun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417809/
https://www.ncbi.nlm.nih.gov/pubmed/34489947
http://dx.doi.org/10.3389/fimmu.2021.700031
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author Shi, Yijun
Ding, Yaowei
Li, Guoge
Wang, Lijuan
Osman, Rasha Alsamani
Sun, Jialu
Qian, Lingye
Zheng, Guanghui
Zhang, Guojun
author_facet Shi, Yijun
Ding, Yaowei
Li, Guoge
Wang, Lijuan
Osman, Rasha Alsamani
Sun, Jialu
Qian, Lingye
Zheng, Guanghui
Zhang, Guojun
author_sort Shi, Yijun
collection PubMed
description OBJECTIVE: Here, we aimed to identify protein biomarkers that could rapidly and accurately diagnose multiple sclerosis (MS) using a highly sensitive proteomic immunoassay. METHODS: Tandem mass tag (TMT) quantitative proteomic analysis was performed to determine the differentially expressed proteins (DEPs) in cerebrospinal fluid (CSF) samples collected from 10 patients with MS and 10 non-inflammatory neurological controls (NINCs). The DEPs were analyzed using bioinformatics tools, and the candidate proteins were validated using the ELISA method in another cohort comprising 160 samples (paired CSF and plasma of 40 patients with MS, CSF of 40 NINCs, and plasma of 40 healthy individuals). Receiver operating characteristic (ROC) curves were used to determine the diagnostic potential of this method. RESULTS: Compared to NINCs, we identified 83 CSF-specific DEPs out of a total of 343 proteins in MS patients. Gene ontology (GO) enrichment analysis revealed that these DEPs are mainly involved in platelet degranulation, negative regulation of proteolysis, and post-translational protein modification. Pathway enrichment analysis revealed that the complement and coagulation cascades, Ras signaling pathway, and PI3K-Akt signaling pathway are the main components. Insulin-like growth factor-binding protein 7 (IGFBP7), insulin-like growth factor 2 (IGF2), and somatostatin (SST) were identified as the potential proteins with high scores, degree, and centrality in the protein-protein interaction (PPI) network. We validated the expression of these three proteins using ELISA. Compared to NINCs, the level of CSF IGFBP7 was significantly upregulated, and the level of CSF SST was significantly downregulated in the MS group. CONCLUSION: Our results suggest that SST and IGFBP7 might be associated with the pathogenesis of MS and would be helpful in diagnosing MS. Since IGFBP7 was used to classify relapsing remitting MS (RRMS) and secondary progressive MS (SPMS) patients, therefore, it may act as a potential key marker and therapeutic target in MS.
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spelling pubmed-84178092021-09-05 Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics Shi, Yijun Ding, Yaowei Li, Guoge Wang, Lijuan Osman, Rasha Alsamani Sun, Jialu Qian, Lingye Zheng, Guanghui Zhang, Guojun Front Immunol Immunology OBJECTIVE: Here, we aimed to identify protein biomarkers that could rapidly and accurately diagnose multiple sclerosis (MS) using a highly sensitive proteomic immunoassay. METHODS: Tandem mass tag (TMT) quantitative proteomic analysis was performed to determine the differentially expressed proteins (DEPs) in cerebrospinal fluid (CSF) samples collected from 10 patients with MS and 10 non-inflammatory neurological controls (NINCs). The DEPs were analyzed using bioinformatics tools, and the candidate proteins were validated using the ELISA method in another cohort comprising 160 samples (paired CSF and plasma of 40 patients with MS, CSF of 40 NINCs, and plasma of 40 healthy individuals). Receiver operating characteristic (ROC) curves were used to determine the diagnostic potential of this method. RESULTS: Compared to NINCs, we identified 83 CSF-specific DEPs out of a total of 343 proteins in MS patients. Gene ontology (GO) enrichment analysis revealed that these DEPs are mainly involved in platelet degranulation, negative regulation of proteolysis, and post-translational protein modification. Pathway enrichment analysis revealed that the complement and coagulation cascades, Ras signaling pathway, and PI3K-Akt signaling pathway are the main components. Insulin-like growth factor-binding protein 7 (IGFBP7), insulin-like growth factor 2 (IGF2), and somatostatin (SST) were identified as the potential proteins with high scores, degree, and centrality in the protein-protein interaction (PPI) network. We validated the expression of these three proteins using ELISA. Compared to NINCs, the level of CSF IGFBP7 was significantly upregulated, and the level of CSF SST was significantly downregulated in the MS group. CONCLUSION: Our results suggest that SST and IGFBP7 might be associated with the pathogenesis of MS and would be helpful in diagnosing MS. Since IGFBP7 was used to classify relapsing remitting MS (RRMS) and secondary progressive MS (SPMS) patients, therefore, it may act as a potential key marker and therapeutic target in MS. Frontiers Media S.A. 2021-08-20 /pmc/articles/PMC8417809/ /pubmed/34489947 http://dx.doi.org/10.3389/fimmu.2021.700031 Text en Copyright © 2021 Shi, Ding, Li, Wang, Osman, Sun, Qian, Zheng and Zhang 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
Shi, Yijun
Ding, Yaowei
Li, Guoge
Wang, Lijuan
Osman, Rasha Alsamani
Sun, Jialu
Qian, Lingye
Zheng, Guanghui
Zhang, Guojun
Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics
title Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics
title_full Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics
title_fullStr Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics
title_full_unstemmed Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics
title_short Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics
title_sort discovery of novel biomarkers for diagnosing and predicting the progression of multiple sclerosis using tmt-based quantitative proteomics
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8417809/
https://www.ncbi.nlm.nih.gov/pubmed/34489947
http://dx.doi.org/10.3389/fimmu.2021.700031
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