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Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

OBJECTIVE: This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. METHODS: In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) o...

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Autores principales: Wu, Shaofeng, Liang, Tuo, Jiang, Jie, Zhu, Jichong, Chen, Tianyou, Zhou, Chenxing, Huang, Shengsheng, Yao, Yuanlin, Guo, Hao, Ye, Zhen, Chen, Liyi, Chen, Wuhua, Fan, Binguang, Qin, Jiahui, Liu, Lu, Wu, Siling, Ma, Fengzhi, Zhan, Xinli, Liu, Chong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280914/
https://www.ncbi.nlm.nih.gov/pubmed/37340462
http://dx.doi.org/10.1186/s12920-023-01566-z
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author Wu, Shaofeng
Liang, Tuo
Jiang, Jie
Zhu, Jichong
Chen, Tianyou
Zhou, Chenxing
Huang, Shengsheng
Yao, Yuanlin
Guo, Hao
Ye, Zhen
Chen, Liyi
Chen, Wuhua
Fan, Binguang
Qin, Jiahui
Liu, Lu
Wu, Siling
Ma, Fengzhi
Zhan, Xinli
Liu, Chong
author_facet Wu, Shaofeng
Liang, Tuo
Jiang, Jie
Zhu, Jichong
Chen, Tianyou
Zhou, Chenxing
Huang, Shengsheng
Yao, Yuanlin
Guo, Hao
Ye, Zhen
Chen, Liyi
Chen, Wuhua
Fan, Binguang
Qin, Jiahui
Liu, Lu
Wu, Siling
Ma, Fengzhi
Zhan, Xinli
Liu, Chong
author_sort Wu, Shaofeng
collection PubMed
description OBJECTIVE: This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. METHODS: In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. RESULTS: The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals. CONCLUSION: PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01566-z.
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spelling pubmed-102809142023-06-21 Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning Wu, Shaofeng Liang, Tuo Jiang, Jie Zhu, Jichong Chen, Tianyou Zhou, Chenxing Huang, Shengsheng Yao, Yuanlin Guo, Hao Ye, Zhen Chen, Liyi Chen, Wuhua Fan, Binguang Qin, Jiahui Liu, Lu Wu, Siling Ma, Fengzhi Zhan, Xinli Liu, Chong BMC Med Genomics Research OBJECTIVE: This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. METHODS: In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. RESULTS: The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value < 0.05). They revealed high diagnostic and predictive values and were closely related to the expression of multiple immune cells (p-value < 0.05). It was inferred that the expression of PSMB9, STAT 1, and TAP1 could be regulated by different medicinal chemicals. CONCLUSION: PSMB9, STAT1, and TAP1, might play a key role in the pathogenesis of TB, including spinal TB, and the protein product of the genes can be served as diagnostic markers and potential therapeutic target for TB. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01566-z. BioMed Central 2023-06-20 /pmc/articles/PMC10280914/ /pubmed/37340462 http://dx.doi.org/10.1186/s12920-023-01566-z Text en © The Author(s) 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wu, Shaofeng
Liang, Tuo
Jiang, Jie
Zhu, Jichong
Chen, Tianyou
Zhou, Chenxing
Huang, Shengsheng
Yao, Yuanlin
Guo, Hao
Ye, Zhen
Chen, Liyi
Chen, Wuhua
Fan, Binguang
Qin, Jiahui
Liu, Lu
Wu, Siling
Ma, Fengzhi
Zhan, Xinli
Liu, Chong
Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning
title Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning
title_full Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning
title_fullStr Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning
title_full_unstemmed Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning
title_short Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning
title_sort proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280914/
https://www.ncbi.nlm.nih.gov/pubmed/37340462
http://dx.doi.org/10.1186/s12920-023-01566-z
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