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Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis
Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as ble...
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/PMC9470879/ https://www.ncbi.nlm.nih.gov/pubmed/36120336 http://dx.doi.org/10.3389/fphar.2022.909784 |
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author | Feng, Chunxiang Wang, Zhixian Liu, Chang Liu, Shiliang Wang, Yuxi Zeng, Yuanyuan Wang, Qianqian Peng, Tianming Pu, Xiaoyong Liu, Jiumin |
author_facet | Feng, Chunxiang Wang, Zhixian Liu, Chang Liu, Shiliang Wang, Yuxi Zeng, Yuanyuan Wang, Qianqian Peng, Tianming Pu, Xiaoyong Liu, Jiumin |
author_sort | Feng, Chunxiang |
collection | PubMed |
description | Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method. |
format | Online Article Text |
id | pubmed-9470879 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94708792022-09-15 Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis Feng, Chunxiang Wang, Zhixian Liu, Chang Liu, Shiliang Wang, Yuxi Zeng, Yuanyuan Wang, Qianqian Peng, Tianming Pu, Xiaoyong Liu, Jiumin Front Pharmacol Pharmacology Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method. Frontiers Media S.A. 2022-08-31 /pmc/articles/PMC9470879/ /pubmed/36120336 http://dx.doi.org/10.3389/fphar.2022.909784 Text en Copyright © 2022 Feng, Wang, Liu, Liu, Wang, Zeng, Wang, Peng, Pu and Liu. 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 | Pharmacology Feng, Chunxiang Wang, Zhixian Liu, Chang Liu, Shiliang Wang, Yuxi Zeng, Yuanyuan Wang, Qianqian Peng, Tianming Pu, Xiaoyong Liu, Jiumin Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis |
title | Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis |
title_full | Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis |
title_fullStr | Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis |
title_full_unstemmed | Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis |
title_short | Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis |
title_sort | integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6a subtype, and predictive drug target signatures for diagnosing renal fibrosis |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9470879/ https://www.ncbi.nlm.nih.gov/pubmed/36120336 http://dx.doi.org/10.3389/fphar.2022.909784 |
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