Exploring the importance of m5c in the diagnosis and subtype classification of COPD using the GEO database
Copyright © 2023 Elsevier B.V. All rights reserved..
BACKGROUND: 5-Methylcytosine (m5C) is an mRNA modifier that is associated with the occurrence and development of viral infection, pulmonary fibrosis, lung cancer, and other diseases. However, the role of m5C regulators in chronic obstructive pulmonary disease (COPD) remains unknown.
METHODS: In this study, by analysing the GSE42057 dataset, the differential expression of m5c regulators in the COPD group and control group was obtained, and a correlation analysis was conducted. The random forest model and support vector machine model were used to predict the occurrence of COPD. A nomogram model was also constructed to predict the prevalence of COPD. The COPD patients were divided into subtypes by consistent cluster analysis based on m5c methylation regulators. Immune cell infiltration was performed on the m5c methylation subtypes. Differentially expressed genes (DEGs) between m5c methylation subtypes were screened, and the DEGs were analysed by Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we verified the expression of several m5C regulators and related pathways using a COPD cell model.
RESULTS: Seven m5c methylation regulators were differentially expressed. The random forest model based on the above genes was the most accurate for predicting the occurrence of COPD. A nomogram model based on the above genes could also accurately predict the prevalence of COPD, and the implementation of these models could benefit COPD patients. The consistent cluster analysis divided the COPD patients into two subtypes (Cluster A and Cluster B). The main component analysis algorithm determined the m5c methylation subtypes and found that patients in Cluster A had a higher m5c score than those in Cluster B. GO analysis of the DEGs between the m5c methylation COPD patient subtypes revealed that DEGS were mainly enriched in leukocyte-mediated immunity and regulation of T-cell activation. KEGG analysis revealed that DEGS were mainly enriched in Th1 and Th2 cell differentiation, neutrophil extracellular trap formation, and the NF-κB signalling pathway. Immunocyte correlation analysis revealed that Cluster B was associated with neutrophil- and macrophage-mediated immunity, while Cluster A was associated with CD4 + T-cell- and CD8 + T-cell-mediated immunity. Cell experiments have also verified some of the above research results.
CONCLUSION: The diagnosis and subtype classification of COPD patients based on m5c regulators may provide a new strategy for the diagnosis and treatment of COPD.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:895 |
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| Enthalten in: |
Gene - 895(2024) vom: 15. Jan., Seite 147987 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wu, Jianjun [VerfasserIn] |
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| Links: |
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| Themen: |
5-Methylcytosine |
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| Anmerkungen: |
Date Completed 02.01.2024 Date Revised 02.01.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.gene.2023.147987 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM364642645 |
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| 245 | 1 | 0 | |a Exploring the importance of m5c in the diagnosis and subtype classification of COPD using the GEO database |
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| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a BACKGROUND: 5-Methylcytosine (m5C) is an mRNA modifier that is associated with the occurrence and development of viral infection, pulmonary fibrosis, lung cancer, and other diseases. However, the role of m5C regulators in chronic obstructive pulmonary disease (COPD) remains unknown | ||
| 520 | |a METHODS: In this study, by analysing the GSE42057 dataset, the differential expression of m5c regulators in the COPD group and control group was obtained, and a correlation analysis was conducted. The random forest model and support vector machine model were used to predict the occurrence of COPD. A nomogram model was also constructed to predict the prevalence of COPD. The COPD patients were divided into subtypes by consistent cluster analysis based on m5c methylation regulators. Immune cell infiltration was performed on the m5c methylation subtypes. Differentially expressed genes (DEGs) between m5c methylation subtypes were screened, and the DEGs were analysed by Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, we verified the expression of several m5C regulators and related pathways using a COPD cell model | ||
| 520 | |a RESULTS: Seven m5c methylation regulators were differentially expressed. The random forest model based on the above genes was the most accurate for predicting the occurrence of COPD. A nomogram model based on the above genes could also accurately predict the prevalence of COPD, and the implementation of these models could benefit COPD patients. The consistent cluster analysis divided the COPD patients into two subtypes (Cluster A and Cluster B). The main component analysis algorithm determined the m5c methylation subtypes and found that patients in Cluster A had a higher m5c score than those in Cluster B. GO analysis of the DEGs between the m5c methylation COPD patient subtypes revealed that DEGS were mainly enriched in leukocyte-mediated immunity and regulation of T-cell activation. KEGG analysis revealed that DEGS were mainly enriched in Th1 and Th2 cell differentiation, neutrophil extracellular trap formation, and the NF-κB signalling pathway. Immunocyte correlation analysis revealed that Cluster B was associated with neutrophil- and macrophage-mediated immunity, while Cluster A was associated with CD4 + T-cell- and CD8 + T-cell-mediated immunity. Cell experiments have also verified some of the above research results | ||
| 520 | |a CONCLUSION: The diagnosis and subtype classification of COPD patients based on m5c regulators may provide a new strategy for the diagnosis and treatment of COPD | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a COPD | |
| 650 | 4 | |a Cell infiltration | |
| 650 | 4 | |a Consistent cluster analysis | |
| 650 | 4 | |a M5c regulators | |
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| 700 | 1 | |a Li, Xiaoning |e verfasserin |4 aut | |
| 700 | 1 | |a Kong, Deyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Xudong |e verfasserin |4 aut | |
| 700 | 1 | |a Du, Weisha |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Jiao, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xin |e verfasserin |4 aut | |
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