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Screening of key genes in childhood asthma based on bioinformatics analysis

BACKGROUND: The key genes of pediatric asthma have not yet been identified and there is a lack of serological diagnostic markers. This may be related to the lack of comprehensive exploration of g The study sought to screen the key genes of childhood asthma using a machine-learning algorithm based on...

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Autores principales: Xia, Yulian, Ling, Chen, Zhan, Shanshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248936/
https://www.ncbi.nlm.nih.gov/pubmed/37305716
http://dx.doi.org/10.21037/tp-23-204
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author Xia, Yulian
Ling, Chen
Zhan, Shanshan
author_facet Xia, Yulian
Ling, Chen
Zhan, Shanshan
author_sort Xia, Yulian
collection PubMed
description BACKGROUND: The key genes of pediatric asthma have not yet been identified and there is a lack of serological diagnostic markers. This may be related to the lack of comprehensive exploration of g The study sought to screen the key genes of childhood asthma using a machine-learning algorithm based on transcriptome sequencing results and explore potential diagnostic markers. METHODS: The transcriptome sequencing results (GSE188424) of pediatric asthmatic plasma samples were downloaded from the Gene Expression Omnibus database, including 43 controlled pediatric asthma serum samples and 46 uncontrolled pediatric asthma samples. R software (AT&T Bell Laboratories) was used to construct the weighted gene co-expression network and screen the hub genes. The penalty model was established by least absolute shrinkage and selection operator (LASSO) regression analysis to further screen the genes in the hub genes. The receiver operating characteristic curve (ROC) was used to confirm the diagnostic value of key genes. RESULTS: A total of 171 differentially expressed genes were screened from the controlled and uncontrolled samples. Chemokine (C-X-C motif) ligand 12 (CXCL12), matrix metallopeptidase 9 (MMP9), and wingless-type MMTV integration site family member 2 (WNT2) were the key genes, which were upregulated in the uncontrolled samples. The areas under the ROC curve of CXCL12, MMP9, and WNT2 were 0.895, 0.936, and 0.928, respectively. CONCLUSIONS: The key genes CXCL12, MMP9, and WNT2 in pediatric asthma were identified by a bioinformatics analysis and machine-learning algorithm, which may be potential diagnostic biomarkers.
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spelling pubmed-102489362023-06-09 Screening of key genes in childhood asthma based on bioinformatics analysis Xia, Yulian Ling, Chen Zhan, Shanshan Transl Pediatr Original Article BACKGROUND: The key genes of pediatric asthma have not yet been identified and there is a lack of serological diagnostic markers. This may be related to the lack of comprehensive exploration of g The study sought to screen the key genes of childhood asthma using a machine-learning algorithm based on transcriptome sequencing results and explore potential diagnostic markers. METHODS: The transcriptome sequencing results (GSE188424) of pediatric asthmatic plasma samples were downloaded from the Gene Expression Omnibus database, including 43 controlled pediatric asthma serum samples and 46 uncontrolled pediatric asthma samples. R software (AT&T Bell Laboratories) was used to construct the weighted gene co-expression network and screen the hub genes. The penalty model was established by least absolute shrinkage and selection operator (LASSO) regression analysis to further screen the genes in the hub genes. The receiver operating characteristic curve (ROC) was used to confirm the diagnostic value of key genes. RESULTS: A total of 171 differentially expressed genes were screened from the controlled and uncontrolled samples. Chemokine (C-X-C motif) ligand 12 (CXCL12), matrix metallopeptidase 9 (MMP9), and wingless-type MMTV integration site family member 2 (WNT2) were the key genes, which were upregulated in the uncontrolled samples. The areas under the ROC curve of CXCL12, MMP9, and WNT2 were 0.895, 0.936, and 0.928, respectively. CONCLUSIONS: The key genes CXCL12, MMP9, and WNT2 in pediatric asthma were identified by a bioinformatics analysis and machine-learning algorithm, which may be potential diagnostic biomarkers. AME Publishing Company 2023-05-22 2023-05-30 /pmc/articles/PMC10248936/ /pubmed/37305716 http://dx.doi.org/10.21037/tp-23-204 Text en 2023 Translational Pediatrics. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Xia, Yulian
Ling, Chen
Zhan, Shanshan
Screening of key genes in childhood asthma based on bioinformatics analysis
title Screening of key genes in childhood asthma based on bioinformatics analysis
title_full Screening of key genes in childhood asthma based on bioinformatics analysis
title_fullStr Screening of key genes in childhood asthma based on bioinformatics analysis
title_full_unstemmed Screening of key genes in childhood asthma based on bioinformatics analysis
title_short Screening of key genes in childhood asthma based on bioinformatics analysis
title_sort screening of key genes in childhood asthma based on bioinformatics analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10248936/
https://www.ncbi.nlm.nih.gov/pubmed/37305716
http://dx.doi.org/10.21037/tp-23-204
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