Deciphering cuproptosis-related signatures in pediatric allergic asthma using integrated scRNA-seq and bulk RNA-seq analysis.
J Asthma
; 61(10): 1316-1327, 2024 Oct.
Article
em En
| MEDLINE
| ID: mdl-38687912
ABSTRACT
OBJECTIVE:
Allergic asthma (AA) is common in children. Excess copper is observed in AA patients. It is currently unclear whether copper imbalance can cause cuproptosis in pediatric AA.METHODS:
The datasets about pediatric AA (GSE40732 and GSE40888) were obtained from Gene Expression Omnibus (GEO) database. The expression of cuproptosis-related genes (CRGs) and immune cell infiltration in pediatric AA samples were analyzed. Single-cell RNA sequencing (scRNA-seq) data (GSE193816) were used to evaluate the expression patterns of CRGs in AA. The identification of differentially expressed genes within clusters was conducted using weighted gene co-expression network analysis. Subsequently, disease progression and cuproptosis-related models were screened using random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and general linear model (GLM) algorithms.RESULTS:
Four CRGs were notably increased in pediatric AA samples. CD4+ T cells, macrophages and mast cells exhibited a lower cuproptosis score in AA samples, indicating that these immune cells may be closely associated with cuproptosis in AA development. Co-expression network of CRGs in AA was constructed. AA samples were divided into two cuprotosis clusters. Following construction of four machine-learning models, SVM model exhibited the highest efficacy of prediction in the testing set (AUC = 0.952). SVM model containing five important variables can be used for prediction of AA.CONCLUSION:
This work provided a machine learning model containing five important variables, which may have good diagnostic efficiency for pediatric AA.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Asma
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RNA-Seq
Limite:
Child
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Female
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Humans
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Male
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article