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Research
Significance of Immune-related Genes in the Diagnosis and Subtype Classification of Childhood Asthma
Yunxiao Shang
Shengjing Hospital of China Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8885-4937
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background Childhood asthma is one of the most common causes of hospitalization in children, causing huge economic losses worldwide. The immune system was closely linked to the occurrence and development of childhood asthma. The main purpose of this research is to explore the role of immune-related genes in the occurrence and treatment of childhood asthma.
Methods GSE40732 dataset and GSE40888 dataset were respectively regarded as training dataset and texting dataset in this article. We performed weighted gene co-expression network analysis (WGCNA) to select immune-related genes associated with the occurrence of childhood asthma based on the training dataset. Random forest (RF) model was established to screen the optimal variables to predict the occurrence of childhood asthma among these significant immune-related genes. Childhood asthma patients were grouped by the consensus clustering method based on the optimal variables. The grouping results were validated in the texting dataset.
Results 69 significant immune-related genes associated with the occurrence of childhood asthma were screened through WGCNA. RF model indicated that 10 optimal variables among these significant immune-related genes can reasonably distinguish normal children and childhood asthma. Childhood asthma patients were classified into two molecular subtypes (Sub1 and Sub2) based on the 10 optimal variables using consensus clustering analysis. More interestingly, childhood asthma patients in Sub1 have higher inflammatory response than Sub2.
Conclusions Our research selected 10 significant immune-related genes related to the occurrence of childhood asthma. We classified childhood asthma patients into two molecular subtypes with different immune cell infiltration based on the 10 significant immune-related genes, which may provide the basis for individualized treatment.
Keywords
Childhood asthma, Immune-related genes, Random forest model, Nomogram, Consensus clustering, Immunocyte
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This is a preprint. It has not completed peer review.
Research
Significance of Immune-related Genes in the Diagnosis and Subtype Classification of Childhood Asthma
Yunxiao Shang
Shengjing Hospital of China Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8885-4937
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 23 Sep, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Translational Medicine
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background Childhood asthma is one of the most common causes of hospitalization in children, causing huge economic losses worldwide. The immune system was closely linked to the occurrence and development of childhood asthma. The main purpose of this research is to explore the role of immune-related genes in the occurrence and treatment of childhood asthma.
Methods GSE40732 dataset and GSE40888 dataset were respectively regarded as training dataset and texting dataset in this article. We performed weighted gene co-expression network analysis (WGCNA) to select immune-related genes associated with the occurrence of childhood asthma based on the training dataset. Random forest (RF) model was established to screen the optimal variables to predict the occurrence of childhood asthma among these significant immune-related genes. Childhood asthma patients were grouped by the consensus clustering method based on the optimal variables. The grouping results were validated in the texting dataset.
Results 69 significant immune-related genes associated with the occurrence of childhood asthma were screened through WGCNA. RF model indicated that 10 optimal variables among these significant immune-related genes can reasonably distinguish normal children and childhood asthma. Childhood asthma patients were classified into two molecular subtypes (Sub1 and Sub2) based on the 10 optimal variables using consensus clustering analysis. More interestingly, childhood asthma patients in Sub1 have higher inflammatory response than Sub2.
Conclusions Our research selected 10 significant immune-related genes related to the occurrence of childhood asthma. We classified childhood asthma patients into two molecular subtypes with different immune cell infiltration based on the 10 significant immune-related genes, which may provide the basis for individualized treatment.
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Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
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