Identification and validation of three potential biomarkers and immune microenvironment for in severe asthma in microarray and single-cell datasets

Abstract

Objective: The aim of this study was to identify genetic biomarkers and cellular communications associated with severe asthma in microarray data sets and single cell data sets. The potential gene expression levels were verified in a mouse model of asthma.Methods: We identified differentially expressed genes from the microarray datasets (GSE130499 and GSE63142) of severe asthma, and then constructed models to screen the most relevant biomarkers to severe asthma by machine learning algorithms (LASSO and SVM-RFE), with further validation of the results by GSE43696. Single-cell datasets (GSE193816 and GSE227744) were identified for potential biomarker-specific expression and intercellular communication. Finally, The expression levels of potential biomarkers were verified with a mouse model of asthma.Results: The 73 genes were differentially expressed between severe asthma and normal control. LASSO and SVM-RFE recognized three genes BCL3, DDIT4 and S100A14 as biomarkers of severe asthma and had good diagnostic effect. Among them, BCL3 transcript level was down-regulated in severe asthma, while S100A14 and DDIT4 transcript levels were up-regulated. The transcript levels of the three genes were confirmed in the mouse model. Infiltration of neutrophils and mast cells were found to be increased in severe asthma and may be associated with bronchial epithelial cells through BMP and NRG signalingConclusions: We identified three differentially expressed genes (BCL3, DDIT4 and S100A14) of diagnostic significance that may be involved in the development of severe asthma and these gene expressions could be serviced as biomarker of severe asthma and investigating the function roles could bring new insights into the underlying mechanisms

Keywords:

• Severe asthma
• immune microenvironment
• candidate biomarkers
• cellular communication
• epithelial cells

Acknowledgments

This study used the GEO database from National Center for Biotechnology Information (NCB1) as the data source. The authors thank the NCBI and all participants.

Ethical approval

All experiments have been conducted in accordance with the Ethics Committee of Hunan Normal University Biomedical Research and conform to the guidelines established in Animal Research: Reporting of In Vivo Experiments. All rat experimental protocols were approved by the Hunan Normal University Biomedical Research (approval no: D2022055) and were performed according to the ethical rules and laws of Hunan Normal University Biomedical Research.

Authors contributions

Conception and design: Mingsheng Lei, Weimin Zhou.

Acquisition, analysis, and interpretation of data: Fuying Zhang, Xiang Weng, Jiabao Zhu. Qin Tang.

Drafting the work and revising: Fuying Zhang, Xiang Weng, Jiabao Zhu, Mingsheng Lei, Weimin Zhou.

Declaration of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Data availability statement

The dataset for this study can be found in the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/).

Additional information

Funding

The research described in this study was funded by Zhangjiajie Yongding District Science and Technology Innovation Plan Project (2021111616).