Identification of cuproptosis-related asthma diagnostic genes by WGCNA analysis and machine learning

Abstract

Objective

Cuproptosis is the latest novel form of cell death. However, the relationship between asthma and cuproptosis is not fully understood.

Methods

In this study, we screened differentially expressed cuproptosis-related genes from the Gene Expression Omnibus (GEO) database and performed immune infiltration analysis. Subsequently, patients with asthma were typed and analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was performed to calculate the module-trait correlations, and the hub genes of the intersection were taken to construct machine learning (XGB, SVM, RF, GLM). Finally, we used TGF-β to establish a BEAS-2B asthma model to observe the expression levels of hub genes.

Results

Six cuproptosis-related genes were obtained. Immune-infiltration analysis shows that cuproptosis-related genes are associated with a variety of biological functions. We classified asthma patients into two subtypes based on the expression of cuproptosis-related genes and found significant Gene Ontology (GO) and immune function differences between the different subtypes. WGCNA selected 2 significant modules associated with disease features and typing. Finally, we identified TRIM25, DYSF, NCF4, ABTB1, CXCR1 as asthma biomarkers by taking the intersection of the hub genes of the 2 modules and constructing a 5-genes signature, which nomograph, decision curve analysis (DCA) and calibration curves, receiver operating characteristic curve (ROC) showed high efficiency in diagnosing the probability of survival of asthma patients. Finally, in vitro experiments have shown that DYSF and CXCR1 expression is up expressed in asthma.

Conclusions

Our study provides further directions for studying the molecular mechanism of asthma.

Keywords:

• Cuproptosis
• asthma
• biomarkers
• WGCNA
• machine learning

Acknowledgements

We are grateful to the GEO database for providing the platform and to the contributors for uploading their meaningful datasets.

Authors’ contributions

WF Wang and CQ Li designed the implementation of the research, drafted preliminary papers, and participated in investigations. WF Wang, QS Su and CQ Li participated in research design and implementation, manuscript revision, manuscript submission, and fund acquisition. All authors read and approved the final manuscript.

Consent for publication

Not applicable.

Data availability

The datasets generated during the current study are available in the Gene Expression Omnibus (GEO) repository, (https://www.ncbi.nlm.nih.gov/geo/), (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134544), (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE182503).

Disclosure statement

The authors declare that the study was conducted without any financial relationships that could be considered as potential conflicts of interest.

Ethics approval and consent to participate

Not applicable.

Additional information

Funding

This research was supported by Guangxi Natural Science Foundation under Grant NO. 2020GXNSFDA238003.