Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches

ABSTRACT

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1‘s pivotal role validated. FDX1‘s high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1‘s connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

KEYWORDS:

• COVID-19
• differential expression genes
• bioinformatics
• immune infiltration
• WGCNA
• machine learning models
• copper-death

Disclosure statement

No potential conflict of interest was reported by the author(s).

Authors’ contributions

Qi Wan conceived and designed paper. Zhenzhong Su collected and analyzed data. Jing Zhang prepared figures. He Yan drafted paper. Jie Zhang edited and revised manuscript. All authors read and approved final version of manuscript.

Data availability statement

The data that supports the findings of this study are available on request from the corresponding author upon reasonable request.

Ethics approval and consent to participate

An ethics statement was not required for this study type, no human or animal subjects or materials were used.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/21645515.2024.2310359.

Additional information

Funding

The work was supported by the Key Project of Science and Technology Committee of Jilin [20220803].