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Similarity of immune-associated markers in COVID-19 and Kawasaki disease: analyses from bioinformatics and machine
Wang Li1, Wenjie Zhu1, Xiangting Tang1
1Department of Clinical laboratory, The Eighth Affiliated Hospital, Sun Yat-sen University, 3025 Shennan Middle Road, Shenzhen, Guangdong, 518000, China.
Insights
This study identified shared molecular pathways and key genes (PGLYRP1, DEFA4, RETN) between COVID-19 and Kawasaki disease (KD). These findings offer insights into the pathogenesis of both diseases, particularly concerning immune responses.
Area of Science:
- Molecular biology
- Immunology
- Bioinformatics
Background:
- SARS-CoV-2 infection causes COVID-19 and can worsen Kawasaki disease (KD), a pediatric vasculitis.
- Understanding shared molecular mechanisms between COVID-19 and KD is crucial for advancing treatment strategies.
Purpose of the Study:
- To investigate the molecular pathogenesis similarities between COVID-19 and Kawasaki disease (KD) using bioinformatics and machine learning.
- To identify shared differentially expressed genes (DEGs) and key molecular pathways implicated in both diseases.
Main Methods:
- Weighted gene co-expression network analysis to identify KD-associated modules.
- Identification and functional annotation of shared DEGs in COVID-19 and KD datasets.
- Protein-protein interaction network analysis, LASSO regression, and ROC curve analysis to identify robust biomarkers.
Main Results:
- Twenty-six shared DEGs were identified between COVID-19 and KD.
- Shared DEGs are involved in immune responses, neutrophil extracellular trap formation, and NOD-like receptor signaling.
- Three key genes (PGLYRP1, DEFA4, RETN) showed positive correlations with immune cells like monocytes and macrophages.
Conclusions:
- PGLYRP1, DEFA4, and RETN are potential immune-associated biomarkers for COVID-19 and KD.
- These biomarkers and their associated pathways may elucidate the shared pathogenesis of both conditions.
- Further research into these shared molecular mechanisms can inform therapeutic approaches for KD and COVID-19.
Background:
Infection by the SARS-CoV-2 virus can cause coronavirus disease 2019 (COVID-19) and can also exacerbate the symptoms of Kawasaki disease (KD), an acute vasculitis that mostly affects children. This study used bioinformatics and machine learning to examine similarities in the molecular pathogenesis of COVID-19 and KD.
Methods:
We first identified disease-associated modules in KD using weighted gene co-expression network analysis. Then, we determined shared differentially expressed genes (DEGs) in training datasets for KD (GSE100154) and COVID-19 (GSE225220), performed functional annotation of these shared DEGs, and used Cytoscape plug-ins (MCODE and Cytohubba) to characterize the protein-protein interaction (PPI) network and identify the hub genes. We performed Least Absolute Shrinkage and Selection Operator(LASSO) regression and receiver operating characteristic (ROC) curve analysis to identify the most robust markers, validated these results by analysis of two other datasets (GSE73461 and GSE18606), and then calculated the correlations of these key genes with immune cells.
Results:
This analysis identified 26 shared DEGs in COVID-19 and KD. The results from functional annotation showed that the shared DEGs primarily functioned in immune responses, the formation of neutrophil extracellular traps, and NOD-like receptor signaling pathways. There were three key genes (PGLYRP1, DEFA4, RETN), and they had positive correlations with monocytes, M0 macrophages, and dendritic cells, which function as immune infiltrating cells in KD.
Conclusion:
The potential immune-associated biomarkers (PGLYRP1, DEFA4, RETN) along with their shared pathways, hold promise for advancing investigations into the underlying pathogenesis of KD and COVID-19.
