Identification of Potential Biomarkers and Immune Cell Signatures in COVID‐19 Myocarditis Through Bioinformatic Analysis

<b>Objective:</b> The present study aims to elucidate the significance of immune cell infiltration in Coronavirus disease 2019 (COVID-19) myocarditis and identify potential diagnostic markers for this condition. Myocarditis, an inflammatory cardiac disease, primarily results from viral infections. Although the association between COVID-19 and myocarditis is well-established, the specific mechanism(s) underlying this relationship remain incompletely understood. <b>Methods:</b> The GSE53607 and GSE35182 datasets were obtained from the GEO database, which contains samples from a mouse model for viral myocarditis. Differentially expressed genes (DEGs) and candidate biomarkers were selected using the LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analysis. Subsequently, the diagnostic potential of these biomarkers was evaluated by calculating the area under the receiver operating characteristic curve (AUC). Further validation of the biomarkers was conducted using the GSE183850 dataset, which consists of samples from patients with COVID-19 myocarditis. In addition, CIBERSORT analysis was employed to estimate the compositional patterns of 22 types of immune cell fractions in merged cohorts. <b>Results:</b> Thirty genes were identified, with a significant proportion of the DEGs being associated with carbohydrate binding, endopeptidase activity, and pathogenic organisms such as <i>Staphylococcus aureus</i> and coronavirus disease. Importantly, gene sets related to the IL6-JAK-STAT3 signaling pathways, inflammatory response, and interferon response exhibited differential activation in viral myocarditis compared to the control group. In addition, in the context of COVID-19 myocarditis patients from the GSE183850 dataset, B2M and C3 were established as diagnostic markers that were subsequently validated (AUC = 0.978 and AUC = 0.956, respectively). Furthermore, analysis of immune cell infiltration revealed correlations between B2M and C3 expression levels and the activation of NK cells, dendritic cells, T cells CD4 memory resting, as well as eosinophils. <b>Conclusion:</b> B2M and C3 have been identified as potential biomarkers for viral myocarditis, providing valuable insights for future investigations into the pathogenesis of COVID-19-associated myocarditis.

Authors

Institutions

Publication Details

Journal
Cardiology Research and Practice
Published
2025-01-01
DOI
https://doi.org/10.1155/crp/2349610
Citations
1
Primary Topic
Viral Infections and Immunology Research
Type
article
Field-Weighted Citation Impact
1.39

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Identification of Potential Biomarkers and Immune Cell Signatures in COVID‐19 Myocarditis Through Bioinformatic Analysis

Yongfei Song, Dongdong Tong, Jiangfang Lian, Xiaojun Jin et al.
1 citations
Cardiology Research and Practice
Viral Infections and Immunology Research
1.39
article

Identification of Potential Biomarkers and Immune Cell Signatures in COVID‐19 Myocarditis Through Bioinformatic Analysis

Yongfei Song, Dongdong Tong, Jiangfang Lian, Xiaojun Jin, Jianhui Liu, Xiaofei Wang, Xiaoyan Huang, Chuanjing Zhang, Chen Huang, Bo Guo
article en
1 citations

Abstract

<b>Objective:</b> The present study aims to elucidate the significance of immune cell infiltration in Coronavirus disease 2019 (COVID-19) myocarditis and identify potential diagnostic markers for this condition. Myocarditis, an inflammatory cardiac disease, primarily results from viral infections. Although the association between COVID-19 and myocarditis is well-established, the specific mechanism(s) underlying this relationship remain incompletely understood. <b>Methods:</b> The GSE53607 and GSE35182 datasets were obtained from the GEO database, which contains samples from a mouse model for viral myocarditis. Differentially expressed genes (DEGs) and candidate biomarkers were selected using the LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analysis. Subsequently, the diagnostic potential of these biomarkers was evaluated by calculating the area under the receiver operating characteristic curve (AUC). Further validation of the biomarkers was conducted using the GSE183850 dataset, which consists of samples from patients with COVID-19 myocarditis. In addition, CIBERSORT analysis was employed to estimate the compositional patterns of 22 types of immune cell fractions in merged cohorts. <b>Results:</b> Thirty genes were identified, with a significant proportion of the DEGs being associated with carbohydrate binding, endopeptidase activity, and pathogenic organisms such as <i>Staphylococcus aureus</i> and coronavirus disease. Importantly, gene sets related to the IL6-JAK-STAT3 signaling pathways, inflammatory response, and interferon response exhibited differential activation in viral myocarditis compared to the control group. In addition, in the context of COVID-19 myocarditis patients from the GSE183850 dataset, B2M and C3 were established as diagnostic markers that were subsequently validated (AUC = 0.978 and AUC = 0.956, respectively). Furthermore, analysis of immune cell infiltration revealed correlations between B2M and C3 expression levels and the activation of NK cells, dendritic cells, T cells CD4 memory resting, as well as eosinophils. <b>Conclusion:</b> B2M and C3 have been identified as potential biomarkers for viral myocarditis, providing valuable insights for future investigations into the pathogenesis of COVID-19-associated myocarditis.

Cardiology Research and PracticeVol. 2025(1)
Ningbo University (CN), Ningbo Medical Center Lihuili Hospital (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Ningbo Municipality, Health Commission of Zhejiang Province, Natural Science Foundation of Ningbo, Natural Science Foundation of Zhejiang Province
Openalex Percentile: Top 21%
Viral Infections and Immunology Research
1.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.