Machine-learning analysis and in vivo validation of shared immune signatures in viral myocarditis and dilated cardiomyopathy

Q: What are the key findings of this study?

Transcriptomic and machine-learning analyses identified 23 shared immune genes in viral myocarditis and dilated cardiomyopathy, with 7 candidates (AUC 0.741-0.909) validated in CVB3 mice.

Q: What is the clinical evidence from this study?

Study design: Letter. Population: Viral myocarditis and dilated cardiomyopathy (n=236). Intervention: Viral myocarditis and dilated cardiomyopathy transcriptomic signatures / CVB3 infection vs. Healthy myocardial controls / phosphate-buffered saline control. Primary outcome: Differentially expressed shared immune-related genes and diagnostic discriminatory performance (AUC) (AUC 0.741-0.909, p=<0.05).

Beibei Du; Rong Miao; Xiaojie Ren; Xinjing Chen; Lemin Zheng

doi:10.5114/aoms/240346

October 22, 2026Archives of Medical ScienceOpen Access

Machine-learning analysis and in vivo validation of shared immune signatures in viral myocarditis and dilated cardiomyopathy

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Key result

Machine learning identifies 7 shared immune genes that accurately discriminate viral myocarditis and dilated cardiomyopathy.

AUC 0.741-0.909
P<0.05
n=236

PICO

P

Population

236 human myocardial transcriptomic samples from dilated cardiomyopathy (n=82), viral myocarditis (n=7), and control (n=147) datasets, alongside experimental validation in 6-week-old male BALB/c mice infected with coxsackievirus B3 followed for 30 days.

E

Exposure / Comparator

Viral myocarditis and dilated cardiomyopathy transcriptomic signatures / CVB3 infection vs Healthy myocardial controls / phosphate-buffered saline control

O

Primary Outcome

Differentially expressed shared immune-related genes and diagnostic discriminatory performance (AUC) — AUC 0.741-0.909, p=<0.05

Main Result

Effect estimate: AUC 0.741-0.909

p-value: p=<0.05

Limitations

GSE1145 had a small sample size and did not specify the causative virus.
The human datasets were cross-sectional and therefore cannot demonstrate progression from viral myocarditis to dilated cardiomyopathy.
Differences in microarray platform, species, sampling time, and clinical characteristics may have affected the comparisons.
Feature selection and diagnostic performance were assessed using retrospective public data without external diagnostic validation.
The drug-gene interaction network represents reported database associations rather than demonstrated treatment efficacy.

Implication

Not ready for clinical adoption; leaves open whether signatures improve myocarditis diagnosis or risk stratification.

Abstract

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Discussion

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Cite This Study

Du et al. (2026) conducted a letter in Viral myocarditis and dilated cardiomyopathy (n=236). Viral myocarditis and dilated cardiomyopathy transcriptomic signatures / CVB3 infection vs. Healthy myocardial controls / phosphate-buffered saline control was evaluated on Differentially expressed shared immune-related genes and diagnostic discriminatory performance (AUC) (AUC 0.741-0.909, p=<0.05). Transcriptomic and machine-learning analyses identified 23 shared immune genes in viral myocarditis and dilated cardiomyopathy, with 7 candidates (AUC 0.741-0.909) validated in CVB3 mice.

synapsesocial.com/papers/6ae28f3bcbadf70daf6c442f https://doi.org/10.5114/aoms/240346

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Machine-learning analysis and in vivo validation of shared immune signatures in viral myocarditis and dilated cardiomyopathy

PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

Machine-learning analysis and in vivo validation of shared immune signatures in viral myocarditis and dilated cardiomyopathy

PICO

Main Result

Limitations

Implication

Abstract

Discussion

Cite This Study

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