Background:
Myocarditis and myocardial infarction (MI) frequently present with overlapping clinical symptoms, elevated troponin levels, and electrocardiographic changes, often requiring invasive diagnostic procedures to establish the underlying diagnosis. Current circulating biomarkers mainly reflect myocardial injury but provide limited insight into the underlying pathophysiology.
Liquid biopsy approaches based on circulating nucleic acids have emerged as promising diagnostic tools across multiple medical fields. While cell-free DNA (cfDNA) predominantly reflects cellular damage and tissue turnover, cell-free RNA (cfRNA) may provide a more dynamic snapshot of ongoing transcriptional and immune activity. Therefore, cfRNA profiling could enable non-invasive characterization of inflammatory cardiac disease processes beyond conventional biomarkers.
Methods:
Plasma samples from healthy controls (n=26), myocarditis patients (n=55), and myocardial infarction (MI) patients (n=16) were collected at the time of diagnosis and processed using standardized centrifugation protocols. Cell-free RNA (cfRNA) was isolated from 1 ml plasma followed by DNase treatment, purification, and Bioanalyzer-based quality assessment. Sequencing libraries were generated using SMARTer Stranded Total RNA-Seq technology and sequenced on Illumina platforms with >35 million paired-end reads per sample. Bioinformatic processing included adapter trimming, quality control, alignment to the human reference genome (GRCh38), duplicate removal, and transcript quantification. Differential gene expression analysis was performed using DESeq2 followed by pathway enrichment analyses. Machine learning models were applied for disease classification.
Results:
Preliminary analyses revealed distinct cfRNA transcriptomic differences between myocarditis and myocardial infarction (MI). Compared with healthy controls, myocarditis showed 3386 upregulated and 3877 downregulated genes, whereas MI demonstrated 2236 upregulated and 2930 downregulated genes.
Initial pathway enrichment analyses suggest myocarditis-associated activation of inflammatory signaling pathways, including IL-17, MAP kinase, and Toll-like receptor signaling. In contrast, pathways shared between myocarditis and MI predominantly reflected general myocardial injury responses such as platelet activation, coagulation, wound healing, and hemostasis. Both disease entities further exhibited signatures related to extracellular matrix organization, collagen formation, and cardiac remodeling. Ongoing analyses will further characterize these disease-specific pathways and refine the transcriptomic differences between inflammatory and ischemic myocardial injury. Machine learning analyses are currently being further optimized to evaluate the discriminatory potential of disease-specific cfRNA signatures.
Conclusion:
Plasma cfRNA profiling identifies disease-specific inflammatory transcriptomic signatures and demonstrates potential as a non-invasive biomarker approach for myocardial injury characterization. Beyond reflecting cardiomyocyte injury alone, cfRNA profiling may capture disease-specific immune and inflammatory activity and thereby support future precision medicine approaches in cardiovascular disease.
