DOI 10.1016/j.fmre.2025.09.008
Accurate pathologic classification of aggressive myocarditis improves insights into patient precise medicine.
Аннотация
Because the disease's heterogeneity is ignored, the current diagnosis of myocarditis with advanced heart failure is imprecise, and there are no established criteria for subtyping this type of myocarditis. We analyzed data from 83 consecutive patients with aggressive myocarditis using a deep learning method to categorize them into three prognostically relevant phenogroups (PGs) based on myocardial whole-slide images: PG1, characterized by structural damage; PG2, dominated by autoimmune response; and PG3, driven by infections. Whole-exome sequencing of all patients revealed a significant concentration of mutations in myocardial structure-related genes in PG1, with the titin gene (TTN) being the most frequently mutated. In contrast, mutations in immune-related genes were more prevalent in PG2 and PG3. RNA sequencing revealed unique molecular signatures: PG1 was associated with apoptosis, fibrosis, and the activation of abnormal metabolic pathways, whereas PG2 and PG3 were linked to cell damage and the activation of divergent immune pathways. Immunohistochemical staining suggested that PG1 and PG2 were characterized by T cell infiltration while PG3 was characterized by macrophage infiltration. In addition, the interleukin-6 signal transducer (IL6ST) was identified as a novel prognostic biomarker for myocarditis. A plasma IL6ST content ≥ 332.45 ng/ml was found to be associated with poor survival from recurrence symptom (hazard ratio [HR] 22.8, 95% confidence interval [CI] 2.3 to 227.2, Log-rank p-value < 0.0001). Finally, we used a decision tree model incorporating four clinical parameters to further validate the effectiveness of these features. This study identified three distinct PGs of aggressive myocarditis with significant differences in pathological manifestations, immunophenotyping, survival outcomes, clinical presentations, and molecular profiles. This approach improves patient selection for novel interventions and thus enables precision medicine.
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