Frontiers in immunology · 2026
Novel phenotypes of immune-mediated necrotizing myopathy identified independent of myositis-specific antibody specificity that improve prognostic stratification.
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Wei Xiaojing, Sun Hui, Pang Zhidan, Bai Na, Guo Tiantian, Nie Changpu, Yang Xuan, Lu Zhen, Bao Liye, Miao Jing, Yu Xuefan
Аннотация
AIM: To systematically investigate the clinical characteristics and prognosis of patients with immune-mediated necrotizing myopathy (IMNM), and to explore the proteomic landscape associated with different clinical phenotypes.
METHODS: A total of 133 IMNM patients were enrolled in this retrospective study. The clinical features and outcomes were compared across three subgroups: anti-HMGCR-positive, anti-SRP-positive, and seronegative patients. Unsupervised machine learning algorithms were applied to cluster patients independently of antibody status. Label-free data-independent acquisition quantitative proteomics was performed on samples from clustered IMNM patients and noninflammatory control subjects.
RESULTS: No significant differences were observed in the overall prognosis among the three antibody-based subtypes. Unsupervised machine learning, independent of myositis-specific antibodies (MSA) status, identified three distinct phenotypes with unique clinical presentations and prognoses. Phenotype 1 (56.4%) corresponded to patients with muscle weakness and a more favorable prognosis. Phenotype 2 (10.5%) was characterized by the highest creatine kinase and lactate dehydrogenase levels, with an intermediate prognosis. Phenotype 3 (33.1%) was marked by a high incidence of ILD and a high mortality rate. Proteomic profiling revealed that these phenotypes exhibited distinct protein expression patterns and associated biological processes.
CONCLUSIONS: MSA are not effective for stratifying IMNM patients into subgroups that are more homogeneous with respect to ILD incidence and prognosis. Novel IMNM phenotypes identified independent of MSA status differ significantly in clinical outcomes and proteomic signatures, providing fresh insights into the pathogenic mechanisms underlying IMNM.
METHODS: A total of 133 IMNM patients were enrolled in this retrospective study. The clinical features and outcomes were compared across three subgroups: anti-HMGCR-positive, anti-SRP-positive, and seronegative patients. Unsupervised machine learning algorithms were applied to cluster patients independently of antibody status. Label-free data-independent acquisition quantitative proteomics was performed on samples from clustered IMNM patients and noninflammatory control subjects.
RESULTS: No significant differences were observed in the overall prognosis among the three antibody-based subtypes. Unsupervised machine learning, independent of myositis-specific antibodies (MSA) status, identified three distinct phenotypes with unique clinical presentations and prognoses. Phenotype 1 (56.4%) corresponded to patients with muscle weakness and a more favorable prognosis. Phenotype 2 (10.5%) was characterized by the highest creatine kinase and lactate dehydrogenase levels, with an intermediate prognosis. Phenotype 3 (33.1%) was marked by a high incidence of ILD and a high mortality rate. Proteomic profiling revealed that these phenotypes exhibited distinct protein expression patterns and associated biological processes.
CONCLUSIONS: MSA are not effective for stratifying IMNM patients into subgroups that are more homogeneous with respect to ILD incidence and prognosis. Novel IMNM phenotypes identified independent of MSA status differ significantly in clinical outcomes and proteomic signatures, providing fresh insights into the pathogenic mechanisms underlying IMNM.