IMNM Patient Clinical and Proteomic Data for Phenotype Stratification

Xiaojing Wei published a dataset on figshare in May 2026 containing clinical and proteomic data from 133 patients with immune-mediated necrotizing myopathy (IMNM). The data was used to identify three novel clinical phenotypes independent of antibody status, which were associated with distinct prognoses and protein expression patterns. The dataset includes results from a retrospective study employing unsupervised machine learning and label-free quantitative proteomics.

Use Cases

• Clustering patients into prognostic subgroups based on clinical features independent of antibody status.
• Identifying protein biomarkers associated with specific IMNM phenotypes and outcomes.
• Training models to predict patient prognosis based on creatine kinase levels, lactate dehydrogenase levels, and incidence of interstitial lung disease.
• Comparing the clinical characteristics of anti-HMGCR-positive, anti-SRP-positive, and seronegative IMNM patient subgroups.

Strengths

• Includes data from 133 IMNM patients, providing a substantial cohort for analysis.
• Integrates clinical data with quantitative proteomic profiling from data-independent acquisition methods.
• Employs unsupervised machine learning to derive novel phenotypes, offering a data-driven stratification approach.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying data tables is unknown, which may limit suitability assessment.
• The retrospective study design may introduce inherent biases in data collection.

Provenance

Source

figshare

Collection Method

Retrospective clinical study combined with label-free data-independent acquisition quantitative proteomics.

Freshness

Last updated 2026-05-14 05:33:03; freshness should be verified.