Stability-Based Machine Learning Identifies a Minimal Two-Protein Serum Signature for Early Silicosis

Background: The early diagnosis of silicosis, an irreversible
fibrotic
lung disease, is challenged by the low sensitivity of current radiological
methods in early-stage disease and their susceptibility to interobserver
variability. Consequently, a pressing need exists for noninvasive,
objective biomarkers to facilitate timely detection and intervention.
Methods: We employed a multistage study design comprising a discovery
cohort (57 Stage I silicosis patients, 57 matched controls) and an
independent, unmatched validation cohort (40 patients, 40 controls).
Serum protein profiles were generated using Olink targeted proteomics.
We utilized a rigorous, stability-based machine learning framework,
which integrated Lasso, Random Forest, and SVM-RFE algorithms over
100 iterations, to perform feature selection and identify a robust
biomarker signature from the discovery cohort. Based on the selected
features, a logistic regression model was subsequently constructed,
and its performance was evaluated using both internal and external
validation. Results: Our discovery strategy identified a two-protein
signature comprising IL8 and CCL3. This signature demonstrated excellent
diagnostic performance in the discovery cohort, achieving a cross-validation
AUC of 0.986 (95% CI: 0.975–1.000). Importantly, the model’s
robustness was confirmed in the heterogeneous validation cohort, where
it achieved an outstanding AUC of 0.973 (95% CI: 0.936–1.000),
with 95.0% specificity and 77.5% sensitivity. Bioinformatic analysis
revealed that decreased serum levels of IL8 and CCL3 were associated
with silicosis, providing novel diagnostic biomarkers and highlighting
a complex, paradoxical shift in circulating chemokines during early-stage
disease.