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

Clinical Snapshot

Key Findings

• P Value: Not explicitly reported

• Effect Size: AUC 0.973 in validation cohort

• Primary Outcome: Diagnostic accuracy of two-protein signature (IL8 and CCL3) for early silicosis detection

• Nnt Or Sensitivity: Sensitivity 77.5%, Specificity 95.0%

• Confidence Interval: 95% CI: 0.936-1.000

Clinical Application

Requires specialized Olink proteomics platform, limiting immediate clinical implementation Relevant for Australian mining and construction industries; would need TGA approval for diagnostic use and cost-effectiveness evaluation for PBS consideration Workers with occupational silica exposure requiring early silicosis screening

Abstract

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.

References

1. 1.Chu, X., Li, Y., Wang, F., Li, W., Wang, N., Zhou, L., Gao, Y., Zhou, P., & Han, L. (2026). Stability-based machine learning identifies a minimal two-protein serum signature for early silicosis. Journal of Proteome Research. https://doi.org/10.1021/acs.jproteome.5c00987