Identifcation of candidate biomarkers for polyarteritis nodosa using data-independent acquisition mass spectrometry.

OBJECTIVES

Polyarteritis nodosa (PAN) is a rare autoimmune disease that can cause severe functional impairment. Early diagnosis and timely intervention are essential to reduce disease severity and improve outcomes.

METHODS

Serum proteins from PAN patients and healthy controls were analyzed using data-independent acquisition mass spectrometry (DIA-MS), identifying 55 differentially expressed proteins. Validation was conducted on an independent set of 35 serum samples (10 healthy controls, 15 disease controls, and 10 PAN patients) to evaluate the diagnostic potential of selected biomarkers.

RESULTS

Eighteen proteins showed significantly altered expression in PAN patients compared to controls. A diagnostic panel of seven proteins - AZGP1, F13B, LBP, RBP4, SERPINF1, PGLYRP2, and PPBP - was identified using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. This panel achieved an area under the receiver operating characteristic (ROC) curve of 0.994, effectively distinguishing PAN patients from controls.

CONCLUSION

By combining DIA-MS technology with the LASSO regression model, this study developed a 7-protein diagnostic panel, providing a highly accurate and efficient tool for PAN diagnosis.