Proteomic profiling of nasal fluids and serum for type 2 nasal polyps diagnosis.

BACKGROUND

Type 2 chronic rhinosinusitis with nasal polyp (T2 CRSwNP) is often associated with severe symptoms and polyp recurrence. Machine learning (ML) framework of biomarkers derived from noninvasive samples has been less evaluated as tools for describing T2 CRSwNP.

OBJECTIVE

To systematically assess the predictive value of protein expression in nasal fluids (NFs) and serum for T2 CRSwNP.

METHODS

T2 and non-T2 CRSwNP were classified using clustering analysis of tissue biomarkers from 82 patients. The expression of 92 inflammation-related proteins was measured in NFs and serum samples from the matched patients using proximity extension assays. ML with 5-fold cross-validation was used to develop a diagnostic model for T2 CRSwNP. Selected biomarkers were further validated using immunohistochemistry, single-cell RNA sequencing, and reverse-transcriptase quantitative polymerase chain reaction.

RESULTS

After defining the T2 and non-T2 CRSwNP groups, we identified 23 dysregulated proteins in NFs and 16 in serum. Four biomarkers-glial cell line-derived neurotrophic factor, monocyte chemoattractant protein-4, transforming growth factor beta 1, and cystatin D-were selected using LASSO regression to predict T2 CRSwNP based on NFs alone. Their expression was validated through immunohistochemistry, single-cell RNA sequencing, and reverse-transcriptase quantitative polymerase chain reaction. The predictive model achieved area under the curve values of 0.91 for the training, 0.91 for the testing, and 0.92 for the validation data sets. Glial cell line-derived neurotrophic factor and monocyte chemoattractant protein-4 were identified as independent prognostic biomarkers for CRSwNP.

CONCLUSION

Proteomic analysis combined with an ML framework identified inflammatory endotypes and recurrence patterns in nasal polyps, offering a simple and noninvasive approach for diagnosing T2 CRSwNP.