Chronic rhinosinusitis with nasal polyps (CRSwNP) is caused by prolonged inflammation of the paranasal sinus mucosa. The epithelial to mesenchymal transition (EMT) is involved in the occurrence and development of CRSwNP. The T-cell immunoglobulin domain and the mucin domain 4 (TIM-4) is closely related to chronic inflammation, but its mechanism in CRSwNP is poorly understood. In our study, we found that TIM-4 was increased in the sinonasal mucosa of CRSwNP patients and, especially, in macrophages. TIM-4 was positively correlated with α-SMA but negatively correlated with E-cadherin in CRS. Moreover, we confirmed that TIM-4 was positively correlated with the clinical parameters of the Lund-Mackay and Lund-Kennedy scores. In the NP mouse model, administration of TIM-4 neutralizing antibody significantly reduced the polypoid lesions and inhibited the EMT process. TIM-4 activation by stimulating with tissue extracts of CRSwNP led to a significant increase of TGF-β1 expression in macrophages in vitro. Furthermore, coculture of macrophages and human nasal epithelial cells (hNECs) results suggested that the overexpression of TIM-4 in macrophages made a contribution to the EMT process in hNECs. Mechanistically, TIM-4 upregulated TGF-β1 expression in macrophages via the ROS/p38 MAPK/Egr-1 pathway. In conclusion, TIM-4 contributes to the EMT process and aggravates the development of CRSwNP by facilitating the production of TGF-β1 in macrophages. Inhibition of TIM-4 expression suppresses nasal polyp formation, which might provide a new therapeutic approach for CRSwNP.

Purpose Chronic rhinosinusitis with nasal polyps (CRSwNP) can be classified into eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (non-eCRSwNP) by tissue biopsy, which is difficult to perform preoperatively. Clinical biomarkers have predictive value for the classification of CRSwNP. We aimed to evaluate the application of artificial neural network (ANN) modeling in distinguishing different endotypes of CRSwNP based on clinical biomarkers. Methods Clinical parameters were collected from 109 CRSwNP patients, and their predictive ability was analyzed. ANN and logistic regression (LR) models were developed in the training group (72 patients) and further tested in the test group (37 patients). The output variable was the diagnosis of eCRSwNP, defined as tissue eosinophil count > 10 per high-power field. The receiver operating characteristics curve was used to assess model performance. Results A total of 15 clinical features from 60 healthy controls, 60 eCRSwNP and 49 non-eCRSwNP were selected as candidate predictors. Nasal nitric oxide levels, peripheral eosinophil absolute count, total immunoglobulin E, and ratio of bilateral computed tomography scores for the ethmoid sinus and maxillary sinus were identified as important features for modeling. Two ANN models based on 4 and 15 clinical features were developed to predict eCRSwNP, which showed better performance, with the area under the receiver operator characteristics significantly higher than those from the respective LR models (0.976 vs. 0.902, P = 0.048; 0.970 vs. 0.845, P = 0.011). All ANN models had better fits than single variable prediction models (all P < 0.05), and ANN model 1 had the best predictive performance among all models. Conclusions Machine learning models assist clinicians in predicting endotypes of nasal polyps before invasive detection. The ANN model has the potential to predict eCRSwNP with high sensitivity and specificity, and is superior to the LR model. ANNs are valuable for optimizing personalized patient management.

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