Nasal secretion biomarker composition for diagnosing type 2 chronic rhinosinusitis with nasal polyps and use thereof

Abstract

The application discloses a nasal secretion biomarker composition for diagnosing type 2 chronic rhinosinusitis with nasal polyps and application thereof, relates to the technical field of biological medicine, and the nasal secretion biomarker composition comprises glial cell-derived neurotrophic factor, cystatin 5, transforming growth factor beta 1 and monocyte chemoattractant protein 4. When the nasal secretion biomarker composition is used for diagnosing CRSwNP intrinsic type, nasal secretion is used as a detection sample, polyp biopsy is not needed, the nasal secretion biomarker composition is non-invasive, simple, convenient, has high sensitivity and specificity, can classify CRSwNP patients before operation without damaging mucosa, can predict the disease intrinsic type of the CRSwNP patient in advance and accurately, and is an ideal non-invasive method.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a biomarker composition for diagnosing type 2 chronic sinusitis with nasal polyps and its application. Background Technology

[0002] Chronic rhinosinusitis (CRS) is a common inflammatory disease of the upper respiratory tract. Based on its clinical phenotype, CRS is mainly divided into polyp-associated and non-polyp-associated types. CRS with nasal polyps (CRSwNP) can be further classified into type 2 CRSwNP (also known as Type 2 CRSwNP or T2 CRSwNP) and non-type 2 CRSwNP (also known as non-Type 2 CRSwNP or non-T2 CRSwNP) based on its intrinsic characteristics. Type 2 CRSwNP typically presents with moderate to severe type 2 helper T cell (Th2) inflammation, accompanied by tissue eosinophilia and elevated IgE (immunoglobulin E) levels. These patients experience more severe clinical symptoms and usually require longer-term, higher-dose drug therapy and / or repeated surgeries. Therefore, predicting the intrinsic type of CRSwNP patients can provide precise guidance for developing clinical treatment plans.

[0003] Currently, common methods for predicting the intrinsic type of CRSwNP are mainly based on inflammatory factors in nasal polyp tissue. In recent years, different classification indicators have been proposed both domestically and internationally, and models ranging from simple to complex have been gradually developed to reveal the characteristics of the intrinsic type of CRS. For example, Tomassen et al. identified ten potential intrinsic CRS characteristics using surgically obtained tissue inflammatory biomarkers (see PMID: 26949058; PMID is a unique identifier in PubMed, representing the document number in the life sciences and medicine fields indexed in the PubMed search engine. Entering the PMID number directly into the PubMed search engine will retrieve the corresponding document, and each PMID number corresponds to a unique document). Wang et al., by assessing the degree of type 2 inflammation, classified CRS patients into non-type 2, low type 2, intermediate type 2, and high type 2, and these classifications showed significant differences in inflammatory molecular characteristics and tissue remodeling (see PMID: 36272582). However, these intrinsic diagnostic methods mainly rely on the detection of tissue inflammatory factors, which require invasive surgery to obtain nasal polyp tissue. This process is subject to various limitations. In addition, the concentration of inflammatory factors may be affected by changes in protein expression in specific locations within the sinus cavity, making it difficult to promote and apply this method on a large scale in clinical practice.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a nasal secretion biomarker composition for diagnosing type 2 CRSwNP and its application, aiming to solve the problem that the diagnosis of intrinsic CRSwNP is mainly based on inflammatory factors in nasal polyp tissue, which requires invasive surgery to obtain nasal polyp tissue.

[0006] The technical solution of the present invention is as follows:

[0007] In a first aspect, the present invention provides a nasal secretion biomarker composition, wherein the nasal secretion biomarker composition comprises glial cell-derived neurotrophic factor, cysteine ​​protease inhibitor 5, transforming growth factor β1, and monocyte chemotactic protein 4.

[0008] A second aspect of the invention provides the use of the nasal secretion biomarker composition of the invention as described above in the preparation of CRSwNP intrinsic diagnostic products.

[0009] Optionally, the intrinsic diagnosis of CRSwNP includes differentiating between type 2 CRSwNP and non-type 2 CRSwNP.

[0010] Optionally, the product may include reagents or kits.

[0011] Optionally, when the product is used to diagnose the intrinsic type of CRSwNP, the sample used is nasal secretion.

[0012] A third aspect of the invention provides the application of the nasal secretion biomarker composition of the invention as described above in the construction of an intrinsic diagnostic model of CRSwNP.

[0013] Optionally, an intrinsic diagnostic model for chronic sinusitis with nasal polyps can be constructed based on Lasso regression, wherein the diagnostic model is logit(π) = 0.031 + 1.04 × log2. A +0.149×log2 B -0.791×log2 C -0.997×log2 D +0.514×E-0.169×F;

[0014] Wherein, logit(π) is the predicted value of the diagnostic model; A is the relative expression level of glial cell-derived neurotrophic factor in the nasal secretions of CRSwNP subjects; B is the relative expression level of monocyte chemoattractant protein 4 in the nasal secretions of CRSwNP subjects; C is the relative expression level of transforming growth factor β1 in the nasal secretions of CRSwNP subjects; D is the relative expression level of cysteine ​​protease inhibitor 5 in the nasal secretions of CRSwNP subjects; E is the age of the CRSwNP subject; and F is the sex of the CRSwNP subject, where male is 1 and female is 0.

[0015] When the value of logit(π) is greater than or equal to 0.53, the intrinsic type of the CRSwNP subject is type 2 CRSwNP.

[0016] Beneficial Effects: The nasal secretion biomarker composition of this invention, used for the diagnosis of intrinsic CRSwNP, employs nasal secretions as the test sample, eliminating the need for polyp biopsy. It is non-invasive, simple, convenient, and exhibits high sensitivity and specificity. It can classify CRSwNP patients before surgery without damaging the mucosa, accurately predicting the intrinsic disease type in advance, making it an ideal non-invasive method. Using the nasal secretion biomarker composition provided by this invention for the diagnosis of intrinsic CRSwNP helps doctors develop personalized treatment plans for patients, thereby avoiding unnecessary over-medication or side effects and improving efficacy; reducing surgical risks; for patients requiring surgical intervention, accurate preoperative classification helps doctors better plan surgical strategies, reducing postoperative complications and recurrence rates; improving resource utilization efficiency; by identifying high-risk patients early, medical resources can be prioritized for those most in need, improving the overall efficiency of medical services; and improving patient prognosis; through more accurate diagnosis and targeted treatment, while reducing disease progression and recurrence rates, it can also significantly improve patients' quality of life. Attached Figure Description

[0017] Figure 1 The graph shows the results of the training set using different evaluation metrics in Xgboost, Logistic Regression, Lasso Regression, Random Forest, and Decision Tree.

[0018] Figure 2 A comparison chart of AUROC values ​​for the test set in Xgboost, Logistic Regression, Lasso Regression, Random Forest, and Decision Tree.

[0019] Figure 3 The graph shows the AUROC values ​​for five-fold cross-validation of Xgboost, Logistic Regression, Lasso Regression, Random Forest, and Decision Tree.

[0020] Figure 4 ROC curve analysis for the training, test, and validation sets.

[0021] Figure 5 This is a calibration curve for the Lasso regression diagnostic model.

[0022] Figure 6 This is a graph showing the relationship between the coefficients and Log(λ).

[0023] Figure 7 The graph shows the relationship between the binomial deviation and Log(λ). Detailed Implementation

[0024] This invention provides a nasal secretion biomarker composition and its application. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0026] Currently, common methods for predicting the intrinsic type of CRSwNP are mainly based on inflammatory factors in nasal polyp tissue. However, this requires invasive surgery to obtain nasal polyp tissue, which is subject to various limitations during the procedure. Furthermore, the concentration of inflammatory factors can be affected by changes in protein expression in specific locations within the sinus cavity, making this method difficult to widely implement in clinical practice. Using nasal secretions as the test sample is not only convenient and not limited by local mucosal inflammation, but also offers the advantage of longitudinal dynamic quantitative assessment. By detecting inflammatory factors in nasal secretions, it is hoped that the subtype of CRSwNP patients can be predicted in advance, thereby providing more personalized and targeted treatment for CRSwNP patients. Based on this, this invention provides a nasal secretion biomarker composition, wherein the nasal secretion biomarker composition includes glial cell-derived neurotrophic factor (GDNF), cysteine ​​protease inhibitor 5 (CST5), transforming growth factor β1 (TGFB1), and monocyte chemotactic protein 4 (MCP-4).

[0027] The nasal secretion biomarker composition of this invention, when used for the diagnosis of intrinsic CRSwNP, uses nasal secretions as the test sample. It is non-invasive, requiring no polyp biopsy, and is simple, convenient, and exhibits high sensitivity and specificity. It can classify CRSwNP patients before surgery without damaging the mucosa, accurately predicting the intrinsic disease type in advance, making it an ideal non-invasive method. Using the nasal secretion biomarker composition provided by this invention for the diagnosis of intrinsic CRSwNP helps doctors develop personalized treatment plans for patients, thereby avoiding unnecessary over-medication or side effects and improving efficacy; reducing surgical risks; for patients requiring surgical intervention, accurate preoperative classification helps doctors better plan surgical strategies, reducing postoperative complications and recurrence rates; improving resource utilization efficiency; by identifying high-risk patients early, medical resources can be prioritized for those most in need, improving the overall efficiency of medical services; and improving patient prognosis; through more accurate diagnosis and targeted treatment, it can significantly improve patients' quality of life while reducing disease progression and recurrence rates.

[0028] This invention also provides an application of the nasal secretion biomarker composition described above in the preparation of CRSwNP intrinsic diagnostic products.

[0029] In some embodiments, the intrinsic type diagnosis of CRSwNP includes differentiating between type 2 CRSwNP and non-type 2 CRSwNP. The nasal secretion biomarker composition of the present invention, as described above, can effectively differentiate between type 2 CRSwNP and non-type 2 CRSwNP.

[0030] In some embodiments, the product includes reagents or kits.

[0031] In some embodiments, the product uses nasal secretions as a sample when used to diagnose the intrinsic type of CRSwNP.

[0032] This invention also provides an application of the nasal secretion biomarker composition described above in constructing a CRSwNP intrinsic diagnostic model.

[0033] In some implementations, an intrinsic diagnostic model for chronic sinusitis with nasal polyps is constructed based on Lasso regression, wherein the diagnostic model is logit(π) = 0.031 + 1.04 × log2. A +0.149×log2 B -0.791×log2 C -0.997×log2 D +0.514×E-0.169×F;

[0034] Wherein, logit(π) is the predicted value of the diagnostic model; A is the relative expression level of GDNF in the nasal secretions of CRSwNP test subjects; B is the relative expression level of MCP-4 in the nasal secretions of CRSwNP test subjects; C is the relative expression level of TGFB1 in the nasal secretions of CRSwNP test subjects; D is the relative expression level of CST5 in the nasal secretions of CRSwNP test subjects; E is the age of CRSwNP test subjects; F is the sex of CRSwNP test subjects, where males are represented by 1 and females by 0; the relative expression levels of the above proteins A, B, C and D were determined by the Olink Target 96 inflammation panel, which (containing reference proteins) was purchased from Olink Proteomics (Uppsala, Sweden), catalog number 95000B, lot number B32027B;

[0035] When the value of logit(π) is greater than or equal to 0.53, the intrinsic type of the CRSwNP subject is type 2 CRSwNP.

[0036] The diagnostic model in this invention has high accuracy and can effectively distinguish between type 2 CRSwNP and non-type 2 CRSwNP, thus enabling the diagnosis of the intrinsic type of CRSwNP in the subject.

[0037] The present invention will be further described below through specific embodiments.

[0038] The following studies were approved by the Research Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University (No. 2023-211). All participants signed written consent forms prior to the procedures. The diagnosis of CRSwNP in participants was based on medical history, physical examination, nasal endoscopy, and sinus CT scans, following the 2020 European Sinusitis and Nasal Polyps Guidelines (EPOS2020) (see PMID: 32077450).

[0039] In the following embodiments, the meanings of English words and English abbreviations are as follows:

[0040] TNF-α: Tumor necrosis factor-α;

[0041] IL-1β: Interleukin-1β;

[0042] IL-5: Interleukin-5;

[0043] IL-6: Interleukin-6;

[0044] IFN-γ: Interferon-γ;

[0045] IL-13: Interleukin-13;

[0046] IL-17A: Interleukin-17A;

[0047] ECP: Human eosinophil cationic protein;

[0048] Periostin: Periosteal protein;

[0049] ALB: Albumin;

[0050] IL-8: Interleukin-8;

[0051] MPO: Myeloperoxidase.

[0052] Example 1

[0053] Exclusion criteria for CRSwNP patients include: (1) posterior maxillary sinus polyps, cystic fibrosis, allergic fungal sinusitis; (2) malignant tumors, severe systemic chronic diseases, infectious diseases; (3) autoimmune diseases and other systemic immune diseases; (4) long-term use of immunosuppressive drugs; (5) age less than 18 years; (6) use of steroids, antibiotics, or nonsteroidal anti-inflammatory drugs within 4 weeks prior to surgery. In other words, CRSwNP patients meeting any of the above criteria are excluded.

[0054] (1) Baseline demographic and general clinical characteristics of 82 patients with CRSwNP were obtained.

[0055] The baseline demographic and general clinical characteristics of the 82 CRSwNP patients used for model construction are shown in Table 1.

[0056] Table 1. Baseline demographic and general clinical characteristics of CRSwNP patients

[0057]

[0058]

[0059] Note: Continuous variables were analyzed using the Mann-Whitney U test, and categorical variables were analyzed using the chi-square test. HPF represents high-power field of view; LM score represents the Lund-Mackay sinus CT score; MLK score represents the modified Lund-Kennedy nasal endoscopy score; SNOT-22 represents the nasal and sinus outcome test 22; VAS represents the visual analog scale.

[0060] Furthermore, the data in Table 1 are presented in the form of median [minimum, maximum] or frequency (percentage %). The data is expressed as "serum neutrophil count, 10..." 9 Taking " / L" as an example, 3.55 [0.300, 5.80] indicates that 3.55 is the median (i.e., the median neutrophil count in non-type 2 CRSwNP patients is 3.55 × 10⁻⁶). 9The minimum value is 0.300 (i.e., in non-type 2 CRSwNP patients, the minimum serum neutrophil count is 0.300 × 10⁹ / L). 9 The maximum value was 5.80 (i.e., the maximum neutrophil count in non-type 2 CRSwNP patients was 5.80 × 10⁹ / L). 9 / L). Additionally, taking "Asthma, is (%)" as an example, 2 (4.8) indicates that 2 represents the frequency (i.e., 2 out of 40 non-type 2 CRSwNP patients have asthma), and 4.8 represents 4.8% (i.e., 4.8% of non-type 2 CRSwNP patients have asthma). The meanings of other data follow the same logic.

[0061] Among the 82 CRSwNP patients listed in Table 1, type 2 CRSwNP patients were identified using the following method, specifically including the following steps:

[0062] a. Protein quantification was performed using enzyme immunoassay (ELISA) and Luminex multifactor liquid microarray technology to determine the protein concentrations of 12 cytokines in nasal polyp tissues from 82 patients with CRSwNP. Based on the protein concentration data of these 12 biomarkers, a consensus cluster analysis was performed to identify and differentiate the patients' immune intrinsic types, specifically including the following steps:

[0063] Nasal tissue biopsies were collected from nasal polyps in patients with CRSwNP and cryopreserved. The frozen tissue samples were weighed and homogenized. 0.1 g of the homogenized tissue was dissolved in 1 mL of 0.9% NaCl solution, centrifuged at 1500 g for 10 minutes, and then used for analysis.

[0064] Referring to the biomarkers used in existing methods (PMID: 26949058, PMID: 36272582), this embodiment selected 12 biomarkers (Table 2) that reflect the inflammation patterns observed in CRSwNP. Specifically, ECP (Cat#abx055137) was detected using an ELISA kit (purchased from Abbexa). MPO (Cat#DY3174), ALB (Cat#DY1455), and Periodin (Cat#DY3548B) were detected using an ELISA kit (purchased from R&D Systems). All other biomarkers were evaluated using a Luminex 100 system (purchased from R&D Systems) to obtain the protein concentrations of these 12 biomarkers.

[0065] Table 2. Names and significance of biomarkers used in tissue cluster analysis

[0066]

[0067] b. Based on the protein concentration data of the above 12 biomarkers, a consensus cluster analysis was performed to identify and differentiate the immune intrinsic type of CRSwNP patients. This included the following steps:

[0068] Consistency matrix was constructed using Consensus-CusterPus (R package), and the following parameters were used to cluster the first pair of samples: 'maXK=10, reps=50, pitem=0.8, oFeature=1.clusterAlgpam', seed=123456, distance='Euclidean'. Based on similarity features and variable order, intra-inflammatory subtyping of CRSwNP patients was performed using tissue biomarker cluster analysis (intra-inflammatory subtyping is defined as the dependent variable here), and each of the 82 CRSwNP patients was grouped and labeled, including 42 non-type 2 CRSwNP patients and 40 type 2 CRSwNP patients.

[0069] (2) Nasal secretions were collected from 82 patients with CRSwNP and analyzed using Olink proteomics.

[0070] Nasal secretions, as clinical samples, may have low protein concentrations, which are affected by various factors (such as collection time and collection method). This invention combines the high sensitivity and accuracy of high-throughput Olink technology, making it very suitable for the analysis of such complex samples. It can effectively detect low-abundance or hard-to-detect proteins associated with type 2 CRSwNP, and can achieve accurate prediction with only 1 μL of sample.

[0071] Nasal secretions were collected using the nasal secretion collection device (also known as the Nasalfluids Absorption Device, NFAD) and sampling method disclosed in the inventor's previous patent (CN116019495A). Then, the Olink immunoassay was used, specifically the Olink Target 96 inflammation panel (purchased from Olink Proteomics, Uppsala, Sweden, catalog number 95000B, batch number B32027B, containing a reference protein) to quantitatively detect 96 proteins in the nasal secretions, obtaining the relative expression levels of each protein. The relative expression levels of each protein were log2 transformed to obtain the normalized protein expression level (NPX) of each protein, where NPX is the logarithm of the relative protein expression level at base 2; a higher NPX indicates a higher protein concentration. Differences in protein expression levels were assessed using an unpaired t-test (Mann-Whitney U test), with p < 0.1 considered statistically significant.

[0072] Then, using the intrinsic type of CRSwNP as the dependent variable and nasal secretion biomarkers detected by Olink technology as independent variables, a predictive model was constructed. Five different machine learning regression algorithms were used, combined with five-fold cross-validation, for model training, optimization, and testing. Model performance was evaluated using metrics including the area under the receiver operating curve (ROC, denoted as AUROC), precision, balanced precision, F-means (F1 score, representing the harmonic mean of precision and recall), NPV (negative predictive value), PPV (positive predictive value), PR AUC (precision-recall area under the curve), precision, recall, sensitivity, and specificity. DeLong's test was used to evaluate the differences in AUROC values ​​among models, and the model with the highest average index was considered the best. To determine the optimal classifier performance, Youden's index was used to determine the optimal cutoff point for positive class predictions in the training dataset. Based on the variable combination selected by the optimal model (a diagnostic model for Lasso regression was constructed using GDNF, CST5, TGFB1, and MCP-4, which exhibits the best diagnostic performance, as detailed in the results below), a diagnostic model for type 2 CRSwNP was constructed, specifically:

[0073] A dataset of 82 CRSwNP patients was randomly divided into a training set (for model training) and a test set (for model testing) in a 7:3 ratio using stratified sampling. Five different machine learning methods were employed: Logistic Regression, Lasso Regression, Decision Tree, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The intrinsic type of CRSwNP was used as the dependent variable, and nasal secretion biomarkers detected by Olink technology were used as independent variables to construct diagnostic models. Each machine learning method utilized its specific feature selection strategy to identify the best predictor. Five-fold cross-validation and Bayesian optimization were used to fine-tune hyperparameters to reduce overfitting and improve model performance. After classifier training, the average AUROC value of the five-fold cross-validation was compared with the AUROC values ​​of the training and test sets for comprehensive performance evaluation. DeLong's test was used to evaluate the statistical difference in AUROC values ​​between models, and the model with the highest average index was considered the best. The Youden index was used to determine the optimal cutoff point for positive class predictions in the training dataset.

[0074] Please refer to Table 3 for relevant parameter information for machine learning algorithms.

[0075] Table 3. Relevant parameter information of machine learning algorithms

[0076]

[0077]

[0078] The training set's performance on different evaluation metrics in Logistic Regression, Lasso Regression, Decision Tree, Random Forest, and XgBoost is as follows: Figure 1 As shown, the AUROC values ​​of the test set in Logistic Regression, Lasso Regression, Decision Tree, Random Forest, and XgBoost are as follows: Figure 2 As shown; using the training set, the AUROC values ​​of the diagnostic model in Logistic Regression, Lasso Regression, Decision Tree, Random Forest, and XgBoost are as follows. Figure 3 As shown.

[0079] The five machine learning algorithms mentioned above use different biomarkers when constructing diagnostic models. However, the results show that the Lasso regression diagnostic model constructed using GDNF, CST5, TGFB1, and MCP-4 has the best performance in predicting type 2 CRSwNP. Figure 1 As shown, the performance evaluation of the Lasso regression diagnostic model on the training set is as follows: AUROC = 0.91, precision = 0.86, balanced precision = 0.86, F-means = 0.86, NPV = 0.86, PPV = 0.86, PR AUC = 0.9, precision = 0.86, recall = 0.86, sensitivity = 0.86, and specificity = 0.86.

[0080] like Figure 2 As shown, the Lasso regression diagnostic model has an AUROC value of 0.91 on the test set. Figure 3 As shown, the five-fold cross-validation of the Lasso regression diagnostic model yielded a maximum average AUROC of 0.85, indicating that the Lasso regression diagnostic model possesses strong predictive power and stability. Furthermore, the AUROC values ​​for the 95% confidence intervals (training set: 0.83–0.98; test set: 0.79–1) are also shown. Figure 4 The results further support the reliability and statistical significance of the Lasso regression diagnostic model under different data partitions.

[0081] In addition, 30 additional CRSwNP patients (14 of whom had type 2 CRSwNP and 16 of whom had non-type 2 CRSwNP) were included as a validation set. These 30 CRSwNP patients were different from the 82 CRSwNP patients mentioned above.

[0082] The accuracy of the Lasso regression diagnostic model was further evaluated using a validation set of 30 CRSwNP patients, with an AUROC value of 0.92 (e.g., ...). Figure 4As shown, the AUROC value of the validation set with a 95% confidence interval is 0.82-1. The Lasso regression diagnostic model constructed above also has good diagnostic performance in the validation set, which further illustrates that the Lasso regression diagnostic model constructed above has high diagnostic efficacy and clinical diagnostic significance.

[0083] Furthermore, the calibration plot of the Lasso regression diagnostic model shows that the diagonal between the predicted probability and the actual frequency of occurrence is close to ( Figure 5 The result indicates that the predicted results are very consistent with the actual observed structure, and the model has good diagnostic capabilities.

[0084] The specific information regarding the Lasso regression diagnostic model constructed above is as follows:

[0085] Set α (i.e., alpha) = 1, and optimize the regularization parameter (λ) (e.g.) Figure 6 and Figure 7 As shown in the figure, four key predictive variables (i.e., biomarkers) were successfully screened: GDNF, MCP-4, TGFB1, and CST5. These variables are closely related to the predictions of the Lasso regression diagnostic model described above. This process first optimized the regularization parameters through cross-validation to ensure that the model maintains high predictive performance while preventing overfitting. Optimizing the regularization parameters helps balance model complexity and fit, especially by constraining the model through L1 (Lasso) and L2 (Ridge) regularization, thereby reducing unnecessary variable introduction and improving the model's generalization ability. These variables showed significant predictive power in the model, indicating that they have a close biological or statistical association with type 2 CRSwNP. In the Lasso regression diagnostic model, after adjusting for gender and age, the final formula of the constructed Lasso regression diagnostic model is: logit(π)=0.031+1.04×log2 A +0.149×log2 B -0.791×log2 C -0.997×log2 D +0.514×E-0.169×F;

[0086] Wherein, logit(π) is the predicted value of the diagnostic model; A is the relative expression level of GDNF in the nasal secretions of the CRSwNP subject; B is the relative expression level of MCP-4 in the nasal secretions of the CRSwNP subject; C is the relative expression level of TGFB1 in the nasal secretions of the CRSwNP subject; D is the relative expression level of CST5 in the nasal secretions of the CRSwNP subject; E is the age of the CRSwNP subject; F is the sex of the CRSwNP subject, where male is 1 and female is 0; the optimal cutoff value based on the Youden index is 0.53.

[0087] In summary, this invention provides a nasal secretion biomarker composition for diagnosing type 2 CRSwNP and its application. When the nasal secretion biomarker composition of this invention is used for the diagnosis of the intrinsic type of CRSwNP, nasal secretions are used as the test sample, eliminating the need for polyp biopsy. It is non-invasive, simple, convenient, and has high sensitivity and specificity. It can classify CRSwNP patients before surgery without damaging the mucosa, and accurately predict the intrinsic type of CRSwNP patients in advance. It is an ideal non-invasive method.

[0088] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. Use of a nasal secretion biomarker composition in constructing a diagnostic model for intrinsic type of chronic rhinosinusitis with nasal polyps; the nasal secretion biomarker composition comprises glial cell-derived neurotrophic factor, cystatin 5, transforming growth factor beta 1 and monocyte chemoattractant protein 4; the intrinsic type of chronic rhinosinusitis with nasal polyps diagnosis comprises distinguishing type 2 chronic rhinosinusitis with nasal polyps from non-type 2 chronic rhinosinusitis with nasal polyps; Based on Lasso regression, a chronic rhinosinusitis with nasal polyps intrinsic type diagnostic model is constructed, and the diagnostic model is logit(π) = 0.031 + 1.04xlog2 A + 0.149xlog2 B - 0.791xlog2 C - 0.997xlog2 D + 0.514xE - 0.169xF; wherein logit(π) is the predicted value of the diagnostic model; A is the relative expression of glial cell-derived neurotrophic factor in the nasal secretion of the chronic rhinosinusitis with nasal polyps to be tested; B is the relative expression of monocyte chemoattractant protein 4 in the nasal secretion of the chronic rhinosinusitis with nasal polyps to be tested; C is the relative expression of transforming growth factor beta 1 in the nasal secretion of the chronic rhinosinusitis with nasal polyps to be tested; D is the relative expression of cystatin 5 in the nasal secretion of the chronic rhinosinusitis with nasal polyps to be tested; E is the age of the chronic rhinosinusitis with nasal polyps to be tested; F is the gender of the chronic rhinosinusitis with nasal polyps to be tested, wherein male is 1 and female is 0; When the value of logit(π) is greater than or equal to 0.53, the intrinsic type of the chronic rhinosinusitis with nasal polyps to be tested is type 2 chronic rhinosinusitis with nasal polyps.

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