Diagnostic marker for glucocorticoid-resistant allergic rhinitis and application thereof

Abstract

The application relates to the field of biomedical technology, and discloses a glucocorticoid-resistant allergic rhinitis diagnostic marker and application thereof.The biological marker comprises a RHEBL1 gene.The application finds and proves that the RHEBL1 gene can be used as a specific diagnostic marker of glucocorticoid-resistant allergic rhinitis, fills the technical blank of lacking a molecular diagnostic marker of the disease, and provides a new molecular target for early diagnosis of glucocorticoid-resistant allergic rhinitis.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to a diagnostic marker for glucocorticoid-resistant allergic rhinitis and its application. Background Technology

[0002] Allergic rhinitis (AR) is a common chronic inflammatory respiratory disease characterized by nasal itching, sneezing, runny nose, and nasal congestion. It not only severely affects patients' sleep quality, learning, and work efficiency, but can also trigger complications such as asthma, sinusitis, and conjunctivitis, placing a heavy burden on patients' families and the social healthcare system. Nasal corticosteroids are first-line drugs for treating allergic rhinitis, possessing strong anti-inflammatory effects and effectively relieving patients' clinical symptoms. However, some patients do not respond well to corticosteroid treatment, exhibiting corticosteroid resistance, and treating these patients presents a clinical challenge.

[0003] Currently, the clinical diagnosis of glucocorticoid-resistant allergic rhinitis mainly relies on the evaluation of the patient's clinical efficacy of glucocorticoid treatment. There is a lack of specific molecular diagnostic markers, leading to diagnostic delays and a high degree of subjectivity. Because it is impossible to quickly and accurately identify glucocorticoid-resistant patients before treatment, inappropriate clinical treatment plans can easily be selected, delaying the condition and increasing the patient's medical burden. Therefore, screening and identifying molecular markers that can specifically diagnose glucocorticoid-resistant allergic rhinitis, and developing rapid, non-invasive diagnostic methods and kits, are of significant clinical importance for achieving individualized and precise treatment of allergic rhinitis.

[0004] RHEBL1 (Ras homolog enriched in brain like 1, also known as Rheb2 or GTPaseRhebL1) is an important member of the Rheb branch of the Ras superfamily of small GTPases. Its human gene is located on chromosome 12q13.12, containing 8 exons and 7 introns, with an Entrez Gene ID of 121268. It encodes a protein composed of 183 amino acids with a molecular weight of approximately 20.7 kDa. RHEBL1 participates in the regulation of multiple core signaling pathways, including mTORC1 and NF-κB, and is closely related to cell proliferation, metabolism, and inflammatory responses. However, there are currently no research reports on the association between the RHEBL1 gene and glucocorticoid-resistant allergic rhinitis, nor have any technical solutions been found that suggest it could be used as a diagnostic biomarker for this disease. Summary of the Invention

[0005] In view of this, the present invention proposes a diagnostic biomarker for glucocorticoid-resistant allergic rhinitis and its application. The biomarker is the RHEBL1 gene, which can achieve non-invasive, rapid and accurate diagnosis of glucocorticoid-resistant allergic rhinitis, providing a basis for individualized clinical treatment.

[0006] The technical solution of the present invention is implemented as follows: In a first aspect, the present invention provides a diagnostic biomarker for glucocorticoid-resistant allergic rhinitis, the biomarker including the RHEBL1 gene.

[0007] Secondly, the present invention provides the application of a diagnostic marker for glucocorticoid-resistant allergic rhinitis in the preparation of products for diagnosing glucocorticoid-resistant allergic rhinitis.

[0008] Based on the above technical solutions, preferably, the glucocorticoid is mometasone furoate nasal spray.

[0009] Thirdly, the present invention provides a kit for diagnosing glucocorticoid-resistant allergic rhinitis, the kit comprising a reagent for detecting the expression level of the RHEBL1 gene in biological samples.

[0010] Based on the above technical solutions, preferably, the reagents include primers that can specifically amplify RHEBL1.

[0011] Based on the above technical solutions, preferably, the biological sample is nasal brush cells.

[0012] Fourthly, the present invention provides a chip for diagnosing glucocorticoid-resistant allergic rhinitis, the chip comprising a reagent for detecting the expression level of the RHEBL1 gene in a biological sample.

[0013] The diagnostic marker for glucocorticoid-resistant allergic rhinitis of the present invention and its application have the following advantages over the prior art: This invention discovers and confirms that the RHEBL1 gene can serve as a specific diagnostic marker for glucocorticoid-resistant allergic rhinitis, filling the technological gap of lacking molecular diagnostic markers for this disease and providing a new molecular target for the early diagnosis of glucocorticoid-resistant allergic rhinitis. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 The flowchart shows the screening and validation process for the RHEBL1 gene. Figure 2 VAS score before treatment; Figure 3 The results of the difference analysis are shown in a graph; Figure 4 The WGCNA results are presented here (only modules with significant differences are shown, and *** indicates p<0.001). Figure 5 Venn diagram for screening differentially expressed genes in nasal brush cell transcriptomics; Figure 6 This is a plot showing the fit of the lasso model. Figure 7 The genome maps of the top 4 genes by priority for screening; Figure 8 The image shows the qPCR results for RHEBL1. Figure 9 The receiver operating characteristic (ROC) curve is shown. Detailed Implementation

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] Screening and validation of the RHEBL1 gene 1. Research Subjects Thirty-two patients with allergic rhinitis who visited the Department of Otolaryngology-Head and Neck Surgery at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology between March and December 2024 were selected. The inclusion criteria are as follows: (1) Age 18-65 years old, gender not limited; (2) Meets the diagnostic criteria of the Chinese Guidelines for the Diagnosis and Treatment of Allergic Rhinitis (2022 Revised Edition), is perennial, persistent, moderate to severe allergic rhinitis, has a history of ≥1 year, and has symptom attacks for ≥12 weeks per year and ≥4 days per week; (3) At least two items in the Total Nasal Symptom Score (TNSS) are ≥2 points, and the sum of the total scores is ≥6 points; Note: The TNSS (Total Nasal Symptom Score) is a standardized tool that assesses the severity of nasal symptoms in patients with allergic rhinitis by evaluating the intensity of four nasal symptoms (nasal congestion, runny nose, nasal itching, and sneezing). Each symptom is scored from 0 to 3 (0: no symptoms; 1: mild (present but not bothersome); 2: moderate (obvious and affecting daily life); 3: severe (unbearable and significantly interferes with activities)). The daily total score ranges from 0 to 12, with higher scores indicating more severe symptoms.

[0018] (4) Skin prick test (SPT) and / or serum specific IgE antibody test, showing positive results for at least one perennial allergen (dust mites, cockroaches, animal dander, etc.).

[0019] The exclusion criteria are as follows: (1) Individuals who have had upper or lower respiratory tract infections, fever, or other systemic symptoms within the past two weeks; (2) Patients with concurrent acute / chronic sinusitis, dry rhinitis, atrophic rhinitis, severe nasal septum deviation, or bronchial asthma; (3) Pregnant women, breastfeeding women, or those who plan to have children in the near future; (4) Patients with severe systemic diseases who are deemed unsuitable for participation in this study by a physician; (5) Patients who have participated in other drug clinical trials within the past 3 months.

[0020] All participants signed written informed consent forms and completed questionnaires on baseline clinical data and nasal symptoms.

[0021] 2 Sample Collection Nasal brush cell samples were collected from all patients. The collection method was as follows: the patient sat with his head slightly tilted back, and a sterile nasal brush was gently inserted into the middle nasal passage of the nasal cavity. After rotating it 3-5 times, the brush was removed and immediately placed into a nuclease-free centrifuge tube containing 1 mL of pre-cooled Trizol lysis buffer. The tube was then stored on ice.

[0022] 3. Treatment and Grouping The enrolled patients underwent a two-week standardized treatment program using mometasone furoate nasal spray (trade name: Nasonex®, manufacturer: Merck Sharp & Dohme). The administration method was as follows: one spray (each spray contains 50 μg of mometasone furoate) into each nostril once daily. Before use, the nasal cavity was cleaned, and the spray nozzle was directed towards the outer wall of the nasal cavity to avoid direct irritation of the nasal septum. Regular, uninterrupted use was maintained throughout the treatment program.

[0023] The degree of decrease in TNSS score after treatment was used as an indicator of efficacy. Symptom improvement was assessed using the TNSS questionnaire two weeks after treatment. Hormone-sensitive allergic rhinitis group (CAR group): TNSS improvement rate ≥30%, 20 cases included.

[0024] Hormone-insensitive allergic rhinitis group (RAR group): TNSS improvement rate <30%, 20 cases included.

[0025] Note: TNSS improvement rate = (TNSS before treatment - TNSS after treatment) / TNSS after treatment × 100%.

[0026] Table 1 Treatment Efficacy

[0027] From Table 1 and Figure 2 It can be seen that there were no significant differences between the two groups of patients in terms of age, sex ratio, medical history, intensity of prick test, family history, and VAS score (a scale used to assess the severity of allergic rhinitis symptoms) before treatment, and the influence of these irrelevant variables can be excluded.

[0028] 4. Transcriptomics sequencing Total RNA was extracted from nasal brush cells of two groups of patients. The RNA concentration, purity and integrity (RIN≥7.0) were detected by Nanodrop and Agilent 2100 Bioanalyzer. mRNA libraries were constructed and PE150 sequencing was performed on the Illumina NovaSeq 6000 platform. The sequencing depth of each sample was ≥6 Gb.

[0029] 5. Data Processing and Gene Screening (1) Data preprocessing and multidimensional statistical analysis The sequencing data were preprocessed using R language version 4.4.2 with Pareto-scaling to eliminate differences in data units. Based on this, principal component analysis (PCA) was used for multidimensional statistics to simplify the data dimensions and uncover its potential structure.

[0030] (2) Differential gene screening The core principle of DEG (Differential Expression Characteristics) differential analysis is based on peptide abundance expression data from samples with different phenotypes (two groups of AR-related samples in this study). Statistical tests are used to quantify the differences in peptide expression levels between groups, and peptides with statistically significant differences in expression levels are screened out. In the early stages of analysis, peptide abundance data undergoes standardization and outlier removal to eliminate interference from sample heterogeneity and detection errors. Subsequently, dedicated analysis packages such as limma in R software are used to calculate the fold change (FC) of peptide expression between groups. Combined with hypothesis testing, p-values ​​and false detection rates (FDR) are calculated. Reasonable thresholds (e.g., FDR < 0.05, |log2FC| > 1) are set to remove peptides with no or weak differences, ultimately obtaining the DEG set.

[0031] In this embodiment, genes that are significantly upregulated must meet the following criteria: fold change (FC) > 1.5 and P < 0.05; genes that are significantly downregulated must meet the following criteria: FC < 1 / 1.5 and P < 0.05. The differentially regulated gene set is obtained (see...). Figure 3 ).

[0032] Figure 3 This study showcases differentially expressed genes between patients with allergic rhinitis who responded to and did not respond to the treatment with sorbitol. Significantly downregulated genes (lower expression in the effective group): CDHR4, EFCAB12, RFX2, CFAP74, PTRH1, RABL2B, CFAP46, MOK, etc. Significantly upregulated genes (higher expression in the effective group): DOCK2, PTPRC, COTL1, RCSD1, NCKAP1L, LCP1, CYBB, RASSF2, THEMIS2, JAMIL, AIF1, CD53, CORO1A, etc.

[0033] (3) Weighted gene co-expression network analysis The core principle of WGCNA (Weighted Gene Co-expression Network Analysis) is based on the assumption that "functionally similar molecules (peptides in this study) have similar expression patterns." By constructing a weighted co-expression network, it can discover co-expression modules and core features that are closely related to the target trait (such as AR trait). Essentially, it reduces the dimensionality of high-dimensional expression data and focuses on a set of biologically significant molecules.

[0034] The key to its core logic lies in "weighting" and "scale-free networks": Unlike the hard threshold of ordinary co-expression analysis, WGCNA converts the Pearson correlation coefficient between peptides into a weighted adjacency matrix by screening for the optimal soft threshold. This results in peptides with higher expression correlation having larger connection weights, and vice versa. At the same time, it ensures that the network conforms to the scale-free characteristics (a few nodes have extremely high connectivity, and most nodes have low connectivity), which is consistent with the natural characteristics of biomolecular networks and reduces interference from spurious associations.

[0035] Subsequently, the topological overlap between peptides was quantified using the Topological Overlap Matrix (TOM) (taking into account both expression correlation and network connectivity characteristics) to reduce the impact of noise. Then, through hierarchical clustering and dynamic tree segmentation, peptides with similar expression patterns and high topological overlap were divided into different co-expression modules. Finally, Spearman correlation analysis between the principal components of the modules and AR traits was used to screen out key modules that are significantly related to the target traits.

[0036] This embodiment uses the R software "WGCNA" package to perform weighted gene co-expression network analysis (WGCNA). First, the Pearson correlation coefficients of peptide abundance expression in eight samples were calculated, and a suitable soft threshold was selected to construct a scale-free co-expression network. Then, an adjacency matrix was generated and converted into a topological overlap matrix (TOM). Through hierarchical clustering and dynamic tree segmentation, genes with similar co-expression relationships were grouped, and the modules were visualized using a dendrogram. The correlation between the module and the AR was determined based on the principal components of the module and the Spearman correlation coefficients between the two groups of samples. (See...) Figure 4 ) (4) Biomarker screening In high-throughput omics research, DEG differential analysis can screen for features (such as genes and metabolites) with significant differences in expression levels between different phenotypes (e.g., disease vs. normal, different treatment groups). WGCNA weighted gene co-expression network analysis can uncover co-expression modules and core features closely related to the target trait. The intersection of these two methods contains both differential information and co-expression correlations, and has initially eliminated a large number of irrelevant and redundant features. However, it may still have problems such as high dimensionality and multicollinearity, and cannot be directly used as biomarkers or key influencing factors. Therefore, it is necessary to further use the LASSO algorithm to accurately screen the intersection data, and by constructing a robust regression model, screen out key common features that significantly contribute to classification, and finally complete the screening and validation of biomarkers or key influencing factors.

[0037] This study used the glmnet package in R software to execute the LASSO algorithm to construct a regression model. This package is a classic tool for regularized regression of high-dimensional data, which can efficiently implement various regularization methods such as LASSO and ridge regression, and supports cross-validation to select the optimal parameters, perfectly adapting to the characteristics of "high-dimensional, small-sample" intersection data. Preprocessing of the data is necessary before analysis: the intersection features of DEG and WGCNA are used as independent variables (e.g., the expression matrix of the intersection genes), and the classification phenotypes of the research subjects (e.g., disease / normal, pathological stage, etc.) are used as dependent variables. The independent variables are standardized to eliminate the influence of dimensions and avoid model bias caused by differences in feature value ranges. Simultaneously, the dependent variable is binary coded (e.g., case group is set to 1, control group to 0) to meet the input requirements of the LASSO binary regression model (results are shown in [see table]). Figure 5 ).

[0038] The core of model construction lies in setting an appropriate regularization parameter λ, whose value directly determines the penalty strength, thus affecting the feature selection results and the model's generalization ability. If λ is too small, the penalty term becomes ineffective, the model degenerates into ordinary least squares regression, and overfitting is likely to occur, failing to remove redundant features. If λ is too large, the penalty strength is too strong, leading to the removal of a large number of effective features, making the model too simple and losing its practical interpretive meaning. Therefore, this study uses 10-fold cross-validation to determine the optimal λ: the preprocessed dataset is randomly divided into 10 subsets, one subset is used as the validation set, and the remaining 9 subsets are used as the training set. A series of non-negative λ values ​​are used to construct the LASSO model, and the prediction error (mean squared error) of the model on the validation set corresponding to each λ is calculated. Finally, the λ that minimizes the prediction error is selected as the optimal regularization parameter, balancing the model's fitting accuracy and generalization ability.

[0039] A LASSO regression model was constructed based on the optimal λ. Utilizing the sparsity-induced properties of its L1 norm penalty term, the regression coefficients of the intersection features were compressed: features with weak categorical contributions and low correlation with the dependent variable had their coefficients compressed to 0, considered redundant and removed; features with significant categorical contributions and high correlation with the dependent variable retained their non-zero coefficients, representing the key common features crucial for classifying the research subjects. These key features possess both the differential expression characteristics of DEG and belong to the core co-expression features related to the target trait in WGCNA, balancing specificity and relevance, and providing a core candidate set for subsequent validation of biomarkers or key influencing factors.

[0040] Figure 6 This is a plot showing the fit of the lasso model. Figure 7 The genome map shows the top 4 genes selected based on priority. Figure 7 It can be seen that the top 4 priority genes are RHEBL1, TUBA4B, and CTXN1 with WDR90, respectively. Among them, the coefficient of RHEBL1 is the highest, far higher than that of other genes, which proves that RHEBL1 is the most critical biomarker for predicting glucocorticoid resistance, and its importance far exceeds that of other candidate genes.

[0041] 6. qPCR validation Total RNA was extracted from nasal brush cells of another 24 patients with allergic rhinitis (12 sensitive and 12 resistant types).

[0042] Nasal brush cell RNA extraction procedure: The entire RNA extraction process from the patient's nasal brush cells must be performed in a nuclease-free environment. All consumables are treated with DEPC water and sterilized at high temperature. Reagents are pre-cooled, and the entire operation is performed in an ice bath. The specific steps are as follows: Take the nasal brush sample collected clinically and immediately place it into a nuclease-free centrifuge tube containing 1 mL of pre-chilled Trizol lysis buffer. Gently squeeze the nasal brush handle and repeatedly scrape the inner wall of the centrifuge tube to ensure that the cells on the nasal brush are fully detached into the lysis buffer. Then vortex the centrifuge tube for 30 seconds to completely disperse the cells and incubate at room temperature for 5 minutes to complete the initial cell lysis. Add 200 μL of chloroform to the centrifuge tube, vortex vigorously for 15 seconds to mix thoroughly, and incubate at room temperature for 3-5 minutes to allow the lysis buffer and chloroform to separate into layers. Place the centrifuge tube in a 4°C high-speed refrigerated centrifuge. Centrifuge at 12000×g for 15 min. After centrifugation, the liquid separates into three layers: an upper colorless aqueous phase containing RNA, a middle layer of protein precipitation, and a lower red organic phase. Carefully aspirate the upper aqueous phase (approximately 400-500 μL, avoiding the middle and organic phases to prevent protein contamination) using a nuclease-free pipette tip and transfer it to a new nuclease-free centrifuge tube. Add an equal volume of pre-chilled isopropanol to the resulting aqueous phase, gently invert the centrifuge tube to mix, and incubate at 4°C for 10 min to allow the RNA to fully precipitate. Place the centrifuge tube back into a 4°C high-speed refrigerated centrifuge at 12000×g for 15 min. Centrifuge at 7500×g for 10 min. A white, flocculent RNA precipitate will appear at the bottom of the centrifuge tube. Carefully discard the supernatant, avoiding aspirating the precipitate. Add 1 mL of pre-chilled 75% nuclease-free ethanol (prepared with DEPC-treated water) to the precipitate, gently vortex the centrifuge tube to wash the precipitate, and centrifuge at 7500×g for 5 min at 4°C. Discard the supernatant and repeat the ethanol washing step once to thoroughly remove residual salt ions and proteins. After discarding the supernatant, place the centrifuge tube opening on ice and let it stand at room temperature for 5-10 min to allow the precipitate to air dry naturally (avoid over-drying, otherwise it will cause...). RNA is difficult to dissolve; add 20-50 μL of nuclease-free water to the dried RNA precipitate, gently pipette to dissolve the precipitate, then incubate the centrifuge tube in a 55-60℃ water bath for 10 min to promote complete RNA dissolution, and then cool it in an ice bath; the dissolved RNA sample can be immediately tested for concentration and purity using a nucleic acid quantification instrument (A260 / A280 ratio of 1.8-2.1 is acceptable, A260 / A230 ratio ≥2.0), or add RNase inhibitor and aliquot at -80℃ to avoid repeated freeze-thaw cycles that could lead to RNA degradation.

[0043] After RNA extraction, the relative expression level of the RAB36 gene was determined using qPCR. The qPCR procedure is as follows: Strict adherence to nucleic acid handling protocols is required for RHEBL1 gene qPCR detection. The entire procedure must be performed in a nuclease-free environment. The steps are as follows: Take an appropriate amount of RNA from the extracted sample that has been tested for concentration and purity. According to the reverse transcription kit instructions, mix RNA, reverse transcription buffer, reverse transcriptase, dNTPs, nuclease-free water, and random / oligo (dT) primers in the specified proportions. Perform reverse transcription on a PCR instrument (generally incubate at 42℃ for 30-60 min, then heat at 85℃ for 5-10 min to inactivate the reverse transcriptase) to obtain the first strand of cDNA. The cDNA product can be appropriately diluted with nuclease-free water for later use.

[0044] The qPCR reaction system was then prepared. According to the requirements of the real-time PCR kit, 10 μL of 2×SYBR Green qPCR premix, RHEBL1 gene-specific upstream and downstream primers (final concentration 0.4 μmol / L), and cDNA template (adjust the amount added according to the sample concentration to avoid excessive template inhibiting the reaction, specifically 2 μL) were added to the nuclease-free PCR tube. Nuclease-free water was added to the total reaction system (20 μL). The mixture was gently mixed throughout the process to avoid generating bubbles. A template-free control (NTC, with nuclease-free water instead of cDNA template) and an internal control gene (GAPDH) were also set up. Three technical replicates were set up for each sample to ensure the reliability of the results.

[0045] RHEBL1 gene-specific upstream and downstream primers: Forward Primer(5'->3'): GCTACGATCCTACAGTGGAGA; Reverse Primer(5'->3'): GGTGACAGAATACACAAGCACA.

[0046] After the system is prepared, place the PCR tubes into the real-time PCR instrument and set the reaction program: first perform pre-denaturation (95℃ for 30s~5min to completely denature the cDNA and activate the Taq enzyme), then perform PCR amplification cycles (40 cycles), each cycle including denaturation at 95℃ for 10~15s, primer annealing (annealing temperature is adjusted according to the Tm value of RHEBL1 primers, the annealing temperature for the above primer concentrations is 60℃, incubation for 20~30s), extension, and collect SYBR Green fluorescence signals in real time during the amplification process.

[0047] After the amplification cycle, melting curve analysis was performed. The program was set to 95℃ for 15s, 60℃ for 1min, followed by a slow temperature increase to 95℃ while continuously acquiring fluorescence signals to verify the specificity of the amplification products and ensure the absence of nonspecific amplification and primer dimer formation. After the reaction, the instrument detection data was saved, and the relative expression level of the RHEBL1 gene in each sample was calculated using the 2^(-ΔΔCt) method with the qPCR instrument's accompanying analysis software. Standardized analysis was performed in conjunction with internal reference gene data. Simultaneously, the melting curve results, the coefficient of variation of Ct values ​​for technical replication, and the fluorescence signal without template control were verified to ensure the validity of the experimental results.

[0048] Figure 8 The results showed that the expression level of the RHEBL1 gene was significantly higher in resistant patients than in sensitive patients, and the difference was statistically significant (P<0.05). Figure 9 ROC curve analysis showed that the AUC value of the RHEBL1 gene was 0.8083. RHEBL1 had a larger AUC than TUBA4B, indicating that RHEBL1 has stronger screening efficacy and confirms that it can be used as a specific diagnostic marker for glucocorticoid-resistant allergic rhinitis.

[0049] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A diagnostic marker for glucocorticoid-resistant allergic rhinitis, characterized in that: The biomarker includes the RHEBL1 gene.

2. The use of the diagnostic marker for glucocorticoid-resistant allergic rhinitis as described in claim 1 in the preparation of products for diagnosing glucocorticoid-resistant allergic rhinitis.

3. The application as described in claim 2, characterized in that, The glucocorticoid mentioned is mometasone furoate nasal spray.

4. A kit for diagnosing glucocorticoid-resistant allergic rhinitis, characterized in that, The kit includes reagents for detecting the expression level of the RHEBL1 gene in biological samples.

5. The kit according to claim 4, characterized in that, The reagents include primers that specifically amplify RHEBL1.

6. The kit according to claim 4, characterized in that, The biological sample was nasal brush cells.

7. A chip for diagnosing glucocorticoid-resistant allergic rhinitis, characterized in that, The chip includes reagents for detecting the expression level of the RHEBL1 gene in biological samples.

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