Adenoid hypertrophy grading diagnosis biomarker based on proteomics and application

Abstract

The invention belongs to the technical field of biology, and relates to a proteomics-based adenoid hypertrophy grading diagnosis biomarker and application thereof. Comprising BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A and MRPS28. The invention further discloses a preparation method of the high-efficiency and high-efficiency biomedical kit. The screened biomarker can accurately diagnose the severity of adenoid hypertrophy in a non-large-area invasive manner, and especially can accurately distinguish severe adenoid hypertrophy from moderate adenoid hypertrophy.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology and relates to a biomarker for grading diagnosis of adenoid hypertrophy based on proteomics and its application. Background Art

[0002] Disclosing the information of this background art section is only intended to increase the understanding of the overall background of the present invention, and is not necessarily regarded as an admission or any form of implication that this information constitutes the prior art already known to those of ordinary skill in the art.

[0003] Currently, the grading of adenoid hypertrophy (AH) in clinical practice is mainly based on the size of the adenoid observed by nasopharyngoscopy. The adenoid is divided into 4 degrees according to the percentage, and degrees III and IV are pathological obstructions clinically. This method mainly measures the percentage based on the relationship between the adenoid and the adjacent soft palate, which has certain subjectivity and requires the judgment of the diagnostician's experience. At the same time, this method is difficult to effectively reflect the compression and obstruction of the adjacent tissues by adenoid hypertrophy. Some clinical evidences show that simply describing the volume of the adenoid is not sufficient to reflect the actual severity of the disease and guide the intervention measures. However, due to its simple operation, it is currently the most widely used. In addition, adenoid hypertrophy and eustachian tube dysfunction are important related factors for secretory otitis media. Studies have found that it is not the size of the adenoid obstructing the posterior nasal aperture that is related to secretory otitis media, but the degree of compression between the adenoid and the eustachian tube orifice. Therefore, only grading the adenoid in children with secretory otitis media by the 4-degree percentage grading method may lead to over-intervention due to incomplete judgment. Therefore, there is currently a lack of biomarkers that can accurately grade and diagnose adenoid hypertrophy in children, which limits its clinical diagnosis and precise treatment. Biomarkers can be used for early diagnosis and increase the understanding of the disease mechanism of the present invention, which may lead to better preventive and therapeutic clinical decisions. Therefore, it is still urgently needed to find new biomarkers to evaluate the occurrence and severity of AH.

[0004] Omics analysis can effectively reveal the potential molecular mechanisms related to the occurrence of diseases, discover new biomarkers and drug targets, and ultimately provide further insights for formulating treatment strategies. Recently, a number of studies have shown that quantitative measurement of proteins in tissues using proteome sequencing technology is expected to discover new biomarkers and deepen the understanding of the complex process of AH development. However, currently, few studies focus on new biomarkers with diagnostic functions to achieve the grading diagnosis of AH patients and verify the accuracy of the biomarkers, and there is no report on biomarkers for adenoid grading diagnosis. Therefore, screening biomarkers for adenoid hypertrophy grading diagnosis and constructing a disease grading strategy will avoid a large number of unnecessary large-scale invasive diagnostic tests. Summary of the Invention

[0005] The present invention first collects specimens of adenoid hypertrophy tissues, then performs proteomic sequencing on all tissue samples, and randomly divides all tissue samples into a discovery set and a validation set. Based on the proteomic data of the discovery set, differential proteins that can significantly distinguish adenoid grades are screened to obtain disease grading biomarkers that can predict the severity of adenoid hypertrophy. Based on the proteomic data of the validation set, the discriminant ability of the candidate biomarkers to distinguish severe and moderate adenoid hypertrophy is verified. The verification shows that the biomarkers screened by the present invention can accurately and non-invasively diagnose the severity of adenoid hypertrophy over a large area, and in particular can accurately distinguish severe and moderate adenoid hypertrophy, providing a scientific basis for early prevention and treatment.

[0006] Based on the above research results, the present invention proposes a proteomics-based adenoid hypertrophy grading diagnosis biomarker and its application. Specifically, the technical solution of the present invention is as follows:

[0007] In a first aspect, a proteomics-based adenoid hypertrophy grading diagnosis biomarker includes at least two of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28.

[0008] In some embodiments, it includes at least three of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28.

[0009] In some embodiments, it includes at least 5 of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28.

[0010] In some embodiments, it includes at least 10 of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28.

[0011] In some embodiments, it includes at least 20 of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28.

[0012] In some embodiments, it includes all of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28. Research shows that when all 30 of the said proteins are selected as biomarkers for grading diagnosis of adenoid hypertrophy, their grading accuracy for severe and moderate adenoid hypertrophy is higher.

[0013] In a second aspect, there is provided an application of the above-mentioned proteomics-based biomarker for grading diagnosis of adenoid hypertrophy in the preparation of a product for detecting or predicting the degree of adenoid hypertrophy.

[0014] In some embodiments, the kit comprises substances for detecting the proteomics-based biomarker for grading diagnosis of adenoid hypertrophy.

[0015] In some embodiments, the product is a drug or a kit.

[0016] In specific embodiments, the kit comprises a biomarker list, a sample incubation solution, a buffer solution, a matrix solution, etc.

[0017] In some embodiments, the product detects whether the adenoid hypertrophy of a subject is severe or moderate by detecting a sample of the subject.

[0018] In specific embodiments, the subject is a human or non-human mammal. Preferably, the subject is a human.

[0019] In specific embodiments, the sample of the subject is a blood or tissue sample of the subject. The tissue sample may be an adenoid.

[0020] Adenoid hypertrophy is one of the main causes of childhood obstructive sleep apnea hypopnea syndrome, and the prevalence rate in children is relatively high. Therefore, in a third aspect, there is provided an application of a substance for detecting the proteomics-based biomarker for grading diagnosis of adenoid hypertrophy in the preparation of a reagent for detecting or predicting childhood obstructive sleep apnea hypopnea syndrome.

[0021] In a fourth aspect, there is provided an application of the proteomics-based biomarker for grading diagnosis of adenoid hypertrophy in constructing a prediction model for the degree of adenoid hypertrophy.

[0022] Preferably, the method for constructing the prediction model for the degree of adenoid hypertrophy comprises:

[0023] Collecting adenoid samples from patients with mild adenoid hypertrophy, patients with moderate adenoid hypertrophy, and patients with severe adenoid hypertrophy;

[0024] Detecting proteins in the adenoid samples of different types of patients, and screening out proteins as biomarkers, wherein the biomarkers include the above-mentioned proteomics-based biomarker for grading diagnosis of adenoid hypertrophy;

[0025] Randomly selecting adenoid samples from patients with moderate adenoid hypertrophy and patients with severe adenoid hypertrophy as a validation set, constructing a corresponding prediction model using a machine learning algorithm, and obtaining the critical value of the prediction model according to the ROC curve;

[0026] Evaluate the prediction effect of the prediction model, and select the prediction model with the best prediction effect as the prediction model for the degree of adenoid hypertrophy.

[0027] The beneficial effects of the present invention are as follows:

[0028] The research of the present invention shows that the biomarker provided by the present invention can be used for the diagnosis of the severity of adenoid hypertrophy, especially for distinguishing severe and moderate adenoid hypertrophy, and has the advantages of high accuracy, high sensitivity, etc. The classification test of the prediction efficiency (AUC) of severe and moderate adenoid hypertrophy by the ROC curve can reach 0.967. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0030] Figure 1 It is a volcano plot of differentially expressed proteins in the proteomic sequencing results of adenoid hypertrophy tissues; a is the comparison between severe adenoid hypertrophy patients (Discoveryset-High grade, DH) and mild patients (Discovery set-Low grade, DL), b is the comparison between DH and moderate patients (Discovery set-Moderate grade, DM), c is the comparison between DM and DL. The abscissa is the fold change (logarithmic transformation with base 1.1), and the ordinate is the significance P-value of the difference (logarithmic transformation with base 10). The red dots are up-regulated significantly differentially expressed proteins, the blue dots are down-regulated significantly differentially expressed proteins, and the gray dots are proteins with no differential changes.

[0031] Figure 2 It is a Venn diagram of differentially expressed proteins in the proteomic sequencing results; a is the Venn diagram of differentially expressed proteins with decreasing expression in the tissues of mild, moderate and severe adenoid hypertrophy patients, and the screening threshold is P <

[0032] 0.05 and FC < 0.909, b is the Venn diagram of differentially expressed proteins with increasing expression in the tissues of mild, moderate and severe adenoid hypertrophy patients, and the screening threshold is P < 0.05 and FC > 1.1.

[0033] Figure 3 It is the expression of the intersection proteins of differentially expressed proteins in the three groups of DH vs DL, DH vs DM and DM vs DL in each group of DH, DM and DL, and the screening threshold is P < 0.05 and |FC| > 1.1.

[0034] Figure 4For validating the performance of validation cohort VH (Validation set - Highgrade) vs VM

[0035] (Validation set - Moderategrade). Specific implementation manners

[0036] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with specific embodiments.

[0037] Example 1: Sample collection and information acquisition

[0038] A total of 50 participants were selected. Inclusion criteria: ① Meeting the diagnostic criteria for childhood adenoid hypertrophy; ② Aged 4 to 12 years old; ③ Having a medical history of no less than three months; ④ Voluntarily participating in this study. Exclusion criteria: ① Having complications including tonsillitis, suppurative otitis media, etc.; ② Those with obvious mental disorders; ③ Those with posterior choanal space-occupying lesions; ④ Those with congenital developmental abnormalities. The evaluation index of adenoid hypertrophy degree is based on the "Diagnosis and Treatment Guidelines for Obstructive Sleep Apnea in Chinese Children (2020)". According to the size and shape of the adenoids under nasopharyngofibroscopy, the adenoid hypertrophy degree is divided into mild obstruction, moderate obstruction, and severe obstruction. Mild obstruction: posterior choanae < 60%; moderate obstruction: posterior choanae 60% - 79%; severe obstruction: posterior choanae 80% - 100%. The participants included 8 mild adenoid hypertrophy patients, 16 moderate patients, and 26 severe patients. The clinical doctor performed adenoidectomy on the patients, and the obtained specimens were placed in tissue preservation solution for cryopreservation and transported to the sequencing company for proteomic sequencing. Among them, 8 mild patients, 10 moderate patients, and 14 severe patients were used as the discovery set; 6 moderate patients and 12 severe patients were used as the validation set.

[0039] Example 2: Sample processing and database establishment

[0040] First, an equal amount of all samples was mixed, followed by sample enzymatic digestion and HPRP fractionation. After data dependent acquisition (DDA) mass spectrometry data collection and database searching for identification, a spectral library was constructed using Spectronaut pulsar X software as the database for subsequent data independent acquisition (DIA) quantification. The mass spectrometry experimental analysis process mainly includes steps such as protein extraction, peptide enzymatic digestion, chromatographic fractionation, liquid chromatography - tandem mass spectrometry (LC - MS / MS) DDA data collection, and database retrieval. Then, after each sample was independently prepared and the protein was enzymatically digested, DIA (data independent acquisition) analysis was performed on the machine separately to obtain the DIA raw files of all samples.

[0041] Example 3: Screening of differentially expressed proteins

[0042] (1) Pretreatment of proteomic sequencing results

[0043] Data preprocessing mainly includes quality control analysis and qualitative and quantitative result analysis. After performing quality control analysis on the raw data, the obtained DIA raw files were imported into Spectronaut Pulsar X for analysis to obtain the total qualitative and quantitative results of all samples.

[0044] (2) Screening of differentially expressed proteins

[0045] To screen for differentially expressed proteins, differential screening was performed on the experimental data of the discovery set (8 mild patients, 10 moderate patients, and 14 severe patients). In the screening of significantly different proteins, the criteria were a fold change (FC) > 1.1 - fold (up - regulation greater than 1.1 - fold or down - regulation less than 0.909 - fold) and a P value < 0.05 (T - test) to obtain the number of up - regulated and down - regulated proteins between the comparison groups. To display the significant differences in proteins between the comparison groups, a volcano plot was drawn with two factors: the fold change in expression and the P value (T - test) of the proteins in the comparison groups, as shown in Figure 1 shown. To find the intersection proteins with differential expression in each group, a Venn diagram was drawn for the differentially expressed proteins in Figure 1 as shown in Figure 2 shown. The differential protein data of each participant in the discovery set are shown in Tables 1 - 4. To more intuitively display the expression differences of the differentially expressed proteins between different groups, a box plot was used to display the differentially expressed proteins, as shown in Figure 3 shown.

[0046] Table 1

[0047]

[0048] Table 2

[0049]

[0050] Table 3

[0051]

[0052] Table 4

[0053]

[0054] Example 4. Prediction of Adenoid Hypertrophy Degree

[0055] The ROC curve is an important indicator for measuring the performance of classification models. To verify the performance of the biomarkers screened in Example 3 in the diagnosis and grading of adenoid hypertrophy, the Monte Carlo cross-validation (MCCV) and balanced subsampling were used to generate the ROC curve, and the accuracy of differential gene diagnosis was understood through the AUC (Area Under Curve). For the validation set (12 severely hypertrophied samples and 6 moderately hypertrophied samples), multiple classification models containing important features were respectively attempted to be constructed and verified. The above process was repeated multiple times, and the performance indicators and confidence intervals of each model were calculated to obtain a comprehensive evaluation of the model performance. The proteomics data was used to distinguish different adenoid hypertrophy patients, and the ROC curve was used for classification test to establish the DH vs DM model.

[0056] The results are as Figure 4 shown. The prediction efficiency (AUC) of severe and moderate adenoid hypertrophy is 0.967. These proteins mainly include BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28.

[0057] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A proteomics-based biomarker for grading and diagnosing adenoid hypertrophy, characterized in that: Includes at least 2 of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A and MRPS28.

2. The proteomics-based biomarker for grading and diagnosing adenoids hypertrophy according to claim 1, characterized in that: including at least 3 of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28; or, comprising at least 5 of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28; or, comprising at least 10 of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28; or, including at least 20 of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A, and MRPS28; Or, including all of the following proteins: BET1, RPS6KA4, HSPA1A, DPP4, OSTC, MRPL21, CMC4, RPL37, PSPH, PTPRS, MFAP5, KIAA1143, EMC4, ZNF460, ABHD13, SH3BP5L, GPC3, DNAJC19, DTD1, H3-7, MKNK1, MFAP2, WRAP73, IGLV4-69, P4HA1, S100A9, QSOX2, RNF25, RMND5A and MRPS28.

3. Use of the proteomics-based adenoids hypertrophy grading diagnostic biomarker according to claim 1 or 2 in the preparation of a product for detecting or predicting the degree of adenoids hypertrophy.

4. The use according to claim 3, characterized in that: The kit includes substances for detecting the proteomics-based adenoids hypertrophy grading diagnostic biomarkers.

5. The use according to claim 3, characterized in that: The product is a medicine or a kit.

6. The use according to claim 5, characterized in that: The kit includes one or more of a biomarker list, a sample incubation solution, a buffer solution, and a matrix solution.

7. The use according to claim 3, characterized in that: The product detects whether the subject's adenoids hypertrophy is severe or moderate by testing the subject's sample.

8. The use according to claim 7, characterized in that: The subject is a human or non-human mammal; Alternatively, the subject sample is a blood or tissue sample from the subject.

9. Use of a substance for detecting the proteomics-based adenoids hypertrophy grading diagnostic biomarker according to claim 1 or 2 in the preparation of a reagent for detecting or predicting obstructive sleep apnea-hypopnea syndrome in children.

10. Application of a proteomics-based adenoids hypertrophy grading diagnostic biomarker in constructing a predictive model for the degree of adenoids hypertrophy.

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