CN117949663A - Screening method and application of novel biomarker for diagnosis of preschool childhood asthma - Google Patents

Abstract

The invention provides a novel biomarker screening method for preschool childhood asthma diagnosis and application thereof, belongs to the field of biological medicine, screens LTBP1, APOA1 and IGFALS proteins as biomarkers for preschool childhood asthma diagnosis, and takes an expression level index of a composition as a basis for judging preschool childhood asthma, so that the accuracy of preschool childhood asthma diagnosis can be improved, the diagnosis process is simplified, and important functions are played in childhood asthma diagnosis and treatment.

Description

Screening method and application of novel biomarker for diagnosis of preschool childhood asthma

Technical Field

The invention belongs to the technical field of biology, and particularly relates to a novel biomarker screening method for preschool childhood asthma diagnosis and application thereof.

Background

Asthma is the most common chronic disease in children and is characterized by airway hyperresponsiveness, reversible airway obstruction and chronic airway inflammation, with the incidence of asthma and hospitalization being higher in preschool children than in the elderly. The pulmonary function track is established in childhood and airway remodeling associated with asthma occurs 3 years ago, therefore preschool children are a critical period for asthma intervention, and accurate identification and diagnosis of preschool children's asthma may help to understand asthma symptoms and improve treatment compliance. Because asthma symptoms are recurrent and fluctuating, their diagnosis in children remains challenging and few effective independent diagnostic tests are available. Recently, the national institute of health and care and Asthma-12 global initiative have proposed diagnostic algorithms that combine available tests such as spirometry, bronchodilator reversibility tests, bronchial excitation tests to measure bronchial hyperreactivity, exhaled nitric oxide fraction (Feno) and allergy tests. However, the diagnostic accuracy of these algorithms for preschool childhood asthma is not yet established.

Thus, there is an urgent need for better methods of diagnosing asthma. To achieve this goal, new studies are needed to identify biomarkers that can distinguish between asthma and healthy preschool children, which can then be incorporated into diagnostic algorithms. In asthma, certain proteins associated with airway obstruction and inflammation are produced in tissue cells and secreted into the circulatory system. Proteomics is therefore a promising approach to identify potential diagnostic biomarkers for preschool childhood asthma. While proteomics has been used in previous studies to analyze blood samples from asthmatic patients, most use Data Dependent Acquisition (DDA) -Mass Spectrometry (MS) and focus on elderly children or adults. The Data Independent Acquisition (DIA) -MS is a time parallel acquisition method, has better consistency and reproducibility than DDA, and is particularly suitable for proteomics research of large sample groups.

Previous studies have identified several asthma protein biomarkers. For example, nieto-Fontarigo et al found serum IGFALS to be a biomarker of allergic asthma in adults. Zamora et al determined that sialyl Interleukins (IL) -8 and IL-10 are biomarkers of childhood asthma. Another study showed that sputum LXA4 could differentiate between children with severe asthma and children with intermittent asthma. Furthermore, by proteomic analysis, C7, C3, C4, α -1-antitrypsin, PDE7, arginase, UK 16-binding protein, phospholipase D and cyclooxygenase were found to accumulate to varying degrees in serum of bronchial asthma patients and healthy individuals. However, few studies have focused on biomarkers of preschool childhood asthma, although diagnosis of asthma is more difficult in this population.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provides a novel biomarker screening method for diagnosing asthma of preschool children, and the Orbitrap Exploris platform is used for carrying out serum proteome analysis based on DIA-MS on the preschool children. Key signaling pathways and biomarker proteins associated with preschool childhood asthma were determined in combination with bioinformatics and statistical analysis.

Based on the technical thought, the technical scheme adopted by the invention is as follows:

the invention provides a novel biomarker screening method for preschool childhood asthma diagnosis, which comprises the following steps:

step 1: analyzing serum proteome characteristics of a subject by adopting independent data acquisition mass spectrum, and determining differential expression proteins between an acute asthma group and a healthy control group after data filtering;

Step 3: a total of 50 differential expression proteins exist between the acute asthma group and the healthy control group, the potential actions of the differential expression proteins on asthmatic individuals and healthy children are analyzed by adopting a supervised orthogonal partial least squares discriminant analysis model, and MMP14, ABHD12B, LTBP1, APOA1, ANG and IGFALS proteins are screened out to be used as biomarkers for distinguishing asthmatic children and healthy children;

Step 4: six biomarkers were mapped using TBtools and subject operating profile analysis was performed on six differentially expressed proteins to assess sensitivity and specificity of the six biomarkers in distinguishing asthmatic individuals from healthy children.

Preferably, in step 1 the subject comprises an acute asthma group, an asthma recovery phase group and a healthy control group, and the serum proteome of the subject is extracted as follows:

(1) The subjects were not treated with the drug, collected blood samples, placed in the dark at room temperature for 1 hour, centrifuged at 3000rpm for 15 minutes at 4 ℃ to separate serum, and finally the serum was aliquoted and stored in a refrigerator at 80 ℃;

(2) Protein extraction and trypsin digestion

First, a serum sample was centrifuged at 12000g for 10 minutes at 4℃to remove cell debris, then the supernatant was retained, the protein concentration of serum was measured using the BCA kit, then a serum protein solution was reduced with 5mM dithiothreitol at 56℃for 30 minutes, and alkylated with 11mM iodoacetamide at dark room temperature for 15 minutes, the first overnight digestion added trypsin at a 1:5 ratio, the second 4 hour digestion added at a 1:100 ratio, the digested peptide was desalted using C18 Ziptips, eluted with 0.1% TFA in 50-70% acetonitrile, then lyophilized and redissolved in 1% formic acid 5% acetonitrile;

(3) High pH-reverse phase fractionation

The treated peptide solutions were combined in equal amounts and further separated by high pH-reverse phase separation using a Dionex UHPLC and ethylene bridge hybrid C18 column at 40 ℃ with a flow rate of 0.2 ml/min and ACN gradient of 60 min in 5mM ammonium formate; fractions were collected at 1 minute intervals and pooled into 12 fractions, and then each fraction was lyophilized and redissolved in 1% formic acid 5% acetonitrile.

It is another object of the present invention to provide a biomarker composition for diagnosing preschool childhood asthma, the composition comprising LTBP1, APOA1 and IGFALS proteins.

It is a further object of the present invention to provide the use of a novel marker composition for diagnosing pre-school-age childhood asthma in the preparation of a reagent for serodiagnosing asthma in a subject.

The invention has the beneficial effects that:

The invention adopts data independent acquisition mass spectrum (DIA-MS) to analyze the serum proteome characteristics of children suffering from acute asthma and children suffering from convalescence, identifies 50 differentially expressed proteins in 46 serum, distinguishes children suffering from asthma and healthy children, confirms the central function of an inflammation-immune mechanism in asthma attack by carrying out function enrichment analysis on the differentially expressed proteins, establishes a supervision orthogonal partial least squares discriminant analysis (OPLS-DA) model, and after 200 permutation tests, R2 and Q2 of the OPLS-DA asthma model and healthy control are respectively 0.53 and 0.45, and R2 and Q2 of the OPLS-DA asthma model and healthy control are respectively 0.766 and 0.491. The model is well fitted and has reliable prediction capability. Thereby, MP14, ABHD12B, LTBP1, APOA1, ANG and IGFALS proteins are screened out as biomarkers by using the model.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.

FIG. 1 is a summary of the proteomic analysis of pediatric asthma serum DIA-MS; (A) An unsupervised model of pentachloroanisole in healthy subjects of asthma, convalescence group.

(B) A disturbed graph of differential protein between asthma, convalescent group and healthy control group. (C) Volcanic plot of 37 altered proteins between asthma and healthy subjects. (unsupervised clustered heatmap of 20 common different altered proteins for convalescent group asthma group versus healthy group.

FIG. 2 is a GO and KEGG enrichment analysis of differentially expressed proteins. (a) biological processes; (B) molecular function; (C) a cellular component; (D) KEGG pathway enrichment.

Figure 3 is a statistical analysis of stable candidate biomarkers. (A) Multivariate principal component analysis score plots of all differential proteins between asthma and healthy subjects; (B) A supervised orthogonal partial least squares discriminant analysis (OPLS-DA) model of differential proteins between preschool asthma and healthy subjects, asthma and health representing asthma and healthy subjects under 5 years of age, respectively; (C) the relative expression level of the candidate biomarker.

FIG. 4 is a prognostic model evaluation of preschool childhood asthma based on protein biomarkers; (A) Abundance heatmaps of 6 candidate biomarkers in preschool asthma and health cohorts; (B) ROC analysis of IGFALS in asthma and health cohorts at all ages; (C) ROC analysis of IGFALS in pre-school-age asthma and health cohorts.

FIG. 5 shows the results of the serum proteome OPLS-DA and substitution test; (A) Proteomic OPLS-DA results for all asthmatic patients with healthy controls; (B) Replacement test results for proteome OPLS-DA for all asthmatic patients with healthy controls; (C) Proteomic OPLS-DA results for preschool childhood asthma patients with healthy controls; (D) Replacement test results for proteomic OPLS-DA of preschool childhood asthma patients with healthy controls.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

The embodiment provides a method for establishing a preschool childhood asthma diagnosis model, which comprises the following steps:

1. Study of clinical characteristics of patients

A total of 46 children were divided into three groups, asthma exacerbation (n=17), convalescent asthma (n=19), and healthy controls (n=10). To determine asthma biomarkers for preschool children, 7, 10, and 7 preschool children (age <5 years) were included in the asthma group, recovery group, and healthy control three groups, respectively. The clinical characteristics of each group are shown in tables 1 and S1. As shown, children with acute asthma had a one second forced expiratory volume (FEV 1%) lower than those of the convalescence and healthy control groups. However, children with acute asthma had higher Fe v 1/forced vital capacity (FEV/FVC) than children in the convalescent and healthy control group (table 1). Compared to the convalescent group and the healthy control group, the neutrophil and eosinophil counts were higher and the lymphocyte counts were lower in the asthmatic group (table 1). Eosinophil counts, feNO and IgE were significantly higher in children of the asthma and convalescent groups than in the healthy group (table 1).

TABLE 1

^a Kruskal-Wallis test p-value

Table S1

2. Proteomic findings associated with asthma

To analyze the serum proteome of asthmatic children, a total of 747 proteins were identified in 46 serum samples using DIA-MS proteome sequencing. Principal Component Analysis (PCA) showed that the serum proteome can differentiate between the three study groups (fig. 1A). To determine asthma biomarkers in children, 50 significantly altered proteins were determined between asthma and healthy controls after data filtration (fold change [ FC ] >1.5, mann-Whitney U test p value < 0.05) (fig. 1B and C; table 2), such as IGFALS, TNXB, LCAT, MMRN1, IGFBP3, APOA1, MADCAM1, PKM, AMY2A, PZP, IGHV-38, ABHD12B, ANTXR2, IGF1 and AGT. At the same time, 63 proteins were significantly differentially expressed between the convalescent group and the healthy control group (fig. 1C and table S3). Interestingly, there were 20 common differentially expressed proteins between the convalescent group and the asthmatic group compared to the healthy group (fig. 1C). Cluster analysis showed that these 20 common differential proteins in asthma, convalescent group and healthy control group could be distinguished in unsupervised cluster analysis (fig. 1D).

TABLE 2

Table S3

3. GO and KEGG enrichment analysis of altered proteins

Based on the results of gene theory (GO) annotation, functional enrichment analysis was performed on the differential protein between asthma and healthy controls. Fisher' S exact test p-value was used to determine significance (Table S4). GO terminology with p value <0.05 is considered significantly rich (fig. 2). As shown, the differential proteins are significantly enriched in immune system-related biological processes such as chemokine-mediated signaling pathways, positive regulation of leukocyte chemotaxis, regulation of granulocyte chemotaxis, positive regulation of leukocyte migration, positive regulation of neutrophil migration, regulation of neutrophil chemotaxis, regulation of leukocyte chemotaxis, and positive regulation of chemotaxis (fig. 2A). In terms of molecular function, differential proteins are significantly enriched in immune related functions such as CXCR chemokine receptor binding, chemokine activity and G protein-coupled receptor binding (fig. 2C). In sarcoplasmic reticulum, growth cone, polarized growth sites and sarcoplasmic cell component entries, differential proteins were significantly enriched (fig. 2B). Next, a KEGG pathway enrichment analysis was performed (Table S5). As expected, the differential proteins are significantly enriched in immune-related pathways such as chemokine signaling pathways, viral protein interactions with cytokines and cytokine receptors, cytokine-cytokine receptor interactions, p53 signaling pathways, and TGF- β signaling pathways. In addition, fat digestion and absorption, cholesterol metabolism, vitamin digestion and absorption pathways are also enriched (fig. 2D).

Table S4

Table S5

4. Biomarkers for preschool children with asthma

A total of 50 differential proteins were found between asthma and healthy controls. Multivariate PCA showed that differential proteins can differentiate between asthma and healthy subjects (fig. 3A and supplemental fig. 5C). To examine the potential role of these proteins in distinguishing asthma from healthy controls, a supervised orthogonal partial least squares discriminant analysis (OPLS-DA) model was constructed (fig. 3B-5A). After 200 permutation tests, R2 and Q2 of the OPLS-DA asthma model and healthy controls were 0.53 and 0.45 respectively fig. 5B), and R2 and Q2 of the OPLS-DA asthma model and healthy controls were 0.766 and 0.491 respectively (fig. 5D), indicating that these models fit well with reliable predictive power. MMP14, ABHD12B, PCYOX1, LTBP1, CFHR4, APOA1, IGHG4, ANG, and IGFALS proteins significantly help differentiate asthmatic children from healthy children (predictive variable importance [ VIP pred ] >1; table 3) and are considered biomarker candidates. The relative expression levels of these candidate biomarkers are shown in the box plot in fig. 3C. Interestingly, these candidates exhibited significantly higher relative abundance in the asthma and convalescence cohorts compared to the healthy cohorts (fig. 3C). In particular, ANG, APOA1, IGFALS, and LTBP1 levels showed a gradual decrease trend from asthma to convalescence to healthy cohorts (fig. 3C).

TABLE 3 Table 3

^a The relative abundance is the LC-MS/MS intensity value

^b The P-value is that of the Kruskal Wallis test

5. Biomarker-based development and evaluation of preschool childhood asthma diagnostic models

To better present the differences in abundance of candidate biomarkers in pre-school-age asthmatic patients, a heat map of six candidate biomarkers was plotted using TBtools (fig. 5A). As shown, all protein abundance was significantly higher in asthmatic preschool children than in healthy preschool children, especially ANG, APOA1 and IGFALS (fig. 5A). Next, subject work character (ROC) curve analysis was performed on 6 candidate proteins to assess their sensitivity and specificity to distinguish asthmatic individuals from healthy children (table 4). Part of the candidate proteins had an area under the curve (AUC) of greater than 0.8 for diagnosis of asthma in whole-age and preschool children. These proteins, including IGFALS, APOA1 and LTBP1, showed good diagnostic value (table 4). To construct a diagnostic model of asthma, we performed a double logistic regression analysis and ROC analysis on 6 protein biomarkers. Thus, IGFALS was suggested as a biomarker for childhood asthma (table 4). For diagnosis of asthma in children (preschool and school-age children), the AUC of the IGFALS model was 0.882, the asymptotic meaning was 0.004 (table 4), and the AUC of the IGFALS model was 0.959 (asymptotic significance: 0.004) for preschool asthma prediction (fig. 5C and table 4). To verify the reliability of the biomarkers, we measured the content of IGFALS, LTBP1 and APOA1 in the samples of the verification set using ELISA. And ROC analysis was performed. As a result, the AUC of the IGFALS model in ELISA data was 0.897, indicating that the IGFALS-based diagnostic model is useful for our cohort and may have significant diagnostic potential for the diagnosis of preschool childhood asthma.

TABLE 4 Table 4

6. Serum protein extraction and treatment

6.1 Subject and serum sample collection during the period 2020 to 2021, a group of 46 children was recruited from the second affiliated hospital of the university of chinese medicine, shanxi. These children were divided into three study groups, asthma attacks (n=17), convalescent asthma (n=19), and healthy controls [ ]

N=10). Children with immune diseases, chronic kidney disease or other diseases affecting serum proteins are excluded. All clinical diagnoses followed the global asthma initiative guidelines in 2019 [10]. On the following morning after the child was admitted without medication, blood samples (4 mL) were collected, left in the dark at room temperature (22-25 ℃) for 1 hour, and then centrifuged at 3,000rpm for 15 minutes at 4℃to isolate serum. Finally, serum was aliquoted and stored in a refrigerator at 80 ℃. The second affiliated hospital ethics committee of the university of shanxi chinese medicine approves the study protocol (ChiCTR 2000033383). All participants provided written informed consent.

6.2 Protein extraction and trypsin digestion first, serum samples were centrifuged at 12,000 g for 10 min at 4 ℃ to remove cell debris. The supernatant was then retained and the protein concentration of the serum was determined using BCA kit (ThermoFisher Scientific, waltham, MA, USA) according to the manufacturer's instructions. The serum protein solution was then reduced with 5mM dithiothreitol at 56℃for 30 minutes and alkylated with 11mM iodoacetamide at dark room temperature for 15 minutes. Trypsin was added in a 1:50 (trypsin/protein mass ratio) ratio for the first overnight digestion and in a 1:100 ratio for the second 4 hours digestion. The digested peptides were desalted using C18Ziptips (Millipore), eluted with 0.1% TFA in 50-70% acetonitrile, then lyophilized and redissolved in 1% formic acid 5% acetonitrile. The iRT peptide (Biognosys, schlieren, switzerland) was added to the samples prior to LC-MS/MS analysis according to the manufacturer's instructions.

6.3 High pH-reverse phase fractionation the treated peptide solutions of subjects were combined in equal amounts and further separated by high pH-reverse phase separation using Dionex UHPLC (ThermoFisher Scientific) and ethylene bridge hybrid C18 column (Waters) at 40℃with a flow rate of 0.2 ml/min and an ACN gradient of 60 min (5-30%) in 5mM ammonium formate (pH 10). Fractions were collected at 1 minute intervals and pooled into 12 fractions. Each component was then lyophilized and redissolved in 1% formic acid 5% acetonitrile.

6.4 Data Dependent Acquisition (DDA) LC-MS/MS analysis and spectral library Generation to generate the spectral library, DDA-MS analysis was used and performed in tandem with Orbitrap Exploris 480 mass spectrometer (ThermoFisher Scientific) on an Easy-nLC 1200UPLC system. First, each peptide fragment was loaded onto Easy-nLC 1200UPLC system and separated from 95% solvent A (0.1% formic acid/2% acetonitrile/98% water) to 28% solvent B (0.1% formic acid/80% acetonitrile) at 50deg.C in a linear gradient of 120 minutes at a flow rate of 250 nL/min. The mass spectrometer operates in a data dependent mode. A full MS scan of 350 to 1500m/z is acquired at high resolution r=120,000 (defined as m/z=400); the resolution of the MS/MS scan was 30,000, the isolation window was 4Da, and the collision energy was 30+ -5% high energy collision dissociation (HCD) fragmentation. Dynamic exclusion was set to 30 seconds using the Pulsar search engine in Spectronaut X (Biognosys, schlieren, switzerland) to process raw data and search the UniProt homo sapiens proteome database within default parameters to generate a spectral library. The digestive enzyme is a specific trypsin with two missing specific cleavage, the fixed modification is an aminomethyl group of cysteine, and the variable modification is the oxidation of methionine. iRT is calculated from the iRT median of all DDA runs. The fragment ions used for target data analysis were selected from 300 to 1800m/z, the minimum relative intensity was set to >5%, and the number of fragment ions >3. The False Discovery Rate (FDR) of protein and peptide profile matches was set to 1%. Protein inference was performed using the ID Picker algorithm in Spectronaut software.

6.5 Data Independent Acquisition (DIA) LC-MS/MS analysis DIA-MS was performed using the same LC-MS system and LC linear gradient method as DDA-MS. For MS/MS acquisition, the DIA mode was set for 50 variable isolation windows according to FWTH (full width at half maximum) and a specific window list was constructed according to the corresponding DDA data of the pooled samples. In the range of m/z from 350 to 1500, the full scan is set to 1200,000 followed by a diameter scan with a resolution of 30,000; 30.5% of administrative officials; AGC target 1e6, maximum injection time 54ms. DIA raw files were processed using Spectronaut X (Biognosys, schlieren, switzerland) and default parameters. The retention time prediction type is dynamic iRT, the correction factor is window 1, and interference correction at the MS2 level is enabled. The system variance is normalized by a local normalization strategy. FDR of peptide precursors and proteins was evaluated at 1% threshold using mProphet method. The total peak area of the peptide MS2 fragment ions was calculated as protein intensity. All results were filtered using Q value and FDR threshold of 1%.

7. Tool for cutting tools

Bioinformatics and statistical analysis all identified serum proteins were annotated using GO (http:// david. Abcc. Ncifcrf. Gov/home. Jsp) and KEGG databases (http:// www.genome.jp/KEGG /). The obtained proteome data were subjected to unsupervised PCA and supervised orthogonal partial least squares discriminant analysis (OPLS-DA) using SIMCA14 software (Umetrics AB in sweden). The standard of FC >1.5 and Mann-Whitney U test p-value <0.05 was used to identify significantly different proteins. Thermal mapping analysis was performed using TB tools (https:// gitsub. Com/CJ-Chen/TB tools). r (version 4.0.3) and SPSS (version 28) are used for functional enrichment and statistical analysis, such as AUC. Fisher's exact test p-value <0.05 was used to test significantly enriched GO functions and KEGG pathways. Availability of data and materials raw data for LC-MS/MS proteomes has been saved to iProX database (https:// www.iprox.cn /), item ID is IPX0004341000.

8. ELISA (enzyme-linked immunosorbent assay) verification

All reagents and components were first returned to room temperature, standards, quality controls and samples, and duplicate wells were recommended. Working solutions of various components of the kit are prepared according to the method described in the specification of the kit. The required strips are taken out of the aluminum foil bags, and the rest strips are put back into the refrigerator by sealing with the self-sealing bags. Setting a standard substance hole, a 0-value hole, a blank hole and a sample hole, wherein 50 mu L of standard substances with different concentrations are respectively added into the standard substance hole, 50 mu L of sample diluent is added into the 0-value hole, 50 mu L of sample to be detected is added into the sample hole without adding the blank hole. In addition to the blank wells, standard wells, 0-value wells, and sample wells, 100 μl of horseradish peroxidase (HRP) -labeled detection antibody was added. The reaction plate is covered by a sealing plate film, and incubated for 60min in a water bath kettle or an incubator at 37 ℃ in a dark place. Uncovering the sealing plate film, discarding the liquid, beating the water absorbing paper, filling the washing liquid in each hole, standing for 20S, throwing the washing liquid, beating the water absorbing paper, and repeating the steps for 5 times. If an automatic plate washer is used, the plate washer is required to be washed according to the operation procedure of the plate washer, and a procedure of soaking for 30s is added, so that the detection precision can be improved. And after the plate washing is finished, before the substrate is added, the reaction plate is fully patted on clean paper without scraps. Substrates A and B were thoroughly mixed in a 1:1 volume, and 100. Mu.L of the substrate mixture was added to all wells. The reaction plate is covered by a sealing plate film, and incubated for 15min in a water bath kettle or an incubator at 37 ℃ in a dark place. And adding 50 mu L of stop solution into all the wells, reading the absorbance (OD value) of each well on an enzyme-labeled instrument, and drawing a standard curve to calculate the content of the molecules to be detected.

Claims (4)

1. A method for screening novel biomarkers for diagnosis of preschool childhood asthma, comprising the steps of:

step 1: analyzing serum proteome characteristics of a subject by adopting independent data acquisition mass spectrum, and determining differential expression proteins between an acute asthma group and a healthy control group after data filtering;

Step 3: a total of 50 differential expression proteins exist between the acute asthma group and the healthy control group, the potential actions of the differential expression proteins on asthmatic individuals and healthy children are analyzed by adopting a supervised orthorhombic least squares discriminant analysis model, and MMP14, ABHD12B, LTBP, APOA1, ANG and IGFALS proteins are screened out as biomarker candidates for distinguishing asthmatic children from healthy children;

Step 4: the six biomarkers were mapped using TBtools and subject operating profile analysis was performed on the six differentially expressed proteins to assess the sensitivity and specificity of the six biomarker candidates in distinguishing asthmatic individuals from healthy children, screening for preschool childhood asthma diagnostic biomarkers.

2. The method of claim 1, wherein in step 1, the subject comprises an acute asthma group, an asthma recovery phase group and a healthy control group, and the serum proteome extraction process of the subject is as follows:

(1) The subjects were not treated with the drug, collected blood samples, placed in the dark at room temperature for 1 hour, centrifuged at 3000rpm for 15 minutes at 4 ℃ to separate serum, and finally the serum was aliquoted and stored in a refrigerator at 80 ℃;

(2) Protein extraction and trypsin digestion

First, a serum sample was centrifuged at 12000g for 10 minutes at 4℃to remove cell debris, then the supernatant was retained, the protein concentration of serum was measured using the BCA kit, then a serum protein solution was reduced with 5mM dithiothreitol at 56℃for 30 minutes, and alkylated with 11mM iodoacetamide at dark room temperature for 15 minutes, the first overnight digestion added trypsin at a 1:5 ratio, the second 4 hour digestion added at a 1:100 ratio, the digested peptide was desalted using C18 Ziptips, eluted with 0.1% TFA in 50-70% acetonitrile, then lyophilized and redissolved in 1% formic acid 5% acetonitrile;

(3) High pH-reverse phase fractionation

The treated peptide solutions were combined in equal amounts and further separated by high pH-reverse phase separation using a Dionex UHPLC and ethylene bridge hybrid C18 column at 40 ℃ with a flow rate of 0.2 ml/min and ACN gradient of 60 min in 5mM ammonium formate; fractions were collected at 1 minute intervals and pooled into 12 fractions, and then each fraction was lyophilized and redissolved in 1% formic acid 5% acetonitrile.

3. A biomarker composition for diagnosing preschool childhood asthma, the composition comprising LTBP1, APOA1, and IGFALS proteins.

4. Use of a biomarker composition for diagnosing preschool childhood asthma in the manufacture of a reagent for serodiagnosing asthma patients in a subject.