Prediction method, system and equipment for neutrophil hormone insensitive asthma

Abstract

The invention provides a method, a system and equipment for predicting neutrophil hormone insensitive asthma, and relates to the field of intelligent medical treatment. The method comprises the following steps: obtaining expression data of a predictive gene of a sample to be detected; performing classification prediction based on the expression data of the prediction gene to obtain a classification result of the risk of the to-be-detected sample suffering from the neutrophil hormone insensitive asthma; if the expression quantity of the predictive gene is higher than a threshold value, obtaining a classification result that the risk of the to-be-detected sample suffering from the neutrophil hormone insensitive asthma is high. According to the method disclosed by the invention, the classification result of predicting the risk of the to-be-detected sample suffering from the neutrophil hormone insensitive asthma is realized by detecting the expression of the prediction gene of the to-be-detected sample, so that the method is used for clinical evaluation and solving related life science problems.

Description

Technical Field

[0001] The present application relates to the field of intelligent medicine, and more specifically, to a method, system and device for predicting neutrophilic hormone-insensitive asthma. Background Art

[0002] Bronchial asthma (asthma) is a common chronic respiratory disease, which brings a great disease burden to mankind. There are about 300 million asthma patients globally, causing more than 400,000 deaths every year, and the disease control rate still needs to be improved.

[0003] As a highly heterogeneous disease, asthma has multiple inflammatory patterns and pathological characteristics, making it difficult to provide precise and personalized treatment measures for asthma patients in clinical diagnosis and treatment. There may be a situation where the treatment plan does not quite match the disease type, resulting in delayed illness and affecting the curative effect. Summary of the Invention

[0004] To solve the above problems, the present application provides a method for predicting neutrophilic hormone-insensitive asthma; the method of the present application realizes the classification result of predicting that the test sample has neutrophilic hormone-insensitive asthma by detecting the expression level of the predicted gene of the test sample, so as to be used for clinical evaluation and solve related life science problems.

[0005] The first aspect of the present application discloses a method for predicting neutrophilic hormone-insensitive asthma, the method comprising:

[0006] Obtaining the expression data of the predicted gene of the test sample;

[0007] Performing classification prediction based on the expression data of the predicted gene to obtain the classification result of the risk level of the test sample having neutrophilic hormone-insensitive asthma. If the expression level of the predicted gene is higher than the threshold, a classification result of high risk of the test sample having neutrophilic hormone-insensitive asthma is obtained.

[0008] In some embodiments, the predicted gene is obtained through an endothelial cell or immune cell-derived targeted gene candidate panel.

[0009] In some embodiments, the immune cells include neutrophils.

[0010] In some embodiments, the endothelial cell or immune cell-derived targeted gene candidate panel is obtained by the following method:

[0011] Obtaining neutrophilic hormone-insensitive asthma endothelial cell or immune cell data;

[0012] The neutrophilic hormone-insensitive asthma endothelial cell or immune cell data is obtained from a single-cell sequencing database;

[0013] The single-cell sequencing database includes differential expression data of mRNA;

[0014] The differential expression data of mRNA were screened using the screening criteria of adj-pvalue < 0.01 and FoldChange > 2 to obtain the screened differential expression data of mRNA and obtain the candidate panel of targeted genes derived from endothelial cells or immune cells.

[0015] In some embodiments, the single-cell sequencing database includes a proprietary sequencing database or a public database. In some embodiments, the single-cell sequencing database is selected from a proprietary sequencing database, and the proprietary sequencing database is obtained by:

[0016] Construct single-cell sequencing library; perform Illumina sequencing after library pooling; perform data quality control; LogNormalize data; perform principal component analysis on the normalized genes; select the first 30 principal components for nonlinear dimensionality reduction and cell clustering analysis; analyze gene expression in endothelial cells or immune cells; and obtain a proprietary sequencing database.

[0017] In some embodiments, the data quality control includes excluding samples: if the expression of all genes in the sample is ≤500 or ≥40,000, the sample is excluded. If the total number of genes detected in the sample is ≤200 or ≥60,000, the sample is excluded. If the mitochondrial expression of genes in the sample is ≥ 15%, the sample is excluded.

[0018] In some embodiments, cell clustering analysis is performed by one or both of TSNE and UMAP.

[0019] In some embodiments, cell clustering analysis divides cells into five cell types: epithelial cells, endothelial cells, stromal cells, immune cells, and red blood cells.

[0020] In some embodiments, the single-cell sequencing database is selected from a public database, including but not limited to Human cell atlas, Jingle Bells, CancerSEA, Single Cell, DISCO, PanglaoDB, SC2disease or CellMarker.

[0021] In some embodiments, the predictor gene comprises Icam1.

[0022] The second aspect of the present application discloses a prediction system for neutrophilic hormone-insensitive asthma, the system comprising:

[0023] An acquisition module is used to obtain the expression data of the predicted gene of the sample to be tested;

[0024] A prediction module, configured to perform classification prediction based on the expression data of the predicted gene, so as to obtain a classification result of the risk level of neutrophilic hormone-insensitive asthma for the sample to be tested. If the expression level of the predicted gene is higher than the threshold, a classification result of high risk of neutrophilic hormone-insensitive asthma for the sample to be tested is obtained.

[0025] In some embodiments, the predicted gene includes Icam1.

[0026] The third aspect of the present application discloses a neutrophilic hormone-insensitive asthma prediction device, which includes: a memory and a processor. The memory is used to store program instructions. The processor is used to call the program instructions, and when the program instructions are executed, the steps of the neutrophilic hormone-insensitive asthma prediction method described in the first aspect of the present application are implemented.

[0027] The fourth aspect of the present application discloses a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the neutrophilic hormone-insensitive asthma prediction method described in the first aspect of the present application are implemented.

[0028] The fifth aspect of the present application discloses a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the neutrophilic hormone-insensitive asthma prediction method described in the first aspect of the present application are implemented.

[0029] Advantages of the present application:

[0030] 1. The present application innovatively discloses a method for predicting neutrophilic hormone-insensitive asthma. By detecting the expression of the predicted gene, this method can well predict the risk level result of a sample having neutrophilic hormone-insensitive asthma, providing guiding information for early intervention and early treatment.

[0031] 2. In order to improve the reliability of the self-owned sequencing database, the present application discloses the standards for data quality control. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0033] Figure 1 It is a schematic flowchart of a method for predicting neutrophilic hormone-insensitive asthma provided by an embodiment of the present application;

[0034] Figure 2 It is a schematic diagram of a neutrophil-type hormone-insensitive asthma prediction system provided by an embodiment of the present application;

[0035] Figure 3 It is a schematic diagram of a neutrophil-type hormone-insensitive asthma prediction device provided by an embodiment of the present application;

[0036] Figure 4 It is a result diagram showing that Icam1 is highly expressed in endothelial cells in neutrophil-type hormone-insensitive asthma (NEU-SRA) samples provided by an embodiment of the present application;

[0037] Figure 5 It is a result diagram showing that Icam1 is highly expressed in neutrophils in neutrophil-type hormone-insensitive asthma (NEU-SRA) samples provided by an embodiment of the present application;

[0038] Figure 6 It is a result diagram showing that the expression of Icam1 in endothelial cells in neutrophil-type hormone-insensitive asthma (NEU-SRA) samples provided by an embodiment of the present application is significantly higher than that in normal control (NC), steroid-sensitive asthma (SSA), and high T2 inflammation-type hormone-insensitive asthma (HT2-SRA);

[0039] Figure 7 It is a result diagram showing that the expression of Icam1 in neutrophils in neutrophil-type hormone-insensitive asthma (NEU-SRA) samples provided by an embodiment of the present application is significantly higher than that in normal control (NC), steroid-sensitive asthma (SSA), and high T2 inflammation-type hormone-insensitive asthma (HT2-SRA). Detailed implementation manners

[0040] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application.

[0041] In some processes described in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations that appear in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0042] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0043] Figure 1 FIG. is a schematic flowchart of a method for predicting neutrophil-type corticosteroid-insensitive asthma provided by an embodiment of the present application. Specifically, the method includes the following steps:

[0044] 101: Obtain the expression data of the predicted gene of the test sample;

[0045] In some embodiments, the test sample refers to a test sample derived from a test subject, which can be obtained from the subject's blood and other fluid samples or tissue samples of biological origin, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as from fresh, frozen, and / or preserved organ or tissue samples, biopsy tissues, or aspirates; blood or any blood component; body fluid; cells at any time during an individual's pregnancy or development; or plasma.

[0046] In some embodiments, the test sample is a cell.

[0047] In some embodiments, the term "subject" as used herein refers to any animal (e.g., a mammal), including but not limited to humans, non-human primates, rodents, etc., that will be the recipient of a particular treatment. Generally, the terms "subject" and "patient" are used interchangeably herein when referring to human subjects.

[0048] In some embodiments, the subject is a human.

[0049] In some embodiments, the predicted gene is obtained through an endothelial cell or immune cell-derived targeted gene candidate panel.

[0050] In some embodiments, the immune cells include neutrophils.

[0051] In some embodiments, the endothelial cell or immune cell-derived targeted gene candidate panel is obtained by the following method:

[0052] Obtain the endothelial cell or immune cell data of neutrophil-type corticosteroid-insensitive asthma;

[0053] The endothelial cell or immune cell data of neutrophil-type corticosteroid-insensitive asthma is obtained from a single-cell sequencing database;

[0054] The single-cell sequencing database includes differential expression data of mRNA;

[0055] The differential expression data of mRNA was screened with the screening criteria of adj-pvalue < 0.01 and FoldChange > 2 to obtain the screened differential expression data of mRNA, and a candidate panel of target genes from endothelial cells or immune cells was obtained.

[0056] In some embodiments, the single-cell sequencing database includes a self-owned sequencing database or a public database;

[0057] In some embodiments, the data of neutrophil-type hormone-insensitive asthma endothelial cells or immune cells is obtained from a self-owned sequencing database, and the self-owned sequencing database is obtained by the following method:

[0058] Construct a single-cell sequencing library; perform Illumina sequencing after pooling the libraries; perform data quality control; perform LogNormalize normalization on the data; perform principal component analysis on the normalized genes; select the top 30 principal components for non-linear dimensionality reduction and cell clustering analysis; analyze the gene expression in endothelial cells or immune cells; obtain a self-owned sequencing database.

[0059] In some embodiments, the data quality control includes excluding samples: if the expression level of all genes in a sample is ≤ 500 or ≥ 40000, the sample is excluded. If the total number of detected genes in a sample is ≤ 200 or ≥ 60000, the sample is excluded. If the mitochondrial-expressed genes in a sample are ≥ 15%, the sample is excluded.

[0060] In some embodiments, the cell clustering analysis is performed by one or both of TSNE and UMAP.

[0061] In some embodiments, the cell clustering analysis divides cells into five cell types: epithelial cells, endothelial cells, stromal cells, immune cells, and red blood cells.

[0062] The specific process of obtaining the self-owned sequencing database is as follows:

[0063] 1. Obtain samples:

[0064] In this application, a total of 108,321 cells from 12 samples (3 normal (HC), 3 hormone-sensitive asthma (SSA), 3 neutrophil-type hormone-insensitive asthma (NEU-SRA), 3 high-T2 inflammatory hormone-insensitive asthma (HT2-SRA)) are included.

[0065] 2. Construct a single-cell sequencing library:

[0066] Cell capture: Using a microfluidic chip, single cells are co-encapsulated with gel microbeads (GEMs) with special labels in tiny water-in-oil droplets. Each GEM contains a unique cell barcode and a molecular identifier (UMI);

[0067] Cell lysis and reverse transcription: The cells in the droplets are lysed, and the released mRNA binds to the oligonucleotide probes on the GEMs. These probes contain cell barcodes, UMIs, and Poly(dT) sequences. Using reverse transcriptase, cDNA is synthesized using mRNA as a template, and each cDNA molecule carries the corresponding cell barcode and UMI;

[0068] Library construction: cDNA amplification: The droplets are broken, and the released cDNA is amplified by PCR to increase the amount of cDNA;

[0069] Fragmentation and end repair: The cDNA is fragmented into small fragments using enzymatic digestion or physical methods, and the fragments are end-repaired to have blunt ends;

[0070] Adapter ligation: Sequencing adapters are ligated to both ends of the cDNA fragments. These adapters contain primer sequences and Index sequences for sequencing, which are used for subsequent sample pooling and sequencing;

[0071] PCR enrichment: PCR amplification is performed again to increase the library yield, and impurities are removed by magnetic bead purification.

[0072] 3. Library quality inspection:

[0073] After the library construction is completed, first perform a preliminary quantification using a Qubit 2.0 Fluorometer, dilute the library to 1.5 ng / ul, and then use an Agilent 2100 / 4200 bioanalyzer to detect the insert size of the library. After the insert size meets the expectations, qRT-PCR is used to accurately quantify the effective concentration of the library (the effective concentration of the library is higher than 2 nM) to ensure the library quality.

[0074] 4. Sequencing on the machine:

[0075] After the library quality inspection is qualified, different libraries are pooled according to the requirements of the effective concentration and the target number of off-machine data, and then Illumina sequencing is carried out. The basic principle of sequencing is Sequencing by Synthesis. Fluorescently labeled dNTPs, DNA polymerase, and adapter primers are added to the flow cell for sequencing to perform amplification. When complementary strands are extended in each sequencing cluster, each added fluorescently labeled dNTP can release the corresponding fluorescence. The sequencer captures the fluorescence signal and converts the optical signal into a sequencing peak through computer software, thereby obtaining the sequence information of the fragment to be tested.

[0076] 5. Data quality control:

[0077] Since low-quality, empty cells, doublets, or multicellular cells will cause cells in the data to show abnormal gene numbers. The total number of all UMIs detected in a cell is highly correlated with the number of genes detected, and will also show abnormal high or low data trends. Low-quality, dead cells may show more mitochondrial contamination, which is manifested as an excessive mitochondrial ratio.

[0078] Use the Seurat package in R language to perform QC and cell filtering based on the values of genes detected in cells, the total value of detected gene Counts, and the mitochondrial ratio. Quality control is carried out according to the following criteria: 1) Retain all cells with gene expression levels > 500 and < 40,000; 2) Retain cells with a total number of detected genes > 200 and < 6,000; 3) Exclude cells with mitochondrial expressed genes ≥ 15%. Finally, 103,981 cells from 12 samples are used for subsequent analysis.

[0079] 6. Data normalization:

[0080] Perform LogNormalize normalization on the data and perform principal component analysis on the normalized genes.

[0081] 7. Cell clustering analysis:

[0082] Subsequently, the top 30 principal components were selected for TSNE and UMAP non-linear dimensionality reduction. According to the cell-specific canonical markers defined in the literature (references PMID: 33208946, 31816636, CellMarker database (http: / / xteam.xbio.top / CellMarker / ), panglaodb database (https: / / panglaodb.se / search.html)), the cell state was roughly divided into five major cell types, namely epithelial cells, endothelial cells, stromal cells, immune cells, and red blood cells. Other clustering algorithms (TSNE) yielded similar results. Among approximately 100,000 cells, the relative proportions of cell types varied. Specifically, the proportion of immune cells was the largest (96.455%; range: lymphocytes 52%, myeloid cells 44.455%), and the proportions of endothelial cells, epithelial cells, stromal cells, and red blood cells were 1.869%, 0.558%, and 0.307%, respectively.

[0083] 8. Analyze the gene expression in endothelial cells or immune cells.

[0084] 9. Obtain an in-house sequencing database.

[0085] In some embodiments, the data of neutrophil-type hormone-insensitive asthma endothelial cells or immune cells are obtained from a public database, and the public database includes Human cell atlas, Jingle Bells, CancerSEA, Single Cell, DISCO, PanglaoDB, SC2disease, or CellMarker.

[0086] The specific process of obtaining the expression data of the predicted genes of the test sample from the public database is as follows:

[0087] Obtain the expression data of the predicted genes of the test sample from Human cell atlas, Jingle Bells, CancerSEA, Single Cell, DISCO, PanglaoDB, SC2disease, or CellMarker.

[0088] In some embodiments, the predicted genes include Icam1.

[0089] 102: Classify and predict based on the expression data of the predicted gene to obtain the classification result of the risk level of neutrophilic corticosteroid-insensitive asthma for the sample to be tested; if the expression level of the predicted gene is higher than the threshold, obtain the classification result of high risk of neutrophilic corticosteroid-insensitive asthma for the sample to be tested.

[0090] In some embodiments, as Figure 4 shown: In endothelial cells, the expression of Icam1 in the NEU−SRA group is higher than that in the other three groups.

[0091] In some embodiments, as Figure 5 shown: In immune cells, the expression of Icam1 in the NEU−SRA group is higher than that in the other three groups.

[0092] For further verification, DSP analysis was further used to verify the difference in Icam1 in endothelial cells (CD34+) and immune cells (CD45+):

[0093] As Figure 6 shown: In endothelial cells, the expression of Icam1 in the NEU−SRA group is significantly higher than that in the other three groups, where NEU−SRA vs NC, p = 0.0051; NEU−SRA vs SSA, p = 0.015; NEU−SRA vs HT2-SRA, p = 0.0029;

[0094] As Figure 7 shown: In immune cells, the expression of Icam1 in the NEU−SRA group is significantly higher than that in the other three groups. Among them, NEU−SRA vs NC, p = 0.00016; NEU−SRA vs SSA, p = 0.0045; NEU−SRA vs HT2-SRA, p = 0.0099.

[0095] Figure 2 is a schematic diagram of a prediction system for neutrophilic corticosteroid-insensitive asthma provided by an embodiment of the present application. The system includes:

[0096] An acquisition module 201 for acquiring the expression data of the predicted gene of the sample to be tested;

[0097] A prediction module 202 for classifying and predicting based on the expression data of the predicted gene to obtain the classification result of the risk level of neutrophilic corticosteroid-insensitive asthma for the sample to be tested; if the expression level of the predicted gene is higher than the threshold, obtain the classification result of high risk of neutrophilic corticosteroid-insensitive asthma for the sample to be tested.

[0098] In some embodiments, the predicted gene includes Icam1.

[0099] Figure 3 It is a schematic diagram of a neutrophil-type hormone-insensitive asthma prediction device provided by an embodiment of the present application. The device includes: a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, it is used to execute the steps of the method in the first aspect of the present application.

[0100] An embodiment of the present application also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method in the first aspect of the present application.

[0101] An embodiment of the present application also discloses a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps of the method in the first aspect of the present application.

[0102] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0103] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0104] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0106] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical discs, etc.

[0107] Those of ordinary skill in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the said program can be stored in a computer-readable storage medium. The storage media mentioned above can be read-only memory, magnetic disks or optical discs, etc.

[0108] The above has introduced in detail a computer device provided by the present application. For those of ordinary skill in the art, according to the idea of the embodiments of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for predicting neutrophilic hormone-insensitive asthma, characterized in that: The method comprises: Obtaining expression data of predicted genes of the sample to be tested; Classification prediction is performed based on the expression data of the predicted gene to obtain a classification result of whether the sample to be tested has a high or low risk of suffering from neutrophilic hormone-insensitive asthma; if the expression amount of the predicted gene is higher than a threshold, a classification result is obtained that the sample to be tested has a high risk of suffering from neutrophilic hormone-insensitive asthma.

2. The method for predicting neutrophilic hormone-insensitive asthma according to claim 1, characterized in that: The predicted genes are obtained through a targeted gene candidate panel derived from endothelial cells or immune cells; optionally, the immune cells include neutrophils.

3. The method for predicting neutrophilic hormone-insensitive asthma according to claim 2, characterized in that: The endothelial cell or immune cell-derived targeted gene candidate panel is obtained by: Obtain data on endothelial cells or immune cells in neutrophil-type hormone-insensitive asthma; The data of neutrophil-type hormone-insensitive asthma endothelial cells or immune cells are obtained from a single-cell sequencing database; Optionally, the single-cell sequencing database includes a proprietary sequencing database or a public database; The single-cell sequencing database includes differential expression data of mRNA; The differential expression data of mRNA were screened using the screening criteria of adj-pvalue < 0.01 and FoldChange > 2 to obtain the screened differential expression data of mRNA and obtain the candidate panel of targeted genes derived from endothelial cells or immune cells.

4. The method for predicting neutrophilic hormone-insensitive asthma according to claim 3, characterized in that: The self-owned sequencing database is obtained by the following method: Construct single-cell sequencing library; perform Illumina sequencing after library pooling; perform data quality control; perform LogNormalize normalization on data; perform principal component analysis on normalized genes; select the first 30 principal components for nonlinear dimensionality reduction and cell clustering analysis; analyze gene expression in endothelial cells or immune cells; obtain self-owned sequencing database; Optionally, the data quality control includes excluding samples: if the expression of all genes in the sample is ≤500 or ≥40,000, the sample is excluded; if the total number of detected genes in the sample is ≤200 or ≥60,000, the sample is excluded; if the mitochondrial expression of genes in the sample is ≥ 15%, the sample is excluded; Optionally, cell clustering analysis was performed by one or both of TSNE and UMAP; Optionally, cell clustering analysis divides the cells into five cell types: epithelial cells, endothelial cells, stromal cells, immune cells, and red blood cells.

5. The method for predicting neutrophilic hormone-insensitive asthma according to claim 3, characterized in that: The public databases include Human cell atlas, Jingle Bells, CancerSEA, Single Cell, DISCO, PanglaoDB, SC2disease or CellMarker.

6. The method for predicting neutrophilic hormone-insensitive asthma according to any one of claims 1 to 5, characterized in that: The predicted genes include Icam1.

7. A prediction system for neutrophilic hormone-insensitive asthma, characterized in that: The system comprises: An acquisition module is used to obtain the expression data of the predicted gene of the sample to be tested; A prediction module is used to perform classification prediction based on the expression data of the prediction gene to obtain a classification result of whether the sample to be tested has a high or low risk of suffering from neutrophilic hormone-insensitive asthma; if the expression amount of the prediction gene is higher than a threshold, a classification result of whether the sample to be tested has a high risk of suffering from neutrophilic hormone-insensitive asthma is obtained; Optionally, the predicted gene comprises Icam1.

8. A device for predicting neutrophilic hormone-insensitive asthma, characterized in that: The device comprises: a memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the steps of the prediction method for neutrophilic hormone-insensitive asthma described in any one of claims 1-6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting neutrophilic hormone-insensitive asthma according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for predicting neutrophilic hormone-insensitive asthma according to any one of claims 1 to 6 are implemented.

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