Asthma prediction and subtype classification method, system and equipment
By detecting predicted gene expression data of asthma patients and using spatial transcriptome sequencing database for classification prediction, the difficulties in asthma diagnosis and subtype classification in the prior art are solved, and support for early diagnosis and personalized treatment is achieved.
Patent Information
- Application Number
- CN202510269162.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-07
- Publication Date
- 2025-06-06
AI Technical Summary
It is difficult for the prior art to accurately diagnose asthma and perform subtype classification, making it difficult to achieve personalized treatment.
By detecting predicted gene expression data from the sample to be tested or the patient, differential expression data were obtained using the spatial transcriptome sequencing database to perform classification predictions to determine the presence risk and subtype of asthma.
It has achieved early diagnosis and subtype classification of asthma, provided a scientific basis for personalized treatment, and improved the accuracy and effectiveness of clinical management.
Smart Images
Figure CN120108757A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of intelligent medical care, and more specifically, to a method, system and device for predicting and classifying asthma subtypes. Background Art
[0002] Asthma is a chronic inflammatory disease involving multiple immune cells and affected by multiple factors. It is one of the most common diseases in the world. The disease often occurs at night and in the early morning. Patients often have symptoms such as coughing and wheezing. In severe cases, it leads to breathing difficulties, shock and even death. As a disease that seriously affects people's health and quality of life, how to accurately diagnose asthma has become an urgent problem to be solved.
[0003] In addition, asthma is a highly heterogeneous chronic respiratory disease with significant differences in clinical manifestations, pathological mechanisms and treatment responses. This heterogeneity not only increases the complexity of clinical management, but also makes it difficult for the traditional "one-size-fits-all" treatment model to meet individual needs. Therefore, the subtype classification of asthma has become an important direction in research and clinical practice in recent years. By identifying different asthma subtypes, we can lay the foundation for precision medicine. Summary of the invention
[0004] To solve the above problems, the present application innovatively proposes a method for predicting and classifying asthma subtypes; the prediction method of the present application detects the expression level of the prediction gene of the sample to be tested to achieve the classification result of predicting that the sample to be tested suffers from asthma, which can be used for the clinical diagnosis of asthma; the subtype classification method of the present application detects the expression level of the patient's prediction gene to obtain the classification result of the patient suffering from different subtypes of asthma, which can be used for the subtype classification of clinical asthma.
[0005] In a first aspect, the present application discloses a method for predicting asthma, the method comprising:
[0006] Obtaining predicted gene expression data of the sample to be tested;
[0007] Classification prediction is performed based on the predicted gene expression data to obtain a classification result of whether the sample to be tested has a high or low risk of asthma; if the expression of the predicted gene is higher than a threshold, a classification result of a high risk of asthma is obtained for the sample to be tested.
[0008] In some embodiments, the predicted genes are obtained through a spatial transcriptome sequencing database.
[0009] In some embodiments, the spatial transcriptome sequencing database includes an own sequencing database or a public database.
[0010] In some embodiments, the spatial transcriptome sequencing database is selected from an own sequencing database.
[0011] In some embodiments, the self-owned sequencing database is obtained by: constructing a spatial transcriptome sequencing library; library quality control; sequencing; gene expression profile analysis; principal component analysis; and obtaining the self-owned sequencing database.
[0012] In some embodiments, the construction of the spatial transcriptome sequencing library includes the following steps: incubating the subject's FFPE sections with morphologically labeled fluorescent antibodies and the entire target mixture in the WTA Panel; performing fluorescence imaging through a DSP system; selecting a region of interest based on the morphologically labeled antibodies; and collecting the Oligos contained in the region of interest as a template for the downstream sequencing process.
[0013] In some embodiments, the FFPE sections are from normal subjects without asthma, subjects with hormone-sensitive asthma, subjects with neutrophilic hormone-insensitive asthma, and subjects with high T2 inflammatory hormone-insensitive asthma. The spatial transcriptome sequencing database includes the differential expression data of mRNA, IncRNA, and miRNA after screening, and the screening parameters are pvalue<0.05 and |log2foldchange|>1.
[0014] In some embodiments, the predicted genes are selected from the intersection of genes in the differential expression data; the intersection of genes in the differential expression data are genes common in the differential expression data of hormone-sensitive asthma (SSA) and normal control (NC), neutrophilic hormone-insensitive asthma (NEU-SRA) and normal control (NC), and high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and normal control (NC).
[0015] In some embodiments, the genes in the differential expression data between hormone-sensitive asthma and normal controls include Hspa1a, Spag6, Retnlb, Zfp998, Dennd3, Bpifa1, Scin, Relb, Itlnb, Scgb3a1, and Cxcl5.
[0016] In some embodiments, the genes in the differential expression data between neutrophilic hormone-insensitive asthma and normal controls include Fcer1g, C1qa, Mpeg1, S100a8, Plac8, Il1b, H2−K1, Lyz2, C1qc, C1qb, Zfp998, Cyp4b1, Gstm1, Sec14l3, Aldh1a1, Enah, Hmgcs2, Tppp3, Selenbp1, Aox3, and Gpx2.
[0017] In some embodiments, the genes in the differential expression data of high T2 inflammatory hormone-insensitive asthma and normal control include Hspa1a, Hsp90aa1, Zfp998, and Igkc.
[0018] In some embodiments, the intersection of genes in the differential expression data includes Zfp998.
[0019] In a second aspect, the present application discloses a method for classifying asthma subtypes, the method comprising:
[0020] Obtain patient predicted gene expression data;
[0021] In some embodiments, the predicted genes are obtained through a spatial transcriptome sequencing database.
[0022] In some embodiments, the spatial transcriptome sequencing database includes an own sequencing database or a public database.
[0023] In some embodiments, the spatial transcriptome sequencing database is selected from an own sequencing database.
[0024] In some embodiments, the self-owned sequencing database is obtained by: constructing a spatial transcriptome sequencing library; library quality control; sequencing; gene expression profile analysis; principal component analysis; and obtaining the self-owned sequencing database.
[0025] In some embodiments, the construction of the spatial transcriptome sequencing library includes the following steps: incubating FFPE sections with morphologically labeled fluorescent antibodies and all target mixtures in the WTA Panel; performing fluorescence imaging through a DSP system; selecting regions of interest based on morphologically labeled antibodies; and collecting Oligos contained in the regions of interest as templates for downstream sequencing processes.
[0026] In some embodiments, the FFPE sections are from a normal subject without asthma, a subject with hormone-sensitive asthma, a subject with neutrophilic hormone-insensitive asthma, or a subject with high T2 inflammatory hormone-insensitive asthma.
[0027] The spatial transcriptome sequencing database includes the differential expression data of mRNA, IncRNA, and miRNA after screening, and the screening parameters are pvalue<0.05 and |log2foldchange|>1.
[0028] In some embodiments, the predicted genes include genes in differential expression data; the genes in the differential expression data include one or more genes in the differential expression data of hormone-sensitive asthma (SSA) and neutrophilic hormone-insensitive asthma (NEU−SRA), hormone-sensitive asthma (SSA) and high T2 inflammatory hormone-insensitive asthma (HT2-SRA), high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and neutrophilic hormone-insensitive asthma (NEU−SRA).
[0029] Classification prediction is performed based on the patient's predicted gene expression data to obtain a first asthma type, a second asthma type, and a third asthma type; the first asthma type is hormone-sensitive asthma, the second asthma type is neutrophilic hormone-insensitive asthma, and the third asthma type is high T2 inflammatory hormone-insensitive asthma.
[0030] In some embodiments, the predicted genes include Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, Chad, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, Itlnb, Txnip, Fmo2, Npr3, Sik1, 1810010H24Rik, Hsp90aa1, Tppp3, Eln, Fcer1g, Mgp, and H2−Ea.
[0031] In some embodiments, Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, and Chad are used to classify and predict patients with hormone-sensitive asthma and patients with neutrophilic hormone-insensitive asthma.
[0032] In some embodiments, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, and Itlnb are used to classify and predict patients with hormone-sensitive asthma and high T2 inflammatory hormone-insensitive asthma.
[0033] In some embodiments, Txnip, Fmo2, Cyp4b1, Npr3, Sik1, 1810010H24Rik, Gsn, Gstm1, Hsp90aa1, Tppp3, Eln, Fcer1g, C1qc, Mgp, Apoe, C1qb, C1qa, Mpeg1, H2−Ea, and Ctss are used to classify and predict patients with high T2 inflammatory hormone-insensitive asthma and neutrophilic hormone-insensitive asthma.
[0034] The third aspect of the present application discloses an asthma prediction system, the system comprising:
[0035] An acquisition module is used to obtain the predicted gene expression data of the sample to be tested;
[0036] The prediction module is used to perform classification prediction based on the predicted gene expression data to obtain a classification result of whether the sample to be tested has a high or low risk of asthma; if the expression of the predicted gene is higher than a threshold, a classification result of a high risk of asthma is obtained for the sample to be tested.
[0037] In some embodiments, the predicted gene comprises Zfp998.
[0038] In a fourth aspect, the present application discloses an asthma subtype classification system, the system comprising:
[0039] An acquisition module, used to obtain patient predicted gene expression data;
[0040] A prediction module is used to perform classification prediction based on the patient's predicted gene expression data to obtain a first asthma type, a second asthma type, and a third asthma type; the first asthma type is hormone-sensitive asthma, the second asthma type is neutrophilic hormone-insensitive asthma, and the third asthma type is high T2 inflammatory hormone-insensitive asthma.
[0041] In some embodiments, the predicted genes include Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, Chad, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, Itlnb, Txnip, Fmo2, Npr3, Sik1, 1810010H24Rik, Hsp90aa1, Tppp3, Eln, Fcer1g, Mgp, and H2−Ea.
[0042] In some embodiments, Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, and Chad are used to classify and predict patients with hormone-sensitive asthma and patients with neutrophilic hormone-insensitive asthma.
[0043] In some embodiments, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, and Itlnb are used to classify and predict patients with hormone-sensitive asthma and high T2 inflammatory hormone-insensitive asthma.
[0044] In some embodiments, Txnip, Fmo2, Cyp4b1, Npr3, Sik1, 1810010H24Rik, Gsn, Gstm1, Hsp90aa1, Tppp3, Eln, Fcer1g, C1qc, Mgp, Apoe, C1qb, C1qa, Mpeg1, H2−Ea, and Ctss are used to classify and predict patients with high T2 inflammatory hormone-insensitive asthma and neutrophilic hormone-insensitive asthma.
[0045] The fifth aspect of the present application discloses a 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 asthma prediction method described in the first aspect of the present application or the steps of the asthma subtype classification method described in the second aspect of the present application are implemented.
[0046] The sixth aspect of the present application discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the asthma prediction method described in the first aspect of the present application or the steps of the asthma subtype classification method described in the second aspect of the present application are implemented.
[0047] The seventh aspect of the present application discloses a computer program product, including a computer program, which, when executed by a processor, implements the steps of the asthma prediction method described in the first aspect of the present application or the steps of the asthma subtype classification method described in the second aspect of the present application.
[0048] Advantages of this application:
[0049] 1. This application innovatively proposes a method for predicting asthma. This method can well predict the risk of a sample suffering from asthma by detecting the expression of predictive genes, providing a scientific strategy for the early diagnosis of asthma.
[0050] 2. This application innovatively proposes a subtype classification method for asthma. This method detects the expression of patients' predicted genes and performs classification prediction based on the patients' predicted gene expression data, providing support for personalized treatment of clinical asthma. BRIEF DESCRIPTION OF THE DRAWINGS
[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
[0052] Figure 1 is a schematic flow chart of the asthma prediction method provided in the embodiment of the present application;
[0053] Figure 2 is a schematic flow chart of the asthma subtype classification method provided in the embodiments of the present application;
[0054] Figure 3 is a schematic diagram of an asthma prediction system provided in an embodiment of the present application;
[0055] Figure 4 is a schematic diagram of the asthma subtype classification system provided in the embodiments of the present application;
[0056] Figure 5 It is a schematic diagram of a device provided in an embodiment of the present application;
[0057] Figure 6 It is a heat map of gene expression profile of ROI provided in the embodiment of the present application;
[0058] Figure 7 It is a principal component analysis diagram provided in an embodiment of the present application;
[0059] Figure 8 It is a volcano plot of differential expression between hormone-sensitive asthma (SSA) and normal control (NC) provided in the examples of the present application;
[0060] Fig. 9 It is a volcano plot of differential expression between neutrophilic hormone-insensitive asthma (NEU-SRA) and normal control (NC) provided in the examples of the present application;
[0061] Fig.10 It is a volcano plot of differential expression between high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and normal control (NC) provided in the examples of the present application;
[0062] Fig.11It is a schematic diagram of the intersection of differentially expressed genes between hormone-sensitive asthma (SSA) and normal control (NC), neutrophil-type hormone-insensitive asthma (NEU-SRA) and normal control (NC), and high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and normal control (NC) provided in the examples of the present application;
[0063] Fig.12 It is a differential expression volcano map of hormone-sensitive asthma (SSA) and neutrophilic hormone-insensitive asthma (NEU-SRA) provided in the examples of the present application;
[0064] Fig.13 It is a differential expression volcano map of hormone-sensitive asthma (SSA) and high T2 inflammatory hormone-insensitive asthma (HT2-SRA) provided in the examples of the present application;
[0065] Fig.14 It is a differential expression volcano map of high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and neutrophilic hormone-insensitive asthma (NEU-SRA) provided in the examples of the present application. DETAILED DESCRIPTION
[0066] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.
[0067] In some of the processes described in the specification and claims of this application and the above-mentioned drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document 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.
[0068] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.
[0069] Figure 1 : is a schematic flow chart of an asthma prediction method provided in an embodiment of the present application. Specifically, the method comprises the following steps:
[0070] 101: Obtain expression data of predicted genes of the sample to be tested;
[0071] In some embodiments, the predicted genes are obtained through a spatial transcriptome sequencing database.
[0072] In some embodiments, the spatial transcriptome sequencing database includes an own sequencing database or a public database.
[0073] In some embodiments, the spatial transcriptome sequencing database is selected from an own sequencing database.
[0074] In some embodiments, the self-owned sequencing database is obtained by: constructing a spatial transcriptome sequencing library; library quality control; sequencing; gene expression profile analysis; principal component analysis; and obtaining the self-owned sequencing database.
[0075] In some embodiments, the construction of the spatial transcriptome sequencing library includes the following steps: incubating the subject's FFPE sections with morphologically labeled fluorescent antibodies and the entire target mixture in the WTA Panel; performing fluorescence imaging through a DSP system; selecting a region of interest based on the morphologically labeled antibodies; and collecting the Oligos contained in the region of interest as a template for the downstream sequencing process.
[0076] In some embodiments, the FFPE section is a paraffin specimen of lung tissue.
[0077] In some embodiments, the FFPE sections are from a normal subject without asthma, a subject with hormone-sensitive asthma, a subject with neutrophilic hormone-insensitive asthma, or a subject with high T2 inflammatory hormone-insensitive asthma.
[0078] In some embodiments, a total of 106 regions of interest are selected.
[0079] In some embodiments, the region of interest includes epithelial cells (ECAD+), endothelial cells (CD34+), immune cells (CD45+).
[0080] In some embodiments, the gene expression profiling results are as follows: Figure 6 shown.
[0081] In some embodiments, the principal component analysis results are as follows: Figure 7 shown.
[0082] The spatial transcriptome sequencing database includes the differential expression data of mRNA, IncRNA, and miRNA after screening, and the screening parameters are pvalue<0.05 and |log2foldchange|>1.
[0083] In some embodiments, the spatial transcriptome sequencing database includes screened epithelial cell (ECAD+) differential expression data.
[0084] In some embodiments, the predicted genes are selected from the intersection of genes in the differential expression data; the intersection of genes in the differential expression data are genes common in the differential expression data of hormone-sensitive asthma (SSA) and normal control (NC), neutrophilic hormone-insensitive asthma (NEU-SRA) and normal control (NC), and high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and normal control (NC).
[0085] In some embodiments, the differential expression data of hormone-sensitive asthma (SSA) and normal control (NC) are as follows Figure 8 As shown, there were 11 differentially expressed genes, including 10 upregulated genes, including Hspa1a, Spag6, Retnlb, Zfp998, Dennd3, Bpifa1, Scin, Relb, Itlnb, and Scgb3a1, and 1 downregulated gene, which was Cxcl5.
[0086] In some embodiments, the differential expression data of neutrophilic hormone-insensitive asthma (NEU-SRA) and normal control (NC) are as follows Fig. 9 As shown in the figure (only 10 up-regulated genes and 10 down-regulated genes are listed in the figure), there are 244 differentially expressed genes, of which 169 are up-regulated genes, including 10 up-regulated genes Fcer1g, C1qa, Mpeg1, S100a8, Plac8, Il1b, H2−K1, Lyz2, C1qc, C1qb, and the up-regulated genes not listed in the figure include Zfp998. There are 75 down-regulated genes, including 10 down-regulated genes Cyp4b1, Gstm1, Sec14l3, Aldh1a1, Enah, Hmgcs2, Tppp3, Selenbp1, Aox3, and Gpx2.
[0087] In some embodiments, the differential expression data of high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and normal control (NC) are as follows Fig.10 As shown, there were 4 differentially expressed genes, including 4 up-regulated genes, including Hspa1a, Hsp90aa1, Zfp998, and Igkc.
[0088] In some embodiments, the common gene results in the differentially expressed gene data of hormone-sensitive asthma (SSA) and normal control (NC), neutrophilic hormone-insensitive asthma (NEU-SRA) and normal control (NC), and high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and normal control (NC) are as follows Fig.11 As shown, there is 1 gene in common in the differentially expressed gene data.
[0089] In some embodiments, the genes common in the differentially expressed gene data of hormone-sensitive asthma (SSA) and normal control (NC), neutrophilic hormone-insensitive asthma (NEU-SRA) and normal control (NC), and high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and normal control (NC) include Zfp998.
[0090] 102: Perform classification prediction based on the predicted gene expression data to obtain a classification result of whether the sample to be tested has a high or low risk of asthma; if the expression of the predicted gene is higher than a threshold, obtain a classification result that the sample to be tested has a high risk of asthma.
[0091] Figure 2 : is a schematic flow chart of the asthma subtype classification method provided in the embodiment of the present application. Specifically, the method comprises the following steps:
[0092] 201: Obtain patient predicted gene expression data;
[0093] In some embodiments, the predicted genes are obtained through a spatial transcriptome sequencing database.
[0094] In some embodiments, the spatial transcriptome sequencing database includes an own sequencing database or a public database.
[0095] In some embodiments, the spatial transcriptome sequencing database is selected from an own sequencing database.
[0096] In some embodiments, the self-owned sequencing database is obtained by: constructing a spatial transcriptome sequencing library; library quality control; sequencing; gene expression profile analysis; principal component analysis; and obtaining the self-owned sequencing database.
[0097] In some embodiments, the construction of the spatial transcriptome sequencing library includes the following steps: incubating FFPE sections with morphologically labeled fluorescent antibodies and all target mixtures in the WTA Panel; performing fluorescence imaging through a DSP system; selecting regions of interest based on morphologically labeled antibodies; and collecting Oligos contained in the regions of interest as templates for downstream sequencing processes.
[0098] In some embodiments, the FFPE section is a paraffin specimen of lung tissue.
[0099] In some embodiments, the FFPE sections are from a normal subject without asthma, a subject with hormone-sensitive asthma, a subject with neutrophilic hormone-insensitive asthma, or a subject with high T2 inflammatory hormone-insensitive asthma.
[0100] In some embodiments, a total of 106 regions of interest are selected.
[0101] In some embodiments, the region of interest includes epithelial cells (ECAD+), endothelial cells (CD34+), immune cells (CD45+).
[0102] In some embodiments, the gene expression profiling results are as follows: Figure 6 shown.
[0103] In some embodiments, the principal component analysis results are as follows: Figure 7 shown.
[0104] In some embodiments, the spatial transcriptome sequencing database includes the differential expression data of mRNA, IncRNA, and miRNA after screening, and the screening parameters are pvalue<0.05 and |log2foldchange|>1.
[0105] In some embodiments, the spatial transcriptome sequencing database includes screened epithelial cell (ECAD+) differential expression data.
[0106] In some embodiments, the predicted genes include genes in differential expression data; the genes in the differential expression data include one or more genes in the differential expression data of hormone-sensitive asthma (SSA) and neutrophilic hormone-insensitive asthma (NEU−SRA), hormone-sensitive asthma (SSA) and high T2 inflammatory hormone-insensitive asthma (HT2-SRA), high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and neutrophilic hormone-insensitive asthma (NEU−SRA).
[0107] In some embodiments, the differential expression data of hormone-sensitive asthma (SSA) and neutrophil-type hormone-insensitive asthma (NEU-SRA) are as follows Fig.12 As shown (only 10 up-regulated genes and 10 down-regulated genes are listed in the figure), among which the up-regulated genes include Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, and the down-regulated genes include Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, and Chad.
[0108] In some embodiments, the differential expression data of hormone-sensitive asthma (SSA) and high T2 inflammatory hormone-insensitive asthma (HT2-SRA) are as follows: Fig.13As shown, the up-regulated genes include H1f4, H1f2, Retnla, Igha, Depp1, and Igkc, and the down-regulated genes include Retnlb, Reg3g, and Itlnb.
[0109] In some embodiments, the differential expression data of high T2 inflammatory hormone-insensitive asthma (HT2-SRA) and neutrophilic hormone-insensitive asthma (NEU-SRA) are as follows Fig.14 As shown (only 10 up-regulated genes and 10 down-regulated genes are listed in the figure), among which the up-regulated genes include Txnip, Fmo2, Cyp4b1, Npr3, Sik1, 1810010H24Rik, Gsn, Gstm1, Hsp90aa1, and Tppp3, and the down-regulated genes include Eln, Fcer1g, C1qc, Mgp, Apoe, C1qb, C1qa, Mpeg1, H2−Ea, and Ctss.
[0110] 202: Classification prediction is performed based on the patient's predicted gene expression data to obtain a first asthma type, a second asthma type, and a third asthma type; the first asthma type is hormone-sensitive asthma, the second asthma type is neutrophilic hormone-insensitive asthma, and the third asthma type is high T2 inflammatory hormone-insensitive asthma.
[0111] Figure 3 : is a schematic diagram of an asthma prediction system provided in an embodiment of the present application, the system comprising:
[0112] 301: an acquisition module, used to acquire predicted gene expression data of a sample to be tested;
[0113] 302: A prediction module is used to perform classification prediction based on the prediction gene expression data to obtain a classification result of whether the sample to be tested has a high or low risk of asthma; if the expression of the prediction gene is higher than a threshold, a classification result of whether the sample to be tested has a high risk of asthma is obtained.
[0114] In some embodiments, the predicted gene comprises Zfp998.
[0115] Figure 4 : is a schematic diagram of an asthma subtype classification system provided in an embodiment of the present application, the system comprising:
[0116] 401: an acquisition module, used to acquire patient predicted gene expression data;
[0117] 402: A prediction module, for performing classification prediction based on the patient's predicted gene expression data to obtain a first asthma type, a second asthma type, and a third asthma type; the first asthma type is hormone-sensitive asthma, the second asthma type is neutrophilic hormone-insensitive asthma, and the third asthma type is high T2 inflammatory hormone-insensitive asthma.
[0118] In some embodiments, the predicted genes include Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, Chad, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, Itlnb, Txnip, Fmo2, Npr3, Sik1, 1810010H24Rik, Hsp90aa1, Tppp3, Eln, Fcer1g, Mgp, and H2−Ea.
[0119] In some embodiments, Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, and Chad are used to classify and predict patients with hormone-sensitive asthma and patients with neutrophilic hormone-insensitive asthma.
[0120] In some embodiments, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, and Itlnb are used to classify and predict patients with hormone-sensitive asthma and high T2 inflammatory hormone-insensitive asthma.
[0121] In some embodiments, Txnip, Fmo2, Cyp4b1, Npr3, Sik1, 1810010H24Rik, Gsn, Gstm1, Hsp90aa1, Tppp3, Eln, Fcer1g, C1qc, Mgp, Apoe, C1qb, C1qa, Mpeg1, H2−Ea, and Ctss are used to classify and predict patients with high T2 inflammatory hormone-insensitive asthma and neutrophilic hormone-insensitive asthma.
[0122] Figure 5This is a schematic diagram of a device provided in an embodiment of the present application, wherein the device comprises: a memory and a processor; the memory is used to store program instructions; the processor is used to call program instructions, and when the program instructions are executed, the steps of the asthma prediction method described in the first aspect of the present application or the steps of the asthma subtype classification method described in the second aspect of the present application are implemented.
[0123] An embodiment of the present application further discloses a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method of the first aspect of the present application are implemented.
[0124] An embodiment of the present application further discloses a computer program product, including a computer program, which implements the steps of the method of the first aspect of the present application when executed by a processor.
[0125] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0126] In the several embodiments provided in 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.
[0127] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0128] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0129] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.
[0130] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.
[0131] The above is a detailed introduction to a computer device provided by the present application. For a person skilled in the art, according to the concept of the embodiments of the present application, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present application.
Claims
1. A method for predicting asthma, characterized in that: The method comprises: Obtaining predicted gene expression data of the sample to be tested; Classification prediction is performed based on the predicted gene expression data to obtain a classification result of whether the sample to be tested has a high or low risk of asthma; if the expression of the predicted gene is higher than a threshold, a classification result of a high risk of asthma is obtained for the sample to be tested.
2. The method for predicting asthma according to claim 1, characterized in that: The predicted genes are obtained through a spatial transcriptome sequencing database; Optionally, the spatial transcriptome sequencing database includes a proprietary sequencing database or a public database; Optionally, the spatial transcriptome sequencing database is selected from an own sequencing database; Optionally, the self-owned sequencing database is obtained by: constructing a spatial transcriptome sequencing library; library quality control; sequencing; gene expression profile analysis; principal component analysis; obtaining a self-owned sequencing database; Optionally, the construction of the spatial transcriptome sequencing library comprises the following steps: incubating FFPE sections with morphologically labeled fluorescent antibodies and all target mixtures in WTAPanel; performing fluorescence imaging through a DSP system; selecting a region of interest based on the morphologically labeled antibodies; and collecting Oligos contained in the region of interest as a template for a downstream sequencing process; The spatial transcriptome sequencing database includes the differential expression data of mRNA, IncRNA, and miRNA after screening, and the screening parameters are pvalue<0.05 and |log2foldchange|>1; Optionally, the predicted genes are selected from the intersection of genes in the differential expression data; the intersection of genes in the differential expression data are the common genes in the differential expression data of hormone-sensitive asthma and normal controls, neutrophilic hormone-insensitive asthma and normal controls, and high T2 inflammatory hormone-insensitive asthma and normal controls.
3. The method for predicting asthma according to any one of claims 1 to 2, characterized in that: The predicted genes include Zfp998.
4. A method for classifying asthma subtypes, characterized in that: The method comprises: Obtain patient predicted gene expression data; Classification prediction is performed based on the patient's predicted gene expression data to obtain a first asthma type, a second asthma type, and a third asthma type; the first asthma type is hormone-sensitive asthma, the second asthma type is neutrophilic hormone-insensitive asthma, and the third asthma type is high T2 inflammatory hormone-insensitive asthma.
5. The method for classifying asthma subtypes according to claim 4, characterized in that: The predicted genes are obtained through a spatial transcriptome sequencing database; Optionally, the spatial transcriptome sequencing database includes a proprietary sequencing database or a public database; Optionally, the spatial transcriptome sequencing database is selected from an own sequencing database; Optionally, the self-owned sequencing database is obtained by: constructing a spatial transcriptome sequencing library; library quality control; sequencing; gene expression profile analysis; principal component analysis; obtaining a self-owned sequencing database; Optionally, the construction of the spatial transcriptome sequencing library comprises the following steps: incubating FFPE sections with morphologically labeled fluorescent antibodies and all target mixtures in WTAPanel; performing fluorescence imaging through a DSP system; selecting a region of interest based on the morphologically labeled antibodies; and collecting Oligos contained in the region of interest as a template for a downstream sequencing process; The spatial transcriptome sequencing database includes the differential expression data of mRNA, IncRNA, and miRNA after screening, and the screening parameters are pvalue<0.05 and |log2foldchange|>1; Optionally, the predicted genes include genes in differential expression data; the genes in the differential expression data include one or more genes in differential expression data of hormone-sensitive asthma and neutrophilic hormone-insensitive asthma, hormone-sensitive asthma and high T2 inflammatory hormone-insensitive asthma, high T2 inflammatory hormone-insensitive asthma and neutrophilic hormone-insensitive asthma; Optionally, the predicted genes include Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, Chad, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, Itlnb, Txnip, Fmo2, Npr3, Sik1, 1810010H24Rik, Hsp90aa1, Tppp3, Eln, Fcer1g, Mgp, H2−Ea; Optionally, the Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, and Chad are used to classify and predict patients with hormone-sensitive asthma and patients with neutrophilic hormone-insensitive asthma; Optionally, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, Itlnb are used to classify and predict patients with hormone-sensitive asthma and high T2 inflammatory hormone-insensitive asthma; Optionally, Txnip, Fmo2, Cyp4b1, Npr3, Sik1, 1810010H24Rik, Gsn, Gstm1, Hsp90aa1, Tppp3, Eln, Fcer1g, C1qc, Mgp, Apoe, C1qb, C1qa, Mpeg1, H2−Ea, and Ctss are used to classify and predict patients with high T2 inflammatory hormone-insensitive asthma and neutrophilic hormone-insensitive asthma.
6. An asthma prediction system, characterized in that: The system comprises: An acquisition module is used to obtain the predicted gene expression data of the sample to be tested; A prediction module is used to perform classification prediction based on the predicted gene expression data to obtain a classification result of whether the sample to be tested has a high or low risk of asthma; if the expression of the predicted gene is higher than a threshold, a classification result of a high risk of asthma is obtained for the sample to be tested; Optionally, the predicted gene includes Zfp998.
7. An asthma subtype classification system, characterized in that: The system comprises: An acquisition module, used to obtain patient predicted gene expression data; A prediction module, for performing classification prediction based on the patient's predicted gene expression data to obtain a first asthma type, a second asthma type, and a third asthma type; the first asthma type is hormone-sensitive asthma, the second asthma type is neutrophilic hormone-insensitive asthma, and the third asthma type is high T2 inflammatory hormone-insensitive asthma; Optionally, the predicted genes include Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, Chad, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, Itlnb, Txnip, Fmo2, Npr3, Sik1, 1810010H24Rik, Hsp90aa1, Tppp3, Eln, Fcer1g, Mgp, H2−Ea; Optionally, the Ctss, Mpeg1, S100a8, C1qc, Cybb, C1qa, Laptm5, Apoe, C1qb, Itgb2, Gstm1, Gsn, Cyp4b1, Scgb3a2, Aldh1a1, Lypd2, Scgb1a1, Cyp2f2, Gabrp, and Chad are used to classify and predict patients with hormone-sensitive asthma and patients with neutrophilic hormone-insensitive asthma; Optionally, H1f4, H1f2, Retnla, Igha, Depp1, Igkc, Retnlb, Reg3g, Itlnb are used to classify and predict patients with hormone-sensitive asthma and high T2 inflammatory hormone-insensitive asthma; Optionally, Txnip, Fmo2, Cyp4b1, Npr3, Sik1, 1810010H24Rik, Gsn, Gstm1, Hsp90aa1, Tppp3, Eln, Fcer1g, C1qc, Mgp, Apoe, C1qb, C1qa, Mpeg1, H2−Ea, and Ctss are used to classify and predict patients with high T2 inflammatory hormone-insensitive asthma and neutrophilic hormone-insensitive asthma.
8. A device, 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 asthma prediction method described in any one of claims 1-3 or the steps of the asthma subtype classification method described in any one of claims 4-5 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 asthma according to any one of claims 1 to 3 or the steps of the method for classifying asthma subtypes according to any one of claims 4 to 5 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 asthma according to any one of claims 1 to 3 or the steps of the method for classifying asthma subtypes according to any one of claims 4 to 5 are implemented.