Gout target biomarker screening and traditional Chinese medicine prediction method and device based on single cell sequencing in combination with medicinal and edible substances and network pharmacology

Abstract

The invention provides a gout target biomarker screening and traditional Chinese medicine prediction method and device based on single cell sequencing in combination with medicinal and edible substances and network pharmacology. The method comprises the following steps: performing data quality control, preprocessing, clustering and grouping on a data set; screening differentially expressed genes through grouping comparison, and taking an intersection of the differentially expressed genes to obtain candidate genes; constructing a protein interaction PP I network, and identifying a potential biomarker treatment target; constructing a network pharmacology model, and performing traditional Chinese medicine or herbal medicine component prediction on the screened chemical small molecules to obtain candidate medicinal materials; according to the standard of homology of medicine and food, a potential traditional Chinese medicine with homology of medicine and food is provided for a gout treatment scheme. By combining with a biomarker screening technology of modern medicine, potential prediction markers are mined, and a theoretical basis and practical guidance are provided for individualized treatment of gout. A plurality of traditional Chinese medicine components with homology of medicine and food are screened through a network pharmacology method and become potential candidate medicines for treating gout.

Description

Technical Field

[0001] The present application relates to the cross - field of bioinformatics, genomics and systems biology, and particularly relates to a method and device for screening gout target biomarkers and predicting traditional Chinese medicine based on single - cell sequencing combined with medicated food homologous substances and network pharmacology. Background Art

[0002] Gout is a common chronic disease caused by disorders of uric acid metabolism, mainly manifested by the deposition of monosodium urate crystals in joints, inducing recurrent acute arthritis. With the change of modern lifestyle, the incidence of hyperuricemia and gout has been increasing year by year, and it has become an important public health problem globally. Urate - lowering therapy (ULT) is recognized as the core strategy for controlling gout. However, although the "Chinese Guidelines for the Diagnosis and Treatment of Hyperuricemia and Gout 2023" has clearly proposed to control the serum uric acid (sUA) level below 360 μmol / L (300 μmol / L for patients with tophi), in reality, only 30% to 40% of patients can achieve this goal, and more than 70% of gout patients still face the dilemma of poor control and frequent acute attacks. This phenomenon reflects multiple challenges in gout treatment, mainly including poor patient compliance with treatment regimens, treatment interruption caused by drug side effects, and clinical troubles brought by fluctuations in uric acid levels during treatment. In response to these problems, how to achieve effective management and prevent recurrence during the inter - critical period of gout has become a major issue in clinical research and practice.

[0003] The inter - critical period of gout refers to the stage between acute attacks. Although patients usually have no obvious symptoms during this period, fluctuations in uric acid levels and potential inflammatory responses still persist. If not effectively controlled, it is easy to induce acute gout attacks or cause chronic joint damage. The management of the inter - critical period of gout requires both reasonable control of serum uric acid levels and comprehensive intervention from the perspectives of immunity and inflammation to avoid repeated aggravation of the condition. At the same time, modern medicine lacks effective markers for predicting the risk of gout recurrence. Especially in patients with not significantly elevated serum uric acid levels, there are still many people facing gout recurrence during the inter - critical period. In response to this phenomenon, exploring new biomarkers and treatment strategies has become the key in the clinical management of gout.

[0004] The idea of "preventive treatment of disease" in traditional Chinese medicine theory provides a new perspective for the management of gout. Traditional Chinese medicine emphasizes taking the strategy of "preventing disease before it occurs and treating it early when it has occurred" through syndrome differentiation and treatment to prevent the occurrence and development of diseases. The occurrence and recurrence of gout are closely related to pathological states such as damp turbidity, blood turbidity, and qi stagnation in the body. The concept of "preventive treatment of disease" in traditional Chinese medicine emphasizes taking personalized intervention measures at different stages of the course of gout. Especially during the inter - critical period of gout, early intervention can not only prevent gout recurrence but also may achieve the balance of uric acid metabolism, reduce drug dependence, and improve the quality of life of patients.

[0005] Currently, more and more studies have shown that the theory of "medicinal and edible homology" of traditional Chinese medicine has unique advantages in the treatment of gout. Traditional Chinese medicine has been proven to play an important role in the treatment of gout by regulating the immune-inflammatory imbalance and inhibiting the deposition of uric acid crystals. Experimental studies have shown that prescriptions containing "medicinal and edible homology" drugs such as Coix Seed, Hawthorn Fruit, and Poria Cocos have significant effects in regulating gout-related inflammatory pathways and alleviating gout symptoms. In addition, clinical studies have also found that certain prescriptions of medicinal and edible homology can effectively slow down the disease fluctuations during the intercritical period of gout, improve uric acid metabolism, and provide new treatment options for the long-term management of gout.

[0006] The present invention aims to analyze the immune-inflammatory imbalance mechanism during the intercritical period of gout, explore the application of the theory of "preventive treatment of disease" in traditional Chinese medicine in the management of the intercritical period of gout, especially through the intervention of traditional Chinese medicine with medicinal and edible homology, and evaluate its potential in controlling uric acid levels and reducing gout recurrence. At the same time, combined with the biomarker screening technology of modern medicine, potential predictive biomarkers are further explored to provide theoretical basis and practical guidance for the individualized treatment of gout. Summary of the Invention

[0007] In view of this, it is necessary to provide a method and device for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with substances of medicinal and edible homology and network pharmacology. Through a comprehensive single-cell sequencing data analysis method, it is specifically designed to finely analyze the heterogeneity between cells, and apply this information to disease diagnosis, optimization of treatment strategies, and formulation of personalized medical plans, so as to solve the technical problem of how to effectively manage and prevent recurrence during the intercritical period of gout in the prior art, and provide a theoretical basis and practical guidance for the individualized treatment of gout.

[0008] In a first aspect, an embodiment of the present application provides a method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with substances of medicinal and edible homology and network pharmacology, the method comprising:

[0009] S1: Collecting a peripheral blood mononuclear cell sample dataset of different categories of subjects, performing data quality control and preprocessing, and identifying genes with significant expression differences among different cell subtypes;

[0010] S2: Using the clustering algorithm of Seurat for clustering, identifying different cell clusters, and using SingleR4 for cell type annotation to identify the main cell types;

[0011] S3: By grouping and comparing the cell datasets, screening out the differentially expressed genes between patients in the remission period and healthy normal controls and the differentially expressed genes between the attack period and the remission period, and taking the intersection of the two to obtain candidate genes that can simultaneously prevent gout attacks and promote the patient's recovery to a normal state;

[0012] S4: Based on candidate gene analysis, analyze the interactions PPI between the proteins encoded by the candidate genes. According to the PPI scores, construct a protein-protein interaction PPI network to identify potential biomarker therapeutic targets;

[0013] S5: Construct a network pharmacology model. In network pharmacology analysis, use the BATMAN database to predict chemical small molecules related to the candidate genes, and use the HERB database to predict traditional Chinese medicine or herbal components of the screened chemical small molecules to obtain candidate medicinal materials;

[0014] S6: According to the criteria of homologous medicines and foods, further screen the candidate medicinal materials to provide potential homologous medicines and foods of traditional Chinese medicine for the treatment plan of gout.

[0015] Optionally, in another implementation manner of the first aspect of the present invention, the preprocessing includes quality control, batch correction, variable gene screening, and dimensionality reduction, and its steps include:

[0016] Perform basic quality control using the Seurat2 software with default threshold settings;

[0017] During the data quality control process, the standard for the number of genes detected in each cell, nFeature_RNA, is set as: nFeature_RNA > 300 and nFeature_RNA < 3000, and at the same time, the proportion of mitochondrial genes is required to be less than 10%;

[0018] Use Harmony3 to correct the batch effect and use

[0019] the FindVariableFeatures function to screen out the top 2000 highly variable genes;

[0020] Perform dimensionality reduction on the data using principal component analysis PCA, use the ElbowPlot method to select the optimal number of principal components, and finally select the top 30 principal components for subsequent analysis.

[0021] Optionally, in another implementation manner of the first aspect of the present invention, in S2: Use the clustering algorithm of Seurat to perform clustering, identify different cell clusters, and use SingleR4 for cell type annotation to identify the main cell types, including:

[0022] Perform cell clustering analysis, set the resolution to 0.3, use the clustering algorithm of Seurat to perform clustering, and then use SingleR4 for cell type annotation;

[0023] Use the RunUMAP function in Seurat and use uniform manifold approximation and projection UMAP to perform two-dimensional visualization of the clusters;

[0024] The distribution of each group of samples in different cell clusters was shown through UMAP cell diagrams to identify the immune cell types present in the gout attack period, remission period, and healthy control group.

[0025] Optionally, in another implementation manner of the first aspect of the present invention, in step S3: through grouped comparison of cell data sets, differentially expressed genes between patients in the remission period and healthy normal controls and differentially expressed genes between the attack period and the remission period were screened, and the intersection of the two was taken to obtain candidate genes that can simultaneously prevent gout attacks and promote the patient's recovery to a normal state, including:

[0026] The multi-source data sets were integrated and divided into three groups: 3 healthy control groups Control, 3 groups of patients in the gout attack period Gout, and 6 groups of patients in the gout remission period GR;

[0027] The samples were divided into two groups for comparison: the first group was 6 GR groups and 3 Control groups, defined as DEG1, for screening genes with differential expression between patients in the remission period and normal controls, and these genes may be related to the patient's recovery to a normal state;

[0028] The second group was 6 GR groups and 3 Gout groups, defined as DEG2, for screening genes with differential expression between the attack period and the remission period, and these genes may be related to the patient's transition from the gout attack period to the remission period;

[0029] By taking the intersection of DEG1 and DEG2, genes that can simultaneously prevent gout attacks and promote recovery to a normal state were obtained, and these genes were abnormally expressed in different cell subtypes;

[0030] Finally, the expression results of the differential genes were shown through a heat map and a trend curve, and duplicates were removed to obtain candidate genes.

[0031] Optionally, in another implementation manner of the first aspect of the present invention, in step S4: based on the candidate genes, the interactions PPI between the proteins encoded by the candidate genes were analyzed, and according to the PPI score, a protein-protein interaction PPI network was constructed to identify potential biomarker treatment targets, including:

[0032] The STRING database was used to predict and analyze the interactions between the proteins encoded by the candidate genes, and according to the PPI score, a protein-protein interaction network was constructed for identifying potential biomarkers;

[0033] Through topological analysis of the PPI network, potential treatment targets were identified.

[0034] Optionally, in another implementation of the first aspect of the present invention, S5: Construct a network pharmacology model. In network pharmacology analysis, use the BATMAN database to predict chemical small molecules related to candidate genes, and use the HERB database to predict traditional Chinese medicine or herbal components for the screened chemical small molecules to obtain candidate medicinal materials, including:

[0035] In network pharmacology analysis, use the BATMAN database to predict chemical small molecules related to candidate genes;

[0036] By screening chemical small molecules with a P-value less than 0.05, obtain small molecules that may interact with candidate genes;

[0037] Use the HERB database to screen the chemical small molecules for prediction of traditional Chinese medicine or herbal components to determine their correlation with candidate genes;

[0038] Perform molecular docking simulation on small molecules and target proteins, and use a genetic algorithm to optimize the docking process; use LigPlot+ software for visualization to further confirm the binding mode of small molecules and target proteins.

[0039] Optionally, in another implementation of the first aspect of the present invention, S6: According to the standards of homologous substances of medicine and food, further screen the candidate medicinal materials to provide potential traditional Chinese medicines of homologous substances of medicine and food for the treatment plan of gout, including:

[0040] Use the data in the table to display the herbal information corresponding to each active ingredient, including the name of the herb, pinyin, medicinal material ID, and whether it has the attribute of homologous substances of medicine and food;

[0041] Mark whether the herb meets the standards of homologous substances of medicine and food of the National Health Commission in the "Homologous Substances of Medicine and Food" column in the table to screen the herb,

[0042] Based on network pharmacology and molecular docking analysis, screen out traditional Chinese medicine components with the property of homologous substances of medicine and food to provide potential traditional Chinese medicines of homologous substances of medicine and food for the treatment plan of gout.

[0043] In the second aspect, an embodiment of the present application provides a gout target biomarker screening and traditional Chinese medicine prediction device based on single-cell sequencing combined with homologous substances of medicine and food and network pharmacology, which is applied to the gout target biomarker screening and traditional Chinese medicine prediction method based on single-cell sequencing combined with homologous substances of medicine and food and network pharmacology as described in the first aspect. The device includes:

[0044] Data acquisition and quality control module: Used to collect peripheral blood mononuclear cell sample datasets of different categories of objects, perform data quality control and preprocessing, and identify genes with significant expression differences in different cell subtypes;

[0045] Cell clustering and subpopulation annotation module: It is used to cluster cells using the clustering algorithm of Seurat, identify different cell clusters, and perform cell type annotation using SingleR4 to identify the main cell types;

[0046] Core regulatory gene identification module: It is used to screen out differentially expressed genes between remission patients and healthy normal controls and between the attack stage and the remission stage by comparing cell data sets in groups, and take the intersection of the two to obtain candidate genes that can simultaneously prevent gout attacks and promote the patient's recovery to a normal state;

[0047] Protein-protein interaction network module: It is used to analyze the interactions PPI between the proteins encoded by candidate genes based on the candidate genes, construct a protein-protein interaction PPI network according to the PPI score, and identify potential biomarker treatment targets;

[0048] Active ingredient-target database screening module: It is used to construct a network pharmacology model. In network pharmacology analysis, use the BATMAN database to predict chemical small molecules related to candidate genes, and use the HERB database to predict traditional Chinese medicine or herbal ingredients for the screened chemical small molecules to obtain candidate medicinal materials;

[0049] Food-medicinal homologous traditional Chinese medicine screening module: It is used to further screen the candidate medicinal materials according to the criteria of food-medicinal homology, and provide potential food-medicinal homologous traditional Chinese medicines for the treatment plan of gout.

[0050] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0051] A processor;

[0052] A memory for storing instructions executable by the processor;

[0053] Wherein, when the processor is configured to execute the instructions, it implements the gout target biomarker screening and traditional Chinese medicine prediction method based on single-cell sequencing combined with food-medicinal homologous substances and network pharmacology as described in the first aspect.

[0054] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a program, and the program instructs the device to execute the gout target biomarker screening and traditional Chinese medicine prediction method based on single-cell sequencing combined with food-medicinal homologous substances and network pharmacology as described in the first aspect.

[0055] In the technical solution provided by the present invention, a method and device for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with medicated food homologous substances and network pharmacology are provided. A peripheral blood mononuclear cell sample dataset of different categories of objects is collected, and data quality control and preprocessing are performed to identify genes with significant expression differences in different cell subtypes. The clustering algorithm of Seurat is used for clustering to identify different cell clusters, and SingleR4 is used for cell type annotation to identify the main cell types. By comparing different groups of cell datasets, differentially expressed genes between remission patients and healthy normal controls and differentially expressed genes between the attack period and the remission period are screened, and the intersection of the two is taken to obtain candidate genes that can simultaneously prevent gout attacks and promote patients to recover to a normal state. Based on the candidate genes, the protein-protein interaction (PPI) between the proteins encoded by the candidate genes is analyzed, and according to the PPI score, a PPI network of protein interactions is constructed to identify potential biomarker treatment targets. A network pharmacology model is constructed. In the network pharmacology analysis, the BATMAN database is used to predict chemical small molecules related to the candidate genes, and the HERB database is used to predict traditional Chinese medicine or herbal components of the screened chemical small molecules to obtain candidate medicinal materials. According to the criteria of medicated food homology, the candidate medicinal materials are further screened to provide potential medicated food homologous traditional Chinese medicine for the treatment plan of gout.

[0056] Beneficial effects:

[0057] (1) By analyzing the immune-inflammatory imbalance mechanism during the gout intercritical period, the application of the traditional Chinese medicine theory of "preventive treatment of disease" in the management of the gout intercritical period is explored. Especially through the intervention of medicated food homologous traditional Chinese medicine, its potential in controlling uric acid levels and reducing gout recurrence is evaluated. At the same time, combined with the biomarker screening technology of modern medicine, potential predictive biomarkers are further explored to provide theoretical basis and practical guidance for the individualized treatment of gout.

[0058] (2) Through network pharmacology methods, a variety of traditional Chinese medicine components with the characteristics of medicated food homology are screened, and their potential interactions with immune targets are determined through molecular docking analysis, becoming potential candidate drugs for the treatment of gout. Description of the drawings

[0059] Figure 1 It is a schematic diagram of the module of the method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with medicated food homologous substances and network pharmacology provided by an embodiment of the present application.

[0060] Figure 2 A-C are schematic diagrams of the results of single-cell RNA sequencing data quality control and principal component analysis provided by an embodiment of the present application.

[0061] Figure 3Analysis of differences in immune cell subsets of PBMCs in the intercritical and attack periods of gout and healthy control groups provided by an embodiment of the present application.

[0062] Figure 4 PPI network of key genes provided by an embodiment of the present application.

[0063] Figure 5 Interaction network diagram of "traditional Chinese medicine - gene - active ingredient" provided by an embodiment of the present application.

[0064] Figure 6 Schematic diagram of the docking of components and target molecules provided by an embodiment of the present application.

[0065] Figure 7 Schematic diagram of the module of the gout target biomarker screening and traditional Chinese medicine prediction device based on single-cell sequencing combined with medicated diet homologous substances and network pharmacology provided by an embodiment of the present application.

[0066] Figure 8 Schematic diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0067] 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 part of the embodiments of the present application, rather than all of the embodiments.

[0068] It should be noted that "at least one" in the embodiments of the present application refers to one or more, and multiple refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application.

[0069] It should be noted that in the embodiments of the present application, terms such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. Features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific manner.

[0070] Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0071] Example 1

[0072] This application provides a method and device for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with medicated diet homologous substances and network pharmacology, including: S1: Collecting peripheral blood mononuclear cell sample datasets of different categories of objects, performing data quality control and preprocessing, and identifying genes with significant expression differences in different cell subtypes; S2: Using the clustering algorithm of Seurat to group, identifying different cell clusters, and using SingleR4 for cell type annotation to identify the main cell types; S3: Through grouped comparison of cell datasets, screening out differentially expressed genes between remission patients and healthy normal controls and differentially expressed genes between the attack period and the remission period, and taking the intersection of the two to obtain candidate genes that can simultaneously prevent gout attacks and promote patients to recover to a normal state; S4: Analyzing the interactions PPI between the proteins encoded by the candidate genes based on the candidate genes, constructing a protein-protein interaction PPI network according to the PPI score, and identifying potential biomarker treatment targets; S5: Constructing a network pharmacology model. In network pharmacology analysis, using the BATMAN database to predict chemical small molecules related to the candidate genes, and using the HERB database to predict traditional Chinese medicine or herbal components of the screened chemical small molecules to obtain candidate medicinal materials; S6: Further screening the candidate medicinal materials according to the criteria of medicated diet homology, and providing potential medicated diet homologous traditional Chinese medicine for the treatment plan of gout.

[0073] Figure 1 It is a schematic flowchart of a method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with medicated diet homologous substances and network pharmacology provided by an embodiment of this application.

[0074] As Figure 1 shown, a method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with medicated diet homologous substances and network pharmacology includes:

[0075] S1: Collecting peripheral blood mononuclear cell sample datasets of different categories of objects, performing data quality control and preprocessing, and identifying genes with significant expression differences in different cell subtypes.

[0076] It can be understood that in this embodiment, the preprocessing includes quality control, batch correction, variable gene screening, and dimensionality reduction, and its steps include:

[0077] Using Seurat2 software with default threshold settings for basic quality control;

[0078] During the data quality control process, the standard for the number of genes detected per cell, nFeature_RNA, is set as: nFeature_RNA > 300 and nFeature_RNA < 3000. At the same time, the proportion of mitochondrial genes is required to be less than 10%;

[0079] Use Harmony3 to correct batch effects and use the FindVariableFeatures function to screen out the top 2000 highly variable genes;

[0080] Perform dimensionality reduction on the data using principal component analysis (PCA), use the ElbowPlot method to select the optimal number of principal components, and finally select the top 30 principal components for subsequent analysis.

[0081] Specifically, the data is sourced from the public database NCBI GEO[1](http: / / www.ncbi.nlm.nih.gov / geo / ). Two datasets were downloaded from it: GSE211783 and GSE217561. The GSE211783 dataset contains peripheral blood mononuclear cell (PBMC) samples from 3 patients in the gout attack phase and the gout remission phase; while the GSE217561 dataset contains PBMC samples from 3 patients in the gout remission phase (excluding the late remission phase) and 3 healthy normal controls. For ease of analysis, these two datasets were integrated and divided into three groups: 3 healthy control groups (Control), 3 groups of patients in the gout attack phase (Gout), and 6 groups of patients in the gout remission phase (GoutRemission, GR).

[0082] Data preprocessing includes multiple steps. First, the Seurat [2] (Version 5.1.0, https: / / cran.r-project.org / web / packages / Seurat / index.html) software is used for basic quality control with default threshold settings (min.cells = 3, min.features = 200). During the data quality control process, the criteria are set as: nFeature_RNA > 300 and nFeature_RNA < 3000, and at the same time, the proportion of mitochondrial genes is required to be less than 10%. Next, Harmony [3] (version: 1.2.0, https: / / github.com / immunogenomics / harmony) is used to correct the batch effect, and the "FindVariableFeatures" function is used to screen out the top 2000 highly variable genes. Then, principal component analysis (PCA) is used to reduce the dimensionality of the data, and the ElbowPlot method is used to select the optimal number of principal components. Finally, the top 30 principal components are selected for subsequent analysis. For cell clustering analysis, the resolution is set to 0.3, and the Seurat clustering algorithm is used for clustering. Subsequently, SingleR [4] (version: 2.4.1, https: / / bioconductor.org / packages / release / bioc / html / SingleR.html) is used for cell type annotation. For visualization convenience, the RunUMAP function in Seurat is used to perform two-dimensional visualization of the clusters using uniform manifold approximation and projection (UMAP).

[0083] Quality control of single-cell transcriptomics data is a crucial step to ensure the reliability of subsequent analysis results. In this study, strict quality control was first performed on the original single-cell RNA sequencing data to remove low-quality cells and potential batch effects. The data quality control was mainly carried out through the following criteria:

[0084] nCount: The total number of transcripts in a cell. By excluding cells with too low or too high nCount, the interference of low-quality cells is avoided. Cells with nCount within a certain range are selected for subsequent analysis to ensure data quality.

[0085] nFeature: The number of genes detected in each cell. Too low a nFeature value may indicate problems in the data collection process of the cell, and too high may be the result of multiplex cell merging. Therefore, a reasonable range of nFeature values is set.

[0086] percent.mt: Mitochondrial gene proportion. A relatively high mitochondrial gene proportion usually indicates that the cells may have died or experienced damage. Therefore, cells with percent.mt greater than 15% were filtered out.

[0087] Through these quality control steps, it was ensured that the single-cell data entering the analysis were high-quality samples. Finally, the dataset contained cells that met the quality control criteria, providing a basis for subsequent clustering and differential expression analysis.

[0088] To further analyze the heterogeneity of cell populations, PCA dimensionality reduction clustering analysis was first performed, and the Harmony algorithm was applied to correct batch effects. Based on the PCA coordinates, UMAP coordinates were calculated and used for cell clustering analysis. After clustering analysis, 19 different cell clusters were identified (as shown in Figure 2 A), and these cell clusters exhibited significant cell type characteristics. Then, these cell clusters were annotated, and 7 major cell types were successfully identified, namely NK cells, CD4+ T cells, CD4+ central memory T cells (Tcm), CD8+ effector memory T cells (Tem), B cells, monocytes, and unknown cell types (as shown in Figure 2 B). Figure 2 Figure C shows the UMAP cell atlas of different grouped samples, clearly showing the distribution of each group of samples in different cell clusters, further revealing the changes in immune cell populations among the gout attack period, remission period, and healthy control group. 3.1.3 Identification of key cell types Based on the analysis of the UMAP atlas, the immune cell types present in the gout attack period, remission period, and healthy control group were identified (as shown in Figure 2 A, Figure 2 B). Through the annotation of cell clusters, a total of 7 major cell types were identified, namely CD4+ T cells, CD4+ central memory T cells (Tcm), CD8+ effector memory T cells (Tem), B cells, NK cells, monocytes, and unknown cell types.

[0089] Figure 2 Figure C shows the UMAP cell atlas of different grouped samples, showing the distribution of each group of samples in different cell clusters. Although there were certain changes in the relative proportions of immune cell subsets between different groups, these changes were not significant. The distribution of each cell type in the control group, attack group, and remission group was relatively similar, suggesting that the proportions of these cell types remained relatively stable under different immune states. These results indicate that although different immune cell populations vary at different stages of gout, overall, the distribution of immune cell subsets does not show significant differences between different groups. This analysis provides a basis for studying the role of immune cells in the immune mechanism of gout in subsequent research.

[0090] S2: Use the clustering algorithm of Seurat to perform clustering, identify different cell clusters, use SingleR4 for cell type annotation, and identify the main cell types.

[0091] It can be understood that in this embodiment, the S2: Use the clustering algorithm of Seurat to perform clustering, identify different cell clusters, use SingleR4 for cell type annotation, and identify the main cell types, including:

[0092] Perform cell clustering analysis, set the resolution to 0.3, use the clustering algorithm of Seurat to perform clustering, and then use SingleR4 for cell type annotation;

[0093] Adopt the RunUMAP function in Seurat, and use uniform manifold approximation and projection UMAP to perform two-dimensional visualization of the clusters;

[0094] Through the UMAP cell map, display the distribution of each group of samples in different cell clusters, and identify the immune cell types existing in the gout attack period, remission period, and healthy control group.

[0095] Specifically, in the analysis of single-cell data, Seurat was first used to preprocess the data, including quality control, batch correction, variable gene screening, and dimensionality reduction. Through these steps, genes with significant expression differences can be identified among different cell subtypes, and they can be annotated and clustered for analysis.

[0096] S3: Through the grouping comparison of cell datasets, screen out the differentially expressed genes between the remission patients and healthy normal controls, and the differentially expressed genes between the attack period and the remission period, and take the intersection of the two to obtain candidate genes that can simultaneously prevent gout attacks and promote the patient's recovery to the normal state.

[0097] It can be understood that in this embodiment, the S3: Through the grouping comparison of cell datasets, screen out the differentially expressed genes between the remission patients and healthy normal controls, and the differentially expressed genes between the attack period and the remission period, and take the intersection of the two to obtain candidate genes that can simultaneously prevent gout attacks and promote the patient's recovery to the normal state, including:

[0098] Integrate multi-source datasets and divide them into three groups: 3 healthy control groups Control, 3 gout attack period patient groups Gout, and 6 gout remission period patient groups GR;

[0099] Divide the samples into two groups for comparison: The first group is 6 GR groups and 3 Control groups, defined as DEG1, used to screen for genes with differential expression between remission patients and normal controls, and these genes may be related to the patient's recovery to the normal state;

[0100] The second group consisted of 6 cases in the GR group and 3 cases in the Gout group, defined as DEG2, which was used to screen for genes with differential expression between the attack phase and the remission phase. These genes may be related to the transition of patients from the gout attack phase to the remission phase.

[0101] By taking the intersection of DEG1 and DEG2, genes that can both prevent gout attacks and promote recovery to the normal state were obtained. These genes were abnormally expressed in different cell subtypes.

[0102] Finally, the expression results of the differential genes were presented through heatmaps and trend curves, and duplicates were removed to obtain candidate genes.

[0103] Specifically, the samples included two sets of comparisons: 6 cases in the GR group and 3 cases in the Control group, defined as DEG1, mainly screening for genes with differential expression between patients in the remission phase and normal controls. These genes may be related to the recovery of patients to the normal state. The second group consisted of 6 cases in the GR group and 3 cases in the Gout group, defined as DEG2, screening for genes with differential expression between the attack phase and the remission phase. These genes may be related to the transition of patients from the gout attack phase to the remission phase. By taking the intersection of DEG1 and DEG2, genes that can both prevent gout attacks and promote recovery to the normal state were obtained. These genes were abnormally expressed in different cell subtypes. Finally, the expression results of these differential genes were presented through heatmaps and trend curves, and duplicates were removed. The obtained candidate genes will be used for subsequent analysis.

[0104] To further screen for differentially expressed genes, the FindMarkers function was used to compare between different groups. The screening threshold was set as |log2FC| > 1 and FDR < 0.05. Specifically, the comparison between 6 GR groups and 3 Control groups was defined as DEG1, screening for differentially expressed genes between patients in the remission phase and healthy normal controls. The comparison between 6 GR groups and 3 Gout groups was defined as DEG2, screening for differentially expressed genes between the attack phase and the remission phase. Finally, by taking the intersection of DEG1 and DEG2, candidate genes that can both prevent gout attacks and promote the recovery of patients to the normal state were screened out, and these genes were presented through heatmaps and trend curves. The genes after removing duplicates were used as candidate genes for subsequent functional analysis.

[0105] To further focus on potential therapeutic targets, the intersection of DEG1 and DEG2 was extracted, obtaining 53 key candidate genes, whose expression patterns were significantly different in the normal, attack, and remission phases. In particular, there were the most differential genes and a wide intersection in B cells, monocytes, and the CD8+ Tem subset, and these cell populations may play a key role in the immune regulation during the intercritical period of gout. The difference in the expression patterns of these genes between the remission and attack phases suggests that they may be key factors regulating the immune response and attack of gout.

[0106] In the disease-specific expression analysis, significant gene expression differences were found between the attack and remission phases, especially the upregulation of immune-related genes during the attack phase and the recovery to the normal state during the remission phase (such as Figure 3 A-B). The expression changes of these differential genes revealed the immune mechanism of gout, especially the immune activation during the attack phase and the immune recovery during the remission phase.

[0107] It was also found that B cells, monocytes, and CD8+ Tem cells showed obvious differential expression between the attack and remission phases, and these cell populations may play a core role in the regulation of the immune response (such as Figure 3 C). The differential expression of these cell subsets provided strong support for further studying the immune mechanism of gout attack and remission.

[0108] S4: Analyze the interactions PPI between the proteins encoded by the candidate genes based on the candidate genes, construct a protein-protein interaction PPI network according to the PPI score, and identify potential biomarker therapeutic targets.

[0109] It can be understood that in this embodiment, the S4: Analyze the interactions PPI between the proteins encoded by the candidate genes based on the candidate genes, construct a protein-protein interaction PPI network according to the PPI score, and identify potential biomarker therapeutic targets, includes:

[0110] Use the STRING database to predict and analyze the interactions between the proteins encoded by the candidate genes, construct a protein-protein interaction network according to the PPI score for identifying potential biomarkers;

[0111] Identify potential therapeutic targets through the topological analysis of the PPI network.

[0112] Specifically, to deeply understand the interactions between the candidate genes, the STRING database [5] (version: 12) (https: / / string-db.org) was used to predict and analyze the interactions between the proteins encoded by the candidate genes. According to the PPI score (set to 0.15), a protein-protein interaction network was constructed to help identify potential biomarkers.

[0113] To further reveal the interactions between candidate genes, a protein - protein interaction (PPI) network was constructed. According to Figure 4 the results in Figure 4 , 23 candidate genes formed 33 protein - protein interaction relationships in the PPI network. The interactions between these genes may play important roles in regulating immune and inflammatory responses. Specifically, in the network, FKBP14 and POLR3B play core roles in multiple immune cell types, especially with high interactions in B cells and monocytes (such as

[0114]

[0115] S5: Construct a network pharmacology model. In network pharmacology analysis, use the BATMAN database to predict chemical small molecules related to candidate genes, and use the HERB database to predict traditional Chinese medicine or herbal components of the screened chemical small molecules to obtain candidate medicinal materials.

[0116] It can be understood that in this embodiment, the S5: Construct a network pharmacology model. In network pharmacology analysis, use the BATMAN database to predict chemical small molecules related to candidate genes, and use the HERB database to predict traditional Chinese medicine or herbal components of the screened chemical small molecules to obtain candidate medicinal materials, including:

[0117] In network pharmacology analysis, use the BATMAN database to predict chemical small molecules related to candidate genes;

[0118] By screening chemical small molecules with a P - value less than 0.05, obtain small molecules that may interact with candidate genes;

[0119] Use the HERB database to screen the chemical small molecules for prediction of traditional Chinese medicine or herbal components to determine their correlation with candidate genes;

[0120] Perform molecular docking simulation on small molecules and target proteins, and use a genetic algorithm to optimize the docking process; use LigPlot+ software for visualization to further confirm the binding mode between small molecules and target proteins.

[0121] ​Specifically, in network pharmacology analysis, the BATMAN database [6] (http: / / lsp.nwu.edu.cn / browse.php?qc=herbs) was used to predict chemical small molecules related to candidate genes. By screening chemical small molecules with a P-value less than 0.05, small molecules that might interact with candidate genes were obtained. Subsequently, the HERB database [7] (http: / / herb.ac.cn / ) was used to predict the herbal (traditional Chinese medicine) components of the screened chemical small molecules to determine their correlation with candidate genes.

[0122] To verify the binding of the key small molecules screened in network pharmacology analysis to target genes, the complex structure file of the relevant target gene protein was downloaded using the PDB database (http: / / www.rcsb.org / ). The PyMol [9] (Version 2.0) software was used to remove other ligands and water molecules to obtain a pure target protein structure. Subsequently, the molecular structure (SDF format) of the key small molecule was downloaded from the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov / ). The AutoDockVina (AutoDock4.2.6)

[11] software was used to perform molecular docking simulation of the small molecule and the target protein, and a genetic algorithm was used to optimize the docking process. Finally, the LigPlot+ [4] (v.2.2, https: / / www.ebi.ac.uk / thornton-srv / software / LigPlus / ) software was used for visualization to further confirm the binding mode of the small molecule and the target protein.

[0123] Based on the above 53 candidate genes, component prediction was performed in the BANTAN database, and 7 active components related to 4 genes were screened out (such as Figure 5 ). These active components include beta-D-Ribofuranose, 4-methylphenol, alpha-D-Mannose, glycerol, (-)-Epicatechin, l-ascorbic acid, and (-)-Epigallocatechin, etc. These components are closely related to immune regulation and inflammatory responses and may play an important role in the treatment of gout. Further, herbal prediction of these components was carried out in the HERB database. The results showed that components such as (-)-Epicatechin and (-)-Epigallocatechin have potential relationships with various herbs (such as Scrophulariae Radix, Smilacis Glabrae Rhizoma, Plantaginis Majoris Herba, etc.) (such as Figure 5)。These herbs are common in traditional Chinese medicine treatment and have multiple effects such as anti - inflammation and immune regulation, providing a basis for subsequent network pharmacology analysis. According to the data in the figure, the associations between different immune cell types and herb components can be seen. For example, POMK and alpha - D - Mannose show a strong interaction in monocytes, while RYR1 and (-)-Epicatechin have a strong association in CD8+Tem cells, which provides a theoretical basis for subsequent treatment strategies. The screening and analysis of these herbs and their active ingredients provide a new perspective for further exploring their potential in gout immune regulation.

[0124] S6: Further screen the candidate medicinal materials according to the criteria of homology between medicine and food, and provide potential traditional Chinese medicines with homology between medicine and food for the treatment plan of gout.

[0125] It can be understood that in this embodiment, the S6: Further screen the candidate medicinal materials according to the criteria of homology between medicine and food, and provide potential traditional Chinese medicines with homology between medicine and food for the treatment plan of gout, including:

[0126] Use the data in the table to display the herb information corresponding to each active ingredient, including the name of the herb, pinyin, medicinal material ID, and whether it has the attribute of homology between medicine and food;

[0127] Mark whether the herb meets the criteria of homology between medicine and food of the National Health Commission in the "Homology between Medicine and Food" column in the table to screen the herbs.

[0128] Based on network pharmacology and molecular docking analysis, screen out traditional Chinese medicine components with the property of homology between medicine and food, and provide potential traditional Chinese medicines with homology between medicine and food for the treatment plan of gout.

[0129] Molecular docking analysis was performed on the above 7 active ingredients. Through PyMol and AutoDock software for molecular docking of target proteins, 5 pairs of targeting relationships were successfully screened out. Specifically, (-)-Epicatechin targets RYR1, (-)-Epigallocatechin targets RYR1, etc., showing a strong binding ability between these active ingredients and their corresponding targets.

[0130] Figure 6 The results in... show that the docking score of each molecular docking is negative, reflecting its strong binding affinity. Among them, the docking score of (-)-Epicatechin and RYR1 is -7.8, the docking score of (-)-Epigallocatechin and RYR1 is -7.7, and the docking score of 4 - methylphenol and RYR1 is -5.1 (as Figure 6)。These results indicate that these components can effectively bind to the targets and may play an important role in the regulation of immune and inflammatory responses.

[0131] In addition, components such as alpha-D-Mannose and POMK, beta-D-Ribofuranose and RYR1 also demonstrated relatively strong binding capabilities, further verifying the application value of these active components in the potential intervention and treatment of gout. The results of these molecular docking provide valuable information for subsequent drug development, especially in exploring intervention and treatment strategies for gout, revealing the potential of these natural active components.

[0132] Example 2

[0133] As Figure 7 shown, this application provides a schematic diagram of a gout target biomarker screening and traditional Chinese medicine prediction device module based on single-cell sequencing combined with homologous substances of medicine and food and network pharmacology. This application provides a gout target biomarker screening and traditional Chinese medicine prediction device based on single-cell sequencing combined with homologous substances of medicine and food and network pharmacology, which is applied to the gout target biomarker screening and traditional Chinese medicine prediction method described in Example 1, including: a data collection and quality control module 11, a cell clustering and subpopulation annotation module 12, a core regulatory gene identification module 13, a protein-protein interaction network module 14, an active ingredient-target database screening module 15, and a homologous traditional Chinese medicine screening module 16.

[0134] Specifically, in this embodiment, the data collection and quality control module 11 is used to collect peripheral blood mononuclear cell sample datasets of different categories of objects, perform data quality control and preprocessing, and identify genes with significant expression differences among different cell subtypes.

[0135] Specifically, in this embodiment, the cell clustering and subpopulation annotation module 12 is used to perform clustering using the clustering algorithm of Seurat to identify different cell clusters, and use SingleR4 for cell type annotation to identify the main cell types.

[0136] Specifically, in this embodiment, the core regulatory gene identification module 13 is used to screen out differentially expressed genes between remission patients and healthy normal controls and differentially expressed genes between the attack period and the remission period through cell dataset grouping comparison, and take the intersection of the two to obtain candidate genes that can simultaneously prevent gout attacks and promote the patient's recovery to a normal state.

[0137] Specifically, in this embodiment, the protein-protein interaction network module 14 is used to analyze the interactions PPI between the proteins encoded by candidate genes based on the candidate genes, construct a protein-protein interaction PPI network according to the PPI score, and identify potential biomarker treatment targets.

[0138] Specifically, in this embodiment, the active ingredient-target database screening module 15 is used to construct a network pharmacology model. In network pharmacology analysis, the BATMAN database is used to predict chemical small molecules related to candidate genes, and the HERB database is used to predict traditional Chinese medicine or herbal ingredients for the screened chemical small molecules to obtain candidate medicinal materials.

[0139] Specifically, in this embodiment, the homologous Chinese medicine and food screening module 16 is used to further screen the candidate medicinal materials according to the criteria of homologous Chinese medicine and food, and provide potential homologous Chinese medicine and food for the treatment plan of gout.

[0140] Figure 8 is an electronic device provided by an embodiment of the present application. As Figure 8 shown, the electronic device at least includes the following parts: a processor 101, a memory 100, a communication interface 103, and a bus 102.

[0141] In the embodiment of the present application, the memory 100 is used to store executable instructions of the processor 101. When the processor 101 is configured to execute the instructions, it realizes a gout target biomarker screening and traditional Chinese medicine prediction device module based on single-cell sequencing combined with homologous substances of Chinese medicine and food as Figure 7 shown.

[0142] In the embodiment of the present application, a computer-readable storage medium includes instructions that instruct the device to execute the method in the first aspect. For example, the instructions instruct the device to execute the artificial intelligence-based device state prediction and risk assessment method shown in the Figure 1 process steps.

[0143] The program operating in the electronic device involved in an embodiment of the present application can be a program that controls a central processing unit (CPU) and the like to realize the functions of the above-mentioned embodiments involved in a solution of the present invention (a program that makes a computer function). Then, the information processed by these devices is temporarily stored in a random access memory (RAM) during its processing, and then stored in various ROMs such as a read-only memory (FlashROM), a hard disk drive (Hard Disk Drive: HDD), and read out, corrected, and written by the CPU as needed.

[0144] It should be noted that a part of the electronic device in the above embodiments can also be implemented by a computer. In this case, the program for implementing the control function can be recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into the computer and executed to achieve the implementation.

[0145] It should be noted that the "computer" mentioned here refers to the computer built into the electronic device, which is a computer including hardware such as an OS and peripheral devices. In addition, the "computer-readable recording medium" refers to removable media such as floppy disks, magneto-optical disks, ROMs, CD-ROMs, and storage devices such as hard disks built into the computer.

[0146] Moreover, the "computer-readable recording medium" may include: a medium that stores a program dynamically for a short time, such as a communication line when sending a program via a network such as the Internet or a communication line such as a telephone line; a medium that stores a program for a fixed time, such as a volatile memory inside a computer of a server or a client in this case. In addition, the above program may be a program for implementing a part of the above functions, and may also be a program that can implement the above functions by combining with a program already recorded in the computer.

[0147] In addition, the electronic device in the above embodiments can also be implemented as an aggregate (device group) composed of multiple devices. Each device constituting the device group may have all or part of the functions or function blocks of the electronic device in the above embodiments. As the device group, it only needs to have all the functions or function blocks of the electronic device.

[0148] Those of ordinary skill in the art in this technical field should recognize that the above embodiments are only used to illustrate the present application, rather than to limit the present application. As long as it is within the scope of the essential spirit of the present application, appropriate changes and variations made to the above embodiments fall within the scope of protection required by the present application.

Claims

1. A method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologous substances and network pharmacology, characterized in that: The method comprises: S1: Collect peripheral blood mononuclear cell sample data sets from different categories of subjects, perform data quality control and preprocessing, and identify genes with significant expression differences in different cell subtypes; S2: Seurat's clustering algorithm was used to perform grouping and identify different cell clusters, and Single R 4 was used to annotate cell types and identify the main cell types; S 3: By comparing the cell data sets in groups, we screened out the differentially expressed genes between patients in remission and healthy controls, and the differentially expressed genes between the attack period and the remission period, and took the intersection of the two to obtain candidate genes that can simultaneously prevent gout attacks and promote patients to return to normal; S 4: Based on the candidate genes, analyze the interactions ( P PI ) between proteins encoded by the candidate genes, construct a protein interaction ( P PI ) network according to the P PI score, and identify potential biomarker therapeutic targets; S 5: Construct a network pharmacology model. In the network pharmacology analysis, use the BATMAN database to predict the chemical small molecules related to the candidate genes, and use the HERB database to predict the components of traditional Chinese medicine or herbal medicine for the screened chemical small molecules to obtain candidate medicinal materials; S 6: Further screening the candidate medicinal materials according to the standard of medicine and food homology to provide potential Chinese medicine and food homology for the treatment of gout.

2. According to claim 1, a method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologous substances and network pharmacology, characterized in that: The preprocessing includes quality control, batch correction, variable gene screening and dimensionality reduction, and the steps include: Basic quality control was performed using Se urat 2 software with the default threshold setting; In the process of data quality control, the number of genes detected in each cell, n Fe atur e_R NA, was set as follows: n Fe atur e_R NA>300 and n Fe atur e_R NA<3000, and the proportion of mitochondrial genes was required to be less than 10%; Harmony 3 was used to correct for batch effects and The Find Variable Features function screened out the top 2000 highly variable genes; Principal component analysis (PCA) was used to reduce the data dimension, and the ElbowPlot method was used to select the optimal number of principal components. Finally, the first 30 principal components were selected for subsequent analysis.

3. The method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologous substances and network pharmacology according to claim 1, characterized in that: S2: Seurat's clustering algorithm was used to perform grouping and identify different cell clusters. Single R 4 was used to annotate cell types and identify the main cell types, including: Cell clustering analysis was performed, with the resolution set to 0.3, using Seurat's clustering algorithm for clustering, and then using Single R 4 for cell type annotation; The Run UMAP function in Seurat was used to visualize the clusters in two dimensions using the uniform manifold approximation and projected UMAP. The UMAP cell map shows the distribution of samples from each group in different cell clusters, identifying the types of immune cells present in gout attacks, remission periods and healthy control groups.

4. According to claim 1, a method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologous substances and network pharmacology, characterized in that: S3: By comparing the cell data sets in groups, differentially expressed genes between patients in remission and healthy controls and differentially expressed genes between the attack period and the remission period are screened, and the intersection of the two is taken to obtain candidate genes that can simultaneously prevent gout attacks and promote patients to return to a normal state, including: The multi-source data sets were integrated and divided into three groups: 3 healthy controls (Contr ol), 3 gout attack patients (Gout), and 6 gout remission patients (GR); The samples were divided into two groups for comparison: the first group consisted of 6 GR group and 3 Control group, which were defined as DEG 1 and used to screen genes with differential expression between patients in remission and normal controls, which may be associated with the recovery of patients to a normal state; The second group consisted of 6 cases in the GR group and 3 cases in the G out group, which was defined as DEG 2 and was used to screen genes with differential expression between the attack and remission phases, which may be associated with the transition of patients from the gout attack phase to the remission phase; By taking the intersection of DEG 1 and DEG 2, genes that can simultaneously prevent gout attacks and promote recovery to a normal state were obtained, and the genes were abnormally expressed in different cell subtypes; Finally, the expression results of differentially expressed genes were displayed through heat maps and trend curves, and duplicates were removed to obtain candidate genes.

5. According to claim 4, a method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologous substances and network pharmacology, characterized in that: S 4: Based on the candidate genes, the interactions P PI between proteins encoded by the candidate genes are analyzed, and according to the P PI scores, a protein interaction P PI network is constructed to identify potential biomarker therapeutic targets, including: The ST RING database was used to predict and analyze the interactions between proteins encoded by candidate genes, and a protein interaction network was constructed based on P PI scores to identify potential biomarkers. Through topological analysis of the P PI network, potential therapeutic targets were identified.

6. According to claim 5, a method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologous substances and network pharmacology, characterized in that: S5: constructing a network pharmacology model, in the network pharmacology analysis, using the BATMAN database to predict chemical small molecules related to the candidate genes, using the HERB database to predict the Chinese medicine or herbal medicine components of the screened chemical small molecules, and obtaining candidate medicinal materials, including: In the network pharmacology analysis, the BAT MA N database was used to predict chemical small molecules associated with candidate genes; By screening chemical small molecules with a P value less than 0.05, small molecules that may interact with candidate genes were obtained; Use the chemical small molecules screened from the HERB database to predict the components of traditional Chinese medicine or herbal medicine to determine their correlation with candidate genes; Molecular docking simulation was performed on small molecules and target proteins, and the docking process was optimized using genetic algorithms. Lig Plot+ software was used for visualization to further confirm the binding mode of small molecules and target proteins.

7. According to claim 1, a method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologous substances and network pharmacology, characterized in that: S6: Further screening the candidate medicinal materials according to the standard of medicine and food homology to provide potential Chinese medicine and food homology for the treatment of gout, including: Use the data in the table to display the herbal information corresponding to each active ingredient, including the name of the herb, its pinyin, herbal ID, and whether it has the property of being a medicine or food. In the "Medicine and Food Same Origin" column of the table, mark whether the herb meets the National Health Commission's standard for medicine and food same origin, and screen the herb. Based on network pharmacology and molecular docking analysis, Chinese herbal medicine ingredients with food-drug homology properties were screened out to provide potential food-drug homology Chinese herbal medicines for the treatment of gout.

8. A device for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologues and network pharmacology, applied to the method for screening gout target biomarkers and predicting traditional Chinese medicine based on single-cell sequencing combined with food-drug homologues and network pharmacology as described in any one of claims 1 to 7, characterized in that: The device comprises: Data collection and quality control module: used to collect peripheral blood mononuclear cell sample data sets of different categories of subjects, perform data quality control and preprocessing, and identify genes with significant expression differences in different cell subtypes; Cell clustering and subpopulation annotation module: used to use Seurat's clustering algorithm to perform clustering, identify different cell clusters, use Single R 4 to perform cell type annotation, and identify the main cell types; Core regulatory gene identification module: used to screen differentially expressed genes between patients in remission and healthy controls and between the attack period and the remission period through group comparison of cell data sets, and take the intersection of the two to obtain candidate genes that can simultaneously prevent gout attacks and promote patients to return to normal state; Protein interaction network module: used to analyze the interactions ( P PI ) between proteins encoded by candidate genes based on candidate genes, construct protein interaction ( P PI ) networks according to P PI scores, and identify potential biomarker therapeutic targets; Active ingredient-target database screening module: used to build a network pharmacology model. In the network pharmacology analysis, the BATMAN database is used to predict the chemical small molecules related to the candidate genes, and the HERB database is used to predict the Chinese medicine or herbal medicine components of the screened chemical small molecules to obtain candidate medicinal materials; The Chinese medicine screening module for both medicine and food is used to further screen the candidate medicinal materials according to the standard of both medicine and food, and provide potential Chinese medicine for the treatment of gout.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the gout target biomarker screening and traditional Chinese medicine prediction method based on single-cell sequencing combined with medicinal and edible homologous substances and network pharmacology as described in any one of claims 1 to 7 when executing the instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and the program instructs the device to execute the gout target biomarker screening and traditional Chinese medicine prediction method based on single-cell sequencing combined with medicinal and edible homologous substances and network pharmacology as described in any one of claims 1 to 7.

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