A method and system for endometriosis subtyping

Abstract

The application relates to an endometriosis subtype identification method and system, relates to the fields of bioinformatics and medical diagnosis auxiliary technologies, and takes transcriptome expression data as input, constructs a standardized expression matrix, and adopts consistency clustering to perform subtype modeling to obtain a molecular subtype label; expression timing structure of a menstrual cycle phase is introduced to perform dynamic gene identification and discriminant power evaluation, and immune cell infiltration inference and path activity scoring are combined to analyze differences of the subtypes in an immune and inflammation network, then gene-pathway / gene-cell correlation is calculated to form multi-dimensional typing information with mechanism orientation. Based on the above results, an interpretable typing identification model is constructed, which provides a new technical means for accurate diagnosis and target identification of endometriosis.

Description

Technical Field

[0001] This application relates to the fields of bioinformatics and medical diagnostic assistance technology, and in particular to a method and system for identifying and classifying endometriosis. Background Technology

[0002] Endometriosis (EMs) is a chronic, hormone-dependent disease characterized by the growth of functional endometrial-like tissue outside the uterine cavity. Current clinical classification of endometriosis is primarily based on the anatomical location of the lesions, such as ovarian, deep-invasive, and peritoneal types. However, this anatomically-based classification method has inherent limitations, failing to effectively identify the disease's potential biological heterogeneity, leading to high uncertainty in patient clinical presentation, treatment response, and prognosis. While current technologies attempt to analyze some patients using gene expression data or immune characteristics, a stable and universally applicable molecular subtyping standard system has not yet been established.

[0003] The existing technology has the following main defects: (1) It ignores the dynamic changes of the disease. Endometriosis is essentially a hormone-dependent disease that fluctuates with the ovarian cycle. Existing studies rarely construct gene feature models from the "time dimension", and cannot capture the fluctuations in the expression of key genes that occur with the menstrual cycle. (2) It lacks the ability to subtype the immune microenvironment. Although some studies have focused on the role of immune dysregulation in endometriosis, existing methods usually focus on a single immune pathway or cell type, and lack the ability to systematically incorporate the immune map into the subtyping model, making it difficult to reveal the core differences in immune regulation between disease subtypes.

[0004] It is evident that existing technologies are limited by flaws such as crude typing and identification mechanisms, lack of temporal information, and weak immune stratification, making it impossible to accurately classify endometriosis. Summary of the Invention

[0005] This application provides a method and system for identifying endometriosis subtypes, establishing a subtype identification mechanism for endometriosis based on multidimensional expression of immune characteristics and dynamic characteristics of the menstrual cycle, achieving accurate subtype identification of endometriosis, and solving the problem that the accuracy and interpretability of subtype identification are limited by existing technologies due to crude subtype identification mechanisms, lack of temporal information and weak immune stratification.

[0006] In a first aspect, this application provides a method for identifying and classifying endometriosis, including:

[0007] The transcriptome chip dataset was preprocessed to obtain a standard expression matrix set. The transcriptome chip dataset includes a patient set, a validation set, and a menstrual cycle phase set. In the standard expression matrix set, each standard expression matrix is ​​organized with genes as rows and samples as columns, and the columns correspond one-to-one with the samples.

[0008] Based on the standard expression matrix corresponding to the patient set, typing modeling and cluster stability analysis were performed through consistent clustering analysis to obtain a molecular subtype set including molecular subtypes;

[0009] Differential expression analysis was performed based on molecular subtype sets to screen differentially expressed genes specific to each subtype, and enrichment analysis results were obtained through functional annotation and pathway enrichment analysis.

[0010] Temporal expression pattern modeling was carried out in the menstrual cycle phase set, and genes with significant dynamic expression were screened to obtain a temporal gene set. The menstrual cycle phase set includes samples with menstrual cycle phase annotations.

[0011] Subtype-specific genes were obtained by dual feature screening and verification based on the intersection of dynamically expressed genes and differentially expressed genes in the time-series gene set.

[0012] Based on predefined immune cell type reference signatures, subtypes are inferred for immune infiltration, and correlation modeling is performed with subtype genes to obtain subtype immune infiltration profiles and key gene-immune cell associations.

[0013] The pathway activities of cytokines and / or chemokines are calculated based on the expression data of patients' concentrated subtype samples, and correlation analysis is performed in combination with subtype-specific genes to output gene-pathway associations and pathway patterns of each subtype. The pathway patterns include activation patterns and inhibition patterns.

[0014] Using expression typing as a framework, and superimposing time-series gene sets, subtype immune infiltration profiles, pathway activities, gene-pathway associations, key gene-immune cell associations, and pathway patterns, a typing identification model is formed to classify endometriosis based on the acquired test data.

[0015] Optionally, based on the standard expression matrix corresponding to the patient set, a set of molecular subtypes including molecular subtypes is obtained by performing typing modeling and cluster stability analysis through consistent clustering analysis. This includes: performing consistent clustering analysis on the standard expression matrix corresponding to the patient set using a consistent clustering algorithm to obtain curve information and change information for different numbers of clusters; selecting the optimal classification based on the curve information and change information, using cluster stability as a benchmark, to obtain the set of molecular subtypes; and validating each molecular subtype in the set of molecular subtypes using the standard expression matrix of the validation set, and updating the set of molecular subtypes based on the validation results.

[0016] Optionally, differential expression analysis is performed based on the molecular subtype set to screen for differentially expressed genes specific to each subtype, including: comparing each molecular subtype with normal control samples from the patient set and determining the differential expression results through differential expression analysis; and screening based on a preset first screening threshold and the differential expression results to obtain differentially expressed genes, including subtype characteristic genes.

[0017] Optionally, enrichment analysis results can be obtained through functional annotation and pathway enrichment analysis, including: term annotation based on subtype characteristic genes, and evaluation of the enrichment characteristics of subtype characteristic genes through pathway enrichment analysis to obtain enrichment analysis results.

[0018] Optionally, the menstrual cycle phase set includes menstrual cycle phase samples. Temporal expression pattern modeling is performed on the menstrual cycle phase set to screen for significantly dynamically expressed genes, resulting in a temporal gene set. This includes: organizing expression data by phase for the standard expression matrix corresponding to the menstrual cycle phase samples; using the phase-organized expression data as input, calculating the intra-group average expression for each phase to construct a phase-ordered input; performing short-term temporal expression pattern modeling on the phase-ordered input; screening for significantly changed expression modules based on a preset significance threshold to determine dynamically expressed genes, thus obtaining the temporal gene set.

[0019] Optionally, a dual feature screening and verification can be performed based on the intersection of each dynamically expressed gene and differentially expressed gene in the time-series gene set to obtain subtype-specific genes. This includes: screening overlapping genes based on the intersection of dynamically expressed genes and differentially expressed genes; screening test genes with subtype-specific expression from the overlapping genes; and evaluating the diagnostic performance of the test genes using receiver operating characteristic curves to obtain subtype-specific genes.

[0020] Optionally, based on a predefined immune cell type reference signature, immune infiltration is inferred for subtypes, and correlation modeling is performed with subtype genes to obtain subtype immune infiltration profiles and key gene-immune cell associations, including: based on the standard expression matrix corresponding to the patient set, the xCell algorithm is used to infer the relative abundance of immune and stromal cells to obtain an immune infiltration score; based on the immune infiltration score, each molecular subtype is tested and evaluated to identify differentially expressed cells between subtypes; correlation analysis is performed between the infiltration score of differentially expressed cells and the expression of subtype-specific genes to form subtype immune infiltration profiles and key gene-immune cell associations; wherein, the key gene-immune cell associations include an association matrix.

[0021] Optionally, correlation analysis can be performed using subtype-specific genes to output gene-pathway associations and pathway patterns for each subtype. This includes: performing correlation analysis based on pathway activity and subtype-specific genes to calculate activity scores for relevant pathways; comparing and summarizing each molecular subtype with normal controls based on the activity scores to analyze gene-pathway associations and pathway patterns for each subtype; wherein, pathway patterns are represented by pathway activation profiles and pathway inhibition profiles.

[0022] Optional, also includes:

[0023] Based on the pathway activation profile, subtype-specific genes were selected as key molecules; the expression levels of key molecules were correlated with the activity scores of each pathway, and the association results between key genes and pathways were output.

[0024] Secondly, this application provides an endometriosis typing and identification system, comprising:

[0025] The data preprocessing module is used to preprocess the transcriptome chip dataset to obtain a standard expression matrix set. The transcriptome chip dataset includes a patient set, a validation set, and a menstrual cycle phase set. In the standard expression matrix set, each standard expression matrix is ​​organized with genes as rows and samples as columns, and the columns correspond one-to-one with the samples.

[0026] The clustering analysis module is used to perform typing modeling and cluster stability analysis based on the standard expression matrix corresponding to the patient set through consistent clustering analysis, and obtain a molecular subtype set including molecular subtypes;

[0027] The differential expression and enrichment analysis module is used to perform differential expression analysis based on molecular subtype sets, screen for differentially expressed genes specific to each subtype, and obtain enrichment analysis results through functional annotation and pathway enrichment analysis.

[0028] The dynamic gene screening module is used to model temporal expression patterns in the menstrual cycle phase set, screen for significantly dynamically expressed genes, and obtain a temporal gene set. The menstrual cycle phase set includes samples with menstrual cycle phase annotations.

[0029] The dual-feature screening module is used to perform dual-feature screening verification based on the intersection of each dynamically expressed gene and differentially expressed gene in the time-series gene set to obtain subtype-specific genes;

[0030] The immune infiltration analysis module is used to infer the immune infiltration of subtypes based on predefined immune cell type reference signatures and to perform correlation modeling with subtype genes to obtain subtype immune infiltration profiles and key gene-immune cell associations.

[0031] The correlation analysis module is used to calculate the pathway activity of cytokines and / or chemokines based on the expression data of patient-focused subtype samples, and to perform correlation analysis in combination with subtype-specific genes, outputting gene-pathway associations and pathway patterns for each subtype, including activation and inhibition patterns.

[0032] The typing identification module is used to form a typing identification model by overlaying time-series gene sets, subtype immune infiltration profiles, pathway activities, gene-pathway associations, key gene-immune cell associations, and pathway patterns on expression typing as a framework, and then typing the acquired test data for endometriosis.

[0033] In summary, this application addresses the technical problems of existing technologies, such as crude typing identification mechanisms, lack of temporal information, and weak immune stratification. It proposes a method for identifying endometriosis subtypes based on multidimensional expression of immune characteristics and dynamic characteristics of the menstrual cycle. This method combines dynamic expression characteristics of the menstrual cycle with the state of the immune microenvironment to achieve high-resolution typing of endometriosis. Specifically, firstly, multiple sets of transcriptome data are preprocessed to establish corresponding standardized expression matrices. Consistent clustering analysis is used for modeling to identify stable clustered molecular subtypes. Based on molecular subtypes, differentially expressed genes with specificity are screened as subtype characteristic genes. Functional annotation and pathway enrichment analysis are performed on these characteristic genes, and significantly enriched entries are visualized to display the functional characteristics of each subtype. Menstrual cycle phase samples are introduced, and dynamically expressed genes are screened through temporal expression modeling. These dynamically expressed genes are then compared with differentially expressed genes to obtain subtype-specific genes. Furthermore, the correlation between immune cell infiltration composition and typing genes is modeled, and the characteristic immune cell composition of different subtypes is analyzed to obtain cellular immune infiltration results. This study assesses the activity scores of cytokine and trending factor-related pathways, calculates the correlation coefficients between menstrual cycle-driven dynamic characteristic genes and pathway activity within each subtype-specific gene group, and establishes a complete endometriosis identification mechanism for endometriosis analysis of test data. As can be seen, this application achieves information integration based on multidimensional expression characteristics, establishing an integrated workflow from expression data to mechanism interpretation. This workflow comprehensively considers dynamic expression during the menstrual cycle, the state of the immune microenvironment, and signaling pathway characteristics, constructing a subtyping system with biological consistency and clinical translational potential. It provides a new technical means for the accurate diagnosis and target identification of endometriosis, aiming to solve the technical problems of limited accuracy and stability of endometriosis subtyping due to single subtyping identification criteria, insufficient incorporation of menstrual cycle temporal information, and insufficient utilization of immune stratification in existing technologies. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating a method for identifying and classifying endometriosis according to an embodiment of this application;

[0037] Figure 2 This is a flowchart illustrating the steps of an optional embodiment of the present application for a method for identifying and classifying endometriosis.

[0038] Figure 3 A classification modeling and validation diagram of endometriosis provided as an example in this application;

[0039] Figure 4 A diagram illustrating the genomic enrichment results of three endometriosis subtypes provided as an example in this application;

[0040] Figure 5 STEM analysis and functional enrichment plot of grouped and clustered genes of GSE51981 sample provided as an example in this application;

[0041] Figure 6 A core gene screening and ROC diagnostic model evaluation diagram is provided as an example for this application;

[0042] Figure 7 A diagram illustrating the distribution of immune cells and correlations of core genes in different subtypes of endometriosis, provided as an example in this application.

[0043] Figure 8 An example of this application provides a diagram illustrating the expression characteristics of cytokines and chemokine pathways in various subtypes of endometriosis.

[0044] Figure 9 This is a structural block diagram of an endometriosis typing and identification system provided in an embodiment of this application. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] To facilitate understanding of the embodiments of this application, further explanations and descriptions will be provided below in conjunction with the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of this application.

[0047] Figure 1 The flowchart illustrates a method for identifying and classifying endometriosis according to an embodiment of this application. The method may specifically include the following steps:

[0048] Step 110: Preprocess the transcriptome microarray dataset to obtain a standard expression matrix set.

[0049] The transcriptome chip dataset includes a patient set, a validation set, and a menstrual cycle phase set. In the standard expression matrix set, each standard expression matrix is ​​organized with genes as rows and samples as columns, and the columns correspond one-to-one with the samples.

[0050] In this embodiment, a transcriptome microarray dataset related to endometriosis can be downloaded from the publicly available gene expression database GEO. This dataset includes GSE6364, GSE37837, GSE51981, and GSE7305. GSE6364 serves as the patient set (or patient samples), including endometrial samples from 21 patients with moderate to severe endometriosis and 14 endometrial tissue samples from patients without endometriosis, all within a normal menstrual cycle. GSE37837 and GSE7305 together form the validation set. GSE37837 contains 18 pairs of 36 samples (paired samples of situ and ectopic endometrial tissue) used to independently validate the typing results. GSE51981, a menstrual cycle sample, includes transcriptomic data of endometrial tissue at different time points during the menstrual cycle, labeled according to physiological phases: proliferative phase, early secretory phase, and mid-secretive phase. It includes 77 endometriosis patients and 37 non-endometriosis patients, and can be used to analyze the relationship between subtype labels and cyclical expression changes. GSE7305 primarily consists of endometriosis and normal endometrial tissue samples, 10 samples each, used to replicate and validate the expression of dynamic core genes.

[0051] In addition, this embodiment can also download pathway gene sets of immune and cytokine pathways from public databases (such as KEGG and Reactome) for pathway activity scoring.

[0052] This embodiment can perform unified preprocessing on the sample data of each sample in the acquired transcriptome microarray dataset, including standardization, to construct a unified expression profile matrix as a standard expression matrix. At least one expression matrix can be built for each type of sample, forming a standard expression matrix set.

[0053] Therefore, in this embodiment, datasets related to endometriosis, such as GSE6364, GSE37837, GSE51981 and GSE7305, were downloaded from the GEO database and used for typing construction, validation, periodicity and immune feature analysis, respectively.

[0054] Step 120: Based on the standard expression matrix corresponding to the patient set, perform typing modeling and cluster stability analysis through consistent clustering analysis to obtain a molecular subtype set including molecular subtypes.

[0055] Step 130: Perform differential expression analysis based on molecular subtype set, screen for differentially expressed genes specific to each subtype, and obtain enrichment analysis results through functional annotation and pathway enrichment analysis.

[0056] Steps 120-130 are described uniformly as follows:

[0057] Based on the established standardized matrix, the expression matrix corresponding to the patient sample was selected, and expression profile-driven subtyping modeling was performed using consistent clustering analysis. Curve information of different cluster numbers was obtained and stability analysis was performed to select stable molecular subtypes.

[0058] Next, differential expression analysis was performed on each molecular subtype. Each molecular subtype was compared with other molecular subtypes to obtain differential expression analysis results. These results include differentially expressed genes (DEGs) specific to different molecular subtypes, enabling the screening of subtype-specific differentially expressed genes. These differentially expressed genes can also be referred to as subtype characteristic genes.

[0059] Subsequently, based on the obtained characteristic genes of each subtype, terminology function annotation and pathway enrichment analysis were performed on differentially expressed genes to evaluate the enrichment characteristics of subtype characteristic genes in cellular components, biological processes and signaling pathways, clarify the unique functional preferences of different subtypes, and obtain the enrichment analysis results.

[0060] Step 140: Perform time-series expression pattern modeling on the menstrual cycle phase set, screen for significantly dynamically expressed genes, and obtain a time-series gene set.

[0061] The menstrual cycle phase set includes samples labeled with menstrual cycle phases.

[0062] To introduce physiological time series analysis on the basis of expression subtyping, this invention conducts time series feature modeling on menstrual cycle phase-labeled samples and performs functional feature analysis on each subtype.

[0063] Specifically, based on the standardized expression matrix of menstrual cycle phase samples, the within-group mean of each gene is calculated according to phase, forming a phase-ordered input matrix. A short time series model is then established, and expression modules with significant changes are screened according to a preset significance threshold to identify dynamically expressed genes. Subsequently, the intersection of these dynamically expressed genes with differentially expressed genes in each subtype is taken to obtain time-driven and subtype-specific candidate genes.

[0064] Step 150: Based on the intersection of each dynamically expressed gene and differentially expressed gene in the time-series gene set, a dual feature screening verification is performed to obtain subtype-specific genes.

[0065] Step 160: Based on the predefined immune cell type reference signature, the subtype is inferred for immune infiltration, and correlation modeling is carried out with the subtype genes to obtain the subtype immune infiltration spectrum and key gene-immune cell association.

[0066] Steps 150 and 160 are described uniformly as follows:

[0067] To incorporate biological mechanism information on the basis of expression typing, this invention further incorporates the state of the immune microenvironment and performs functional feature modeling and mechanism analysis for each subtype.

[0068] Specifically, a dual-feature gene screening and diagnostic capability assessment based on menstrual cycle and subtype characteristics was introduced. Dynamically expressed genes were screened and compared with characteristic genes of each subtype for dual-feature gene screening. Overlapping genes were selected through intersection analysis. Then, the selected genes were validated to obtain subtype-specific genes that simultaneously possess menstrual cycle expression characteristics, subtype differences, and high discriminative ability.

[0069] To further analyze the immune microenvironment characteristics corresponding to each molecular subtype, we first inferred immune infiltration of the samples based on a standardized expression matrix and immune cell type reference signatures to obtain the relative abundance of each cell type; subtype-differential cells were then identified. Subsequently, we performed correlation analysis between the expression levels of subtype-specific genes and the infiltration scores of differentially expressed cells to construct a gene-cell association matrix, obtaining the immune infiltration results. These immune infiltration results suggest a subtype-specific association between different subtype-specific genes and the immune microenvironment, indicating potential regulatory relationships.

[0070] Step 170: Calculate the pathway activities of cytokines and / or chemokines based on the expression data of the patient's centralized subtype samples, and perform correlation analysis in combination with subtype-specific genes to output gene-pathway associations and pathway patterns for each subtype. The pathway patterns include activation patterns and inhibition patterns.

[0071] Step 180: Using expression typing as the framework, time-series gene sets, subtype immune infiltration profiles, pathway activities, gene-pathway associations, key gene-immune cell associations, and pathway patterns are superimposed to form a typing identification model, and endometriosis typing is performed on the acquired test data.

[0072] Steps 170-180 are described uniformly as follows:

[0073] To further analyze the activation patterns of immune pathways and the synergistic expression of subtype-specific genes, the activity of cytokine and chemokine-related pathways was scored based on a standardized expression matrix in subtyped patient samples, and the differences in pathway activity among subtypes were compared. The correlation between subtype-specific gene expression and pathway activity was calculated to obtain gene-pathway correlation results.

[0074] Finally, using the above process, a classification and identification model for endometriosis was established by integrating the results of cellular immune infiltration and correlation. Based on this model, the patient's test data were classified and the endometriosis classification results were output.

[0075] As can be seen, the high-resolution typing method for endometriosis based on the combination of the expression temporal structure of the menstrual cycle and the state of the immune microenvironment in this application breaks through the traditional classification approach based on a single static expression profile. This method introduces the expression temporal structure of the menstrual cycle as the typing basis and integrates immune cell infiltration characteristics and functional pathway analysis to generate a typing label with mechanism orientation. Through marker gene extraction, immune calibration and pathway mapping, an integrated process from expression data to mechanism interpretation is realized, providing a new technical means for the accurate diagnosis and target identification of endometriosis. It also provides an improvement path for addressing the problems of single typing basis, lack of temporal information and insufficient immune stratification in the existing technology.

[0076] Reference Figure 2 This illustration shows a flowchart of an optional embodiment of an endometriosis typing and identification method provided in this application. The method specifically may include the following steps:

[0077] Step 210: Preprocess the transcriptome microarray dataset to obtain a standard expression matrix set.

[0078] The transcriptome chip dataset includes a patient set, a validation set, and a menstrual cycle phase set. In the standard expression matrix set, each standard expression matrix is ​​organized with genes as rows and samples as columns, and the columns correspond one-to-one with the samples.

[0079] In this embodiment, the limma package in R language can be used to standardize each dataset, remove low-expression or missing probes, and take the average value for cases where multiple probes correspond to the same gene to construct a unified standard expression matrix.

[0080] Step 220: Perform consistent clustering analysis on the standard expression matrix corresponding to the patient set using the consistent clustering algorithm to obtain curve information and change information for different numbers of clusters.

[0081] Step 230: Based on cluster stability, the optimal classification is selected based on curve information and change information to obtain the molecular subtype set.

[0082] Step 240: Validate each molecular subtype in the molecular subtype set using the standard expression matrix of the validation set, and update the molecular subtype set based on the validation results.

[0083] Steps 220-240 are described uniformly as follows:

[0084] In the specific implementation, based on the constructed expression matrix, a clustering algorithm is used to perform consistent clustering analysis. The range of the number of clusters is predetermined, and multiple resampling is performed under different numbers of clusters to obtain curve information and change information.

[0085] Subsequently, the cluster stability of the curve and change information was evaluated. The optimal number of classifications was determined by combining cluster stability and sample separation, and consistent clustering was performed to obtain the molecular subtype set. Finally, the standard expression moments of the validation samples were used to perform external validation on each molecular subtype, and the molecular subtypes were updated as necessary based on the validation results.

[0086] Take endometriosis as an example:

[0087] Reference Figure 3 ( Figure 3 A to Figure 3 As shown in E), for the expression matrix of GSE6364, ConsensusClusterPlus package is used for consistent clustering analysis. The clustering algorithm used can be K-means, and initially, K can be set to K=2-9. The number of clusters can be set to 1000 to enhance robustness. 1000 resamplings are performed under different numbers of clusters to obtain CDF curves (as curve information) and Delta Area changes (as change information). Next, the clustering stability is evaluated by CDF and Delta Area curves. Finally, K=3 is selected as the optimal number of clusters. In GSE6364, stable clustering is achieved into three subtypes: molecular subtype 1 (Cluster C3, hereinafter referred to as subtype 1 or C3, n=6), molecular subtype 2 (Cluster C4, hereinafter referred to as subtype 2 or C4, n=6), and molecular subtype 3 (Cluster C2, hereinafter referred to as subtype 3 or C2, n=9), resulting in a molecular subtype set.

[0088] Reference Figure 3As shown in F, the reproducibility was verified using GSE37837. Specifically, the verification was performed using the GSE37837 dataset, and the results showed that the endometriosis subtypes were still divided into three categories: Cluster A (n = 21), Cluster B (n = 11), and Cluster C (n = 4).

[0089] It should be noted that, in Figure 3 middle, Figure 3 A to Figure 3 C represents the evaluation of cluster stability for different numbers of clusters using the cumulative distribution function curve; Figure 3 D represents the sample cluster heatmap displayed when the number of clusters is 3; Figure 3 E represents the cluster consistency histogram under different numbers of clusters; Figure 3 F indicates that the genotyping results were validated on the independent expression dataset GSE37837. The genotyping was modeled based on periodic expression trends and immune infiltration characteristics.

[0090] Step 250: Differential expression analysis is performed based on the molecular subtype set to screen for differentially expressed genes specific to each subtype, and enrichment analysis results are obtained through functional annotation and pathway enrichment analysis.

[0091] Optionally, the above differential expression analysis based on molecular subtype sets to screen for differentially expressed genes specific to each subtype may include the following substeps:

[0092] Sub-step 2501 involves comparing the molecular subtypes with normal control samples and determining the differential expression results through differential expression analysis.

[0093] Sub-step 2502 involves screening based on a preset first screening threshold and differential expression results to obtain subtype characteristic genes.

[0094] A unified description is provided for sub-steps 2501 and 2502:

[0095] This embodiment, based on the obtained different molecular subtypes, compares each subtype with a normal control sample and performs differential expression analysis to obtain differential expression values ​​as the differential expression results. A preset screening threshold for subtype genes can be used as the first screening threshold. This screening threshold is compared with the differential expression analysis values ​​to screen genes that meet the threshold conditions. The upregulation and downregulation of genes in different molecular subtypes are analyzed to determine the characteristic genes of the subtypes.

[0096] For example, refer to Figure 4As shown, based on the previously obtained subtypes 1, 2, and 3, each subtype was compared with normal control samples. Differential expression analysis was performed using the limma package method, with the first screening threshold set as P < 0.05 and |log2 FC| > 1. The differential expression analysis results showed that 183 genes were upregulated and 488 genes were downregulated in subtype 1; 306 genes were upregulated and 455 genes were downregulated in subtype 2; and 151 genes were upregulated and 277 genes were downregulated in subtype 3. Specifically, subtype 1 significantly expressed genes involved in ion transport and channel regulation, such as SLC30A2, SLC1A1, and SLC18A2; subtype 2 contained cell proliferation-related factors such as MKI67, TOP2A, and AURKB; and subtype 3 significantly expressed genes related to inflammation activation and immune response, such as SLC27A6, SLC46A2, and ATP1A2. Therefore, in this embodiment, it can be understood that all three subtypes in the molecular subtype set can be used as subtype characteristic genes for subsequent processing.

[0097] in, Figure 4 It shows the top two genomic clustering features among the three subtypes. Figure 4 A represents the genomic enrichment result of subtype 1; Figure 4 B indicates the genomic enrichment result of subtype 2; Figure 4 B indicates the genome enrichment result for subtype 3.

[0098] Optionally, the enrichment analysis results obtained through functional annotation and pathway enrichment analysis may include the following sub-steps:

[0099] Sub-step 2503 involves terminology annotation based on subtype characteristic genes and evaluation of the enrichment characteristics of subtype characteristic genes through pathway enrichment analysis to obtain enrichment analysis results.

[0100] In this embodiment, to explore the functional differences between different subtypes, terminology annotation and pathway enrichment analysis can be performed on the characteristic genes of each subtype to evaluate the enrichment characteristics of the characteristic genes of each subtype in cellular components, biological processes and signaling pathways, determine that different subtypes have their own unique functional preferences, and obtain the enrichment analysis results.

[0101] For example, based on the examples above:

[0102] Based on the characteristic genes of each subtype obtained through screening, GO terminology annotation and KEGG pathway enrichment analysis can be performed using the "clusterProfiler" package in R software (example version v3.6.3). Initial parameters include setting the threshold to P-value < 0.05. Furthermore, the significantly enriched entries are visualized using the "GOplot" and "ggplot2" packages to demonstrate the functional characteristics of each subtype.

[0103] In actual experiments, enrichment analysis results showed that:

[0104] Subtype 1 is primarily enriched in GO items such as cellular zinc ion homeostasis, collagen-containing extracellular matrix, and carbohydrate transmembrane transporter activity, as well as KEGG pathways such as mineral uptake and the IL-17 signaling pathway, suggesting that this subtype has a unique function in the regulation of ion metabolism. See Table 1 below for details.

[0105]

[0106] Table 1. Results of GO and KEGG functional enrichment analysis of differentially expressed genes in subtype 1

[0107] Subtype 2 was significantly enriched in proliferation-related pathways such as mitosis, chromosome segregation, and spindle structure regulation, suggesting that it is characterized by active cell cycle activity. See Table 2 below:

[0108]

[0109] Table 2. Results of GO and KEGG functional enrichment analysis of differentially expressed genes in subtype 2

[0110] Subtype 3 is primarily enriched in pathways such as inflammatory response regulation, cytokine-mediated signal transduction, and innate immune response, exhibiting characteristics of chronic inflammatory activation. See Table 3 below for details.

[0111]

[0112] Table 3. Results of GO and KEGG functional enrichment analysis of differentially expressed genes in subtype 3

[0113] In summary, subtype 1 is enriched in ion metabolism, subtype 2 in cell cycle, and subtype 3 in inflammatory and immune regulation.

[0114] Step 260: Perform time-series expression pattern modeling on the menstrual cycle phase set, screen for significantly dynamically expressed genes, and obtain a time-series gene set.

[0115] The menstrual cycle phase set includes samples labeled with menstrual cycle phases.

[0116] In one optional embodiment, the menstrual cycle phase set includes menstrual cycle phase samples. Temporal expression pattern modeling is performed on the menstrual cycle phase set, and significantly dynamically expressed genes are screened to obtain a temporal gene set. Specifically, this may include: organizing expression data according to phases for the standard expression matrix corresponding to the menstrual cycle phase samples; using the phase-organized expression data as input, calculating the intra-group average expression of each phase to construct a phase-ordered input; performing short-temporal expression pattern modeling on the phase-ordered input, screening expression modules with significant changes based on a preset significance threshold, determining dynamically expressed genes, and obtaining a temporal gene set.

[0117] Specifically, based on the standardized expression matrix corresponding to the menstrual cycle phase samples, a mean expression matrix is ​​established according to the within-group mean. Based on this, time-series expression pattern modeling is performed, and expression modules with significant changes are screened according to a preset significance threshold to obtain dynamically expressed genes.

[0118] In practical implementation, the standardized expression matrix of menstrual cycle phase-annotated samples can be summarized and expressed in the order of proliferative phase, early secretory phase, and mid-secretionary phase to form a phase-ordered input. A Short Time-series Expression Miner (STEM) model is built on the phase-ordered input and pattern recognition is performed. Modules with significant changes are screened based on a significance threshold to obtain a dynamic expression gene set. Subsequently, the intersection of this gene set with differentially expressed genes of each subtype is taken as time-driven and subtype-specific candidate genes for further evaluation.

[0119] For example, based on the aforementioned example and using the GSE51981 dataset, the STEM online analysis tool provided by the OmicStudio platform was used to model short-time series expression patterns. A significance threshold of P < 0.01 could be set to filter expression modules that showed significant changes over the period. In the actual experiment, the analysis identified 16 expression trend patterns (let's assume they are named Profile 0-15), such as... Figure 3 As shown in Figure A, Profiles 3, 7, and 2 are statistically significant (P = 0.00028, P = 0.00111, and P = 0.001850, respectively). Figure 5 B to Figure 5 As shown in Figure D, these expression trend patterns exhibit dynamic characteristics such as a continuous decline or a decline followed by stabilization within the cycle. Further KEGG pathway enrichment analysis of these expression pattern-related genes revealed that Group 3 (Profile 3) contained 16 genes, primarily enriched in pathways such as progesterone-mediated oocyte maturation, oocyte meiosis, tryptophan metabolism, and O- and N-glycan biosynthesis. Figure 5 As shown in E, Profile 7 contains 24 genes enriched in single-carbon metabolic pathways such as folate metabolism, mucin-type O-glycan synthesis, and folate resistance. Figure 5 As shown in Figure F, Profile 2 is associated with eight pathways, including PI3K-Akt signaling, the p53 pathway, glutathione and pyrimidine metabolism, drug metabolism, and EGFR tyrosine kinase inhibitor resistance. It is evident that the selected dynamically expressed gene enrichment pathways cover key regulatory axes such as oocyte maturation, carbohydrate metabolism, and PI3K-Akt and p53 signaling. Figure 5 As shown in G.

[0120] It should be noted that, in Figure 5 middle, Figure 5 A represents three significant time expression patterns (marked in red) selected through STEM analysis. Figure 5 B Figure 5 C and Figure 5 D shows the expression trends of representative differentially expressed transcripts corresponding to Profile 3, Profile 7, and Profile 2, respectively. Figure 5 E to Figure 5 G corresponds to Profile3, Profile7 and Profile2 respectively.

[0121] Step 270: Based on the intersection of each dynamically expressed gene and differentially expressed gene in the time-series gene set, a dual feature screening verification is performed to obtain subtype-specific genes.

[0122] Optionally, this embodiment performs dual feature screening and verification based on the intersection of each dynamically expressed gene and differentially expressed gene in the time-series gene set to obtain subtype-specific genes. Specifically, it may include: performing intersection processing based on dynamically expressed genes and differentially expressed genes to screen overlapping genes; screening test genes with subtype-specific expression from the overlapping genes; and evaluating the diagnostic performance of the test genes using receiver operating characteristic curves to obtain subtype-specific genes.

[0123] In practice, the intersection of the screened dynamically expressed genes and the characteristic genes of each subtype is taken to screen overlapping genes that simultaneously exhibit menstrual cycle expression characteristics and subtype differences (also known as dual-characteristic genes, candidate regulatory genes, etc.). Then, the dual-characteristic genes are analyzed and verified to determine whether each dual-characteristic gene has obvious subtype-specific expression. Genes with subtype-specific expression are selected as test genes.

[0124] Next, receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the target genes in order to screen for biomarkers with high diagnostic potential and obtain subtype-specific genes with high regionalization ability. After screening subtype-specific genes, the expression trends of each subtype-specific gene can be independently validated. Validation samples can be used for testing to compare the expression trends of different subtype-specific genes and clarify the strong generalization ability and biological stability of each subtype-specific gene.

[0125] For example, by taking the intersection of the 55 dynamically expressed genes and the 1658 subtype characteristic genes, 22 overlapping dual-characteristic genes were obtained. Figure 6As shown in Figure A, 14 dual-signature genes exhibited significant subtype-specific expression. Next, pROC analysis was performed on these dual-signature genes. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminative or diagnostic performance of these 14 genes within the subtypes. Genes with an AUC (area under the curve) value greater than 0.9 were selected as high-diagnostic-potential biomarkers, i.e., marker genes. Ultimately, 10 core typing genes with high discriminative ability were obtained. These core typing genes can serve as subtype-specific genes and are assigned to three different typing subgroups.

[0126] Among them, reference Figure 6 B and Figure 6 As shown in Figure C, representative genes for subtype 1 include: NEFM (AUC=0.930), HELLS (AUC=0.960), PPP2R2C (AUC=0.990), and CPM (AUC=0.950); representative genes for subtype 2 include: KIF11 (AUC=0.990), DLGAP5 (AUC=0.990), TOP2A (AUC=0.980), and ASPM (AUC=0.980); representative genes for subtype 3 include: STXBP6 (AUC=1) and MMP26 (AUC=1). Figure 4 As shown in BC

[0127] Reference Figure 6 As shown in Figure D, GSE7305 from the aforementioned validation sample was used as an external dataset to independently validate the expression trends or levels of the 10 subtype-specific genes. For independent validation, the Wilcoxon test (p<0.01) was used to compare the expression trends of different subtypes. The results showed that these genes were generally downregulated in endometriosis samples, suggesting their important diagnostic and stratification value in disease subtyping. It is evident that this expression trend is consistent with the representative genes of each subtype, indicating that the subtyping genes possess strong generalization ability and biological stability.

[0128] It should be noted that, in Figure 6 middle, Figure 6 A represents the intersection of differentially expressed genes in each subtype of endometriosis with menstrual cycle-related expressed genes to obtain three groups of subtype-specific cycle-driving candidate genes. Figure 6 B and Figure 6 C represents the ROC curves and AUC analysis of the three subtype-specific core genes selected based on the intersection genes; Figure 6 D indicates that the expression of high-performance core genes with an AUC greater than 0.9 was validated in an independent validation set.

[0129] Step 280: Based on the predefined immune cell type reference signature, the subtype is inferred for immune infiltration, and correlation modeling is carried out with the subtype genes to obtain the subtype immune infiltration spectrum and key gene-immune cell association.

[0130] In one optional embodiment, immune infiltration is inferred from subtypes based on predefined immune cell type reference signatures, and correlation modeling is performed with subtype genes to obtain subtype immune infiltration profiles and key gene-immune cell associations. Specifically, this may include: inferring the relative abundance of immune and stromal cells using the xCell algorithm based on the standard expression matrix corresponding to the patient set to obtain an immune infiltration score; evaluating each molecular subtype based on the immune infiltration score to identify differentially expressed cells between subtypes; and performing correlation analysis between the infiltration scores of differentially expressed cells and the expression of subtype-specific genes to form subtype immune infiltration profiles and key gene-immune cell associations; wherein, the key gene-immune cell associations include an association matrix.

[0131] After identifying key genes of subtypes with periodic dynamic characteristics, this embodiment further systematically models the immune cell infiltration composition, key immune regulatory pathways and their association with the expression of periodic genes in the three types of subtype samples from the perspective of the immune microenvironment, in order to reveal the potential differences in inflammatory response and local immune mechanisms among the subtypes.

[0132] To further explore the immune microenvironment characteristics corresponding to each molecular subtype, the xCell algorithm was used to assess the infiltration levels of different immune and stromal cell types in each molecular subtype. First, based on the ssGSEA principle, the relative enrichment scores of each cell type in the sample were inferred at the transcriptome level. Enrichment analysis could be performed in R software (v3.6.3), and the expression correlation between the obtained subtype-specific genes and immune cells could be further evaluated using the SangerBox platform to obtain the immune cell infiltration results.

[0133] In practical implementation, based on the standard expression matrix corresponding to the patient set, the xCell algorithm is used to score the immune infiltration of GSE6364 samples, inferring the relative enrichment of 64 types of immune cells and stromal cells (such as Th1 / Th2 cells, dendritic cells, macrophages, etc.), thus obtaining the immune infiltration score. Based on the immune infiltration score, the Kruskal-Wallis test is used to assess the differences in immune cell infiltration among the three subtypes, identifying differentially expressed cells among subtypes. Using the analyzed differentially expressed cells, Spearman correlation analysis is performed in conjunction with subtype-specific genes to identify multiple significant gene-immune cell pairs, obtaining the subtype immune infiltration profile and the association between key genes and immune cells, which serves as the result of immune cell infiltration.

[0134] For example, actual experimental results show that different subtypes have characteristic immune compositions, such as subtype 1 being enriched with Th2 cells and dendritic cells, subtype 2 being enriched with hematopoietic precursors and mast cells, and subtype 3 having upregulated M2 macrophages and dendritic cells.

[0135] Throughout the immune infiltration process, the results showed that all three subtypes exhibited different immune cell infiltration characteristics: Subtype 1 was enriched with astrocytes, iDCs, M1 macrophages, and Th2 cells, while Tregs, preadipocytes, etc., were significantly reduced, such as... Figure 7 As shown in A; subtype 2 is characterized by increased Th2 cells, pre-B cells, and MEP, while decreased CD4+ Tcm, CD8+ T cells, and M2 macrophages, such as... Figure 7 As shown in B; in subtype 3, activated dendritic cells, M2 macrophages, and mast cells are upregulated, while CD8+Tem and GMP levels are relatively low, such as Figure 7 As shown in C. Regarding subtype-specific genes, different genes showed significant correlations with specific immune cells. For example, HELLS, CPM, and PPP2R2C in subtype 1 were significantly negatively correlated with various precursor and inflammatory cells, such as... Figure 7 As shown in A; in subtype 2, KIF11, DLGAP5, and TOP2A are mainly negatively correlated with epithelial cells and positively correlated with erythroid or myeloid precursor cells, such as Figure 7 As shown in B; subtype 3 STXBP6 is negatively correlated with basophils and hepatocytes, such as Figure 7 As shown in Figure C, this result suggests that different subtypes possess characteristic immune cell compositions and can regulate the remodeling process of the local immune microenvironment through key marker genes.

[0136] It should be noted that, Figure 7 middle, Figure 7 A, Figure 7 B Figure 7 C shows the distribution of immune cell infiltration in subtypes 1, 2, and 3, and the correlation analysis between the expression of subtype-specific core genes and various immune cell types. Statistical significance is indicated by: * P < 0.05; ** P < 0.01; *** P < 0.001; ns indicates no statistical difference (P > 0.05).

[0137] Step 290: Calculate the pathway activities of cytokines and / or chemokines based on the expression data of the patient's centralized subtype samples, and perform correlation analysis in combination with subtype-specific genes to output gene-pathway associations and pathway patterns of each subtype.

[0138] The pathway modes include activation and inhibition modes.

[0139] In practice, on patient samples that have been classified, the activity of cytokine and chemokine-related pathways is scored based on a standardized expression matrix, and the differences in pathway activity among different subtypes are compared. The correlation between subtype-specific gene expression and pathway activity is calculated to obtain gene-pathway correlation results.

[0140] Optionally, correlation analysis can be performed using subtype-specific genes to output gene-pathway associations and pathway patterns for each subtype. This includes: performing correlation analysis based on pathway activity and subtype-specific genes to calculate activity scores for relevant pathways; comparing and summarizing each molecular subtype with normal controls based on the activity scores to analyze gene-pathway associations and pathway patterns for each subtype; wherein, pathway patterns are represented by pathway activation profiles and pathway inhibition profiles.

[0141] To further analyze the differences in inflammatory regulatory pathways among different subtypes, this embodiment uses the standardized expression matrices of subtyped patient samples to assess the differences in expression activity of cytokine and chemokine-related signaling pathways among each subtype, obtaining pathway activity scores. Based on the activity scores, correlation analysis is used to calculate the correlation coefficients between menstrual cycle-driven dynamic characteristic genes and pathway activity within each subtype.

[0142] For example, using the genotyped samples from GSE6364 as a benchmark, the differences in expression activity of each subtype in cytokine and chemokine-related signaling pathways were assessed, and the correlation between genotype-specific genes and these pathways was analyzed. The GSVA method was used to calculate the enrichment score of each sample in cytokine-related pathways. When calculating the enrichment score, pathway gene sets derived from the publicly available databases Reactome and KEGG could be used. Then, Spearman correlation analysis was used to calculate the correlation coefficients between menstrual cycle-driven dynamic characteristic genes and pathway activity within each subtype.

[0143] For example, in actual experiments, the results showed that different subtypes exhibited significant differences in inflammatory pathway activity and characteristic gene-pathway co-expression:

[0144] Reference Figure 8As shown in Figure A, subtype 1 exhibits low expression of multiple cytokine signaling pathways, including IL-6, IL-11, IL-15, and CXCR4. The characteristic gene NEFM is positively correlated with the IL-8 / CXCR1 pathway (r=0.84), while the characteristic genes PPP2R2C and CPM are strongly negatively correlated with multiple cytokine pathways (such as IL-1R, IL-27, IL-12, and IL-21) (r ranges from -0.82 to -0.99, with a minimum of -0.99). The characteristic gene HELLS is also negatively correlated with the IL-36 pathway (r=-0.83).

[0145] Reference Figure 8 As shown in Figure B, subtype 2 exhibits a more extensive state of inflammatory suppression compared to normal endometrium, with an overall suppression of inflammatory signals. Specifically, the IL-4, IL-6, IL-7, IL-9, IL-10, IL-15, IL-21, and IL-27 pathways are all significantly downregulated, while only the CCL18 pathway is upregulated. The characteristic genes of this subtype, KIF11, DLGAP5, TOP2A, and ASPM, show significant negative correlations with multiple cytokine pathways (such as IL-6, CXCR4, IL-9, and IL-15) (r ranges from -0.60 to -0.89), indicating that these genes are generally negatively correlated with inflammatory pathways.

[0146] Reference Figure 8 As shown in Figure C, subtype 3 exhibits high inflammatory activation characteristics, showing multi-pathway activation. The activated and enriched pathways include IL-2, IL-2R, IL-5, IL-6, IL-8 / CXCR1 and CXCR2, IL-15, IL-18, IL-22BP, IL-23, and IL-37. Despite the overall enhanced inflammatory signal, some dynamic genes, including STXBP6 and MMP26, are still moderately to strongly negatively correlated with these pathways (r is approximately -0.62 to -0.84). Among them, characteristic genes such as STXBP6 are still negatively correlated with multiple pathways (such as IL-2, IL-4, IL-6, and IL-15) (r is approximately -0.6 to -0.84), and MMP26 is also significantly negatively correlated with the IL-2R, CXCR1 / 2, and IL-18 pathways.

[0147] It should be noted that, Figure 8 This study demonstrates the enrichment of cytokine and chemokine-related signaling pathways in three subtypes of endometriosis and analyzes the expression differences of pathway-related subtype-specific genes. Figure 8 A, Figure 8 B Figure 8C represents the expression patterns of significantly enriched pathways and representative genes in subtypes 1, 2, and 3, respectively. Statistical significance is indicated by: *P < 0.05; **P < 0.01; ns indicates no statistical difference (P > 0.05).

[0148] It is evident that the three subtypes exhibit significant differences in cytokine network activation status, indicating different immune regulatory mechanisms and degrees of inflammatory involvement, which can provide a reference for targeted intervention strategies.

[0149] In an optional embodiment, the method may further include: selecting subtype-specific genes as key molecules based on the pathway activation profile; performing correlation analysis between the expression level of the key molecules and the activity scores of each pathway, and outputting the association results between the key genes and pathways.

[0150] Step 300: Using expression typing as the framework, time-series gene sets, subtype immune infiltration profiles, pathway activities, gene-pathway associations, key gene-immune cell associations, and pathway patterns are superimposed to form a typing identification model, and endometriosis typing is performed on the acquired test data.

[0151] In practical implementation, this embodiment can utilize the process of each step to establish a classification and identification mechanism for endometriosis based on the results of cellular immune infiltration and correlation coefficients. This classification and identification mechanism can be used to classify and identify the patient's test data to obtain the patient's endometriosis classification result.

[0152] In summary, this application proposes a high-resolution classification scheme for endometriosis based on a combination of dynamic expression characteristics of the menstrual cycle and the state of the immune microenvironment. It breaks through the traditional classification approach based on a single static expression profile, introducing for the first time the temporal structure of menstrual cycle expression as a classification basis, and integrating immune cell infiltration characteristics and functional pathway analysis to generate classification tags with biological mechanism orientation. This is achieved through marker gene extraction, immune calibration, and pathway mapping. Thus, this invention realizes an integrated process from expression data to mechanism interpretation, providing a new technical means for the accurate diagnosis and target identification of endometriosis.

[0153] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should know that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps may be performed in other orders or simultaneously.

[0154] like Figure 9 As shown in the embodiment of this application, an endometriosis typing and identification system 900 is also provided, including:

[0155] The data preprocessing module 910 is used to preprocess the transcriptome chip dataset to obtain a standard expression matrix set. The transcriptome chip dataset includes a patient set, a validation set, and a menstrual cycle phase set. In the standard expression matrix set, each standard expression matrix is ​​organized with genes as rows and samples as columns, and the columns correspond one-to-one with the samples.

[0156] The clustering analysis module 920 is used to perform typing modeling and cluster stability analysis based on the standard expression matrix corresponding to the patient set through consistent clustering analysis, and obtain a molecular subtype set including molecular subtypes;

[0157] The differential expression and enrichment analysis module 930 is used to perform differential expression analysis based on molecular subtype sets, screen for differentially expressed genes specific to each subtype, and obtain enrichment analysis results through functional annotation and pathway enrichment analysis.

[0158] The dynamic gene screening module 940 is used to perform temporal expression pattern modeling in the menstrual cycle phase set, screen for significantly dynamically expressed genes, and obtain a temporal gene set. The menstrual cycle phase set includes samples with menstrual cycle phase annotations.

[0159] The dual-feature screening module 950 is used to perform dual-feature screening verification based on the intersection of each dynamically expressed gene and differentially expressed gene in the time-series gene set to obtain subtype-specific genes;

[0160] The immune infiltration analysis module 960 is used to infer the immune infiltration of subtypes based on predefined immune cell type reference signatures and to perform correlation modeling with subtype genes to obtain subtype immune infiltration profiles and key gene-immune cell associations.

[0161] The correlation analysis module 970 is used to calculate the pathway activity of cytokines and / or chemokines based on the expression data of the patient's concentrated subtype sample, and to perform correlation analysis in combination with subtype-specific genes, outputting gene-pathway associations and pathway patterns of each subtype, including activation patterns and inhibition patterns.

[0162] The typing identification module 980 is used to form a typing identification model by superimposing time-series gene sets, subtype immune infiltration profiles, pathway activities, gene-pathway associations, key gene-immune cell associations, and pathway patterns on expression typing as a framework, and to perform endometriosis typing on the acquired test data.

[0163] It should be noted that the endometriosis typing and identification system provided in this application embodiment can execute the endometriosis typing and identification method provided in any embodiment of this application, and has the corresponding functions and beneficial effects of the method.

[0164] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0165] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for classifying and identifying endometriosis, characterized in that, include: The transcriptome chip dataset was preprocessed to obtain a standard expression matrix set. The transcriptome chip dataset includes a patient set, a validation set, and a menstrual cycle phase set. In the standard expression matrix set, each standard expression matrix is ​​organized with genes as rows and samples as columns, and the columns correspond one-to-one with the samples. Based on the standard expression matrix corresponding to the patient set, typing modeling and cluster stability analysis were performed through consistent clustering analysis to obtain a molecular subtype set including molecular subtypes; Differential expression analysis was performed based on molecular subtype sets to screen differentially expressed genes specific to each subtype, and enrichment analysis results were obtained through functional annotation and pathway enrichment analysis. Temporal expression pattern modeling was carried out in the menstrual cycle phase set, and genes with significant dynamic expression were screened to obtain a temporal gene set. The menstrual cycle phase set includes samples with menstrual cycle phase annotations. Subtype-specific genes were obtained by dual feature screening and verification based on the intersection of dynamically expressed genes and differentially expressed genes in the time-series gene set. Based on predefined immune cell type reference signatures, subtypes are inferred for immune infiltration, and correlation modeling is performed with subtype genes to obtain subtype immune infiltration profiles and key gene-immune cell associations. The pathway activities of cytokines and / or chemokines are calculated based on the expression data of patients' concentrated subtype samples, and correlation analysis is performed in combination with subtype-specific genes to output gene-pathway associations and pathway patterns of each subtype. The pathway patterns include activation patterns and inhibition patterns. Using expression typing as a framework, and superimposing time-series gene sets, subtype immune infiltration profiles, pathway activities, gene-pathway associations, key gene-immune cell associations, and pathway patterns, a typing identification model is formed to classify endometriosis based on the acquired test data.

2. The method according to claim 1, characterized in that, Based on the standard expression matrix corresponding to the patient set, typing modeling and cluster stability analysis were performed using consistent clustering analysis to obtain a molecular subtype set including molecular subtypes, including: Consistent clustering analysis was performed on the standard expression matrix corresponding to the patient set using a consistent clustering algorithm to obtain curve information and change information for different numbers of clusters; Based on cluster stability, and using curve and change information, the optimal classification is selected to obtain the molecular subtype set; The molecular subtypes in the molecular subtype set are validated using the standard expression matrix of the validation set, and the molecular subtype set is updated based on the validation results.

3. The method according to claim 1, characterized in that, Differential expression analysis was performed based on molecular subtype sets to screen for differentially expressed genes specific to each subtype, including: The molecular subtypes were compared with normal control samples from a patient group, and the differential expression results were determined through differential expression analysis. Based on a preset first screening threshold and differential expression results, differentially expressed genes are obtained, including subtype characteristic genes.

4. The method according to claim 3, characterized in that, Enrichment analysis results were obtained through functional annotation and pathway enrichment analysis, including: Terminology annotation was performed based on subtype characteristic genes, and the enrichment characteristics of subtype characteristic genes were evaluated through pathway enrichment analysis to obtain enrichment analysis results.

5. The method according to claim 1, characterized in that, The menstrual cycle phase set includes menstrual cycle phase samples. Temporal expression pattern modeling is performed on the menstrual cycle phase set, and significantly dynamically expressed genes are screened to obtain a temporal gene set, including: For the standard expression matrix corresponding to the menstrual cycle phase samples, the expression data is organized according to phase. Using phase organization expression data as input, the intra-group average expression of each phase is calculated to construct an ordered phase input. Short-time expression pattern modeling is performed on phase-ordered inputs. Expression modules with significant changes are screened based on preset significance thresholds to determine dynamically expressed genes and obtain a time-series gene set.

6. The method according to claim 1, characterized in that, Based on the intersection of dynamically expressed genes and differentially expressed genes in the time-series gene set, dual feature screening and verification were performed to obtain subtype-specific genes, including: Overlapping genes were screened by intersecting dynamically expressed genes and differentially expressed genes. Subtype-specific genes are obtained by screening for subtype-specific expression from overlapping genes and evaluating the diagnostic performance of the subtype-specific genes using receiver operating characteristic (ROC) curves.

7. The method according to claim 6, characterized in that, Immune infiltration was inferred from subtypes based on predefined immune cell type reference signatures, and correlation modeling was performed with subtype genes to obtain subtype immune infiltration profiles and key gene-immune cell associations, including: Based on the standard expression matrix corresponding to the patient set, the xCell algorithm is used to infer the relative abundance of immune and stromal cells and obtain the immune infiltration score. Based on the immune infiltration score, each molecular subtype was tested and evaluated to identify differentially expressed cells between subtypes; Correlation analysis was performed between differential cell infiltration scores and subtype-specific gene expression to form a subtype immune infiltration profile and a key gene-immune cell association. Among them, the key gene-immune cell association includes an association matrix.

8. The method according to claim 1, characterized in that, Correlation analysis was performed using subtype-specific genes to output gene-pathway associations and pathway patterns for each subtype, including: Correlation analysis was performed based on pathway activity and subtype-specific genes to calculate the activity scores of relevant pathways; Based on activity scores, each molecular subtype was compared with the normal control and the results were summarized to analyze gene-pathway associations and pathway patterns of each subtype. Among them, the pathway pattern is represented by the pathway activation spectrum and the pathway inhibition spectrum.

9. The method according to claim 8, characterized in that, Also includes: Based on the pathway activation profile, subtype-specific genes were selected as key molecules; Correlation analysis was performed between the expression levels of key molecules and the activity scores of each pathway to output the association results of key genes and pathways.

10. A system for classifying and identifying endometriosis, characterized in that, include: The data preprocessing module is used to preprocess the transcriptome chip dataset to obtain a standard expression matrix set. The transcriptome chip dataset includes a patient set, a validation set, and a menstrual cycle phase set. In the standard expression matrix set, each standard expression matrix is ​​organized with genes as rows and samples as columns, and the columns correspond one-to-one with the samples. The clustering analysis module is used to perform typing modeling and cluster stability analysis based on the standard expression matrix corresponding to the patient set through consistent clustering analysis, and obtain a molecular subtype set including molecular subtypes; The differential expression and enrichment analysis module is used to perform differential expression analysis based on molecular subtype sets, screen for differentially expressed genes specific to each subtype, and obtain enrichment analysis results through functional annotation and pathway enrichment analysis. The dynamic gene screening module is used to model temporal expression patterns in the menstrual cycle phase set, screen for significantly dynamically expressed genes, and obtain a temporal gene set. The menstrual cycle phase set includes samples with menstrual cycle phase annotations. The dual-feature screening module is used to perform dual-feature screening verification based on the intersection of each dynamically expressed gene and differentially expressed gene in the time-series gene set to obtain subtype-specific genes; The immune infiltration analysis module is used to infer the immune infiltration of subtypes based on predefined immune cell type reference signatures and to perform correlation modeling with subtype genes to obtain subtype immune infiltration profiles and key gene-immune cell associations. The correlation analysis module is used to calculate the pathway activity of cytokines and / or chemokines based on the expression data of patient-focused subtype samples, and to perform correlation analysis in combination with subtype-specific genes, outputting gene-pathway associations and pathway patterns for each subtype, including activation and inhibition patterns. The typing identification module is used to form a typing identification model by overlaying time-series gene sets, subtype immune infiltration profiles, pathway activities, gene-pathway associations, key gene-immune cell associations, and pathway patterns on expression typing as a framework, and then typing the acquired test data for endometriosis.

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