A method and apparatus for treating chronic hepatitis b based on a molecular typing model

Abstract

The application provides a chronic hepatitis B treatment method and device based on a molecular typing model, and the method comprises the following steps: obtaining a tissue sample of a chronic hepatitis B patient; determining the type of chronic hepatitis B of the chronic hepatitis B patient according to the tissue sample of the chronic hepatitis B patient based on a constructed molecular typing model; and determining the targeted treatment drug for the chronic hepatitis B patients of each type according to the type of chronic hepatitis B of the chronic hepatitis B patient. The application can perform molecular typing on the tissue sample of the chronic hepatitis B patient through the construction of the molecular typing model and the molecular typing model, and give the targeted treatment drug according to the type of chronic hepatitis B of the chronic hepatitis B patient. From the perspective of disease heterogeneity, the application provides a new idea for the treatment of drug-resistant patients and a new stratification suggestion for CHB patients.

Description

Technical Field

[0001] This application relates to the field of pharmaceutical technology, and more specifically, to a treatment method and device for chronic hepatitis B based on a molecular typing model. Background Technology

[0002] Hepatitis B virus (HBV) is a hepatotropic DNA virus that primarily resides within hepatocytes and damages them, causing hepatocyte inflammation, necrosis, and fibrosis. HBV is a global public health problem, with approximately 350 million people persistently infected. The pathogenesis of chronic hepatitis B (CHB) is complex and not yet fully understood.

[0003] Antiviral therapy is a crucial treatment for chronic hepatitis B (CHB) patients. Currently, clinically available medications include nucleoside analogues (Nas) and interferon alpha (IFN-α). Other treatment options include anti-inflammatory, antioxidant, hepatoprotective, anti-fibrotic, and immunomodulatory therapies. Common Nas treatments include entecavir (ETV), tenofovir disoproxil fumarate (TDF), tenofovir alafenamide fumarate (TAF), and tenofovir amibufenamide (TMF). Meanwhile, Peg-IFN-α and IFN-α have been approved for CHB treatment in my country. However, the use of nucleoside analogues is limited by drug resistance and nephrotoxicity during long-term treatment, and IFN-α treatment is prone to adverse reactions. Therefore, effectively improving the clinical cure rate remains a challenge in clinical treatment.

[0004] Taking into account virological, biochemical, and histological characteristics, chronic HBV infection is generally divided into four stages: HBeAg-positive chronic HBV infection, HBeAg-positive chronic HBV infection (CHB), HBeAg-negative chronic HBV infection, and HBeAg-negative CHB (also known as the reactivation phase). However, not all HBV-infected individuals sequentially experience these four stages, and the currently used virological, biochemical, and histological indicators are insufficient for clearly staging all infected individuals. Research on CHB molecular typing mainly focuses on DNA subtypes, with limited exploration of CHB subtype differentiation at the transcriptomic level. For example, Yin Yonghua et al. divided HBV into one genotype (AJ) and three subgenotypes (A1-A5; B1-B6; C1-C6; D1-D4; and F1-F4).

[0005] A growing body of research is attempting to provide classification recommendations by identifying and characterizing patients with similar target states. However, previous studies have not explored the drug sensitivity of patients after omics-based classification. Summary of the Invention

[0006] In view of this, the purpose of this application is to provide a treatment method and device for chronic hepatitis B based on a molecular subtyping model. This method can perform molecular subtyping on tissue samples from patients with chronic hepatitis B by constructing a molecular subtyping model, and provide targeted therapeutic drugs according to the type of chronic hepatitis B in the patients. This addresses the problem in the prior art that previous studies did not explore the drug treatment sensitivity of patients after subtyping at the omics dimension. It provides a new approach to the treatment strategy for drug-resistant patients with poor treatment response from the perspective of disease heterogeneity, and provides new stratification suggestions for CHB patients.

[0007] In a first aspect, embodiments of this application provide a treatment method for chronic hepatitis B based on a molecular subtyping model. The method includes: obtaining tissue samples from patients with chronic hepatitis B; determining the type of chronic hepatitis B in patients based on the constructed molecular subtyping model and the tissue samples; and determining targeted therapeutic drugs for each subtype of chronic hepatitis B based on the type of chronic hepatitis B in patients.

[0008] Optionally, the types of chronic hepatitis B include immune-activated chronic hepatitis B and metabolic-activated chronic hepatitis B. Immune-activated chronic hepatitis B is chronic hepatitis B that is enriched in immune cells and inflammatory response pathways, while metabolic-activated chronic hepatitis B is chronic hepatitis B that is enriched in intracellular metabolism and endothelial cell proliferation.

[0009] Optionally, the molecular subtyping model is constructed through the following steps: obtaining a training dataset, which includes a subset of chronic hepatitis B patients and a subset of healthy controls; identifying the chronic hepatitis B patient samples and healthy control samples in the training dataset using the Limma package in R software, and determining differentially expressed genes between chronic hepatitis B patients and healthy controls; performing enrichment analysis on the upregulated differentially expressed genes using the online database Metascape, identifying significant enrichment pathways in chronic hepatitis B patients, and constructing a protein-protein interaction (PPI) network using the STRING online database to... This study revealed the inter-regulatory relationships between proteins; gene enrichment analysis was performed on each chronic hepatitis B patient sample to identify potential enriched pathways associated with upregulated differentially expressed genes between chronic hepatitis B patients and healthy controls, and these pathways were screened to obtain target pathways rich in upregulated differentially expressed genes; hierarchical clustering of target pathways was performed using the R package "ConsensuClusterPlus" to identify immune-activated and metabolically activated chronic hepatitis B patient samples in the chronic hepatitis B patient subset.

[0010] Optionally, the molecular subtyping model is validated through the following steps: obtaining a validation dataset, which includes multiple liver biopsy samples from chronic hepatitis B patients and the chronic hepatitis B type of each liver biopsy sample; inputting each liver biopsy sample from the validation dataset into the molecular subtyping model based on the molecular subtyping model, determining the accuracy of the molecular subtyping model based on the chronic hepatitis B type and the chronic hepatitis B type determined by the molecular subtyping model; determining whether the accuracy of the molecular subtyping model is greater than a preset target value; if the accuracy of the molecular subtyping model is greater than the preset target value, then the construction of the molecular subtyping model is deemed practical.

[0011] Optionally, the targeted therapy drugs for patients with different subtypes of chronic hepatitis B can be determined through the following steps: obtaining the treatment response rate of each targeted therapy drug for immune-activated chronic hepatitis B and the treatment response rate of each targeted therapy drug for metabolically activated chronic hepatitis B; determining the chronic hepatitis B subtypes with high treatment response rates and those with poor treatment response rates based on the chronic hepatitis B type of the patients, and performing drug prediction for the chronic hepatitis B subtypes with poor treatment response rates.

[0012] Optionally, the immune cells and inflammatory response pathways in immune-activated chronic hepatitis B include: cytokine-cytokine receptor interactions, interferon signaling pathway, interleukin-10 signaling pathway, and Toll-like receptor signaling pathway. Immune cells, including B cells, CD8+ T cells, dendritic cells, Th2 cells, and M1 macrophages, are extensively infiltrated in immune-activated chronic hepatitis B. Metabolic-activated chronic hepatitis B is enriched in metabolic-related pathways including: JAK-STAT signaling pathway, the metabolic effects of cytochrome p450 on exogenous drugs, PPAR signaling pathway, and other enzyme pathways involved in drug metabolism. In metabolic-activated chronic hepatitis B, endothelial cell, hepatocyte, preadipocyte, and Th1 cell pathways are highly activated.

[0013] Optionally, the steps for hierarchical clustering of the target pathway based on the R package "ConsensuClusterPlus" include: using the Pam algorithm combining Euclidean and Ward-D2, we perform multiple iterations using the R package "ConsensuClusterPlus" and determine the cumulative distribution function value and incremental area after each iteration based on the cumulative distribution function; determining the optimal number of clusters based on the cumulative distribution function value and incremental area after each iteration; and performing hierarchical clustering of the target pathway based on the optimal number of clusters using the R package "ConsensuClusterPlus".

[0014] Optionally, differentially expressed genes between patients with chronic hepatitis B and healthy controls can be determined through the following steps: calculating the fold change in gene expression regulation between patients with chronic hepatitis B and healthy controls, and the parameter value of the Wilcox test result; differentially expressed genes whose fold change in gene expression regulation regulation is greater than a first threshold and whose parameter value of the Wilcox test result is less than a second threshold are identified as differentially expressed genes between patients with chronic hepatitis B and healthy controls.

[0015] Optionally, the method further includes: constructing a protein-protein interaction network based on the STRING online database to determine the mutual regulatory relationship between proteins, wherein the mutual regulatory relationship between proteins is determined by the following steps: constructing an interaction network of multiple nodes and multiple edges using multiple upregulated genes, wherein a node represents a gene and an edge represents the interaction between genes, and determining the mutual regulatory relationship between proteins based on the interaction network of multiple nodes and multiple edges.

[0016] Secondly, embodiments of this application also provide a treatment device for chronic hepatitis B based on a molecular typing model, the device comprising:

[0017] The tissue sample acquisition module is used to acquire tissue samples from patients with chronic hepatitis B.

[0018] The subtype determination module is used to determine the type of chronic hepatitis B in patients based on tissue samples from patients with chronic hepatitis B using a molecular subtyping model.

[0019] The treatment recommendation module is used to determine targeted treatment drugs for patients with different subtypes of chronic hepatitis B based on their chronic hepatitis B type.

[0020] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform the steps of the above-described treatment method for chronic hepatitis B based on a molecular typing model.

[0021] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the above-described treatment method for chronic hepatitis B based on a molecular typing model.

[0022] The chronic hepatitis B treatment method and device based on molecular subtyping models provided in this application can perform molecular subtyping of tissue samples from patients with chronic hepatitis B by constructing a molecular subtyping model, and provide targeted therapeutic drugs according to the type of chronic hepatitis B in the patients. This solves the problem in the prior art that previous studies did not explore the drug treatment sensitivity of patients after subtyping at the omics dimension. It provides a new approach to the treatment of drug-resistant patients from the perspective of disease heterogeneity and provides new stratification suggestions for CHB patients.

[0023] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1This is a schematic diagram of data for identifying differentially expressed genes between chronic hepatitis B (CHB) patients and healthy controls (HC) provided in an embodiment of this application.

[0026] Figure 2 This is a schematic diagram of the consistent clustering data of the chronic hepatitis B (CHB) cohort provided in the embodiments of this application;

[0027] Figure 3 A schematic diagram of transcriptomic characteristics of two subtypes of chronic hepatitis B (CHB) patients provided in the embodiments of this application;

[0028] Figure 4 A schematic diagram showing significant differences in disease staging, histological activity score, and liver fibrosis histological score for the subtype classification provided in the embodiments of this application;

[0029] Figure 5 This application provides a schematic diagram of the pathway-driven characteristics of chronic hepatitis B (CHB) patient subtypes.

[0030] Figure 6 A schematic diagram of data on cell subpopulation-driven characteristics of chronic hepatitis B (CHB) patient subtypes provided in an embodiment of this application;

[0031] Figure 7 This is a schematic diagram illustrating the response data of patients with chronic hepatitis B (CHB) subtypes to various biological therapies, provided in an embodiment of this application.

[0032] Figure 8 A flowchart illustrating a treatment method for chronic hepatitis B based on a molecular typing model, provided as an embodiment of this application;

[0033] Figure 9 A schematic diagram of the structure of a chronic hepatitis B treatment device based on a molecular typing model provided in an embodiment of this application;

[0034] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] 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, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0036] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of pharmaceutical technology.

[0037] Research has revealed that hepatitis B virus (HBV) is a hepatotropic DNA virus that primarily resides within hepatocytes and damages them, causing hepatocyte inflammation, necrosis, and fibrosis. HBV is a global public health problem, with approximately 350 million people persistently infected. The pathogenesis of chronic hepatitis B (CHB) is complex and not yet fully understood.

[0038] Antiviral therapy is a crucial treatment for chronic hepatitis B (CHB) patients. Currently, clinically available medications include nucleoside analogues (Nas) and interferon alpha (IFN-α). Other treatment options include anti-inflammatory, antioxidant, hepatoprotective, anti-fibrotic, and immunomodulatory therapies. Common Nas treatments include entecavir (ETV), tenofovir disoproxil fumarate (TDF), tenofovir alafenamide fumarate (TAF), and tenofovir amibufenamide (TMF). Meanwhile, Peg-IFN-α and IFN-α have been approved for CHB treatment in my country. However, the use of nucleoside analogues is limited by drug resistance and nephrotoxicity during long-term treatment, and IFN-α treatment is prone to adverse reactions. Therefore, effectively improving the clinical cure rate remains a challenge in clinical treatment.

[0039] Taking into account virological, biochemical, and histological characteristics, chronic HBV infection is generally divided into four stages: HBeAg-positive chronic HBV infection, HBeAg-positive chronic HBV infection (CHB), HBeAg-negative chronic HBV infection, and HBeAg-negative CHB (also known as the reactivation phase). However, not all HBV-infected individuals sequentially experience these four stages, and the currently used virological, biochemical, and histological indicators are insufficient for clearly staging all infected individuals. Research on CHB molecular typing mainly focuses on DNA subtypes, with limited exploration of CHB subtype differentiation at the transcriptomic level. For example, Yin Yonghua et al. divided HBV into one genotype (AJ) and three subgenotypes (A1-A5; B1-B6; C1-C6; D1-D4; and F1-F4).

[0040] A growing body of research is attempting to provide classification recommendations by identifying and characterizing patients with similar target states. However, previous studies have not explored the drug sensitivity of patients after omics-based classification.

[0041] Based on this, embodiments of this application provide a treatment method and device for chronic hepatitis B based on a molecular subtyping model. This method can perform molecular subtyping on tissue samples from patients with chronic hepatitis B by constructing a molecular subtyping model, and provide targeted therapeutic drugs based on the type of chronic hepatitis B in each patient. This addresses the problem in existing technologies where previous studies did not explore the drug treatment sensitivity of patients after subtyping at the omics dimension. It provides a new approach to treating drug-resistant patients from the perspective of disease heterogeneity and offers new stratified treatment recommendations for CHB patients.

[0042] It should be noted that all statistical analyses were performed using R software (version 4.1.3). The Wilcoxon test was used to compare the two groups, and due to the limited sample size, Fisher's exact test was used to examine differences in drug sensitivity distribution between subtypes. Two-tailed tests were used to determine statistical significance; a p-value < 0.05 was considered statistically significant.

[0043] Please see Figure 8 , Figure 8 This is a flowchart illustrating a treatment method for chronic hepatitis B based on a molecular typing model, provided as an embodiment of this application. Figure 8 As shown in the embodiments of this application, the treatment method for chronic hepatitis B based on a molecular subtyping model includes:

[0044] S101. Obtain tissue samples from patients with chronic hepatitis B.

[0045] S102. Based on the constructed molecular subtyping model, determine the type of chronic hepatitis B in patients with chronic hepatitis B according to tissue samples.

[0046] Here, chronic hepatitis B types include immune-activated chronic hepatitis B (CHB-A) and metabolic-activated chronic hepatitis B (CHB-B).

[0047] Among them, immune-activated chronic hepatitis B is chronic hepatitis B enriched in immune cells and inflammatory response pathways, while metabolic-activated chronic hepatitis B is chronic hepatitis B enriched in intracellular metabolism and endothelial cell proliferation.

[0048] Specifically, the molecular subtyping model can be constructed through the following steps: Obtain a training dataset, which includes a subset of chronic hepatitis B patients and a subset of healthy controls; using the Limma package in R software, identify the chronic hepatitis B patient samples and healthy control samples in the training dataset to determine differentially expressed genes between chronic hepatitis B patients and healthy controls; perform enrichment analysis on the upregulated differentially expressed genes using the online database Metascape to determine the significantly enriched pathways in chronic hepatitis B patients, and construct a protein-protein interaction (PPI) network using the STRING online database. This study aimed to demonstrate the inter-regulatory relationships between proteins. Gene enrichment analysis was performed on each chronic hepatitis B patient sample to identify potential enriched pathways associated with upregulated differentially expressed genes between chronic hepatitis B patients and healthy controls. These potential enriched pathways were then screened to identify target pathways rich in upregulated differentially expressed genes. Hierarchical clustering of the target pathways was performed using the R package "ConsensuClusterPlus" to identify immune-activated and metabolically activated chronic hepatitis B patient samples within the chronic hepatitis B patient subset.

[0049] For example, publicly available gene expression data and clinical annotations were retrieved from the Gene Expression Omnibus (GEO) database. This included CHB patient datasets (GSE84044, GSE65359, GSE83148, GSE101685, GSE112790, GSE89377, GSE64041, GSE33006, GSE25097, GSE120652, and GSE83148) and datasets for biotherapy responses (GSE66700 and GSE27555). The data was divided into a training dataset (GSE84044, GSE65359, GSE83148, GSE101685, GSE112790, GSE89377, GSE64041, GSE33006, GSE25097, GSE120652) and an independent validation dataset (GSE83148). The original CEL files for all datasets were obtained, and background adjustment and normalization were performed using a robust multi-array averaging method with the Affy and Simpleaffy packages. The "Combat" algorithm from the sva package in R was used to mitigate the batch effect caused by merging data from different datasets.

[0050] The training dataset initially consisted of 207 patients with chronic hepatitis B (CHB) and 58 healthy controls (HC); after data filtering and selection, a total of 203 CHB samples and 58 HC samples were retained for further analysis.

[0051] Here, the 203 CHB samples represent the subset of patients with chronic hepatitis B, and the 58 HC samples represent the subset of healthy control groups.

[0052] In particular, based on the Limma package in R software, samples of chronic hepatitis B patients and healthy control groups in the training dataset are identified. After determining the differentially expressed genes between chronic hepatitis B patients and healthy control groups, the false discovery rate (FDR) can be used to correct false positive results.

[0053] Specifically, differentially expressed genes between patients with chronic hepatitis B and healthy controls can be identified through the following steps: Calculate the fold change in gene expression regulation between patients with chronic hepatitis B and healthy controls, and the parameter value of the Wilcox test result; identify differentially expressed genes whose fold change in gene expression regulation regulation is greater than a first threshold and whose parameter value of the Wilcox test result is less than a second threshold as differentially expressed genes between patients with chronic hepatitis B and healthy controls.

[0054] For example, differentially expressed genes (DEGs) can be defined as DEGs with |logFC|>1.5 and a corrected p-value<0.05.

[0055] Here, FC stands for Fold Change, which is the fold change in gene expression regulation. |logFC| is the absolute value of the fold change in gene expression regulation, base 2, mainly used to indicate the degree of fold change in gene expression regulation. The p-value here represents a parameter of the Wilcox test result, used to indicate the significance of the statistical test result. The corrected p-value is the p-value obtained after applying the Benjamin-Hochberg correction method, which can effectively correct for false positives. Enrichment analysis of upregulated DEGs was performed using the online database Metascape, including Gene Ontology Annotation (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway enrichment analysis.

[0056] For example, please see Figure 1 , Figure 1 This is a schematic diagram of data for identifying differentially expressed genes between chronic hepatitis B (CHB) patients and healthy controls (HC) provided in an embodiment of this application.

[0057] like Figure 1 As shown, Figure 1 The graphs in AB represent heatmaps and volcano maps of differentially expressed genes between CHB patients and HC patients. Figure 1 The CE analysis of 173 upregulated differentially expressed genes was performed using Gene Ontology (GO), reaction genome, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Figure 1 F represents the three highly correlated protein clusters identified using the MCODE algorithm.

[0058] like Figure 1 As shown in AB, Figure 1 AB indicated that 173 upregulated DEGs were screened from CHB patients and HC samples. GO enrichment analysis showed that these upregulated DEGs were mainly enriched in metabolic processes, inflammatory responses, and biosynthetic processes (such as...). Figure 1 (as shown in C).

[0059] Furthermore, KEGG and Reactome enrichment analyses showed that the upregulated genes were significantly involved in the PPAR signaling pathway and metabolic processes (e.g., Figure 1 (As shown in DE). Therefore, pathways with a corrected P-value < 0.05 are considered significantly enriched.

[0060] Optionally, the method further includes: constructing a protein-protein interaction network based on the STRING online database to determine the mutual regulatory relationships between proteins.

[0061] The process involves determining the regulatory relationships between proteins through the following steps: constructing an interaction network of multiple nodes and edges using multiple upregulated genes, where each node represents a gene and each edge represents an interaction between genes; and determining the regulatory relationships between proteins based on this interaction network.

[0062] Here, to elucidate the complex relationships of protein-protein inter-regulation, a protein-protein interaction (PPI) network was constructed using the STRING online database. Furthermore, the constructed PPI network was visualized and analyzed using Cytoscape, and key modules highlighting critical protein interactions were identified through the MOCDE plugin in Cytoscape.

[0063] For example, an interaction network of 172 nodes and 239 edges can be constructed using 173 upregulated genes, where each node represents a gene and each edge represents an interaction between genes. The MOCDE algorithm was used to select three core modules (such as...). Figure 1 (as shown in F).

[0064] Here, to investigate potential enrichment pathways associated with DEG upregulation between CHB patients and HC, single-sample gene enrichment analysis (ssGSEA) was performed on each sample. ssGSEA generated scores for each gene set, enabling the assessment of significant enrichment pathways associated with HBV infection.

[0065] Genomic information about signaling pathways or biological processes can be obtained from the KEGG and Reactome databases. Pathways with a corrected P-value < 0.05 and rich in upregulated differentially regulated genes can be screened out, and these pathways can then be selected for further analysis.

[0066] Here, to explore the heterogeneity of molecular subtypes defined by differentially upregulated genes associated with CHB, the R package "ConsensuClusterPlus" was used for hierarchical clustering to divide CHB samples into two types.

[0067] Specifically, the steps for hierarchical clustering of the target pathway based on the R package "ConsensuClusterPlus" include: using the Pam algorithm combining Euclidean and Ward-D2, we perform multiple iterations using the R package "ConsensuClusterPlus" and determine the cumulative distribution function value and incremental area after each iteration based on the cumulative distribution function; determining the optimal number of clusters based on the cumulative distribution function value and incremental area after each iteration; and performing hierarchical clustering of the target pathway based on the optimal number of clusters using the R package "ConsensuClusterPlus".

[0068] The clustering method used was the Pam algorithm, a combination of Euclidean and Ward-D2 clustering, which was repeated 1000 times to ensure accuracy. The optimal cluster was determined by the cumulative distribution function (CDF). Principal component analysis (PCA) was performed to confirm the unsupervised classification results. Furthermore, to investigate differences in molecular processes and biological functions between the two subtypes, differential analysis was conducted between the subtype results. DEGs (Devices, Genes, and Genes) were then selected and subjected to functional enrichment analysis.

[0069] For example, please refer to Figure 2 , Figure 2 This is a schematic diagram of the consistent clustering of the chronic hepatitis B (CHB) cohort provided in the embodiments of this application.

[0070] like Figure 2 As shown, Figure 2 A is the consistency score matrix of the CHB samples when k=2. Figure 2 B represents the consistent clustering of the cumulative distribution function for k values ​​ranging from 2 to 9. Figure 2 C is used to analyze the change in the area under the cumulative distribution function curve, with k ranging from 2 to 9. Figure 2 D is a heatmap of 203 patients with chronic hepatitis B, showing the distribution of gene transcripts in the two subtypes.

[0071] To obtain the optimal number of clusters, we evaluated all clusters with k ranging from 2 to 9 through 1000 iterations using the "ConsensusterPlus" package. Based on the CDF value and incremental area, we determined that k=2 was the optimal clustering result. Figure 2 AC). Differential analysis was performed on the two subtypes, and a heatmap of significantly upregulated genes between the subtypes was plotted. Figure 2 D). Subtype A (CHB-A) had 266 upregulated genes, and subtype B (CHB-B) had 138 upregulated genes. To further explore the potential functions of differentially expressed genes and the dysregulated biological processes and signaling pathways between the two subtypes, we performed enrichment analyses on Gene Ontology Bioprocesses (GO-BP), KEGG, and Reactome using the Metascape online database. CHB-A was significantly enriched in immune inflammatory pathways, including inflammatory responses, interleukin signaling pathways, and lymphocyte proliferation.

[0072] In addition, please see Figure 3 , Figure 3 for Figure 3 This is a schematic diagram of transcriptomic characteristics of two subtypes of chronic hepatitis B (CHB) patients provided in the embodiments of this application.

[0073] like Figure 3 As shown, Figure 3AC represents the analysis of differentially expressed genes (DEGs) upregulated by CHB-A using Gene Ontology (GO), Reactome, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Figure 3 DF represents the GO enrichment, reactionome, and KEGG analysis of CHB-B-upregulated DEGs.

[0074] CHB-A is also enriched in cell cycle and virus infection-related pathways. Figure 3 AC). CHB-B, on the other hand, is significantly enriched in metabolism-related pathways, such as drug metabolism, amino acid and derivative metabolism, and interferon signaling pathways. Figure 3 DF).

[0075] Furthermore, statistical tests showed a significant difference between subclass classification and disease progression (P<0.001).

[0076] For example, please refer to Figure 4 , Figure 4 This is a schematic diagram showing significant differences in disease staging, histological activity score, and liver fibrosis histological score for the subtype classification provided in the embodiments of this application.

[0077] like Figure 4 As shown, Figure 4 * indicates P < 0.05; ** indicates P < 0.01; *** indicates P < 0.001. Figure 4 A represents the proportion of the difference in disease stage between the two disease subtypes. Figure 4 B represents the proportion of the difference in histological activity scores between the two disease subtypes. Figure 4 C represents the proportion of the difference in liver fibrosis histological scores between the two disease subtypes.

[0078] like Figure 4 As shown in Figure A, CHB-A patients were predominantly in the immune clearance phase (100%), while CHB-B patients were predominantly in the immune tolerant phase (45%), with a mixed subset of patients in the immune clearance phase (32%) and inactive phase (23%). Significant differences were also observed between subtype classifications and histological activity scores, as well as between these subtypes and histological scores for liver fibrosis (P<0.001). In histological activity scores, CHB-A patients were predominantly in the G2 (50%) and G3 (24%) stages, while CHB-B patients were predominantly in the G0 (47%) and G1 (31%) stages. Figure 4B). In the histological scoring of liver fibrosis, CHB-A was mainly seen in patients at stages S2 (41%) and S3 (22%), while CHB-B was mainly seen in patients at stages S0 (51%) and S2 (19%). Figure 4 C).

[0079] To investigate differences in immune inflammation and metabolic processes among subtypes, pathways and processes associated with chronic hepatitis B (CHB) were retrieved from the KEGG and Reactome databases, and enrichment scores of CHB-related immune pathways and cellular components were compared. The "Xcell" package in R software was used to quantify the immune cell infiltration score of liver tissue samples from CHB patients. The Xcell package can achieve enrichment scores encompassing 64 immune cell and stromal cell types. Single-sample gene enrichment analysis (ssGSEA) was used to assess the enrichment level of signaling pathway gene sets in each sample. The ssGSEA enrichment score quantifies the co-regulation or downregulation level of genes within a specific gene set in a given sample. The Wilcoxon test was used to assess the enrichment scores of specific immune cell infiltration types and signaling pathways in each subtype; a p-value <0.05 was considered statistically significant.

[0080] Specifically, the immune cells and inflammatory response pathways in immune-activated chronic hepatitis B include: cytokine-cytokine receptor interactions, interferon signaling pathway, interleukin-10 signaling pathway, and Toll-like receptor signaling pathway. Immune-activated chronic hepatitis B is characterized by a large infiltration of immune cells, including B cells and CD8+ cells. + T cells, dendritic cells, Th2 cells, and M1 macrophages are enriched in metabolic-related pathways in metabolically activated chronic hepatitis B, including the JAK-STAT signaling pathway, the metabolic effects of cytochrome p450 on exogenous drugs, the PPAR signaling pathway, and other enzyme pathways involved in drug metabolism. In metabolically activated chronic hepatitis B, endothelial cell, hepatocyte, preadipocyte, and Th1 cell pathways are highly activated.

[0081] For example, please refer to Figure 5 , Figure 5 This application provides a schematic diagram of the pathway-driven characteristics of chronic hepatitis B (CHB) patient subtypes.

[0082] like Figure 5 The box plots in the figure show the pathway enrichment fractions for the two subtypes. * indicates P < 0.05; ** indicates P < 0.01; *** indicates P < 0.001.

[0083] like Figure 5As shown, CHB-A is mainly enriched in immune inflammation-related pathways, such as cytokine-cytokine receptor interactions, interferon signaling pathway, interleukin-10 signaling pathway, and Toll-like receptor signaling pathway. CHB-B, on the other hand, is significantly enriched in pathways such as the JAK-STAT signaling pathway, the metabolism of exogenous drugs by cytochrome p450, PPAR signaling pathway, and other enzymes involved in drug metabolism.

[0084] Furthermore, consistent with previous enrichment results, immune cells were activated to varying degrees in both subtypes.

[0085] Please see Figure 6 , Figure 6 This is a schematic diagram of data illustrating the cell subpopulation-driven characteristics of chronic hepatitis B (CHB) patient subtypes provided in an embodiment of this application. Figure 6 The box plots in the figure show the pathway enrichment fractions for the two subtypes. * indicates P < 0.05; ** indicates P < 0.01; *** indicates P < 0.001.

[0086] like Figure 6 As shown, CHB-A shows a large infiltration of immune cells, including B cells, CD8+ T cells, dendritic cells, Th2 cells, and M1 macrophages. In contrast, CHB-B shows high activation of pathways such as endothelial cells, hepatocytes, preadipocytes, and Th1 cells.

[0087] Optionally, the molecular subtyping model can be validated through the following steps: obtaining a validation dataset, which includes multiple liver biopsy samples from chronic hepatitis B patients and the chronic hepatitis B type of each liver biopsy sample; inputting each liver biopsy sample from the validation dataset into the molecular subtyping model based on the molecular subtyping model, determining the accuracy of the molecular subtyping model based on the chronic hepatitis B type and the chronic hepatitis B type determined by the molecular subtyping model; determining whether the accuracy of the molecular subtyping model is greater than a preset target value; if the accuracy of the molecular subtyping model is greater than the preset target value, then the construction of the molecular subtyping model is deemed practical.

[0088] Here, the accuracy of the classification results was confirmed through validation on an independently published dataset of CHB liver biopsy samples. Patients in this dataset were divided into two subtypes using 173 upregulated genes (subtype A [n=44] and subtype B [n=77]). A comprehensive analysis of the enrichment scores of CHB-related pathways and cell subsets in both subtypes yielded the same conclusion as before: CHB-A is mainly characterized by an immune-activated state and frequent inflammatory activity, while CHB-B is mainly concentrated in metabolic-related signaling pathways.

[0089] We employed the decision tree method in Xgboost to construct an Xgboost model for molecular subtyping of CHB patients based on upregulated differentially expressed gene characteristics. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the model's performance. A 10-fold cross-validation was used to control overfitting for each training unit, and the fitted model was applied for assignment.

[0090] For example, 203 CHB patient samples were used as the training set, and then the model constructed in this invention was validated using an independent dataset (containing 122 CHB patient samples). The training data was divided into two subtypes using a classifier trained with 173 upregulated gene features. The classifier was then applied to an independent validation set, and the model accurately distinguished between the two subtypes, achieving an AUC value as high as 96.8%, indicating that the model has a certain generalization ability.

[0091] S103. Based on the type of chronic hepatitis B in patients with chronic hepatitis B, determine the targeted therapy drugs for each subtype of chronic hepatitis B.

[0092] In this step, targeted therapies for patients with chronic hepatitis B can be determined through the following steps: obtaining the treatment response rate of each targeted therapy for immune-activated chronic hepatitis B and the treatment response rate of each targeted therapy for metabolically activated chronic hepatitis B; determining the chronic hepatitis B subtypes with high treatment response rates and those with poor treatment response rates based on the type of chronic hepatitis B in the patients, and performing drug prediction for the chronic hepatitis B subtypes with poor treatment response rates.

[0093] Here, the therapeutic effects of various biological agents on CHB patients vary, depending on the pathological specificity and molecular activity of liver tissue in different subtypes of patients.

[0094] For example, please refer to Figure 7 , Figure 7 This is a schematic diagram illustrating the response data of patients with chronic hepatitis B (CHB) subtypes to various biological therapies, provided as an embodiment of this application. Figure 7As shown, responders are those who responded to the biotherapy; non-responders are those who did not respond to the biotherapy. ns indicates insignificant, and * indicates P<0.05. Figure 7 A represents the response / non-response to IFN-α: CHB-A is 54% / 46% [7 / 6], CHB-B is 94% / 6% [16 / 1]. Figure 7 B represents the response / no response to combination therapy with pegylated interferon and adefovir: CHB-A was 45% / 55% [5 / 6], CHB-B was 100% / 0% [4 / 0].

[0095] like Figure 7 As shown, this invention evaluated the therapeutic effects of two biologics (IFN-α and peg-IFN combined with adefovir) on different subtypes, finding that both drugs were more effective in treating CHB-B than in CHB-A. Figure 7 As shown in Figure A, when treated with INF-α, 54% of patients in CHB-A responded to treatment, while 46% did not. In CHB-B, the vast majority of patients (94%) responded well to INF-α treatment, and there was a significant difference in the response rate between these two patient subtypes (P<0.05). Figure 7 As shown in Figure B, when peg-IFN was combined with adefovir, 55% of patients diagnosed with CHB-A did not respond to treatment, while all CHB-B patients responded after treatment. However, due to the insufficient sample size, the therapeutic effect of the biologic peg-IFN combined with adefovir may not be statistically significant.

[0096] Meanwhile, given the poor response rates of the two treatment methods in CHB-A subtyped patients, drug prediction can be performed using a reverse transcriptional regulation prediction method. This method screens drugs that have a reverse regulatory effect on the patient's transcriptome expression model, specifically by upregulating abnormally downregulated transcriptome genes and downregulating abnormally upregulated transcriptome genes to achieve a balance in transcriptional regulation. First, using the Limma package in R software, differentially expressed genes (DEGs) were screened in CHB-A subtyped patient and healthy control group samples. Differentially expressed genes (DEGs) were defined as those with |logFC|>1.5 and a corrected p-value <0.05. DEGs with logFC>1.5 were defined as upregulated differentially expressed genes (up-DEGs), and those with logFC<-1.5 were defined as downregulated differentially expressed genes (down-DEGs). Subsequently, the up-DEGs and down-DEGs were input into the DGIdb (Drug-Gene Interaction database) for drug prediction. For drugs predicted by up-DEGs, inhibitors, antagonists, blockers, and inverse agonists were selected based on the principle of reverse transcriptional regulation, resulting in a total of 121 drugs or small molecule compounds. For drugs predicted by down-DEGs, activators, partial agonists, and receptor agonists were selected based on the principle of reverse transcriptional regulation, resulting in a total of 249 drugs or small molecule compounds. The intersection of the 121 and 249 drugs or small molecule compounds yielded two drugs: asenapine and agomelatine. Previous studies have shown that these two drugs are predicted to have some inhibitory effect on HBV. Furthermore, their combination with IFN-α is expected to restore immune tolerance in patients with poor treatment response rates and improve the treatment response rate of CHB subtypes with poor treatment response rates.

[0097] In conclusion, the different subtype classifications of HBV-infected CHB patients may be correlated with drug sensitivity, and this can be considered as a key factor for future clinical medication of HBV-infected CHB patients.

[0098] The chronic hepatitis B treatment method based on molecular subtyping models provided in this application can perform molecular subtyping of tissue samples from chronic hepatitis B patients by constructing molecular subtyping models, and provide targeted therapeutic drugs according to the chronic hepatitis B type of the patients. This solves the problem in the prior art that previous studies did not explore the drug treatment sensitivity of patients after subtyping at the omics dimension. It provides a new approach to the treatment of drug-resistant patients from the perspective of disease heterogeneity and provides new stratification suggestions for CHB patients.

[0099] Based on the same inventive concept, this application also provides a chronic hepatitis B treatment device based on a molecular subtyping model, which corresponds to the chronic hepatitis B treatment method based on a molecular subtyping model. Since the principle of the device in this application is similar to the chronic hepatitis B treatment method based on a molecular subtyping model described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0100] Please see Figure 9 , Figure 9 This is a schematic diagram of a chronic hepatitis B treatment device based on a molecular typing model, provided in an embodiment of this application. Figure 9 As shown, the chronic hepatitis B treatment device 900 based on a molecular typing model includes:

[0101] Tissue sample acquisition module 901 is used to acquire tissue samples from patients with chronic hepatitis B;

[0102] Subtype determination module 902 is used to determine the type of chronic hepatitis B in patients based on tissue samples from patients with chronic hepatitis B using a molecular typing model.

[0103] The treatment recommendation module 903 is used to determine targeted treatment drugs for patients with different subtypes of chronic hepatitis B based on the type of chronic hepatitis B in the patient.

[0104] The chronic hepatitis B treatment device based on a molecular subtyping model provided in this application can perform molecular subtyping of tissue samples from patients with chronic hepatitis B by constructing a molecular subtyping model, and provide targeted therapeutic drugs according to the type of chronic hepatitis B in the patients. This solves the problem in the prior art that previous studies did not explore the drug treatment sensitivity of patients after subtyping at the omics dimension. It provides a new approach to the treatment of drug-resistant patients from the perspective of disease heterogeneity and provides new stratification suggestions for CHB patients.

[0105] Please see Figure 10 , Figure 10This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 10 As shown, the electronic device 1000 includes a processor 1010, a memory 1020, and a bus 1030.

[0106] The memory 1020 stores machine-readable instructions executable by the processor 1010. When the electronic device 1000 is running, the processor 1010 communicates with the memory 1020 via the bus 1030. When the machine-readable instructions are executed by the processor 1010, they can perform the operations described above. Figure 8 The specific implementation of the steps in the method embodiment for treating chronic hepatitis B based on molecular typing models can be found in the method embodiment, and will not be repeated here.

[0107] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 8 The specific implementation of the steps in the method embodiment for treating chronic hepatitis B based on molecular typing models can be found in the method embodiment, and will not be repeated here.

[0108] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0112] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining targeted therapies for patients with different subtypes of chronic hepatitis B based on a molecular subtyping model, characterized in that, The method includes: Obtain tissue samples from patients with chronic hepatitis B; Based on the constructed molecular subtyping model, the type of chronic hepatitis B in patients with chronic hepatitis B was determined according to tissue samples. Based on the type of chronic hepatitis B in patients, targeted therapy drugs are determined for each subtype of chronic hepatitis B. The targeted therapy drugs for patients with different subtypes of chronic hepatitis B are determined through the following steps: To obtain the treatment response rate of each targeted therapy for immune-activated chronic hepatitis B and the treatment response rate of each targeted therapy for metabolic-activated chronic hepatitis B; Based on the type of chronic hepatitis B in patients with chronic hepatitis B, the targeted therapy drugs with the highest treatment response rate for each type of chronic hepatitis B were identified, and targeted therapy drugs were predicted for patients with chronic hepatitis B types with low treatment response rates. Among these, targeted therapy drug prediction for patients with chronic hepatitis B who have a low treatment response rate includes: Based on the Limma package in R software, differentially expressed genes were screened in samples from patients with chronic hepatitis B and healthy control groups. The differentially expressed genes included upregulated differentially expressed genes and downregulated differentially expressed genes. The upregulated and downregulated differentially expressed genes were input into the DGIdb database for drug prediction. For drugs that upregulate differentially expressed genes, inhibitors, antagonists, blockers and reverse agonists were selected based on the principle of reverse transcription regulation, and a total of 121 drugs or small molecule compounds were obtained through screening. For drugs predicted to downregulate differentially expressed genes, activators, partial agonists and receptor agonists were selected based on the principle of reverse transcription regulation, and a total of 249 drugs or small molecule compounds were screened. The intersection of 121 drugs or small molecule compounds and 249 drugs or small molecule compounds was screened together; Chronic hepatitis B includes two main types: immune-activated chronic hepatitis B and metabolically activated chronic hepatitis B. Among them, immune-activated chronic hepatitis B is chronic hepatitis B enriched in immune cells and inflammatory response pathways, while metabolic-activated chronic hepatitis B is chronic hepatitis B enriched in intracellular metabolism and endothelial cell proliferation. The molecular typing model is constructed using the following steps: Obtain a training dataset, which includes a subset of chronic hepatitis B patients and a subset of healthy control groups; Based on the Limma package in R software, samples of patients with chronic hepatitis B and healthy control groups in the training dataset were identified to determine differentially expressed genes between patients with chronic hepatitis B and healthy control groups. Enrichment analysis was performed on upregulated differentially expressed genes using the online database Metascape to identify significant enrichment pathways in patients with chronic hepatitis B. A protein-protein interaction (PPI) network was constructed using the STRING online database to demonstrate the mutual regulatory relationships between proteins. Gene enrichment analysis was performed on each chronic hepatitis B patient sample to identify potential enrichment pathways associated with upregulated differentially expressed genes between chronic hepatitis B patients and healthy controls. Potential enrichment pathways associated with upregulated differentially expressed genes between chronic hepatitis B patients and healthy controls were screened to obtain target pathways rich in upregulated differentially expressed genes. Based on the R package "ConsensuClusterPlus", hierarchical clustering of target pathways was performed to identify immune-activated chronic hepatitis B patient samples and metabolic-activated chronic hepatitis B patient samples in the chronic hepatitis B patient subset; The molecular typing model was validated using the following steps: Obtain a validation dataset, which includes multiple liver tissue biopsy samples from patients with chronic hepatitis B and the type of chronic hepatitis B in each liver tissue biopsy sample from a patient with chronic hepatitis B; Based on the molecular typing model, liver tissue biopsy samples from each chronic hepatitis B patient in the validation dataset are input into the molecular typing model. The accuracy of the molecular typing model is determined based on the chronic hepatitis B type and the chronic hepatitis B type of each liver tissue biopsy sample from the molecular typing model. Determine whether the accuracy of the molecular typing model is greater than a preset target value; If the accuracy of the molecular typing model is greater than the preset target value, then the construction of the molecular typing model is determined to be practical.

2. The method according to claim 1, characterized in that, The immune cells and inflammatory response pathways in immune-activated chronic hepatitis B include: cytokine-cytokine receptor interactions, interferon signaling pathway, interleukin-10 signaling pathway, and Toll-like receptor signaling pathway. Immune cells, including B cells, CD8+ T cells, dendritic cells, Th2 cells, and M1 macrophages, infiltrate extensively in immune-activated chronic hepatitis B. Metabolic activation chronic hepatitis B is enriched in metabolic-related pathways including: JAK-STAT signaling pathway, the metabolic effects of cytochrome p450 on exogenous drugs, PPAR signaling pathway, and other drug metabolism enzyme pathways. In metabolic activation chronic hepatitis B, endothelial cell, hepatocyte, preadipocyte, and Th1 cell pathways are highly activated.

3. The method according to claim 1, characterized in that, The steps for hierarchical clustering of target pathways based on the R package "ConsensuClusterPlus" include: The Pam algorithm, based on the combination of Euclidean and Ward-D2, is used in the R package "ConsensuClusterPlus" to perform multiple iterations and determine the cumulative distribution function value and incremental area after each iteration based on the cumulative distribution function. The optimal number of clusters is determined based on the cumulative distribution function value and incremental area after each iteration; Based on the optimal number of clusters, hierarchical clustering of the target pathway is performed using the R package "ConsensuClusterPlus".

4. The method according to claim 1, characterized in that, The following steps were used to identify differentially expressed genes between patients with chronic hepatitis B and healthy controls: Calculate the fold change in gene regulatory expression and the parameter values ​​of the Wilcox test results between patients with chronic hepatitis B and healthy controls; Differentially expressed genes between patients with chronic hepatitis B and healthy controls were defined as those whose fold change in gene regulatory expression was greater than a first threshold and whose Wilcox test parameter value was less than a second threshold.

5. The method according to claim 2, characterized in that, The method further includes: A protein-protein interaction network was constructed based on the STRING online database to determine the regulatory relationships between proteins. The following steps are used to determine the regulatory relationships between proteins: By constructing an interaction network with multiple nodes and edges using multiple upregulated genes, a node represents a gene and an edge represents the interaction between genes. Based on the interaction network of multiple nodes and multiple edges, the mutual regulatory relationships between proteins are determined.

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