A method of identifying convergent module nodes in a biomolecular network

Abstract

The application discloses a method for identifying convergent nodes in a biomolecular network, comprising the following steps: (1) determining a biomolecular network in which convergent nodes need to be identified; (2) calculating the collective influence (CI) value, the integrated value index (IVI) value and the CI-IVI value of each node in the biomolecular network; ranking according to the calculation results, removing the node with the largest CI-IVI value; and then calculating lambda (λ); (4) if λ<=1, ending the identification, and the removed node being the convergent node; if λ>1, performing step (5); (5) re-calculating the CI-IVI value of each node in the remaining biomolecular network, ranking, removing the node with the largest CI-IVI value, and calculating lambda (λ); (6) if λ<=1, ending the identification, and all the removed nodes being the convergent node set; if λ>1, repeating step (5) until λ<=1. Compared with the known method for identifying convergent nodes in a biomolecular network, the method disclosed by the application is more effective.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics. Specifically, this invention relates to a method for identifying convergence module nodes in complex molecular networks of diseases and drug interventions, such as protein-protein interaction networks, gene expression regulation networks, drug metabolism and drug target networks. Background Technology

[0002] With the rise of omics and the continuous advancement of high-throughput technologies, a vast amount of biological datasets have been generated. Using biological items (such as proteins, genes, and metabolites) as nodes and the relationships between node pairs as edges, various virtual biomolecular networks have been formed, such as protein-protein interaction networks, gene expression regulation networks, and drug metabolism networks. Effective analysis of these data networks can help reveal mechanisms of gene expression regulation, protein-protein interactions, and metabolite interactions, which can then be applied in fields such as disease mechanism research and treatment, and drug development.

[0003] Research has shown that modular organizational structures exist within biomolecular networks. Identifying these modular structures and key nodes within each module helps simplify and reduce the dimensionality of complex networks, aids in understanding biological systems, systematically reveals the complex mechanisms of drug action, and facilitates the development of new drugs, especially those targeting complex diseases. The inventors have disclosed a method for identifying key nodes in biomolecular networks in Chinese invention patent application CN105956413A (publication date September 21, 2016). This method involves first identifying key modules of the biomolecular network, and then identifying key nodes within those modules.

[0004] As research into biomolecular networks deepens, researchers have recognized the existence of a convergence concept within these networks, meaning that there is an upper limit to the number of modules. When the number of modules exceeds this limit, even if the number of nodes continues to increase, the impact will reach saturation.

[0005] In complex situations, identifying the principal contradiction is key to solving the problem. Researchers have already conducted work on identifying convergence modules or nodes based on the K-kernel algorithm and the CI (Collective Influence) algorithm. However, these algorithms often neglect the joint propagation factors of module nodes, fail to consider the impact of inter-module relationships on convergence modules in the local, semi-local, and global topological dimensions of the network, and lack quantitative analysis of convergence module nodes. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a novel method for identifying convergent module nodes in biomolecular networks. This method can capture all topological dimensions of the network and simultaneously resolve the positional bias of global centrality metrics, thereby identifying convergent module nodes in biomolecular networks.

[0007] definition:

[0008] In this paper, the term "biomolecular network" refers to a network existing in biological systems in different organizational forms, consisting of nodes representing various types of biomolecules and edges representing the interactions between biomolecules. Common biomolecular networks include gene transcription regulatory networks, metabolic networks, epigenetic networks, phenotypic networks, signal transduction networks, and protein-protein interaction networks.

[0009] In this paper, the term "converging module node" refers to a module node in the network that occupies a critical position in terms of both structure and function. For example, it has an important influence on the information transmission and interaction of nodes in the network. The impact of deleting this node on the network structure and information transmission is greater than the impact of deleting other nodes in the network.

[0010] In this paper, the term "K-kernel algorithm" refers to a heuristic algorithm for converging a set of nodes in a network. Its core idea is to measure a node's influence based on its coreness within the network, where nodes located in the core region have higher influence than peripheral nodes. The specific implementation process is as follows: First, isolated nodes in the network are considered to have a coreness of 0 and are removed from the network. Then, a kernel decomposition operation is performed. In this process, all nodes with a coreness of 1 are first removed from the network, and then nodes with a residual degree less than or equal to 1 are continuously removed until all remaining nodes have a residual degree greater than 1. These removed nodes are collectively referred to as 1-core nodes, with a coreness value of 1. Next, nodes with a coreness of 2 are processed in the same way. This process continues until all nodes in the network have been removed. Under this method, nodes with higher coreness are typically located in the center of the network and have greater influence.

[0011] In this paper, the term “CI” refers to Collective Influence, and the CI value of each node in a molecular biological network can be calculated using formula (1).

[0012] ,

[0013] in, , Let i and j represent the degrees of nodes i and j, respectively. It is a sphere boundary composed of nodes whose shortest path to node i is of length l, and is usually set to l=1.

[0014] The term "CI algorithm" refers to a network convergence set based on the collective influence (CI) value. The main idea of ​​this algorithm is to remove the node with the highest CI value in each iteration to quickly collapse the network until the value calculated by formula (2) is reached. .

[0015] ,

[0016] Where N is the total number of nodes. It is the average degree of the network. This is the collective influence value of node i, typically set to l=1. When... At that point, the removal process will stop, meaning that most of the giant components will disappear.

[0017] In this paper, the term "IVI value" refers to the Integrated Value if Influence, which reflects degree centrality (DC), cluster rank (CR), neighborhood connectivity (NC), local H index (H), betweenness centrality (BC), and collective influence (CI). The IVI value of each node in the molecular biological network can be calculated using formula (3).

[0018] .

[0019] in, , , , , and These are nodes The range-normalized degree centrality, range-normalized local H-index, range-normalized domain connectivity, range-normalized cluster rank, range-normalized betweenness centrality, and range-normalized collective influence are among them. It is calculated using formula (1).

[0020] The present invention adopts the following technical solution:

[0021] A method for identifying convergence nodes in a biomolecular network includes the following steps:

[0022] (1) Identify the biomolecular network for which convergence nodes need to be identified;

[0023] (2) Calculate the collective influence CI value of each node in the biomolecular network according to formula (1).

[0024] ,

[0025] in, , Let i and j represent the degrees of nodes i and j, respectively. It is a sphere boundary consisting of nodes whose shortest path to node i is of length l, where l=1;

[0026] Then, substitute the values ​​into formula (3) to calculate the overall comprehensive impact index (IVI) value for each node.

[0027] ,

[0028] in, , , , , and These are nodes Range normalized degree centrality, range normalized local H-index, range normalized domain connectivity, range normalized cluster rank, range normalized betweenness centrality, and range normalized collective influence.

[0029] Then substitute the IVI value into formula (4) to calculate the value of each node. value;

[0030] ,

[0031] in, and These are the overall comprehensive impact index values ​​for node i and node j, respectively. It is a sphere boundary consisting of nodes whose shortest path to node i is of length l, where l=1;

[0032] Sort the results according to the calculation results, remove the node with the largest CI-IVI value; then calculate according to formula (5). ,

[0033] ,

[0034] Where N is the total number of nodes. It is the average degree of the network. Calculated using formula (4);

[0035] (4) If If the identification ends, the removed node is the convergence node; if Then proceed to step (5);

[0036] (5) Recalculate the remaining nodes of the biomolecular network according to formulas (1), (3) and (4). Value, Sort, Remove The node with the largest value is calculated according to formula (5). ;

[0037] (6) If If the identification ends, the set of all removed nodes is the converged node set; if Then repeat step (5) until... .

[0038] Preferably, the biomolecular network is selected from gene expression regulatory networks, protein-protein interaction networks, biological metabolic networks, epigenetic networks, phenotypic networks, signal transduction networks, etc.; more preferably, it is a gene expression regulatory network.

[0039] The biomolecular network can be any known in the art, such as those reported in patent or non-patent literature. It can also be constructed for a specific purpose. For example, the biomolecular network can be the "lung adenocarcinoma metastatic prognostic differential gene regulatory network" constructed in the examples.

[0040] Typically, the biomolecular network contains ≥3 nodes and ≥3 edges. For example, the "Lung Adenocarcinoma Metastatic Prognostic Differential Gene Regulatory Network" constructed in this embodiment contains 90 nodes and 157 edges.

[0041] The method for identifying convergence nodes in biomolecular networks described in this invention innovatively integrates the collective influence (CI) value and the overall comprehensive influence (IVI) value. A basic flowchart is shown below. Figure 1 As shown, its basic idea can be summarized as follows:

[0042] (1) Calculate the overall comprehensive impact index (IVI) of the node, and further calculate the CI-IVI value of the node based on the overall comprehensive impact index (IVI) of the node.

[0043] (2) Remove the node with the largest CI-IVI value in each iteration to quickly disintegrate the network. That is, the node with the largest CI-IVI value is removed, and then the new CI-IVI value of each remaining node in the network is calculated; and the network is updated. Value, when A value ≤1 indicates that most of the giant components disappeared during the removal process. The final result is a sorted list of convergent nodes.

[0044] Complex networks are composed of nodes and the relationships (edges) between them. Within these networks, module nodes have varying roles. In fact, convergent module nodes have different functions for the network, and removing a convergent module node can cause the network to experience critical collapse. Therefore, identifying convergent module nodes is crucial for understanding the overall network's operational mechanisms. Thus, research on convergent module nodes lays the foundation for exploring the main pharmacological mechanisms underlying the synergistic effects of combined drug applications for complex diseases; it also lays the groundwork for further analysis of drug interactions, guiding combined drug use and disease treatment, and providing a scientific basis for guiding disease treatment and drug development.

[0045] To address this, this invention proposes an innovative concept for identifying convergence module nodes in complex networks of diseases and drug interventions. Specifically, it provides a novel method integrating CI (Centralization Indicator) and IVI (Internal Visibility) to identify convergence module nodes from biomolecular networks. This identification process captures all topological dimensions of the network, resolves locational biases in centrality measures, and quantitatively identifies convergence module nodes based on the percolation theory of complex networks. The method of this invention comprehensively considers multiple important network centrality measures, including degree centrality, cluster registration, neighborhood connectivity, local H-index, betweenness centrality, and collective influence. Each centrality measure captures different topological dimensions of the network, including local, semi-local, and global topological dimensions. Attached Figure Description

[0046] The present invention will now be described in detail with reference to the accompanying drawings.

[0047] Figure 1 The flowchart of the convergence module node identification method of the present invention is shown.

[0048] Figure 2 The constructed prognostic gene regulatory network for lung adenocarcinoma metastasis is shown.

[0049] Figure 3 This diagram shows the identification results of convergence module nodes in the prognostic differential gene regulatory network for lung adenocarcinoma metastasis based on the K-kernel method. Nodes with colors different from other nodes in the diagram are the identified convergence module nodes.

[0050] Figure 4 The diagram shows the identification results of convergence module nodes in the prognostic differential gene regulatory network for lung adenocarcinoma metastasis based on the CI method. Nodes with colors different from other nodes in the diagram are the identified convergence module nodes.

[0051] Figure 5 The diagram shows the identification results of convergence module nodes in the prognostic differential gene regulatory network for lung adenocarcinoma metastasis based on the CI-IVI method of this invention. Nodes with colors different from other nodes in the diagram are the identified convergence module nodes.

[0052] Figure 6This paper illustrates the changes in the size G(q) and number C(q) of the largest connected component in a lung adenocarcinoma metastatic prognostic differential gene regulatory network after removing nodes from various convergence modules using different methods. In Figures A and B, the x-axis q represents the proportion of nodes removed; the y-axis G(q) in Figure A represents the size of the giant connected component, which is the proportion of the giant connected component in the network after removing nq nodes based on a certain convergence method, relative to the initial network size G; and the y-axis C(q) in Figure B represents the number of connected components in the network after removing nq nodes based on a certain convergence method. Detailed Implementation

[0053] The present invention will be described below with reference to specific embodiments. Those skilled in the art will understand that these embodiments are for illustrative purposes only and do not limit the scope of the invention in any way.

[0054] Example 1 Construction of a gene regulatory network for differential prognostic outcomes in lung adenocarcinoma metastasis

[0055] 1.1 Differentially expressed genes associated with lung adenocarcinoma metastasis

[0056] A total of 344 (M0) and 25 (M1) RNAseq data for lung adenocarcinoma and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA). 1444 differentially expressed genes between metastatic and non-metastatic lung adenocarcinomas were calculated and identified.

[0057] 1.2 Prognostic genes among differentially expressed genes associated with lung adenocarcinoma metastasis

[0058] Multivariate Cox survival analysis was performed based on differentially expressed gene levels and patient overall survival (OS). 423 genes associated with patient survival outcomes were identified (P-value < 0.05).

[0059] 1.3 Construction of a gene regulatory network for differential prognostic outcomes in lung adenocarcinoma metastasis

[0060] A differential regulatory network for lung adenocarcinoma metastasis was constructed by mapping 423 prognostic differential genes to a human disease regulatory network. Analysis showed that this network contained 171 nodes and 157 edges. To maximize node perturbation in the disease network, isolated nodes and unconnected parts of the network were removed. Figure 2 The nodes in the red box (the final prognostic differential network for lung adenocarcinoma metastasis) consist of 90 nodes and 157 edges, as shown in Table 1 and... Figure 2 As shown.

[0061]

[0062] For details on the construction of the gene regulatory network for prognostic differences in lung adenocarcinoma metastasis, please refer to: Liu Xi, Research on the optimization of traditional Chinese medicine formulations for inhibiting lung adenocarcinoma metastasis based on network target convergence and its efficacy mechanism [D], China Academy of Chinese Medical Sciences, 2024.

[0063] Example 2 Identification of convergence module nodes in the differential prognostic gene regulatory network for lung adenocarcinoma metastasis constructed in Example 1

[0064] 2.1 Identifying Convergence Module Nodes Based on the K-Kernel Method

[0065] Isolated nodes in the network are considered to have a coreity of 0 and are removed from the network. Then, a kernel decomposition operation is performed. In this process, all nodes with a coreity of 1 are first removed from the network, and then nodes with a residual degree less than or equal to 1 are continuously removed until all remaining nodes in the network have a residual degree greater than 1. These removed nodes are collectively called 1-core nodes, with a coreity value of 1. Next, nodes with a coreity of 2 are processed in the same way. This process continues until all nodes in the network have been removed. Therefore, the maximum coreity of the network is 4, and the nodes (genes) included are shown in Table 2. This set of nodes is the set of convergent nodes identified by the K-sum method, as detailed in [see Table 2]. Figure 3 .

[0066]

[0067] 2.2 CI Method for Identifying Convergence Module Nodes

[0068] The collective influence CI value of each node in the differential prognostic gene regulatory network for lung adenocarcinoma metastasis is calculated according to formula (1).

[0069] ,

[0070] in, , Let i and j represent the degrees of nodes i and j, respectively. It is a sphere boundary consisting of nodes whose shortest path to node i is of length l, where l=1.

[0071] Remove the node with the largest CI value (iteration) to quickly collapse the network, calculated by formula (2). value;

[0072] ,

[0073] Where N is the total number of nodes. It is the average degree of the network. It is the collective influence value of node i, and is usually set to l=1.

[0074] After the first removal Continue calculating the CI values ​​of all remaining nodes in the network, then remove the node with the highest CI value for the second time, and calculate... The value is then iterated in this way until the 11th node is removed. The removal process was then stopped. Therefore, the 11 removed nodes (genes) constitute the convergence module nodes, forming the set of convergence module nodes identified by the CI method. See Table 3 for details. Figure 4 .

[0075]

[0076] 2.3 CI-IVI Method for Identifying Convergence Module Nodes

[0077] The collective influence (CI) value of each node in the differential prognostic gene regulatory network for lung adenocarcinoma metastasis is calculated according to formula (1). Then, the calculated CI value is substituted into formula (3) to calculate the overall comprehensive influence index (IVI) value of each node.

[0078] ,

[0079] in, , , , , and These are nodes Range normalized degree centrality, range normalized local H-index, range normalized domain connectivity, range normalized cluster rank, range normalized betweenness centrality, and range normalized collective influence.

[0080] Then substitute the IVI value into formula (4) to calculate the CI-IVI value of each node;

[0081] ,

[0082] in, and These are the overall comprehensive impact index values ​​for node i and node j, respectively. It is a sphere boundary consisting of nodes whose shortest path to node i is of length l, where l=1.

[0083] Sort the results according to the calculation results, remove the node with the largest CI-IVI value (first iteration); then calculate λ according to formula (5).

[0084] ,

[0085] Where N is the total number of nodes. It is the average degree of the network. It is calculated using formula (4).

[0086] Calculation results The CI-IVI values ​​of each remaining node in the network are recalculated using formulas (1), (3), and (4), sorted, and the node with the highest CI-IVI value is removed (second iteration). The λ value is calculated using formula (5). This iterative cycle continues until the 16th node is removed. The removal process was then stopped. Therefore, the 16 removed nodes (genes) constitute the convergence module nodes, forming the set of convergence module nodes identified by the CI-IVI method. See Table 4 for details. Figure 5 .

[0087]

[0088] 2.4 Performance Comparison of Different Methods for Identifying Converging Node Sets

[0089] The performance of the three methods for identifying convergent module nodes is compared by the proportion (q) of network nodes removed after deleting each convergent module node from the prognostic differential gene regulatory network of lung adenocarcinoma metastasis, the size of the largest connected component (G(q)), and the number of connected components (C(q)) of the network.

[0090] like Figure 6 As shown in Figure A, in the network, when q is approximately 0.51, the CI-IVI method directly reduces the network size G(q) from 74 to 4 (these two values ​​are the product of the vertical axis value in Figure A and the initial network size, respectively). After processing with the CI method, to achieve the same degree of network damage, the required node deletion ratio q needs to reach 0.52, while using the K-core method, the node deletion ratio needs to be increased to 0.74. Therefore, it can be seen that the CI-IVI method improves performance compared to both the CI and K-core methods under the same degree of network damage. Figure 6 The B-plot shows that, corresponding to G(q), C(q) increases sharply with increasing q in the initial stage, reaches an extreme value, and then decreases. Under this metric, CI-IVI significantly outperforms the CI method and the K-kernel method.

[0091] Furthermore, Table 5 shows the performance of convergence module node sets identified by different methods in network destructive experiments using node immunity. Specifically, when the convergence node set identified by the K-core method was deleted from the network, G(q) was 0.48 and C(q) was 16; when the convergence node set identified by the CI method was deleted, G(q) was 0.11 and C(q) was 29; while when the convergence node set identified by the CI-IVI method of this invention was deleted, G(q) was 0.10 and C(q) was 29. In network analysis, when the value of G(q) is small and the value of C(q) is large, it indicates that the network splitting phenomenon is more severe. This phenomenon reflects that the deleted node set plays a more critical role in the network, that is, it has a greater influence. Therefore, from the perspective of evaluating the impact of node immunity strategies on network destructiveness, the CI-IVI method shows higher effectiveness than the K-core and CI methods.

[0092]

[0093] In conclusion, the CI-IVI method performed best among all methods.

[0094] Example 3: Application and Verification of Convergence Module Node Identification

[0095] 3.1 Application of the convergence module node of the differential prognostic gene regulatory network for lung adenocarcinoma metastasis

[0096] Using the convergence module node identified in Example 2 as the target, compound information from the TCMSP, TCMID, and CTD databases was used to establish the correlation between the target and chemical components. Compounds were screened based on rigorous scientific criteria such as OB > 20 and Druglike ≥ 0.18. After integrating compound information from the three databases and comparing it with the existing TCMSP database, compounds related to traditional Chinese medicine components were successfully screened. To more clearly reveal the interrelationships between the target, compounds, and traditional Chinese medicine, the corresponding traditional Chinese medicines were preliminarily evaluated, and the relationships between "core target - compound" and "compound - traditional Chinese medicine" were mapped to establish a correspondence between "convergence module node (target) - compound - traditional Chinese medicine".

[0097] The 16 convergence module nodes (targets) identified by the CI-IVI method correspond to a total of 1136 compounds in the CTD, TCMSP, and TCMID databases. According to their names, 36 compounds can be found in the TCMSP database, and by PubMed ID, 9909 IDs can be obtained, corresponding to 541 compounds. Based on the constraints of OB≥20, Druglike≥0.18, and frequency of traditional Chinese medicine ≥8, the following traditional Chinese medicine formula was selected: Lobelia chinensis, Atractylodes lancea, Amomum tsao-ko, Atractylodes macrocephala, Lysimachia christinae, and Chrysanthemum morifolium, named "Bai Lian Fang" (White Lotus Formula). See: Liu Xi, Research on the Optimization and Efficacy Mechanism of Traditional Chinese Medicine Formulas for Inhibiting Lung Adenocarcinoma Metastasis Based on Network Target Convergence [D], China Academy of Chinese Medical Sciences, 2024.

[0098] The K-kernel method identified 6 convergence module nodes (targets) that corresponded to a total of 722 compounds in the CTD, TCMSP, and TCMID databases. According to their names, 28 compounds were found in the TCMSP database. Using PubMed IDs, 6735 IDs were obtained, corresponding to 367 compounds. Based on constraints such as OB≥30, Druglike≥0.18, and a frequency of 6 or more traditional Chinese medicines, the following traditional Chinese medicine formula was selected: Lobelia chinensis, Chrysanthemum morifolium, Glycyrrhiza uralensis, Lysimachia christinae, Elsholtzia ciliata, Inula japonica, and Ginkgo biloba, named "Yinlian Formula". See: Liu Xi, Research on the Optimization and Efficacy Mechanism of Traditional Chinese Medicine Formulas for Inhibiting Lung Adenocarcinoma Metastasis Based on Network Target Convergence [D], China Academy of Chinese Medical Sciences, 2024.

[0099] 3.2 Pharmacodynamic Experiment Verification of Zebrafish

[0100] 3.2.1 Materials

[0101] 3.2.1.1 Experimental drugs:

[0102] ① Traditional Chinese medicine composition: Extract powder 1 (brownish-yellow, Bailianfang traditional Chinese medicine composition): content is 2.45g / g (raw amount / extract powder), Extract powder 2 (brownish-yellow, Yinlianfang traditional Chinese medicine composition): content is 3.40g / g (raw amount / extract powder).

[0103] The above-mentioned method for preparing extract powder is as follows:

[0104] Extract Powder 1: 15g of Lobelia chinensis, 15g of Lysimachia christinae, 10g of Chrysanthemum morifolium, 10g of Atractylodes macrocephala, 9g of Atractylodes lancea, and 6g of Amomum tsao-ko. Mix all the herbs together, add 5-10 times their weight of water, heat to a boil and simmer for 25 minutes, then filter while hot. Add 5-10 times their weight of water to the dregs, heat to a boil and simmer for 20-30 minutes, then filter while hot. Combine the two decoctions and concentrate to a relative density of 1.3-1.4 at 80℃ to obtain the final product.

[0105] Extract powder 2: 15g of Lobelia chinensis, 15g of Lysimachia christinae, 10g of Chrysanthemum morifolium, 3g of Glycyrrhiza uralensis, 6g of Mosla chinensis, 6g of Inula japonica, 10g of Ginkgo biloba; mix all the medicinal materials, add 5-10 times the weight of water, heat to boiling and decoct for 25 minutes, filter while hot; add 5-10 times the weight of water to the medicinal dregs, heat to boiling, decoct for 20-30 minutes, filter while hot, combine the two filtrates, then concentrate to a relative density of 1.3-1.4 at 80℃, to obtain the final product.

[0106] ② Paclitaxel, white powder, Shanghai Aladdin Biochemical Technology Co., Ltd., batch number J2126744, stored at 4℃. Prepare a 20.0 mM mother solution with DMSO, dilute as needed, and store at -20℃.

[0107] 1.2 Laboratory animals

[0108] ① Wild-type AB strain zebrafish, propagated by natural paired mating, a total of 300 individuals, 30 individuals per experimental group, 3 dpf in age, used for the maximum test concentration (MTC) determination of extract powder 1.

[0109] ② Wild-type AB strain zebrafish, propagated by natural paired mating, a total of 1200 individuals, 30 individuals per experimental group, 2 dpf in age, used for evaluating the inhibitory effect of extract powder 1 on non-small cell lung cancer metastasis.

[0110] Raised in fish breeding water at 28℃ (water quality: add 200 mg of instant sea salt to every 1 L of reverse osmosis water, conductivity 480~510 μS / cm; pH 6.9~7.2; hardness 53.7~71.6 mg / L CaCO3), the laboratory animal use license number is: SYXK (Zhe) 2022-0004. The feeding management complies with the requirements of international AAALAC accreditation.

[0111] 3.2.1.3 Laboratory instruments:

[0112] ① Dissecting microscope (SZX7, OLYMPUS, Japan);

[0113] ② CCD camera (VertA1, Shanghai Tusen Vision Technology Co., Ltd., China);

[0114] ③ Micromanipulator for microinjection (IM300, NARISHIGE, Japan);

[0115] ④ Motorized focusing continuous zoom fluorescence microscope (AZ100, Nikon, Japan);

[0116] ⑤ Precision electronic balance (CP214, OHAUS, America);

[0117] ⑥ 6-well plate (Nest Biotech, Shanghai, China).

[0118] 3.2.2 Methods

[0119] (1) Concentration group

[0120] Experiment 1 Model Control Group

[0121] Experimental Group 2 positive control drug paclitaxel 1.00 pmol / tail

[0122] In experimental group 3, the positive control drug paclitaxel was 2.00 pmol / tail.

[0123] In Experiment 4, the positive control drug paclitaxel was 4.00 pmol / tail.

[0124] Experimental group 5 extract powder 1 31.2 µg / mL

[0125] Experimental group 6 extract powder 1 62.5 µg / mL

[0126] Experiment 7 group extract powder 1 125 µg / mL

[0127] Experimental group 8: Extract powder 2 31.2 µg / mL

[0128] Experimental group 9: Extract powder 2 62.5 µg / mL

[0129] Experiment 10: Extract powder 2 125 µg / mL

[0130] (2) Model making

[0131] Human non-small cell lung cancer (NCI-H460 and A549) cells were labeled with green fluorescent dye and transplanted into the yolk sac of zebrafish via microinjection, with approximately 200 cells transplanted per zebrafish, to establish a zebrafish lung cancer tumor cell transplantation model.

[0132] (3) Experimental methods

[0133] ① Measurement of MTC

[0134] Three hundred and thirty wild-type AB strain zebrafish at 3 days post-flop (dpf) were randomly selected and placed in six-well plates. Each well (experimental group) contained thirty zebrafish, and the extract powder 1 was administered at concentrations of 125, 250, 500, 1000, and 2000 µg / mL. A normal control group was also included. Each well (experimental group) contained 3 mL of extract powder. The zebrafish were incubated at 35 ℃ until 5 dpf. Mortality was recorded, and the mean toxicity (MTC) of extract powder 1 in normal zebrafish was determined based on the zebrafish toxicity response and mortality.

[0135] ②Evaluate the inhibitory effect of traditional Chinese medicine composition on the growth and metastasis of non-small cell lung cancer (NCI-H460 and A549).

[0136] Human non-small cell lung cancer (NCC) cells (NCI-H460 and A549) were labeled with green fluorescent dyes and microinjected into the yolk sacs of wild-type AB strain zebrafish at 2 days post-flop (dpf). Approximately 200 cells were transplanted per zebrafish to establish a zebrafish model of human NCC transplantation. The zebrafish injected with human NCC cells were cultured at 35°C for 3 days post-flop. At 3 days post-flop, zebrafish with good tumor uniformity were selected under a microscope and randomly assigned to 6-well plates, 30 zebrafish per well, with a water volume of 3 mL per well. Extract powder 1 and extract powder 2 were administered at concentrations of 31.2, 62.5, and 125 µg / mL, respectively. The positive control group received paclitaxel at doses of 1.00 pmol / zebrafish, 2.00 pmol / zebrafish, and 4.00 pmol / zebrafish. A model control group was also established. Zebrafish were incubated at 35℃ until 5 dpf. Ten zebrafish from each experimental group were randomly selected for observation, photography, and image saving under a fluorescence microscope. Image analysis was performed using Nikon NIS-Elements D 4.30.00 advanced image processing software to calculate the fluorescence intensity of zebrafish xenografts and the metastasis distance (M) of tail vein tumor cells. The statistical analysis results of fluorescence intensity and metastasis distance were used to evaluate the inhibitory effect of extract powder 1 on the growth and metastasis of human non-small cell lung cancer in zebrafish. Statistical results are expressed as mean ± SE. The formula for calculating the inhibitory effect on zebrafish tumor growth and metastasis is as follows:

[0137]

[0138] Statistical analysis was performed using one-way ANOVA and Dunnett's T test. p < 0.05 indicated statistical significance, providing a representative experimental profile.

[0139] (3) Experimental results

[0140] ① Measurement of MTC

[0141] Extract powder 1 induced no more than 10% mortality and significant toxicity in normal zebrafish at a concentration of 500 µg / mL, while it did not induce mortality or toxicity phenotypes in tumor model zebrafish at a concentration of 250 µg / mL. Therefore, the MTC for the inhibitory effect of extract powder 1 on the growth of metastatic non-small cell lung cancer in zebrafish was determined to be 250 µg / mL, and the MTC for the inhibitory effect of extract powder 2 on the growth of metastatic non-small cell lung cancer in zebrafish was determined to be 125 µg / mL. See Table 6 for details.

[0142]

[0143] ② The growth-inhibiting effect of the traditional Chinese medicine composition on NCI-H460 transplanted tumors

[0144] The fluorescence intensities of zebrafish NCI-H460 xenografts at paclitaxel concentrations of 1.00 pmol / tail, 2.00 pmol / tail, and 4.00 pmol / tail were 255176±15040, 181161±21683, and 144306±11680 pixels, respectively. The high-dose paclitaxel group showed a significant difference compared to the model control group (27962.50±24644 pixels) (p < 0.01), with a tumor growth inhibition rate of 48.4%, indicating that the positive control drug can inhibit the growth of NCI-H460 xenografts.

[0145] The fluorescence intensities of zebrafish NCI-H460 xenografts at concentrations of 31.2, 62.5, and 125 µg / mL of extract powder 1 were 275152±28973, 162435±19859, and 171974±18511 pixels, respectively. The medium- and high-dose groups showed highly significant differences compared to the model control group (27962.50±24644 pixels) (p < 0.01 and p < 0.01, respectively), with tumor growth inhibition rates of 41.91% and 38.50%, respectively. See Table 7 for details.

[0146] The fluorescence intensities of zebrafish NCI-H460 xenografts at concentrations of 31.2, 62.5, and 125 µg / mL of extract powder 2 were 228483±32757, 185791±24387, and 155179±12587 pixels, respectively. The high-dose group showed a significant difference compared to the model control group (27962.50±24644 pixels) (p < 0.01), with a tumor growth inhibition rate of 44.51%. See Table 7 for details.

[0147] The medium-dose group of extract powder 1 showed a significant difference in tumor growth inhibition against zebrafish NCI-H460 xenografts compared to the model group (p < 0.01); while the high-dose groups of extract powder 2 and paclitaxel showed significant differences in tumor growth inhibition compared to the model control group. See Table 7 for details.

[0148]

[0149] ③ The inhibitory effect of traditional Chinese medicine composition on the metastasis of NCI-H460 transplanted tumors

[0150] The metastatic distances of NCI-H460 tumor cells in the tail vein of zebrafish at paclitaxel concentrations of 1.00 pmol / tail, 2.00 pmol / tail, and 4.00 pmol / tail were 119950±10289, 93602±13052, and 80031±9542 pixels, respectively. There were no statistically significant differences between these concentrations and the model control group (119209±17587 pixels) (p > 0.05), indicating that the positive control drug at these concentrations could not inhibit the metastasis of NCI-H460 xenografts. See Table 8 for details.

[0151] The metastatic distances of NCl-H460 tumor cells in the tail vein of zebrafish at concentrations of 31.2, 62.5, and 125 µg / mL of extract powder 1 were 71861±14093, 27380±3142, and 27755±2845, respectively. The medium- and high-dose groups showed highly significant differences compared to the model control group (119209±17587 pixels) (p < 0.001 and p < 0.001, respectively), with tumor metastasis inhibition rates of 77.03% and 76.72%, respectively. This indicates that medium- and high-dose extract powder 1 has a significant inhibitory effect on tumor metastasis. See Table 8 for details.

[0152] The metastatic distances of NCl-H460 tumor cells in the tail vein of zebrafish at concentrations of 31.2, 62.5, and 125 µg / mL of extract powder 2 were 76867±10817, 34809±3662, and 35008±5693, respectively. The medium and high dose groups showed highly significant differences compared to the model control group (119209±17587 pixels) (p < 0.001 and p < 0.001, respectively), with tumor metastasis inhibition rates of 70.80% and 70.63%, respectively, indicating that medium and high doses of extract powder 2 also had a significant inhibitory effect on tumor metastasis. However, compared with extract powder 1 at the same dose level, the tumor inhibition rates of the medium and high dose groups of extract powder 2 were significantly lower than those of extract powder 1 at the same dose level (p < 0.05), indicating that extract powder 1 had a significantly stronger inhibitory effect on the growth of zebrafish NCl-H460 xenografts than extract powder 2. See Table 8 for details.

[0153]

[0154] Therefore, extract powder 1 showed stronger inhibitory effects on the metastasis of zebrafish NCI-H460 xenografts than extract powder 2 and the positive control paclitaxel group.

[0155] ④ The growth-inhibiting effect of the traditional Chinese medicine composition on A549 transplanted tumors

[0156] The fluorescence intensities of zebrafish A549 xenografts at concentrations of paclitaxel (1.00 pmol / tail, 2.00 pmol / tail, and 4.00 pmol / tail) were 326139±37347, 269894±24063, and 249623±25639 pixels, respectively, which were all highly significant compared with the model control group (494191±44230 pixels) (p < 0.01, p < 0.001, and p < 0.001, respectively). The tumor growth inhibition rates were 34.01%, 45.39%, and 49.49%, respectively, indicating that the positive control drug significantly inhibited the growth of A549 xenografts. See Table 9 for details.

[0157] The fluorescence intensities of zebrafish A549 xenografts at concentrations of 32.5, 62.5, and 125 µg / mL of extract powder 1 were 408184±56037, 262577±31503, and 248242±20246 pixels, respectively. The medium and high dose groups showed highly significant differences compared to the model control group (494191±44230 pixels) (p < 0.01 and p < 0.01, respectively), with tumor growth inhibition rates of 46.87% and 49.77%, respectively. This indicates that extract powder 1 significantly inhibits the growth of zebrafish A549 xenografts. See Table 9 for details.

[0158] The fluorescence intensities of zebrafish A549 xenografts at concentrations of 32.5, 62.5, and 125 µg / mL of extract powder 2 were 405119±39223, 299785±31821, and 196138±29992 pixels, respectively. Among them, the medium and high dose groups showed significant differences from the model control group (494191±44230 pixels) (p < 0.01 and p < 0.001, respectively), with tumor growth inhibition effects of 39.34% and 60.31%, respectively.

[0159]

[0160] Therefore, extract powder 1 and extract powder 2 both have an inhibitory effect on the growth of zebrafish A549 transplanted tumors.

[0161] (4) The metastatic effect of the traditional Chinese medicine composition on A549 transplanted tumors

[0162] The metastatic distances of zebrafish A549 tail vein tumor cells at paclitaxel concentrations of 1.00 pmol / tail, 2.00 pmol / tail, and 4.00 pmol / tail were 31673±3467, 31137±3553, and 29941±2953 pixels, respectively, which were all highly significant compared with the model control group (70006±8870 pixels) (p < 0.001, p < 0.001, and p < 0.001, respectively). The tumor metastasis inhibition effects were 54.76%, 55.52%, and 57.23%, respectively, indicating that the positive control drug can significantly inhibit the metastasis of A549 xenografts. See Table 10 for details.

[0163] The metastatic distances of zebrafish A549 tail vein tumor cells at concentrations of 32.5, 62.5, and 125 µg / mL of extract powder 1 were 37236±5113, 30906±2519, and 23285±2334 pixels, respectively. The medium- and high-dose groups showed highly significant differences compared to the model control group (70006±8870 pixels) (p < 0.05 and p < 0.001, respectively), with tumor growth inhibition rates of 55.85% and 66.74%, respectively. This indicates that extract powder 1 has a significant inhibitory effect on the metastasis of zebrafish A549 xenografts. See Table 10 for details.

[0164] The metastatic distances of zebrafish A549 tail vein tumor cells at concentrations of 32.5, 62.5, and 125 µg / mL of extract powder 2 were 36060±4114, 33075±2991, and 26201±3574 pixels, respectively. The high-dose groups showed significant differences from the model control group (70006±8870 pixels) (p < 0.05 and p < 0.001), with tumor metastasis inhibition rates of 52.75% and 62.57%, respectively. However, the tumor metastasis inhibition of zebrafish A549 xenografts at all doses of extract powder 2 was significantly lower than that of extract powder 1 at the same dose level (p < 0.05), indicating that extract powder 1 had a significantly stronger inhibitory effect on zebrafish A549 xenografts than extract powder 2. See Table 10 for details.

[0165]

[0166] The above examples demonstrate that the method of this invention can effectively identify convergence target modules of diseases in biological networks, and has advantages over other convergence module node identification methods, providing a scientific basis for guiding disease treatment and drug development.

[0167] The above description of specific embodiments of the present invention does not limit the present invention. Those skilled in the art can make various changes or modifications based on the present invention, and as long as they do not depart from the spirit of the present invention, they should all fall within the scope of the appended claims.

Claims

1. A method for identifying convergent nodes in a biomolecular network, wherein the biomolecular network is a prognostic differential gene regulatory network for lung adenocarcinoma metastasis, comprising the following steps: (1) Identify the biomolecular network for which convergence nodes need to be identified; (2) Calculate the collective influence CI value of each node in the biomolecular network according to formula (1). ， in, , Let i and j represent the degrees of nodes i and j, respectively. It is a sphere boundary consisting of nodes whose shortest path to node i is of length l, where l=1; Then, substitute the values ​​into formula (3) to calculate the overall comprehensive impact index (IVI) value for each node. ， in, , , , , and These are nodes Range normalized degree centrality, range normalized local H-index, range normalized domain connectivity, range normalized cluster rank, range normalized betweenness centrality, and range normalized collective influence. Then substitute the IVI value into formula (4) to calculate the CI-IVI value of each node; ， in, and These are the overall comprehensive impact index values ​​for node i and node j, respectively. It is a sphere boundary consisting of nodes whose shortest path to node i is of length l, where l=1; Sort the results according to the calculation results, remove the node with the largest CI-IVI value; then calculate according to formula (5). , ， Where N is the total number of nodes. It is the average degree of the network. Calculated using formula (4); (4) If If the identification ends, the removed node is the convergence node; if Then proceed to step (5); (5) Recalculate the remaining nodes of the biomolecular network according to formulas (1), (3) and (4). Value, Sort, Remove The node with the largest value is calculated according to formula (5). ; (6) If If the identification ends, the set of all removed nodes is the converged node set; if Then repeat step (5) until... .

2. The method according to claim 1, characterized in that, The biomolecular network contains ≥3 nodes and ≥3 edges.

3. The method according to claim 1, characterized in that, The differential gene regulatory network for metastatic lung adenocarcinoma contains 90 nodes and 157 edges.

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