Method for analyzing action target of artemisinin for treating renal clear cell carcinoma based on network pharmacology and molecular docking technology

Abstract

The invention belongs to the technical field of biological medicine, and discloses a method for analyzing an action target of artemisinin for treating renal clear cell carcinoma based on network pharmacology and a molecular docking technology, and the method comprises the following steps: S1, screening the action target of an artemisinin active component; s2, identifying candidate disease targets; s3, acquiring an intersection target point; s4, constructing a protein-protein interaction network and performing network topology analysis; s5, core target biological function and molecular action signal path analysis and visualization; s6, constructing an artemisinin-renal clear cell carcinoma-target spot-pathway network diagram; s7, exploring an action mechanism of artemisinin for improving the renal clear cell carcinoma based on a molecular docking technology; s8, carrying out survival analysis on the core target spot based on the TCGA database, and drawing a Kaplan-Meier curve; and S9, observing the influence of artemisinin and derivatives thereof on the activity of renal clear cell carcinoma 786-O cells by adopting a CCK-8 method. Network pharmacology and molecular docking technologies are applied, multiple omics data are integrated from the perspective of system biology, a drug-target-disease network is constructed, the interaction between artemisinin and renal clear cell carcinoma related targets is deeply analyzed, and the action mechanism of artemisinin on the aspects of genes, proteins, signal channels and the like is comprehensively revealed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biological medicine, and particularly relates to a method for analyzing the action target point of artemisinin in treating renal clear cell carcinoma based on network pharmacology and molecular docking technology. BACKGROUND

[0002] Renal clear cell carcinoma (ccRCC) is a common malignant tumor of the urinary system. At present, the preferred treatment method for local ccRCC is surgery, radiotherapy and chemotherapy, while metastatic ccRCC is resistant to conventional treatment methods and is still the main cause of cancer-related deaths. Although great efforts have been made in the past few decades, significant progress has been made in the treatment of metastatic ccRCC, including sorafenib, sunitinib and everolimus, etc. Targeted tyrosine kinase inhibitors or nivolumab, etc. Immunotherapy has been approved. However, the exact molecular mechanism of ccRCC progression is still unclear.

[0003] Molecular docking technology can predict ligand-receptor binding mode through computational chemistry energy scoring, which has shown key value in the development of kinase inhibitors such as EGFR (epidermal growth factor receptor) and ALK (anaplastic lymphoma kinase). Compared with traditional high-throughput screening, its advantages are: ①It can construct a three-dimensional pharmacophore model of artemisinin to realize cross-proteomic virtual screening; ②It can optimize the calculation of binding free energy by combining molecular dynamics simulation, and the prediction accuracy reaches the experimental level The root mean square deviation of two molecular structures is less than 2 angstroms It is a key indicator for measuring the similarity of molecular three-dimensional structure); ③Integrate the renal clear cell carcinoma (ccRCC) differential expression gene database (TCGA-KIRC) to preferentially screen disease-related target points. At present, there is no innovative application of this technology in artemisinin-ccRCC target point discovery.

[0004] At present, in the research of artemisinin in the treatment of polycystic ovary syndrome, Pubchem, SwissTargetPrediction and PharmMapper databases are used to predict artemisinin target points, GeneCards and DisGeNET databases are used to obtain disease target points, and PPI network analysis and molecular docking technology are used to reveal the potential mechanism. The accuracy of the data depends on the integrity of the basic data, and the network pharmacology and molecular docking technology depend on the huge bioinformatics data and databases. Therefore, the integrity and accuracy of the data are crucial to the reliability of the results of target point prediction and mechanism analysis. SUMMARY

[0005] The application aims to provide a method for analyzing the action target points of artemisinin in treating renal clear cell carcinoma based on network pharmacology and molecular docking technology.

[0006] To solve the above technical problems, the embodiment of the application provides a method for analyzing the action target points of artemisinin in treating renal clear cell carcinoma based on network pharmacology and molecular docking technology, which comprises the following steps:

[0007] S1, screening the action target points of active components of artemisinin

[0008] S1.1, searching in the database by taking artemisinin as a keyword, querying and recording the SMILES number of artemisinin by means of the Pubchem database;

[0009] S1.2, inputting the obtained SMILES number into the SwissTargetPrediction database for searching, and predicting the potential action target points of artemisinin;

[0010] S1.3, introducing the PharmMapper database, integrating the analysis results obtained by S1.2 with the prediction results of the SwissTargetPrediction database, and comprehensively determining the target points of artemisinin acting on diseases;

[0011] S1.4, importing the target point information determined in S1.3 into the Uniprot database, and performing conversion operation to obtain the Uniprot ID corresponding to each target point;

[0012] S2, identifying disease candidate target points

[0013] Mining the public renal clear cell carcinoma disease data set, searching in the GeneCards database by taking renal clear cell carcinoma as a keyword, predicting the action points of renal clear cell carcinoma, and screening the disease target points of renal clear cell carcinoma by taking the correlation score greater than or equal to 5 as a criterion;

[0014] S3, obtaining intersection target points

[0015] Applying the Wayne software to obtain the intersection target points of the target points of artemisinin acting on diseases and the receptor target points of renal clear cell carcinoma;

[0016] S4, constructing a protein-protein interaction network and performing network topology analysis

[0017] S4.1, import the intersection target points through the String platform, set the object as Homo sapiens, take the highest confidence 0.900, hide the free gene node, and obtain a protein-protein interaction network;

[0018] S4.2, import the protein-protein interaction network into Cytoscape3.6.1 software, select network analysis, obtain network topology parameters, and then export a TSV file;

[0019] S4.3, import the TSV file into Cytoscape3.6.1 software to make a protein-protein interaction (PPI) graph, and screen the top three core gene targets according to the Degree value, which are PIK3R1, SRC and GRB2;

[0020] S5, biological function, molecular action signal pathway analysis and visualization of core target points

[0021] The intersection target points of artemisinin and renal clear cell carcinoma obtained in step S3 are imported into the DAVID database for GO function enrichment analysis and KEGG signal pathway analysis;

[0022] S6, construction of artemisinin-renal clear cell carcinoma-target-pathway network graph

[0023] Enumerate the top 20 KEGG pathways corresponding to all intersection target points of artemisinin target points and renal clear cell carcinoma disease target points with a correlation score of 5 or more, and construct a visual artemisinin-renal clear cell carcinoma-target-pathway network graph;

[0024] S7, exploring the mechanism of artemisinin in improving renal clear cell carcinoma based on molecular docking technology

[0025] S7.1, extract the three-dimensional structure of artemisinin from the PubChem database, obtain the core protein structure of the core gene target PIK3R1, SRC and GRB2 from the PDB online database, remove the water and chemical groups combined with the core protein, and measure the distance between artemisinin and the core protein;

[0026] S7.2, dock and calculate the binding energy of artemisinin and the core protein, evaluate the binding affinity between artemisinin and the core protein, and prove that artemisinin acts on the core gene target;

[0027] S7.3, visualize the docking graph of artemisinin and the core protein binding, and construct the binding site of artemisinin and the core protein;

[0028] S7.4, molecular docking experiment verification: artemisinin has binding ability with PIK3R1, SRC and GRB2, the three core gene targets.

[0029] S8. Survival analysis and verification of core target points based on TCGA database

[0030] S8.1, Obtain the transcriptome data and corresponding clinical survival information of renal clear cell carcinoma patients from the TCGA database;

[0031] S8.2, Based on the transcript expression of core target points PIK3R1, SRC and GRB2, the patients are divided into high expression group and low expression group according to the median;

[0032] S8.3, Survival curve is drawn by Kaplan-Meier method, and the overall survival difference of the two groups of patients is compared by Logrank test;

[0033] S8.4, The survival analysis result shows that the survival prognosis of PIK3R1 high expression group is significantly better than that of low expression group (HR=0.56, p=0.00031), and the survival prognosis of GRB2 high expression group is also significantly better than that of low expression group (HR=0.71, p=0.025), which indicates that the two genes are significantly related to the prognosis of renal clear cell carcinoma patients (p<0.05);

[0034] S9, In vitro cell experiment verification of the anti-proliferation activity of artemisinin and its derivatives based on CCK-8 method

[0035] S9.2, Blank control group, negative control group and different concentration drug treatment groups are set respectively, and the drugs include artemisinin, artesunate, dihydroartemisinin and artemether, and the concentrations are set as 10 μM, 50 μM and 100 μM respectively, and at least 3 replicate wells are set in each group;

[0036] S9.3, After adding the drug, continue to culture for 24 hours, add 10 μL CCK-8 reagent to each well, incubate for 1 hour, and then measure the absorbance value of each well at 450 nm wavelength by enzyme label instrument;

[0037] S9.4, The results show that artemisinin and its derivatives can significantly inhibit the proliferation of 786-O cells, and the inhibition is in a concentration-dependent manner; among them, artemether has the strongest inhibitory effect, and the cell survival rate is reduced to about 40% (p<0.001) at the concentration of 100 μM; dihydroartemisinin and artesunate also show significant inhibitory effect, and the survival rate is reduced to 60-65% (p<0.001); the inhibitory effect of artemisinin is relatively mild, but it is still statistically significant (p<0.001).

[0038] The protein-protein interaction (PPI) network constructed in step S4.1 includes 292 nodes and 519 edges, the average node degree is 3.55, and the PPI enrichment degree p value is less than 1.0e -16 .

[0039] In step S5, the GO function enrichment analysis includes biological process BP, cell component CC and molecular function MF.

[0040] In step S5, the KEGG pathway enrichment analysis enriches 163 pathways, and 20 results are selected from the 163 pathways according to P value (PValue, a parameter used to determine the result of hypothesis testing) to draw a bubble chart.

[0041] In step S7, the core protein structure is converted into MOL2 format by using OpenBable 3.1.1 software in step S5.1, and then PyMol software is used to remove water and chemical groups combined with the core protein.

[0042] In step S7, AutoDockTool 1.5.7 is used to dock and calculate the binding energy of artemisinin and the core protein; and PyMol software is used to visualize the docking diagram of artemisinin and the core protein.

[0043] In step S8, survival analysis is performed on the core target based on the TCGA database, and the Kaplan-Meier curve shows that the overall survival of patients in the high expression group of PIK3R1 and GRB2 is significantly better than that in the low expression group (p<0.05).

[0044] In step S9, CCK-8 method is used to verify the inhibitory effect of artemisinin and its derivatives (artemether, dihydroartemisinin and artesunate) on the proliferation of 786-O cells, and the results show that each drug can significantly inhibit cell activity (p<0.001), and is in a concentration-dependent manner.

[0045] The beneficial effects of the above technical solutions of the present application are as follows:

[0046] 1. The present application provides a method for analyzing the target points of artemisinin in treating renal clear cell carcinoma based on network pharmacology and molecular docking technology, which integrates multi-omics data from the perspective of systems biology, constructs a drug-target-disease network, and deeply analyzes the interaction between artemisinin and renal clear cell carcinoma related target points, and comprehensively reveals the action mechanism at the levels of genes, proteins and signal pathways.

[0047] 2、The application introduces big data analysis of network pharmacology, and combines molecular docking technology to accurately simulate intermolecular interaction at atomic level, quickly and comprehensively screen potential target points of artemisinin acting on renal clear cell carcinoma, make up for the shortcomings of traditional methods, provide clues for subsequent experimental verification, and speed up the drug research and development process.

[0048] 3、Based on the clear mechanism and potential target points determined by network pharmacology in the application, artemisinin can be structurally modified and optimized to design new anticancer drugs with stronger effect, lower toxicity and higher targeting, improve the treatment effect, and provide more choices and scientific basis for clinical medication.

[0049] 4、The application first confirms that the core target points PIK3R1 and GRB2 are significantly related to the prognosis of renal clear cell carcinoma patients through the integration of TCGA database survival analysis, provides important biomarkers and prognosis evaluation basis for the clinical conversion of artemisinin in the treatment of renal clear cell carcinoma. And the significant inhibitory effect of artemisinin and its derivatives on the proliferation of renal clear cell carcinoma 786-O cells is verified by in vitro CCK-8 experiment, which confirms the reliability of the prediction result of network pharmacology, and provides solid experimental evidence for the practical application of artemisinin drugs in the treatment of renal clear cell carcinoma. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The Wayne diagram of the intersection of the biological active ingredient drug target gene of artemisinin with therapeutic effect on renal clear cell carcinoma and the ccRCC disease target gene in the application;

[0051] Figure 2 The PPI network diagram of the ccRCC target gene treated by artemisinin in the application;

[0052] Figure 3 The core target point network diagram of artemisinin acting on ccRCC in the application;

[0053] Figure 4 The bar chart of ccRCC and artemisinin GO enrichment function analysis in the application;

[0054] Figure 5 The bubble chart of 20 pathways obtained by KEGG enrichment analysis of the ccRCC target gene treated by artemisinin in the application;

[0055] Figure 6 The disease-pathway-target-drug network diagram of artemisinin acting on ccRCC in the application;

[0056] Figure 7 The docking diagram of artemisinin and the core protein molecule PIK3R1 in the application;

[0057] Figure 8Artemisinin docking diagram with core protein molecule SRC in the application;

[0058] Figure 9 Artemisinin docking diagram with core protein molecule GRB2 in the application;

[0059] Figure 10 Survival curve diagram of core protein molecule PIK3R1 under different expressions in the application;

[0060] Figure 11 Survival curve diagram of core protein molecule GRB2 under different expressions in the application;

[0061] Figure 12 CCK-8 cell viability diagram of 786-O cells treated by artemisinin in the application;

[0062] Figure 13 CCK-8 cell viability diagram of 786-O cells treated by artemether in the application;

[0063] Figure 14 CCK-8 cell viability diagram of 786-O cells treated by artemisinin in the application;

[0064] Figure 15 CCK-8 cell viability diagram of 786-O cells treated by artemether in the application. DETAILED DESCRIPTION

[0065] In order to make the technical problems, technical solutions and advantages of the application clearer, specific embodiments will be described in detail below with reference to the drawings.

[0066] The embodiment of the application provides a method for analyzing the target point of artemisinin in treating renal clear cell carcinoma based on network pharmacology and molecular docking technology, and the method is characterized in that the method comprises the following steps:

[0067] S1, screening the active ingredient of artemisinin acting target point

[0068] S1.1, searching in the database by taking artemisinin (Artemisinin) as a keyword, querying and recording the SMILES number of artemisinin by means of Pubchem database;

[0069] S1.2, inputting the obtained SMILES number into SwissTargetPrediction database for searching, and predicting the potential acting target point of artemisinin;

[0070] S1.3, import the PharmMapper database, integrate the analysis results obtained by prediction in S1.2 with the prediction results of the SwissTargetPrediction database, and comprehensively determine the target points of artemisinin acting on diseases;

[0071] S1.4, import the target point information determined in step S1.3 into the Uniprot database, and perform conversion operation to obtain the Uniprot ID corresponding to each target point.

[0072] S2, identify disease candidate target points

[0073] The public kidney clear cell carcinoma (ccRCC) disease data set is mined, the GeneCards database is searched with kidney clear cell carcinoma (ccRCC) as the keyword, the action points of kidney clear cell carcinoma are predicted, and the kidney clear cell carcinoma disease target points are screened with relevance scores (Relevance Scores) ≥5.

[0074] S3, obtain intersection target points

[0075] The Venny (https: / / bioinfogp.cnb.csic.es / tools / venny / ) software is used to obtain the intersection target points of artemisinin acting on diseases and kidney clear cell carcinoma;

[0076] S4, construct protein-protein interaction (PPI) network and network topology analysis

[0077] S4.1, import the intersection target points through the String (https: / / string-db.org / ) platform, set the object as Homo sapiens, take the highest confidence 0.900, hide the free gene node, and obtain the protein-protein interaction (PPI) network; the obtained protein-protein interaction (PPI) network includes 292 nodes and 519 edges, the average node degree is 3.55, and the PPI enrichment degree p value is less than 1.0e -16 .

[0078] S4.2, import the protein-protein interaction (PPI) network into Cytoscape3.6.1 software, select network analysis “networkAnalyzer”, obtain network topology parameters, and then export TSV file;

[0079] S4.3, import the TSV file into Cytoscape3.6.1 software to make a protein-protein interaction (PPI) graph, and screen the top three core gene target points according to the Degree value (relevance score), which are PIK3R1, SRC and GRB2.

[0080] S5, Analysis and visualization of the biological function of the core target point, molecular action signal pathway

[0081] S5.1, GO functional enrichment and KEGG enrichment analysis of the biological process BP, cellular component CC, and molecular function MF of the artemisinin treatment ccRCC target genes using the DAVID database (https: / / david.ncifcrf.gov);

[0082] S5.2, Import the obtained artemisinin treatment ccRCC target genes into the database research preview, and specify the species as Homo sapiens, and the exported results are visualized by the online mapping tool MicroXplore (http: / / www.bioinformatics.com.cn / ).

[0083] S6, Construction of artemisinin-renal clear cell carcinoma (ccRCC)-target-pathway network diagram

[0084] Enumerate the top 20 KEGG pathways corresponding to all intersection target points of artemisinin targets and renal clear cell carcinoma disease targets with relevance scores (Relevance Scores) ≥5, and construct a visual artemisinin-renal clear cell carcinoma (ccRCC)-target-pathway network diagram.

[0085] S7, Exploring the mechanism of artemisinin improving renal clear cell carcinoma based on molecular docking technology

[0086] Molecular docking of the core proteins of the 3 core gene targets (PIK3R1, SRC, GRB2) screened by Cytosape3.6.1 software in step S4 and artemisinin. Obtain the protein structure of the core target pathway and the 3D structure of artemisinin for molecular docking. Obtain the core protein structure of the above 3 gene targets from the PDB online database (https: / / www.rcsb.org). Preferably, low-resolution X-ray structures, human structures, and nucleotide binding sites are selected to bind artemisinin structures. At the same time, format conversion of the 2 core targets is required to enable use in subsequent software.

[0087] Obtain the SDF format of the 3D structure of artemisinin from the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov / ), and convert it to MOL2 format using OpenBable3.1.1 software.

[0088] The distance between artemisinin and core protein was measured by removing water and chemical groups bound to core protein using PyMol software. Then, the binding energy was calculated by docking artemisinin with core protein using AutoDockTool1.5.7. The exported file in PDBQT format was converted to PDB format. The docking figure of artemisinin binding to core protein was visualized using PyMol software, and the binding site of artemisinin and protein was constructed. Through molecular docking, it was identified that artemisinin had strong binding capacity with PIK3R1, SRC and GRB2 three core target points.

[0089] S8. Survival analysis and verification of core target points based on TCGA database

[0090] S8.1, Transcriptional data and corresponding clinical survival information of patients with renal clear cell carcinoma (ccRCC) were obtained from TCGA database (https: / / portal.gdc.cancer.gov / );

[0091] S8.2, Based on the transcript expression (TPM) of core target points PIK3R1, SRC and GRB2 screened out in step S4.3, the patients were divided into high expression group and low expression group according to the median expression value;

[0092] S8.3, Kaplan-Meier method was used to draw survival curve, and Logrank test was used to compare the overall survival difference between the two groups of patients, and online platform (http: / / kmplot.com / analysis / ) was used for visualization;

[0093] S8.4, The survival analysis results showed that the overall survival of PIK3R1 high expression group was significantly longer than that of low expression group (HR=0.56, p=0.00031), and the overall survival of GRB2 high expression group was also significantly longer than that of low expression group (HR=0.71, p=0.025), indicating that the two core target points were significantly related to the prognosis of patients with renal clear cell carcinoma (p<0.05).

[0094] S9.1, Human renal clear cell carcinoma cell line 786-O was cultured in RPMI-1640 medium containing 10% fetal bovine serum at 37℃, 5% CO2 incubator;

[0095] S9.2, Cells in logarithmic growth phase were inoculated in 96-well plates at a density of 5000 cells per well, with a volume of 100 μL per well, and pre-cultured in a 37℃, 5% CO2 incubator for 24 hours;

[0096] S9.3, Set up blank control group, negative control group and drug treatment group respectively, the drugs include artemisinin, artesunate, dihydroartemisinin and artemether, dissolved with DMSO and then diluted with culture medium to working concentration (10 μM, 50 μM, 100 μM), at least 3 replicate wells are set in each group;

[0097] S9.4, continue to culture for 24 hours after adding drugs, add 10 μL CCK-8 reagent to each well, incubate for 1 hour, then measure the absorbance value (OD value) of each well at 450 nm wavelength with an enzyme-labeled instrument;

[0098] S9.5, calculate cell survival rate (%) = (experimental group OD value-blank group OD value) / (negative control group OD value-blank group OD value) x 100%, and use GraphPad Prism 9.0 software for statistical analysis, the data are expressed as mean ± SD, and one-way ANOVA is used for comparison between groups;

[0099] S9.6, the experimental results show that artemisinin and its derivatives can significantly inhibit the proliferation of 786-O cells, and show concentration-dependent effect; among them, artemether has the strongest inhibitory effect, and the cell survival rate is reduced to about 40% (p<0.001) at the concentration of 100 μM; dihydroartemisinin and artesunate also show significant inhibitory effect, and the survival rate is reduced to 60-65% (p<0.001); artemisinin itself has relatively mild inhibitory effect, but still has statistical significance (p<0.001).

[0100] In the drawings of the specification, Figure 1 is a Wayne diagram of the intersection of the biological active ingredient drug target gene of artemisinin having therapeutic effect on renal clear cell carcinoma (ccRCC) and the ccRCC disease target gene in the present application;

[0101] Figure 2 is a PPI network diagram of the ccRCC target gene of artemisinin treating ccRCC in the present application; Figure 3 is a core target point network diagram of artemisinin acting on ccRCC in the present application; Figure 4 is a bar chart of GO enrichment function analysis of ccRCC and artemisinin in the present application; Figure 5 is a bubble chart of 20 pathways obtained by KEGG enrichment analysis of the ccRCC target gene of artemisinin treating ccRCC in the present application; Figure 6 is a disease-pathway-target-drug network diagram of artemisinin acting on ccRCC in the present application; Figure 7The docking diagram of artemisinin and the core protein molecule PIK3R1 in the application; Figure 8 The docking diagram of artemisinin and the core protein molecule SRC in the application; Figure 9 The docking diagram of artemisinin and the core protein molecule GRB2 in the application. Figure 10 The survival curve diagram of the core protein molecule PIK3R1 under different expressions in the application; Figure 11 The survival curve diagram of the core protein molecule GRB2 under different expressions in the application; Figure 12 The CCK-8 cell viability diagram of artemisinin acting on 786-O cells in the application; Figure 13 The CCK-8 cell viability diagram of artesunate acting on 786-O cells in the application; Figure 14 The CCK-8 cell viability diagram of dihydroartemisinin acting on 786-O cells in the application; Figure 15 The CCK-8 cell viability diagram of artemether acting on 786-O cells in the application.

[0102] The technical key points of the application are:

[0103] 1. Multi-source data aggregation and standardized integration: Collect multi-source information such as chemical component data of artemisinin, gene and protein data related to renal clear cell carcinoma, and signal pathway data associated with the disease, etc. in all directions. Standardize the collected data, unify the data format and quality standard, eliminate the differences and contradictions between the data, and realize the efficient integration of the data, laying a solid foundation for subsequent in-depth analysis.

[0104] 2. Complex network construction and deep analysis: Carefully construct the drug-target-disease network, use complex network analysis methods, and use degree centrality, intermediate centrality and other indicators to accurately identify the core target points in the network. At the same time, through pathway enrichment analysis and other methods, the key signal pathways are excavated, and the potential action mechanism of artemisinin in treating renal clear cell carcinoma is revealed in depth from the system and whole angle.

[0105] 3. Molecular docking precise simulation and verification: Use professional molecular docking software to carry out high-precision molecular docking simulation of artemisinin and its derivatives and the potential target points screened out. Analyze the interaction mode between molecules at the atomic level, such as hydrogen bond, hydrophobic interaction, etc., strictly verify the binding activity and specificity of the target points and drugs, and ensure the reliability of the research results.

[0106] 4. Clinical correlation analysis and experimental verification: Integrate the information of clinical databases such as TCGA, verify the clinical correlation of the core target points and the prognosis of renal clear cell carcinoma patients through survival analysis; further verify the antiproliferative activity of artemisinin and its derivatives on renal clear cell carcinoma cells through in vitro cell experiments (such as CCK-8 method), realize the complete research closed loop from bioinformatics prediction to experimental verification.

[0107] The protection point of the present application is:

[0108] 1. Innovation protection of fusion research method: The innovative research method of network pharmacology combined with molecular docking is the core competitiveness of this research. The specific operation process and algorithm of data mining process, network construction steps, molecular docking parameter setting and other specific operation processes and algorithms involved in this method are comprehensively protected by intellectual property rights to prevent technology from being copied and stolen.

[0109] 2. Protection of new target and pathway discovery rights: The new target of artemisinin for treating renal clear cell carcinoma and the associated signaling pathway discovered through this research have great scientific research and application value. The discovery rights of these targets and pathways, as well as related applications based on them, are strictly protected to maintain the exclusivity of research results.

[0110] 3. Protection of drug research and development innovation ideas: The drug design optimization ideas and combination drug strategies based on research results are key innovative points for promoting drug research and development. These ideas are protected as technological innovation achievements, and no one is allowed to use the ideas to carry out related drug research and development work without authorization.

[0111] 4. Protection of experimental data and model intellectual property rights: The vast amount of experimental data generated during the research, as well as various models used for simulation and prediction, such as network models and molecular docking models, are important intellectual property rights. Encryption storage, access control and other means are used to ensure the security and integrity of data and models to prevent data leakage and illegal use of models.

[0112] 5. Protection of clinical verification and experimental system: The survival analysis model based on TCGA database, CCK-8 in vitro experimental verification system and other clinical and experimental verification methods and related parameter settings are protected to prevent unauthorized use of the specific verification system and experimental scheme of this research.

Claims

1.A method for analyzing target points of action of artemisinin in treating renal clear cell carcinoma based on network pharmacology and molecular docking technology, characterized in that, It comprises the following steps: S1, screening artemisinin active ingredient target S1.1, artemisinin as the keyword in the database, with the help of Pubchem database, query and record artemisinin SMILES number; S1.2, the SMILES number obtained is input into SwissTargetPrediction database for retrieval, and the potential target of artemisinin is predicted; S1.3, introduce PharmMapper database, integrate the analysis results obtained by S1.2 with the prediction results of SwissTargetPrediction database, and comprehensively determine the target of artemisinin acting on renal clear cell carcinoma; S1.4, the target information determined in step S1.3 is imported into Uniprot database for conversion operation, and the Uniprot ID corresponding to each target point is obtained; S2, identify disease candidate target Mining public renal clear cell carcinoma disease data set, searching in GeneCards database with renal clear cell carcinoma as the keyword, predicting the action point of renal clear cell carcinoma, and screening renal clear cell carcinoma disease target with correlation score ≥ 5; S3, get intersection target The intersection target of artemisinin acting on disease and renal clear cell carcinoma receptor target is obtained by using the software of wean, that is, artemisinin-renal clear cell carcinoma intersection target; S4, construct protein-protein interaction network and network topology analysis S4.1, import the intersection target through String platform, set the object as Homo sapiens, take the highest confidence 0.900, hide the free gene node, and obtain the protein-protein interaction network; S4.2, import the protein-protein interaction (PPI) network into Cytoscape3.6.1 software, select network analysis, obtain network topology parameters, and then export TSV file; S4.3, import TSV file into Cytoscape3.6.1 software to make protein-protein interaction (PPI) graph, and screen the top three core gene targets according to Degree value, which are PIK3R1, SRC and GRB2 respectively; S5, biological function, molecular action signal pathway analysis and visualization of core target The artemisinin-renal clear cell carcinoma intersection target obtained in step S3 is imported into DAVID database for GO function enrichment analysis and KEGG signal pathway analysis; S6, construct artemisinin-renal clear cell carcinoma-target-pathway network diagram Enumerate the top 20 KEGG pathways corresponding to all intersection targets of artemisinin target and renal clear cell carcinoma disease target with correlation score ≥ 5, and construct visual artemisinin-renal clear cell carcinoma-target-pathway network diagram; S7, explore the mechanism of artemisinin in treating renal clear cell carcinoma based on molecular docking technology S7.1, extract the three-dimensional structure of artemisinin from the PubChem database, obtain the core protein structure of the core gene target PIK3R1, SRC, and GRB2 from the PDB online database, remove the water and chemical groups bound to the core protein, and measure the distance between artemisinin and the core protein; S7.2, dock and calculate the binding energy of artemisinin and the core protein, evaluate the binding affinity between artemisinin and the core protein, and prove that artemisinin acts on the core gene target; S7.3, visualize the docking diagram of artemisinin and the core protein binding, and construct the binding site of artemisinin and the core protein; S7.4, molecular docking experiment verification: artemisinin has binding ability with PIK3R1, SRC, and GRB2 three core gene targets. S8, survival analysis and verification of core targets based on TCGA database S8.1, obtain the transcriptome data and corresponding clinical survival information of renal clear cell carcinoma patients from the TCGA database; S8.2, based on the transcript expression of core targets PIK3R1, SRC, and GRB2, divide the patients into high expression group and low expression group according to the median; S8.3, draw the survival curve by using Kaplan-Meier method, and compare the overall survival difference between the two groups by Logrank test; S8.4, the survival analysis results show that the survival prognosis of PIK3R1 high expression group is significantly better than that of low expression group (HR=0.56, p=0.00031), and the survival prognosis of GRB2 high expression group is also significantly better than that of low expression group (HR=0.71, p=0.025), indicating that these two genes are significantly related to the prognosis of renal clear cell carcinoma patients (p<0.05); S9, in vitro cell experiment verification of the anti-proliferative activity of artemisinin and its derivatives based on CCK-8 method S9.1, culture human renal clear cell carcinoma cell line 786-O, inoculate in 96-well plate at a density of about 5000 cells per well, and pre-culture in 37℃, 5% CO2 incubator for 24 hours; S9.2, set up blank control group, negative control group and different concentration drug treatment group respectively, the drugs include artemisinin, artesunate, dihydroartemisinin and artemether, the concentration is set to 10μM, 50μM and 100μM respectively, at least 3 replicate wells for each group; S9.3, after adding drug, continue to culture for 24 hours, add 10μL CCK-8 reagent per well, incubate for 1 hour, and then measure the absorbance value of each well at 450nm wavelength by enzyme label instrument; S9.4、The results showed that artemisinin and its derivatives could significantly inhibit the proliferation of 786-O cells in a concentration-dependent manner. Among them, artesunate had the strongest inhibitory effect, and the cell survival rate decreased to about 40% at a concentration of 100 μM (p < 0.001). Dihydroartemisinin and artesunate also showed significant inhibitory effects, and the survival rate decreased to 60-65% (p < 0.001). Artemisinin itself had a relatively mild inhibitory effect, but it was still statistically significant (p < 0.001). 2.The method for analyzing the target points of the effect of artemisinin on renal clear cell carcinoma based on network pharmacology and molecular docking technology according to claim 1, characterized in that, The protein-protein interaction network constructed in step S4.1 includes 292 nodes and 519 edges, with an average node degree of 3.55, and a PPI enrichment p-value less than 1.0e -16 . 3.The method for analyzing the target points of the effect of artemisinin on renal clear cell carcinoma based on network pharmacology and molecular docking technology according to claim 1, characterized in that, In step S5, the GO functional enrichment analysis includes biological process BP, cellular component CC, and molecular function MF. 4.The method for analyzing the target points of the effect of artemisinin on renal clear cell carcinoma based on network pharmacology and molecular docking technology according to claim 1, characterized in that, In step S5, KEGG pathway enrichment analysis enriched 163 pathways in total. According to the P value, 20 results were selected from the 163 pathways to draw a bubble chart. 5.The method for analyzing the target points of the effect of artemisinin on renal clear cell carcinoma based on network pharmacology and molecular docking technology according to claim 1, characterized in that, In step S7, the core protein structure was converted to MOL2 format using OpenBable 3.1.1 software in step S5.1, and then PyMol software was used to remove water and chemical groups bound to the core protein. 6.The method for analyzing the target points of the effect of artemisinin on renal clear cell carcinoma based on network pharmacology and molecular docking technology according to claim 1, characterized in that, In step S7, AutoDockTool 1.5.7 was used to dock and calculate the binding energy of artemisinin and the core protein. In step S7.3, PyMol software was used to visualize the docking diagram of artemisinin and the core protein binding. 7.The method for analyzing the target points of the effect of artemisinin on renal clear cell carcinoma based on network pharmacology and molecular docking technology according to claim 1, characterized in that, In step S8.4, survival analysis was based on the TCGA database. The Kaplan-Meier curve showed that the overall survival of patients in the high expression group of PIK3R1 was significantly longer than that in the low expression group (HR = 0.56, p = 0.00031), and the overall survival of patients in the high expression group of GRB2 was also significantly longer than that in the low expression group (HR = 0.71, p = 0.025). 8.The method for analyzing the target points of the effect of artemisinin on renal clear cell carcinoma based on network pharmacology and molecular docking technology according to claim 1, characterized in that, In step S9.2, the artemisinin and its derivatives used were: Artemisinin, Artesunate, Dihydroartemisinin, and Artemether, and the treatment concentrations were set to 10 μM, 50 μM, and 100 μM.

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