Method for analyzing action mechanism of Tianma III formula for treating colorectal cancer

Abstract

The invention discloses a method for analyzing an action mechanism of a Tianma III formula for treating colorectal cancer. The method comprises the following steps: (1) screening colorectal cancer disease targets; (2) searching the medicine composition of the Tianma III prescription to obtain effective active ingredient targets of the Tianma III prescription; (3) taking an intersection to obtain an intersection target; (4) constructing a component-target-disease co-expression network diagram and a multi-protein-protein interaction network diagram to obtain a potential core target; carrying out GO and KEGG pathway enrichment analysis on an intersection target spot; carrying out molecular docking on the potential core target and a key compound molecule; an in-vitro experiment verifies that the Tianma formula III inhibits colorectal cancer cell proliferation and promotes apoptosis; a real-time fluorescence quantification method is used for verifying the influence of the Tianma No.III formula on the mRNA expression level of a potential core target. The method is comprehensive in analysis system, few in error and high in prediction speed and efficiency, reduces drug screening and development cost, provides scientific basis for prevention and treatment of colorectal cancer, and provides reference value for research of other compounds.

Description

Technical Field

[0001] The present invention relates to an analysis method for the action mechanism of drugs in treating cancer, and in particular to an analysis method for the action mechanism of Tianma III prescription in treating colorectal cancer. Background Art

[0002] It is of great practical significance to clarify the molecular mechanisms of the occurrence and development of colorectal cancer (CRC) and to find drugs with significant efficacy, low price and few toxic side effects.

[0003] Tianma Formula No. 3 is a new type of Chinese medicine prescription based on traditional Chinese medicine theory and combined with modern medical research. It is mainly used to treat specific types of tumors or related diseases.

[0004] Network pharmacology refers to the use of high-throughput technology, systems biology technology and network analysis technology under the guidance of multi-omics technology to determine the mechanism of action between drugs and diseases by constructing the relationship between drug components, targets, diseases and pathways. The research methods of traditional Chinese medicine and network pharmacology are consistent. By combining the two, it provides a strong guarantee and new ideas for studying the mechanism of action and scientific connotation of traditional Chinese medicine and its compound.

[0005] At present, the methods for screening disease targets in the prior art are mostly limited to obtaining data from one or two databases, and these databases have significant differences in data sources, update frequency, and the range of disease targets covered. For example, CN117352045A discloses an analysis method for the molecular mechanism of action of Xinmai Shu No. 1 oral liquid in the treatment of heart disease, which is to obtain the therapeutic target of heart disease by screening using the Gene Cards database; CN117409854A discloses a method for analyzing the mechanism of action of chrysin in the treatment of colorectal cancer based on network pharmacology and molecular docking, which selects GEO, DisGENET, PharmGkb and TTD databases to screen targets for colorectal cancer. However, due to the inconsistency of data between these databases and incomplete coverage, the colorectal cancer genes obtained by the above method are not comprehensive. The former only uses a single database Gene Cards, and although the latter has more database options, it still has the problem of incomplete coverage, especially not including the largest disease target database GeneCards, and the screening results of the GEO database may produce large errors due to the selection of different data sets. In addition, the screening results of the GEO database may have large errors due to the selection of different data sets, which increases the uncertainty of the analysis. Therefore, the existing methods not only have defects in coverage, but also easily lead to instability in screening results due to differences within and across databases.

[0006] Therefore, in view of the incomplete selection of existing disease targets, it is urgent to find an analytical method for the mechanism of action of Tianma III prescription in the treatment of colorectal cancer that has a comprehensive analysis system, few analytical errors, high prediction speed and efficiency, reduces drug screening and development costs, provides a scientific basis for the prevention and treatment of colorectal cancer, and provides reference value for the research of other compound prescriptions. Summary of the invention

[0007] The technical problem to be solved by the present invention is to overcome the above-mentioned defects of the prior art and provide an analytical method for the mechanism of action of Tianma III prescription in treating colorectal cancer, which has a comprehensive analytical system, few analytical errors, high prediction speed and efficiency, reduces the cost of drug screening and development, provides a scientific basis for the prevention and treatment of colorectal cancer, and provides reference value for the research of other compound prescriptions.

[0008] The technical solution adopted by the present invention to solve the technical problem is as follows: a method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer, comprising the following steps: (1) First, colorectal cancer disease targets are screened from multiple databases respectively, and then duplicate values ​​of colorectal cancer disease targets obtained from the multiple databases are deleted to obtain screened colorectal cancer disease targets; (2) First, the drug composition of Tianma III prescription was searched through multiple databases to obtain the effective active ingredients and their targets respectively, and then the duplicate values ​​of the effective active ingredients and their targets obtained from the above multiple databases were integrated and deleted to obtain the effective active ingredient targets of Tianma III prescription; (3) Intersecting the colorectal cancer disease targets screened in step (1) with the effective active ingredient targets of Tianma III prescription obtained in step (2) to obtain intersection targets; (4) constructing a "component-target-disease" co-expression network diagram of the colorectal cancer disease target obtained in step (1) and the active ingredients of Tianma No. III prescription and their targets obtained in step (2) for the treatment of colorectal cancer, thereby obtaining co-expressed targets; performing protein-protein interaction analysis on the co-expressed targets to obtain multiple protein-protein interaction network diagrams, and further using plug-in analysis to obtain potential core targets of Tianma No. III prescription for the treatment of colorectal cancer; Analysis method 1: First, the intersection targets obtained in step (3) are converted into target IDs, and then the converted IDs are subjected to GO and KEGG pathway enrichment analysis, and the KEGG enrichment network relationship is further visualized for analysis; Analysis method 2: Molecular docking is performed between the 3D structure of the potential core target obtained in step (4) and the key compound molecule, and the docking results are visualized; Analysis method three: in vitro experiments were used to verify that Tianma III prescription inhibited the proliferation of colorectal cancer cells and promoted the apoptosis of colorectal cancer cells; Analysis method 4: Real-time fluorescence quantitative method was used to verify the effect of Tianma III prescription on the expression level of potential core target mRNA obtained in step (4).

[0009] The inventive idea of ​​the method of the present invention is: the core of the method of the present invention is to comprehensively utilize multiple disease target databases and network pharmacology methods to systematically screen the active ingredients and targets in Tianma No. III prescription that have potential effects on colorectal cancer; through multi-database cross-validation and integrated analysis, the problem of incomplete information or large deviation in a single database can be overcome to ensure that more comprehensive and accurate target data are obtained; combined with network pharmacology tools, a multi-level network of ingredients-targets-pathways of the compound can be constructed to systematically analyze the pharmacological action pathways of Tianma No. III prescription. In order to determine whether this combined analysis method is close to the real biological mechanism, the present invention further verifies it through in vitro experiments; in vitro experiments can verify at the cellular level whether the inhibitory effect of the prescription on colorectal cancer cells is consistent with the targets and pathways predicted by network pharmacology analysis, so that the accuracy of theoretical analysis can be supported and verified by experimental data, so that the revealed mechanism is closer to the real biological action process. Therefore, the method of the present invention can effectively reveal the real mechanism of Tianma No. III prescription in inhibiting colorectal cancer through comprehensive analysis and experimental verification of the synergistic effects of multiple targets, multiple pathways, and multiple pathways, and improve the scientificity and credibility of mechanism research.

[0010] Preferably, in step (1), the databases include: TCGA, GEO, GeneCards, OMIM, PharmGkb, TTD and DisGeNET. Compared with the currently selected databases, the method of the present invention is significantly different from the existing research that only integrates one or more databases by comprehensively integrating the disease target data of TCGA, GEO and other five databases (GeneCards, OMIM, PharmGkb, TTD and DisGeNET) for the first time. This integration not only introduces high-quality patient sample data, but also enhances the ability of cross-platform data comparison, promotes the understanding of disease mechanisms and the efficiency of target identification. In addition, the analysis technology combined with multiple data sources not only contributes to basic research, but also promotes the development of clinical applications.

[0011] Preferably, the screening method of the TCGA database is: downloading colorectal cancer RNA patient sequencing data from the TCGA database, using Perl scripts to process the transcriptome data and convert it into gene symbols, and extracting mRNA data, further using Log |FC|≥1, adjP≤0.05 as screening conditions, and analyzing by R language to obtain differentially expressed disease targets. The method of the present invention collects TCGA data as network pharmacology disease data for the first time.

[0012] Preferably, the screening method of the GEO database is: in the GEO database, using "colorectal cancer" as the search term, obtaining the GSE37364 data set, downloading the gene expression data of normal adjacent cancer tissues and tumor tissues, further grouping through Perl software and analyzing with R language, using Log |FC|≥1, adjP≤0.05 as screening conditions, to obtain differentially expressed genes in colon cancer patient tissues, and taking the up-regulated genes and down-regulated genes therein as potential disease targets of GEO.

[0013] Preferably, the screening method of the GeneCards, OMIM, PharmGkb, TTD or DisGeNET database is: in the above five databases, "colorectal cancer" is used as the search term, and the relevant genes obtained from the five databases are respectively taken and combined through the R language Venn package to obtain colorectal cancer disease targets.

[0014] Preferably, in step (2), the database includes: one or more of: a traditional Chinese medicine system pharmacology database and analysis platform, a China National Knowledge Infrastructure database, a Wanfang database, a VIP database, a Chinese biomedical literature database, a PubChem database or a PubMed database.

[0015] Preferably, in step (2), the search is performed with oral bioavailability ≥ 30% and drug similarity ≥ 0.18 as filtering conditions.

[0016] Preferably, in step (2), the drug composition of the Tianma III prescription is: Pinellia, Hedyotis diffusa, Scutellaria barbata, Trillium, Sargassum, Astragalus and Rhubarb.

[0017] Preferably, in step (2), duplicate values ​​of effective active ingredients and their targets obtained from the above-mentioned multiple databases are deleted through integration using a Perl script.

[0018] Preferably, in step (3), the intersection is obtained by using the Venn package of R language.

[0019] Preferably, in step (4), the construction refers to: integrating and classifying the effective active ingredients of Tianma III prescription and its targets and colorectal cancer disease targets into genomes, type groups and network relationship files through Perl scripts, and integrating the above groups using Cytoscape software.

[0020] Preferably, in step (4), the co-expression targets are analyzed through the STING database, the species information in the setting conditions is selected as human, and the unconnected nodes are hidden, and protein-protein interaction analysis is performed.

[0021] Preferably, in step (4), the analysis is performed using the cytoHubba plug-in of the Cytoscape software.

[0022] Preferably, in step (4), the top five ranked by centrality degree analysis are selected as potential core targets.

[0023] Preferably, in the analysis method 1, the intersection targets obtained in step (3) are converted into target IDs using the R language org.hs.eg package.

[0024] Preferably, in the analysis method 1, clusterProfiler, DOSE and enrichment plot packages are used with p < 0.05 as the screening condition, and the converted ID is subjected to GO and KEGG pathway enrichment analysis.

[0025] Preferably, in analysis method one, the KEGG enrichment network relationship is further visualized using Cytoscape software. Using Cytoscape software for visualization analysis of the KEGG enrichment network can identify key targets and interactions between different pathways, revealing the complex biological regulatory mechanism of Tianma III in the treatment of colorectal cancer. By analyzing the nodes in the network, the targets that have the most significant impact on specific biological processes can be determined. At the same time, the topological structure of the network shows the relationship between signal pathways, helps locate functional modules, discovers potential biomarkers for the treatment of colorectal cancer with Tianma III, and provides new hypotheses for subsequent research.

[0026] Preferably, in the second analysis method, the 3D structure of the potential core target obtained in step (4) is downloaded from the Pubchem database, and the protein is subjected to the removal of crystal water and the original ligand in the protein receptor and the hydrogenation operation of the receptor protein using the AutoDock Tools 1.5.6 software. Then, the 3D structure of the potential core target is molecularly docked with the key compound molecule using the AutoDock Vina 1.1.2 and Pymol 2.1, Discovery studio V2019 software. The docking result uses the binding free energy as the evaluation criterion for the degree of binding with the key compound molecule. The binding affinity between the potential core target and the key compound is evaluated, and the redder the color, the more stable the binding. In addition, the docking results can be used to screen out compounds that bind to the core target with high affinity, helping to identify potential drug candidate molecules.

[0027] Preferably, in the second analysis method, the key compound molecules include one or more of quercetin, luteolin, kaempferol, wogonin, baicalein or beta sitosterol, etc. The number of key compounds to be screened can be determined by oneself, for example, the method of the present invention selects the top six in terms of effective active ingredient content.

[0028] Preferably, in the second analysis method, the visualization comprises: using the R language pacman package to visualize the binding free energy through a heat map, and respectively visualizing the docking patterns of the potential core target obtained in step (4) and the key compound molecules.

[0029] Preferably, in analysis method three, the CCK8 method is used to verify the inhibition of colorectal cancer cell proliferation by Tianma No. Ⅲ prescription. By verifying the inhibitory effect of Tianma No. Ⅲ prescription on colorectal cancer cell proliferation by CCK8 method, its anticancer activity can be quantitatively evaluated, including inhibitory effect and dose dependence. The experimental results will reveal that Tianma No. Ⅲ prescription exhibits significant cell proliferation inhibition and provide preliminary evidence for its use as a potential therapeutic drug. In addition, this study can provide further support for exploring its mechanism of action and help understand the biological pathways through which it inhibits cell proliferation.

[0030] Preferably, the CCK8 method is specifically as follows: colorectal cancer cells are cultured at a rate of 1×10 3 ~10×10 3 The cells were seeded at a density of 10 cells / well on a 96-well plate and divided into multiple groups with preset concentrations of Tianma III prescription-containing serum of 0-20%, each group with 4-6 replicate wells. After overnight culture, the cells were grouped for intervention according to the preset concentrations of the drug-containing serum. After 20-28 hours of intervention, 80-120 μL / well of CCK8 solution prepared in complete culture medium were added. After incubation for 1-2 hours at 35-39°C and 4-6% CO2, the absorbance values ​​at 450 nm were analyzed and the average of the replicate wells in the group was taken.

[0031] Preferably, in analysis method three, flow cytometry is used to verify the effect of Tianma No. III prescription on promoting apoptosis of colorectal cancer cells. By verifying the promoting effect of Tianma No. III prescription on apoptosis of colorectal cancer cells by flow cytometry, the percentage and type of cell apoptosis can be quantitatively evaluated, revealing its pro-apoptotic effect. This analysis can show a significant increase in the apoptosis rate of cells after treatment with Tianma No. III prescription, provide preliminary clues to related signaling pathways, and lay the foundation for further exploration of its mechanism of action. These results help to establish the potential application value of Tianma No. III prescription in the treatment of colorectal cancer.

[0032] Preferably, the flow cytometry is as follows: the serum containing Tianma III prescription is used to intervene in colorectal cancer cells and negative control group cells at gradient concentrations of 0 to 20% for 20 to 28 hours, respectively, the colorectal cancer cells and negative control group cells after intervention are washed 2 to 3 times with pre-cooled PBS, 180 to 220 μL of 1×Annexin V binding solution are added to resuspend the cells, and the concentration is adjusted to 1×10 6 ~5×10 6 After the cell suspension reaches 1 cell / mL, add 8-12 μL Annexin V-FITC staining solution to the cell suspension, mix gently, and incubate at 3-5°C in the dark for 5-10 min. Finally, add 8-12 μL PI staining solution, mix gently, and incubate at 3-5°C in the dark for 5-10 min. Immediately detect by flow cytometer.

[0033] Preferably, the pretreatment method of the negative control group cells is: washing with PBS, digesting the cells with trypsin, centrifuging at 35-39° C. and 800-1200 rpm / min for 8-12 min, and collecting the trypsin-digested cells.

[0034] Preferably, in the analysis method three, the colorectal cancer cells include: one or more of HCT116, SW480, DLD1, SW620 or HT29.

[0035] Preferably, in the fourth analysis method, the specific method of the real-time fluorescence quantitative method verification is: collect the colorectal cancer cells after the intervention of Tianma No. Ⅲ prescription obtained in the third analysis method, extract the total RNA with Trizol, further perform the reverse transcription reaction according to the cDNA reverse transcription kit, establish a total reaction system volume of 18-22 μL, perform 8-12min pre-denaturation treatment at 23-27°C, and the amplification conditions are: 93-97°C, react for 25-35s, and anneal for 25-35s at 55-65°C for 35-45 cycles; use β-actin as the internal reference gene, and use the 2-ΔΔCt method to analyze the relative expression of mRNA. The intervention effect of Tianma No. Ⅲ prescription on colorectal cancer cells can be verified by real-time fluorescence quantitative method, and the relative expression of BCL2, JUN, Caspase3, XIAP and Caspase9 mRNA after intervention can be quantitatively analyzed. The results will reveal the effects of Tianma III on the expression of specific genes, provide important information for understanding its anti-cancer mechanism, and help identify potential biomarkers. β-actin was selected as the internal reference gene because it has a stable expression level in various cell types and under different treatment conditions and is not affected by experimental conditions.

[0036] The beneficial effects of the method of the present invention are as follows: the method of the present invention makes full use of bioinformatics methods and computer technology, combines seven disease-related databases for the first time to obtain targets, reduces subsequent analysis errors caused by imperfect screening, demonstrates a new research method, and constructs a "component-target-disease" network diagram of Tianma III prescription for the treatment of colorectal cancer for the first time, indicating that its efficacy process involves multiple processes, mainly involving multiple targets such as BCL2, JUN, Caspase3, XIAP and Caspase9, and the main pathways include PI3K-Akt, IL-17, TNF, MAPK, p53, cell senescence and apoptosis. The core compounds that play a role include quercetin, luteolin, kaempferol, wogonin, baicalein and β-sitosterol, which provide new research ideas for exploring the mechanism of Tianma III in inhibiting colorectal cancer. Combined with experimental verification, the system and comprehensive disclosure of the mechanism of Tianma III in inhibiting colorectal cancer through the synergistic effect of "multi-target, multi-pathway and multi-way" not only improves the prediction speed and efficiency, but also reduces the cost of drug screening and development. These results will be further verified in subsequent studies, so as to provide a scientific basis for the prevention and treatment of colorectal cancer, and at the same time provide reference value, new ideas and insights for the research of other compound prescriptions.

[0037] Abbreviations in the present invention: CRC: colorectal cancer; TMⅢ: Tianma III prescription; ID: identification number; Degree: degree; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; TCGA: Cancer Genome Atlas; GEO: Gene Expression Omnibus; GeneCards: Human Genome Database; OMIM: Human Gene and Inherited Disease Database; PharmGkb: Drug Genome Knowledge Base; TTD: Drug Target Database; DisGeNET: Disease Gene Network Database; Perl: Practical Report Extraction Language; Log |FC|: Difference fold after Log; adjP: Corrected P value; TCMSP: Traditional Chinese Medicine System Pharmacology Database and Analysis Platform; Pubchem: Public Chemical Database; PubMed: NCBI PubMed biomedical information retrieval platform; STING: stimulating interferon gene database; CCK8: cell counting kit-8; 2-ΔΔCt method: negative ΔΔCt method of 2; β-actin: β-actin; BP: biological process; CC: cellular component; MF: molecular function; PI3K-Akt: phosphatidylinositol 3-kinase-protein kinase B pathway; IL-17: interleukin-17; TNF: tumor necrosis factor; MAPK: mitogen-activated protein kinase; P53: tumor suppressor gene P53; BCL2: B-cell lymphoma 2; JUN: JUN protein; Caspase3 (CASP3): caspase 3; XIAP: X-linked inhibitor of apoptosis protein; Caspase9 (CASP9): caspase 9; PBS: phosphate buffered saline. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is the mRNA heat map of the differentially expressed disease targets of colorectal cancer screened from the TCGA database in step (1) of Example 1 of the present invention; Figure 2 is the volcano map of differentially expressed genes in colon cancer patient tissues screened from the GEO database GSE37364 data set in step (1) of Example 1 of the present invention; Figure 3 is a heat map of genes differentially expressed in colon cancer patient tissues screened from the GEO database GSE37364 data set in step (1) of Example 1 of the present invention; Figure 4 is a Venn diagram of the union of colorectal cancer disease targets obtained from five databases in step (1) of Example 1 of the present invention; Figure 5 It is a Venn diagram of the intersection of the colorectal cancer disease target and the effective active ingredient target of Tianma III prescription in step (3) of Example 1 of the present invention; Figure 6is a "component-target-disease" co-expression network diagram of the Tianma III prescription for treating colorectal cancer obtained in step (4) of Example 1 of the present invention; Figure 7 is a plurality of protein-protein interaction network diagrams obtained in step (4) of Example 1 of the present invention; Figure 8 It is a potential core target for treating colorectal cancer by the Tianma III prescription obtained in step (4) of Example 1 of the present invention; Fig. 9 It is a bubble chart of GO pathway enrichment analysis of the intersection target obtained in step (3) of the analysis method 1 in Example 1 of the present invention; Fig.10 The KEGG pathway enrichment analysis and target network diagram of the intersection targets obtained in step (3) of the analysis method 1 in Example 1 of the present invention; Fig.11 is a heat map visualization of the molecular docking binding free energy in the second analysis method of Example 1 of the present invention; Fig.12 It is a visualized conformational diagram of the molecular docking mode of BCL2 and luteolin in the second analysis method of Example 1 of the present invention; Fig.13 It is a visualized conformational diagram of the molecular docking mode of Caspase3 and luteolin in the second analysis method of Example 1 of the present invention; Fig.14 It is a visualized conformational diagram of the docking mode of Caspase3 and β-sitosterol molecules in the analytical method 2 of Example 1 of the present invention; Fig.15 It is a visualized conformational diagram of the docking mode of Caspase3 and quercetin molecules in the analytical method 2 of Example 1 of the present invention; Fig.16 It is a visualized conformational diagram of the molecular docking mode of JUN and luteolin in the analytical method 2 of Example 1 of the present invention; Fig.17 is a visualized conformational diagram of the molecular docking mode of XIAP and luteolin in the analytical method 2 of Example 1 of the present invention; Fig.18 It is a line graph of the CCK8 method in the third analysis method of Example 1 of the present invention to verify that the Tianma III prescription inhibits the proliferation of colorectal cancer cells; Fig.19 This is a flow cytometric graph of flow cytometry used in the third analysis method of Example 1 of the present invention to verify that Tianma III prescription promotes apoptosis of colorectal cancer cells; Fig. 20 This is a relative expression bar graph of the effect of Tianma III prescription on the potential core target mRNA level obtained in step (4) verified by the real-time fluorescence quantitative method in the analysis method 4 of Example 1 of the present invention. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the embodiments and drawings.

[0040] The drug composition of Tianma No. III prescription used in the embodiment of the present invention is: Pinellia, Hedyotis diffusa, Scutellaria barbata, Trillium, Sargassum, Astragalus and Rhubarb, which were purchased from the Affiliated Hospital of Hunan Institute of Traditional Chinese Medicine; the raw materials or chemical reagents used in the embodiment of the present invention, unless otherwise specified, were obtained through conventional commercial channels.

[0041] Reference Example 1 for Preparation of Tianma III Freeze-dried Powder Add 10g of French Pinellia, 20g of Hedyotis diffusa, 20g of Scutellaria barbata, 10g of Vinegar Trillium, 10g of Sargassum, 30g of Astragalus, and 10g of Rhubarb (a total of 110g) into 15 times the amount of water, boil for 1.5h, filter while hot, add the residue into 12 times the amount of water, boil for 1h, filter while hot, combine the filtrate, concentrate to a small volume, and freeze-dry to obtain Tianma III prescription extract powder, which should be stored under dry conditions at room temperature.

[0042] Reference Example 2 for the Preparation of Serum Containing Drugs from Tianma III Formula Thirty SPF SD male rats were evenly divided into 20 drug-containing serum groups and 10 blank serum groups; the dosage of Tianma III prescription was equivalent to the 70kg adult dosage, and was calculated according to the human-animal body surface area conversion (the human-animal body surface area conversion is used to convert the drug dosage in animal experiments into human dosage, which ensures that the drug dosage derivation from animal models to humans has a reasonable physiological basis. Through this conversion, it can be concluded that the effective drug dosage in animal experiments corresponds to the appropriate dosage for humans, ensuring the subsequent dosage Safety and efficacy), distilled water was used to prepare Tianma III solution with a concentration of 0.24g Tianma III extract powder / mL, and the gavage dose was 12.5mL / kg, twice a day, for 7 consecutive days. The blank group was gavaged with distilled water, with the same gavage dose, frequency and days. 2h after gavage on the 7th day, abdominal aortic blood was collected, allowed to stand at room temperature for 2h, centrifuged, serum was extracted, and complement was inactivated in a water bath at 56℃ for 30min. After sterilization by filtration with a 0.2μm microporous filter membrane, it was packaged and stored at -80℃.

[0043] Example 1 (1) First, screen colorectal cancer disease targets from TCGA, GEO, GeneCards, OMIM, PharmGkb, TTD and DisGeNET databases: The screening method of the TCGA database is as follows: colorectal cancer RNA patient sequencing data were downloaded from the TCGA database (https: / / portal.gdc.cancer.gov), transcriptome data were processed using Perl scripts, and converted into gene symbols, and mRNA data were extracted. Log |FC|≥1, adjP≤0.05 were further used as screening conditions, and 3681 differentially expressed disease targets were obtained through R language analysis, of which 2180 were up-regulated genes and 1501 were down-regulated genes, and an mRNA heat map was drawn, as shown in FIG. Figure 1 As shown in the figure, the screening not only visually demonstrated the changes in gene expression, but also helped identify potential biomarkers and therapeutic targets, indicating that there are significant differences in gene expression in colorectal cancer; The screening method of the GEO database is as follows: in the GEO database (https: / / www.ncbi.nlm.nih.gov / gds / ), "colorectal cancer" is used as the search term to obtain the GSE37364 data set, and the gene expression data of normal adjacent cancer tissues and tumor tissues are downloaded. This method ensures the pertinence of the research and the reliability of the data; further, when grouped by Perl software and analyzed by R language, Log |FC|≥1, adjP≤0.05 are used as screening conditions, indicating that the screened genes have significant expression differences (Log |FC| represents fold change, adjP is the corrected P value, ensuring that the screening results are statistically significant), and 31,290 differentially expressed genes in colon cancer patient tissues are obtained, of which 5,736 are up-regulated genes and 4,625 are down-regulated genes, and a volcano map of differentially expressed genes is drawn (such as Figure 2 ) and gene heatmap (as shown in Figure 3 As shown); the volcano map of differentially expressed genes shows the number and distribution of up-regulated and down-regulated genes, while the gene heat map further shows the expression patterns of these genes in different samples; 5736 up-regulated genes and 4625 down-regulated genes, a total of 10361 genes, were selected as potential disease targets of GEO. Through this screening method, the study identified a large number of differentially expressed genes related to CRC, which may play a key role in the occurrence and development of tumors and their differences from normal tissues. The screened genes will help follow-up functional studies and further understand the molecular mechanism of Tianma III prescription in treating CRC; The screening method of the GeneCards, OMIM, PharmGkb, TTD or DisGeNET database is as follows: in the GeneCards (https: / / www.genecards.org / ), OMIM (https: / / www.omim.org / ), PharmGkb (https: / / www.pharmgkb.org / ), TTD (https: / / db.idrblab.net / ttd / ) and DisGeNET (https: / / www.disgenet.org / ) databases, "colorectal cancer" is used as the search term, and the related genes obtained from the five databases are respectively taken and combined by the R language Venn package, and 12054 colorectal cancer disease targets are obtained from the GeneCards database, 411 colorectal cancer disease targets are obtained from OMIM, 287 colorectal cancer disease targets are obtained from PharmGkb, 99 colorectal cancer disease targets are obtained from the TTD database, and 5473 colorectal cancer disease targets are obtained from the DisGeNET database; Then, the duplicate values ​​of colorectal cancer disease targets obtained from the above seven databases were deleted, and 18,480 screened colorectal cancer disease targets were obtained, such as Figure 4 As shown; This method ensures comprehensive screening of relevant genes from multiple authoritative databases, providing a broad library of potential colorectal cancer targets for subsequent research and analysis; (2) First, the drug composition of Tianma III prescription was searched through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, https: / / old.tcmsp-e.com / tcmsp.php), China National Knowledge Infrastructure, Wanfang Database, VIP Database, China Biomedical Literature Database, PubChem Database and PubMed Database, with oral bioavailability ≥ 30% and drug similarity ≥ 0.18 as filtering conditions, and the active ingredients and their targets were obtained respectively. A total of 94 chemical active ingredients were collected, including 13 from Pinellia, 7 from Hedyotis diffusa, 29 from Scutellaria, 5 from Trillium, 4 from Seaweed, 20 from Astragalus and 16 from Rheum officinale. Then, the duplicate values ​​of the active ingredients and their targets obtained from the above six databases were deleted through Perl script integration, and 190 active ingredient targets of Tianma III prescription were obtained; The above-mentioned targets of effective active ingredients were obtained by predicting and screening the targets of the 94 collected chemical active ingredients; the targets corresponding to each active ingredient of each drug can be obtained through literature retrieval and database query (such as TCMSP, PubChem, etc.); finally, the targets of all active ingredients of drugs were integrated and duplicates were removed to obtain the targets of 190 active ingredients. The amount of data is large and cannot be fully displayed, but this step is completed by processing the data through standardized scripts to ensure the accuracy and completeness of the targets; (3) The 18,480 colorectal cancer disease targets obtained in step (1) and the 190 Tianma III active ingredient targets obtained in step (2) were intersected using the R language Venn package to obtain 181 intersection targets, as shown in the following figure: Figure 5 As shown, this suggests that the active ingredients of Tianma III formula exert potential therapeutic effects on colorectal cancer through these overlapping targets; (4) The effective active ingredients and their targets of Tianma III prescription and colorectal cancer disease targets were integrated and classified into genomes, type groups and network relationship files through Perl scripts, and the above groups were integrated using Cytoscape software (http: / / www.cytoscape.org / ) to construct a "ingredient-target-disease" co-expression network diagram of Tianma III prescription for the treatment of colorectal cancer, which included the colorectal cancer disease targets obtained in step (1) and the active ingredients and their targets of Tianma III prescription obtained in step (2), as shown in the figure. Figure 6 As shown, the co-expression target is Figure 6 The middle rectangular part is obtained by integrating the overlapping parts of the colorectal cancer disease targets and the active ingredient targets of Tianma III prescription. These targets refer to the genes that intersect between the two. The circular nodes of different colors represent different drug compositions and colorectal cancer disease targets. Among them, blue represents Pinellia ternata, red represents Hedyotis diffusa, green represents Scutellaria barbata, purple represents Tripterygium wilfordii, blue-green represents Sargassum, orange represents Astragalus membranaceus, and rose-red represents Rhubarb. The lines in the network represent the connection between the active ingredients of these drugs and the co-expressed targets, showing the potential interactions and action mechanisms of each drug with colorectal cancer-related targets. The co-expression targets were analyzed through the STING database (https: / / cn.string-db.org / ), the species information in the conditions was set to human, unconnected nodes were hidden, and protein-protein interaction analysis was performed to obtain multiple protein-protein interaction network diagrams, such as Figure 7 As shown, the figure shows the interaction relationship between these targets, reveals their synergistic effects or potential regulatory networks in cells, and displays key targets with strong associations, indicating that these proteins play an important role in the treatment of colorectal cancer with Tianma III prescription; Further analysis was performed using the cytoHubba plug-in of the Cytoscape software, and the top 5 rankings of the centrality degree analysis were taken, and the potential core targets of Tianma III prescription for the treatment of colorectal cancer were BCL2, JUN, Caspase3 (CASP3), XIAP, and Caspase9 (CASP9), such as Figure 8 As shown, the redder the rectangular box is, the higher the possibility that the gene is a core target, indicating that it has a higher centrality and importance in the network.

[0044] Analysis method 1: First, the intersection targets obtained in step (3) were converted into target IDs using the R language org.hs.eg package. Then, clusterProfiler, DOSE, and enrichment plot packages were used with p < 0.05 as the screening condition to perform GO (gene ontology) and KEGG (Kyoto encyclopedia of genes and genomes) pathway enrichment analysis on the converted IDs. like Fig. 9 As shown in the figure, 2615 related entries were obtained through GO analysis, including 2283 biological process (BP) related entries, 110 cell component (CC) related entries, and 220 molecular function (MF) related entries. The bubble chart expresses the top ten of BP, CC, and MF. The bubble color is related to the corrected P value. The smaller the value, the redder the color. The bubble size is proportional to the number of enrichments. BP is mainly reflected in reactive oxygen, oxidative stress, oxygen metabolism, etc.; CC is mainly reflected in various membranes such as cytoplasm and mitochondrial outer membrane; MF is mainly reflected in transcription factors, etc., indicating that these entries play an important role in the treatment of colorectal cancer with Tianma No. Ⅲ prescription. The Cytoscape software was further used to visualize the KEGG enrichment network relationship, KEGG pathway enrichment analysis and target network diagram, such as Fig.10As shown in the figure, KEGG enrichment obtained 37 related entries. The expression of targets and pathways is proportional to the size of the icons. The connecting line represents the correlation between the two, indicating the correlation between targets and pathways, indicating that some targets play a regulatory role in multiple signaling pathways; red rectangles represent targets, indicating potential core targets identified in the study, which are closely related to diseases or specific biological processes; orange V-shaped represents pathway expression, representing the enrichment results of KEGG pathways, and the size of the pathway is proportional to the expression, which means that the higher the expression, the more important the pathway is in the biological process; the pathway-related enrichments mainly include PI3K-Akt, IL-17, TNF, MAPK, p53, cell senescence and apoptosis, indicating that these pathways are closely related to the functions of targets, and these signaling pathways play an important role in various pathological and physiological states such as cancer, cell damage, and metabolic regulation; the correlation between targets and pathways not only reflects the regulatory network at the molecular level, but also reveals the regulatory role of targets in important signaling pathways, which is of great guiding significance for understanding disease mechanisms and discovering potential therapeutic targets.

[0045] Analysis method 2: The 3D structures of the potential core targets BCL2, JUN, Caspase3, XIAP and Caspase9 obtained in step (4) were downloaded from the Pubchem database (https: / / pubchem.ncbi.nlm.nih.gov / ). The AutoDockTools1.5.6 software was used to remove the crystal water and the original ligands in the protein receptor and hydrogenate the receptor protein. Then, the 3D structures of the potential core targets were docked with the key compound molecules quercetin, luteolin, kaempferol, wogonin, baicalein and β-sitosterol using AutoDock Vina1.1.2, Pymol2.1 and Discovery studio V2019 software (Dassault Systemes Biovia, San Diego, CA, USA). The docking results used the binding free energy as the evaluation criterion for the degree of binding with the key compound molecules. The R language pacman package was used to visualize the binding free energy through a heat map, as shown in Figure 2. Fig.11 As shown; the docking modes of the potential core targets obtained in step (4) and the key compound molecules are visualized, respectively, BCL2 and luteolin molecule docking (such as Fig.12 As shown in the figure), Caspase3 and luteolin molecular docking (as shown in the figure Fig.13 As shown in Figure 2), Caspase3 docks with β-sitosterol molecules (as shown in Figure 2 Fig.14 As shown in the figure), Caspase3 and quercetin molecule docking (as shown in the figure Fig.15 As shown in the figure), JUN and luteolin molecular docking (as shown in the figure Fig.16As shown in the figure), XIAP and luteolin molecular docking (as shown in the figure Fig.17 As shown); through the heat map of molecular docking binding free energy, core targets with higher binding free energy are selected for docking with key compounds; the selection of different targets and compounds is based on their molecular structure and functional suitability. At the same time, the difference in binding free energy between different targets and compounds reflects the affinity of their interaction. Therefore, binding free energy can help explain why certain compounds are selected for docking with specific targets; the binding affinity between potential core targets and key compounds is evaluated. The redder the color, the more stable the binding. It can be seen that quercetin and Caspas9 bind most stably, followed by luteolin and Caspas3; in addition, compounds with high affinity binding to core targets can be screened out through docking results, helping to identify potential drug candidate molecules as quercetin, luteolin, kaempferol, wogonin, baicalin and β-sitosterol.

[0046] Analysis method 3: CCK8 method was used to verify the inhibition of colorectal cancer cell proliferation by Tianma III prescription. Specifically, HCT116, HT29, SW480, SW620 and DLD1 colorectal cancer cells were cultured at 5×10 3 The cells were seeded at a density of 100 cells / well on a 96-well plate and divided into six groups with preset concentrations of Tianma III prescription-containing serum of 0%, 2.5%, 5%, 10%, 15% and 20%, respectively. Each group had 5 replicate wells. After overnight culture, the cells were grouped and intervened according to the preset concentrations of the drug-containing serum. After 24 hours of intervention, CCK8 solution prepared in complete culture medium was added at 100 μL / well. After incubation for another 2 hours at 37°C and 5% CO2, the cells were placed in a Bio-Tek microplate reader and the absorbance at 450 nm was analyzed. The average of the replicate wells in the group was taken. Fig.18 The results showed that the higher the serum concentration of Tianma III prescription, the higher the inhibitory effect on the proliferation of multiple colorectal cancer cells; Flow cytometry was used to verify the effect of Tianma III prescription on promoting apoptosis of colorectal cancer cells. Specifically, the serum containing Tianma III prescription was used to intervene in HCT116 and SW480 colorectal cancer cells and negative control group cells at gradient concentrations of 0, 10%, 15% and 20% for 24 h, respectively. The colorectal cancer cells and negative control group cells after intervention were washed twice with pre-cooled PBS, and 220 μL of 1×Annexin V binding solution was added to resuspend the cells, and the concentration was adjusted to 1×10 6After the cells / mL were added, 10 μL of Annexin V-FITC staining solution was added to the cell suspension, and the mixture was gently mixed. The mixture was incubated at 4°C in the dark for 10 min. Finally, 10 μL of PI staining solution was added. The mixture was gently mixed. The mixture was incubated at 4°C in the dark for 5 min, and the cells were immediately detected by flow cytometry. The pretreatment method of the cells in the negative control group was as follows: the cells were washed with PBS and digested with trypsin, and the cells were centrifuged at 36°C and 1000 rpm / min for 10 min to collect the cells digested with trypsin. Fig.19 As shown, the results showed that with the increase of the concentration of Tianma III prescription, the apoptosis rate of the two colorectal cancer cells also continued to increase.

[0047] Analysis method 4: Real-time fluorescence quantitative method was used to verify the effect of Tianma No. III prescription on the expression level of potential core target mRNA obtained in step (4). The specific method was as follows: HCT116 colorectal cancer cells after intervention with Tianma No. III prescription obtained in analysis method 3 were collected, total RNA was extracted using Trizol, and reverse transcription reaction was further performed according to the cDNA reverse transcription kit to establish a total reaction system volume of 20 μL. Pre-denaturation treatment was performed at 25°C for 10 minutes, and the amplification conditions were: 40 cycles of reaction at 95°C for 30 seconds and annealing at 60°C for 25 seconds; β-actin was used as the internal reference gene, and the relative expression of mRNA was analyzed using the 2-ΔΔCt method; Fig. 20 As shown, the mRNA results showed that the expression levels of BCL2 and JUN were lower than those of the control group (p < 0.05), and the expression levels of Caspase3, XIAP and Caspase9 were higher than those of the control group (p < 0.05); these results indicate that Tianma III prescription can effectively regulate the expression of core target-related genes in colorectal cancer cells, mainly inhibiting the expression of oncogenes BCL2 and JUN, promoting the mRNA expression of tumor suppressor genes Caspase3, XIAP and Caspase9, and thereby inhibiting the progression of colorectal cancer.

Claims

1. A method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer, characterized in that: The following steps are involved: (1) First, colorectal cancer disease targets are screened from multiple databases respectively, and then duplicate values ​​of colorectal cancer disease targets obtained from the multiple databases are deleted to obtain screened colorectal cancer disease targets; (2) First, the drug composition of Tianma III prescription was searched through multiple databases to obtain the effective active ingredients and their targets respectively, and then the duplicate values ​​of the effective active ingredients and their targets obtained from the above multiple databases were integrated and deleted to obtain the effective active ingredient targets of Tianma III prescription; (3) Intersecting the colorectal cancer disease targets screened in step (1) with the effective active ingredient targets of Tianma III prescription obtained in step (2) to obtain intersection targets; (4) constructing a "component-target-disease" co-expression network diagram of the colorectal cancer disease target obtained in step (1) and the active ingredients of Tianma No. III prescription and their targets obtained in step (2) for the treatment of colorectal cancer, thereby obtaining co-expression targets; performing protein-protein interaction analysis on the co-expression targets to obtain multiple protein-protein interaction network diagrams, and further using plug-in analysis to obtain potential core targets of Tianma No. III prescription for the treatment of colorectal cancer; Analysis method 1: First, the intersection targets obtained in step (3) are converted into target IDs, and then the converted IDs are subjected to GO and KEGG pathway enrichment analysis, and the KEGG enrichment network relationship is further visualized for analysis; Analysis method 2: Molecular docking is performed between the 3D structure of the potential core target obtained in step (4) and the key compound molecule, and the docking results are visualized; Analysis method three: in vitro experiments were used to verify that Tianma III prescription inhibited the proliferation of colorectal cancer cells and promoted the apoptosis of colorectal cancer cells; Analysis method 4: Real-time fluorescence quantitative method was used to verify the effect of Tianma III prescription on the expression level of potential core target mRNA obtained in step (4).

2. The method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer according to claim 1, characterized in that: In step (1), the databases include: TCGA, GEO, GeneCards, OMIM, PharmGkb, TTD and DisGeNET; the screening method of the TCGA database is: downloading the sequencing data of colorectal cancer RNA patients from the TCGA database, using Perl script to process the transcriptome data and convert it into gene symbols, and extracting mRNA data, and further using Log |FC|≥1, adjP≤0.05 as screening conditions, and using R language analysis to obtain differentially expressed disease targets; the screening method of the GEO database is: in the GEO database, the colorectal cancer "colorectal "cancer" was used as the search term to obtain the GSE37364 data set, download the gene expression data of normal adjacent cancer tissues and tumor tissues, and further grouped by Perl software and analyzed by R language, with Log|FC|≥1, adjP≤0.05 as the screening conditions to obtain the differentially expressed genes in the tissues of patients with colon cancer, and the up-regulated genes and down-regulated genes therein were used as potential disease targets of GEO; the screening method of the GeneCards, OMIM, PharmGkb, TTD or DisGeNET databases was as follows: in the above five databases, "colorectal cancer" was used as the search term respectively, and the related genes obtained from the five databases were respectively taken and combined by the R language Venn package to obtain colorectal cancer disease targets respectively.

3. The method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer according to claim 1 or 2, characterized in that: In step (2), the database includes: one or more of the traditional Chinese medicine system pharmacology database and analysis platform, China National Knowledge Infrastructure database, Wanfang database, VIP database, Chinese biomedical literature database, PubChem database or PubMed database; the search is performed with oral bioavailability ≥ 30% and drug similarity ≥ 0.18 as filtering conditions; the drug composition of the Tianma No. III prescription is: Pinellia, Hedyotis diffusa, Scutellaria barbata, Trillium, Sargassum, Astragalus and Rhubarb; and the duplicate values ​​of the effective active ingredients and their targets obtained from the above multiple databases are deleted through Perl script integration.

4. The method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer according to any one of claims 1 to 3, characterized in that: In step (3), the intersection is obtained using the R language Venn package.

5. The method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer according to any one of claims 1 to 4, characterized in that: In step (4), the construction refers to: integrating and classifying the effective active ingredients of Tianma III prescription and its targets and colorectal cancer disease targets into genomes, type groups and network relationship files through Perl scripts, and integrating the above groups using Cytoscape software; performing protein-protein interaction analysis on the co-expressed targets through the STING database, setting the species information in the conditions to select humans, hiding unconnected nodes; using the cytoHubba plug-in of Cytoscape software for analysis; and taking the top 5 ranked by centrality degree analysis as potential core targets.

6. The method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer according to any one of claims 1 to 5, characterized in that: In analysis method 1, the intersection targets obtained in step (3) were converted into target IDs using the R language org.hs.eg package. The converted IDs were subjected to GO and KEGG pathway enrichment analysis using clusterProfiler, DOSE, and enrichment plot packages with p < 0.05 as the screening condition. The KEGG enrichment network relationship was further visualized using Cytoscape software.

7. The method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer according to any one of claims 1 to 6, characterized in that: In the second analysis method, the 3D structure of the potential core target obtained in step (4) is downloaded from the Pubchem database, and the protein is subjected to the removal of crystal water and the original ligand in the protein receptor and the hydrogenation operation on the receptor protein using the AutoDock Tools 1.5.6 software. Then, the 3D structure of the potential core target is subjected to molecular docking with the key compound molecule using the AutoDock Vina 1.1.2 and Pymol 2.1, and Discovery studio V2019 software. The docking result uses the binding free energy as the evaluation criterion for the degree of binding with the key compound molecule. The key compound molecule includes one or more of quercetin, luteolin, kaempferol, wogonin, baicalein or β-sitosterol. The visualization includes: using the R language pacman package to visualize the binding free energy through a heat map, and respectively visualizing the docking mode of the potential core target obtained in step (4) and the key compound molecule.

8. The method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer according to any one of claims 1 to 7, characterized in that: In the third analysis method, the CCK8 method was used to verify the inhibition of colorectal cancer cell proliferation by Tianma III prescription. The CCK8 method is as follows: colorectal cancer cells were cultured at a rate of 1×10 3 ~10×10 3 The cells were inoculated on a 96-well plate at a density of 10 cells / well and divided into multiple groups with preset concentrations of Tianma No. III drug-containing serum of 0-20%, each group having 4-6 replicate wells. After overnight culture, the cells were grouped and intervened according to the preset concentrations of drug-containing serum. After intervention for 20-28 hours, 80-120 μL / well of CCK8 solution prepared with complete culture medium was added. After incubation for 1-2 hours at 35-39°C and 4-6% CO2, the absorbance values ​​at 450 nm were analyzed respectively, and the average of the replicate wells in the group was taken. Flow cytometry was used to verify the promotion of apoptosis of colorectal cancer cells by Tianma No. III drug-containing serum at gradient concentrations of 0-20% for 20-28 hours in colorectal cancer cells and negative control group cells, the colorectal cancer cells and negative control group cells after intervention were washed 2-3 times with pre-cooled PBS, and 180-220 μL 1×Annexin was added respectively. Resuspend the cells in V binding buffer and adjust the concentration to 1×10 6 ~5×10 6 After the cells / mL are obtained, 8-12 μL of Annexin V-FITC staining solution is added to the cell suspension, the mixture is gently mixed, and the mixture is incubated at 3-5°C in the dark for 5-10 min. Finally, 8-12 μL of PI staining solution is added, the mixture is gently mixed, and the mixture is incubated at 3-5°C in the dark for 5-10 min. The mixture is immediately detected by flow cytometry. The pretreatment method of the cells in the negative control group is as follows: the cells are washed with PBS and digested with trypsin, and the cells are centrifuged at 35-39°C and 800-1200 rpm / min for 8-12 min to collect the digested cells. The colorectal cancer cells include one or more of HCT116, SW480, DLD1, SW620 or HT29.

9. The method for analyzing the mechanism of action of Tianma III prescription in treating colorectal cancer according to any one of claims 1 to 8, characterized in that: In analysis method four, the specific method for verifying the real-time fluorescence quantitative method is: collecting colorectal cancer cells after intervention with Tianma No. III prescription obtained in analysis method three, extracting total RNA with Trizol, further performing reverse transcription reaction according to the cDNA reverse transcription kit, establishing a total reaction system volume of 18 to 22 μL, performing pre-denaturation treatment at 23 to 27°C for 8 to 12 minutes, and amplification conditions: reacting at 93 to 97°C for 25 to 35 seconds, and annealing at 55 to 65°C for 25 to 35 seconds for 35 to 45 cycles; using β-actin as an internal reference gene, the relative expression level of mRNA was analyzed using the 2-ΔΔCt method.

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