A method for screening active ingredient groups of traditional Chinese medicines against gastric cancer in combination with poly-pk and msi

Abstract

The application discloses a traditional Chinese medicine anti-gastric cancer active ingredient group screening method combined with Poly-PK and MSI, and the specific process is as follows: traditional Chinese medicine compound pretreatment, Poly-PK multi-component metabolism detection, animal model construction and MSI imaging, and bioinformatics correlation analysis; the application takes the traditional Chinese medicine 'formula-syndrome-disease' diagnosis and treatment logic as the core, integrates the dynamic metabolism analysis advantage of Poly-PK and the spatial distribution tracking advantage of MSI, realizes the integrated screening of'syndrome type matching-dynamic metabolism-spatial distribution-target point correlation' through systematic bioinformatics correlation analysis, accurately excavates the core active ingredient group related to the treatment of gastric cancer in the traditional Chinese medicine compound, solves the technical pains that the existing screening methods ignore the syndrome type, the analysis is not comprehensive, and the positioning is not accurate, and provides scientific and reliable technical support for the research and development of traditional Chinese medicine anti-gastric cancer new drugs.

Description

Technical Field

[0001] This invention relates to the field of screening technology for active ingredients in traditional Chinese medicine, and in particular to a method for screening groups of active ingredients in traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI. Background Technology

[0002] Stomach cancer is one of the most common malignant tumors worldwide. According to the World Health Organization, there were approximately 1.1 million new cases of stomach cancer and 769,000 deaths worldwide in 2020. Among them, my country accounted for 43.9% of new cases and 48.5% of deaths, seriously threatening human health.

[0003] Currently, the main treatments for gastric cancer are surgery, chemotherapy, and targeted therapy. However, chemotherapy drugs have problems such as strong toxic side effects and significant drug resistance, and targeted therapy is applicable to a limited population. Therefore, it is of great significance to find safe and effective alternative treatment options.

[0004] Traditional Chinese medicine (TCM) has unique advantages in the treatment of gastric cancer, especially in improving patients' clinical symptoms, enhancing efficacy and reducing toxicity, and prolonging survival. However, TCM compound formulas are complex, with multiple components, multiple targets, and synergistic effects. Screening for their core active ingredient groups is a key bottleneck in the development of new TCM drugs.

[0005] Existing methods for screening active ingredients in traditional Chinese medicine have the following main technical shortcomings:

[0006] (1) Ignoring the core logic of TCM “syndrome type”: TCM treatment emphasizes “differentiation of syndromes and treatment”. Different syndrome types of the same disease have different treatment prescriptions. However, the existing screening methods are mostly directly targeting the “gastric cancer” disease itself, without considering the “prescription-syndrome” matching. This may result in the selected ingredients not matching the clinical syndrome type, and the actual efficacy is not good.

[0007] For example, the pathogenesis of gastric cancer of the qi stagnation and blood stasis type is different from that of gastric cancer of the spleen and stomach deficiency type. The active ingredients of the applicable prescriptions are significantly different. If the syndrome screening is ignored, it may lead to the misscreening of ineffective ingredients.

[0008] (2) Incomplete multi-component analysis: Traditional pharmacokinetics only focuses on the in vivo process of a single component, which cannot reflect the characteristics of multi-component synergistic metabolism of Chinese medicine compound; although some multi-component analysis methods can detect multiple components, they lack the tracking of secondary metabolites, making it difficult to fully elucidate the in vivo action process of the drug.

[0009] (3) Lack of tissue distribution information: Existing methods mostly analyze drug components through body fluid samples such as blood and urine, which cannot clearly identify the spatial distribution and enrichment of components in tumor tissues and target organs, making it difficult to accurately locate the core components that exert the drug effect.

[0010] (4) The screening logic is not systematic: existing methods are mostly based on the direct correlation between "components and efficacy", lacking systematic correlation analysis of "components-metabolism-targets-efficacy". The scientificity and reliability of the screening results are insufficient, making it difficult to support the modern research and development of traditional Chinese medicine compound prescriptions.

[0011] Multi-component pharmacokinetic analysis (Poly-PK) enables dynamic metabolic analysis of multiple components in traditional Chinese medicine (TCM) formulas, while mass spectrometry (MSI) allows for in-situ spatial distribution tracking of drug components within tissues. The combination of these two techniques provides a new pathway for multi-component analysis of TCM. However, there are currently no reports on integrating the TCM logic of "formula-syndrome-disease" with Poly-PK and MSI techniques for screening active ingredient groups in TCM for gastric cancer treatment.

[0012] Therefore, developing a screening method that takes into account the matching of "prescription-syndrome-disease", dynamic metabolism of multiple components, spatial distribution of tissues and system correlation analysis is the key to solving the bottleneck of screening active ingredients of traditional Chinese medicine for gastric cancer, and has important academic value and application prospects. Summary of the Invention

[0013] The purpose of this invention is to provide a method for screening active ingredients in traditional Chinese medicine (TCM) for gastric cancer treatment using a combination of poly-PK and MSI. This invention is based on the TCM diagnostic logic of "prescription-syndrome-disease," integrating the advantages of dynamic metabolic analysis of poly-PK with the spatial distribution tracking advantages of MSI. Through systematic bioinformatics correlation analysis, it achieves integrated screening of "syndrome matching, dynamic metabolism, spatial distribution, and target association," accurately identifying core active ingredient groups in TCM compound prescriptions related to gastric cancer treatment. This addresses the technical pain points of existing screening methods, such as neglecting syndrome differentiation, incomplete analysis, and inaccurate localization, providing scientific and reliable technical support for the development of new TCM drugs for gastric cancer treatment.

[0014] The technical solution of this invention:

[0015] A method for screening active components of traditional Chinese medicine for gastric cancer using a combination of poly-PK and MSI is proposed. This method is based on the "formula-syndrome-disease" logic of traditional Chinese medicine and integrates multi-component pharmacokinetic (Poly-PK) and mass spectrometry (MSI) technology. The specific steps include the following:

[0016] A. Pretreatment of Traditional Chinese Medicine Compound: Take the traditional Chinese medicine compound, and prepare the test drug through extraction and purification;

[0017] B. Poly-PK multi-component metabolism assay: Urine samples were collected at multiple time points, and UPLC-QTOF / MS technology was used to detect the qualitative and quantitative data of the original drug component, secondary metabolites and endogenous metabolites of the test drug, screen differential metabolites, and clarify the dynamic metabolic process of the drug in the body and its impact on the body's metabolism.

[0018] C. Animal Model Construction and MSI Imaging: Animal models are constructed and biological samples are prepared after drug intervention; MSI imaging is performed on the biological samples to obtain MSI tissue distribution data; in situ imaging and quantitative analysis of drug components in tumor tissue and major organs are achieved to clarify the tissue distribution pattern of core components;

[0019] D. Bioinformatics association analysis: Integrate metabolic data, distribution data and target data to construct a multi-dimensional association network, clarify the mechanism of action of components through GO / KEGG enrichment analysis, and screen core active ingredient groups by combining multiple indicators.

[0020] In the aforementioned method for screening active ingredients of traditional Chinese medicine against gastric cancer using a combination of Poly-PK and MSI, the extraction process of the traditional Chinese medicine compound pretreatment described in step A is as follows: the traditional Chinese medicine compound is soaked in 8-12 times the amount of purified water for 30-60 minutes, then decocted 2-3 times for 1-2 hours each time. The decoctions are combined, filtered through a 400-mesh filter, and concentrated under reduced pressure using a rotary evaporator to a crude drug concentration of 1.0-2.0 g / mL. The solution is then filtered through a 0.22 μm organic phase filter membrane and stored at 4°C for later use. The reduced pressure concentration is specifically performed at a vacuum degree of -0.08 to -0.06 MPa and a temperature of 50-60°C.

[0021] The test drug is prepared by using a standardized extraction process to ensure the stability and reproducibility of the drug components.

[0022] In the aforementioned method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI, the specific procedure for the Poly-PK multi-component metabolic detection in step B is as follows:

[0023] B1. Sample collection: Take 5 mL of urine samples from 0 h before administration and 1 h, 3 h, 6 h, 9 h, 12 h and 24 h after administration, respectively, place them in enzyme-free centrifuge tubes, and freeze them in an ultra-low temperature freezer at -80℃ to avoid repeated freeze-thaw cycles.

[0024] B2. UPLC-Q-TOF / MS detection: Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used to detect the test drug and urine samples to obtain qualitative and quantitative data of the drug's original chemical components, secondary metabolites and endogenous metabolites;

[0025] B3. Screening of differential metabolites: The raw mass spectrometry data were imported into the software for preprocessing. Using healthy volunteers as controls, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to screen for endogenous differential metabolites before and after drug administration. The screening criteria were variable importance projection value (VIP) > 1.0, P < 0.05 and Fold Change (FC) > 2.0 or FC < 0.5.

[0026] In the aforementioned method for screening active ingredients of traditional Chinese medicine against gastric cancer using a combination of Poly-PK and MSI, the specific detection conditions for the UPLC-Q-TOF / MS detection described in step B2 are as follows:

[0027] The UPLC detection conditions were as follows: a Waters ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.8 μm) was used; the mobile phase consisted of acetonitrile phase A and 0.1% formic acid aqueous solution phase B (v / v);

[0028] The gradient elution program was as follows: 0-5 min, 5%-20% A; 5-15 min, 20%-50% A; 15-25 min, 50%-80% A; 25-30 min, 80%-95% A; flow rate 0.3 mL / min.

[0029] Column temperature 35℃;

[0030] Injection volume: 5 μL;

[0031] Mass spectrometry detection conditions: electrospray ionization (ESI) source, simultaneous scanning in positive and negative ion modes, scanning range m / z 100-1500;

[0032] The capillary voltage for positive ions is 3.0 kV, and for negative ions it is 2.5 kV.

[0033] Tapered hole voltage 40V;

[0034] The temperature of the desolventizing gas is 400℃, and the flow rate of the desolventizing gas is 800L / h;

[0035] Collision energy 10-40 eV, data collected using MSE mode.

[0036] In the aforementioned screening method for active ingredients of traditional Chinese medicine against gastric cancer using a combination of Poly-PK and MSI, the efficient separation and accurate qualitative and quantitative analysis of multiple components are ensured by optimizing the UPLC-Q-TOF / MS detection parameters.

[0037] In the aforementioned method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI, the specific details of step C, namely, the construction of the animal model and MSI imaging, are as follows:

[0038] C1. Animal model construction: After feeding mice, human gastric cancer SGC-7901 cells were injected and inducing qi stagnation and blood stasis syndrome until the model was successfully established.

[0039] C2. Drug intervention and sample preparation: Mice that successfully developed the model were randomly divided into a drug administration group and a model control group. The drug administration group was administered the test drug by gavage, while the model control group was administered an equal volume of physiological saline by gavage. The drugs were administered continuously for 8 weeks, once a day. After the drug administration was completed, the mice were euthanized and dissected to obtain tumor tissue, stomach tissue, liver and kidney. The surface bloodstains were rinsed with physiological saline, the moisture was absorbed with filter paper, and the mice were frozen at -80℃ for 30 min. The tissue sections were then cut into 10 μm thick sections using a cryostat and mounted on conductive glass slides. The sections were stored at -20℃ for later use.

[0040] C3. MSI Imaging Detection: MSI tissue distribution data were obtained by detecting tissue sections using matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOFMSI) technology.

[0041] In the aforementioned method for screening active ingredients of traditional Chinese medicine against gastric cancer using a combination of Poly-PK and MSI, step C ensures imaging quality and quantitative accuracy through successful model construction (simultaneously satisfying the characteristics of "gastric cancer" and the characteristics of "qi stagnation and blood stasis" syndrome) and optimization of MSI detection parameters.

[0042] In the aforementioned method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI, the specific details of the MSI imaging detection in step C3 are as follows:

[0043] Matrix preparation: α-cyano-4-hydroxycinnamic acid (CHCA) was dissolved in acetonitrile-0.1% formic acid aqueous solution (1:1, v / v) to prepare a matrix solution of 10 mg / mL; the matrix was uniformly sprayed onto the surface of the tissue section using a matrix sprayer at a spray pressure of 0.3 MPa and a spray distance of 5 cm. After each spraying, the tissue section was dried at room temperature for 30 s, for a total of 5 spraying cycles.

[0044] Imaging and detection conditions: laser wavelength 337nm, laser frequency 1000Hz, scanning resolution 50μm, imaging range covering the entire tissue section, quality resolution ≥15000 (m / z 200), detection mode is reflection mode, m / z scanning range 100-1500, and the quality axis is calibrated using the external standard method (calibration materials: Angiotensin I, Glu-fibrinopeptide B).

[0045] In the aforementioned screening method for active ingredients of traditional Chinese medicine used in combination with Poly-PK and MSI for gastric cancer, the bioinformatics association analysis in step D is as follows:

[0046] D1. Data Integration: Based on the qualitative and quantitative data obtained in step B, the data on endogenous differential metabolites, and the MSI tissue distribution data obtained in step C, construct a correlation network of "prototype drug components - secondary metabolites - endogenous differential metabolites".

[0047] D2. Target Association Analysis: Gastric cancer-related targets were retrieved from the GeneCards, OMIM, and DisGeNET databases. Potential targets corresponding to drug components were obtained from the SymMap and TCMSP databases. Venny 2.1.0 software was used to screen drug-gastric cancer intersection targets. David 6.8 database was used to perform GO functional enrichment analysis (biological process BP, cellular component CC, molecular function MF) and KEGG pathway enrichment analysis on drug-gastric cancer intersection targets. The screening criterion was P<0.05.

[0048] D3. Screening of Core Active Ingredients: Core active ingredient groups are screened based on the following three core indicators:

[0049] D3.1 The concentration of the drug component in tumor tissue is ≥ 2.0 times the concentration in blood;

[0050] D3.2. The binding affinity (KD value) of the component to the core targets of gastric cancer (PRKACA, VEGFR-2, c-Met, ADRB2, NCOA2) is ≤1.0×10⁻⁶. -6 mol / L;

[0051] D3.3 The components can significantly regulate the expression of AKT, ERK1 / 2, PCNA or Caspase-3, which are related to the VEGF / VEGFR-2 or HGF / c-Met signaling pathway. The inhibition rate of PCNA, AKT and ERK1 / 2 protein expression is ≥30%, and the promotion rate of Caspase-3 protein expression is ≥30%.

[0052] In the aforementioned screening method for active ingredients of traditional Chinese medicine used in combination with Poly-PK and MSI for gastric cancer, step D ensures the reliability and effectiveness of the screening results through systematic data integration and scientific screening criteria.

[0053] In the aforementioned method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI, the traditional Chinese medicine compound in step A is characterized in that the traditional Chinese medicine compound is a traditional Chinese medicine compound for gastric cancer of the qi stagnation and blood stasis type, specifically including 3-5 parts by weight of Shenqu, 2-4 parts of Xiangchacai, 3-6 parts of Zhike, 2-5 parts of Zhidahuang and 3-6 parts of Houpo.

[0054] The aforementioned screening method for active ingredients of traditional Chinese medicine in the combination of Poly-PK and MSI for gastric cancer included 4 parts by weight of Shenqu, 3 parts of Xiangchacai, 5 parts of Zhike, 4 parts of processed Dahuang and 5 parts of Houpo.

[0055] Compared with the prior art, the beneficial effects of this application are as follows:

[0056] 1) Filling a technological gap: This invention is the first to integrate the TCM "prescription-syndrome-disease" diagnosis and treatment logic with Poly-PK and MSI technologies, clarifying the correspondence between "syndrome type-component-target", and solving the core defect of traditional screening methods that ignore syndrome matching;

[0057] It achieves dual analysis of the time dimension (dynamic metabolism) and the spatial dimension (tissue distribution), filling the technical gaps in existing methods where multi-component analysis is incomplete and tissue localization is inaccurate.

[0058] 2) High scientific rigor and reliability: This invention clarifies the detailed parameters of key technologies such as UPLC-QTOF / MS and MSI, and formulates standardized differential metabolite screening criteria (VIP>1.0, P<0.05, FC>2.0 or <0.5) and core active ingredient group screening indicators (tumor enrichment concentration, target binding affinity, pathway regulation ability). The experimental process is reproducible and the results are verifiable.

[0059] By using bioinformatics systems to analyze and establish a network linking "components-metabolism-targets-efficacy", the screening results can be made more scientifically based.

[0060] 3) High practicality: This method is applicable to the screening of active ingredient groups in various traditional Chinese medicine compound prescriptions related to gastric cancer with qi stagnation and blood stasis. It does not require complicated pretreatment steps and has high detection efficiency (UPLC-QTOF / MS can identify dozens of components in one detection, and MSI can achieve whole-area tissue imaging).

[0061] The core active ingredient groups identified can be directly used for the formulation development and quality control standard setting of new traditional Chinese medicine drugs, shortening the research and development cycle and reducing research and development costs.

[0062] It can also provide experimental data support for elucidating the mechanism of action of traditional Chinese medicine compound prescriptions, and promote the modernization of traditional Chinese medicine.

[0063] 4) Significant industrialization value: The core active ingredient group screened by this method has clear anti-gastric cancer targets and mechanisms, and can be developed into innovative Chinese medicine drugs, improved Chinese medicine drugs or health products, providing new drug options for the treatment of gastric cancer;

[0064] It can be used to build a research and development platform for new traditional Chinese medicine drugs and has broad market application prospects. Attached Figure Description

[0065] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0066] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0067] Example

[0068] A method for screening active components of traditional Chinese medicine (TCM) against gastric cancer using a combination of poly-PK and MSI is proposed. This method is based on the TCM "formula-syndrome-disease" logic and integrates multi-component pharmacokinetic (Poly-PK) and mass spectrometry (MSI) technologies. Figure 1 As shown, it includes the following steps:

[0069] A. Pretreatment of Traditional Chinese Medicine Compound: Take the traditional Chinese medicine compound, and prepare the test drug through extraction and purification;

[0070] B. Poly-PK multi-component metabolism assay: Urine samples were collected at multiple time points, and UPLC-QTOF / MS technology was used to detect the qualitative and quantitative data of the original drug component, secondary metabolites and endogenous metabolites of the test drug, and to screen for differential metabolites.

[0071] C. Animal Model Construction and MSI Imaging: After constructing an animal model and administering drug intervention, biological samples are prepared; MSI imaging is performed on the biological samples to obtain MSI tissue distribution data;

[0072] D. Bioinformatics association analysis: Integrate metabolic data, distribution data and target data to construct a multi-dimensional association network, clarify the mechanism of action of components through GO / KEGG enrichment analysis, and screen core active ingredient groups by combining multiple indicators.

[0073] The extraction process of the traditional Chinese medicine compound pretreatment described in step A is as follows: the traditional Chinese medicine compound is soaked in 8-12 times the amount of purified water for 30-60 minutes, then decocted 2-3 times for 1-2 hours each time. The decoctions are combined, filtered through a 400-mesh filter, and concentrated under reduced pressure using a rotary evaporator to a crude drug concentration of 1.0-2.0 g / mL. The solution is then filtered through a 0.22 μm organic phase filter membrane and stored at 4°C for later use. The reduced pressure concentration is specifically performed at a vacuum degree of -0.08 to -0.06 MPa and a temperature of 50-60°C.

[0074] The specific procedure for the Poly-PK multi-component metabolic assay described in step B is as follows:

[0075] B1. Sample collection: Take 5 mL of urine samples from 0 h before administration and 1 h, 3 h, 6 h, 9 h, 12 h and 24 h after administration, respectively, place them in enzyme-free centrifuge tubes, and freeze them in an ultra-low temperature freezer at -80℃ to avoid repeated freeze-thaw cycles.

[0076] B2. UPLC-Q-TOF / MS detection: Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used to detect the test drug and urine samples to obtain qualitative and quantitative data of the drug's original chemical components, secondary metabolites and endogenous metabolites;

[0077] B3. Screening of differential metabolites: The raw mass spectrometry data were imported into Progenesis QI software for preprocessing (preprocessing included peak alignment, noise reduction, and peak identification). Using healthy volunteers as controls, orthogonal partial least squares discriminant analysis (OPLS-DA) was used to screen for endogenous differential metabolites before and after drug administration. The screening criteria were variable importance projection value (VIP) > 1.0, P < 0.05, and Fold Change (FC) > 2.0 or FC < 0.5.

[0078] Step B1 involves sample collection from eligible volunteers, as detailed below:

[0079] Ten to fifteen patients with stage II-III gastric cancer who met the diagnostic criteria for Qi stagnation and blood stasis type in the "Chinese Expert Consensus on Early Diagnosis and Treatment of Gastric Cancer (2023 Edition)" (primary symptom: stabbing pain in the stomach, fixed and unmoving; secondary symptoms: abdominal distension, dark purple tongue or ecchymosis, and choppy pulse; meeting one primary symptom and two or more secondary symptoms) and were confirmed by pathological biopsy were selected. At the same time, five to eight age- and sex-matched healthy volunteers were selected as controls. All participants signed informed consent forms.

[0080] Patients and volunteers took 200 mL of the test drug orally on an empty stomach.

[0081] In step B2, the specific detection conditions for UPLC-Q-TOF / MS detection are as follows:

[0082] The UPLC detection conditions were as follows: a Waters ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.8 μm) was used.

[0083] The mobile phase consists of acetonitrile phase A and 0.1% formic acid aqueous solution (v / v) phase B;

[0084] The gradient elution program was as follows: 0-5 min, 5%-20% A; 5-15 min, 20%-50% A; 15-25 min, 50%-80% A; 25-30 min, 80%-95% A; flow rate 0.3 mL / min.

[0085] Column temperature 35℃;

[0086] Injection volume: 5 μL;

[0087] Mass spectrometry detection conditions: electrospray ionization (ESI) source, simultaneous scanning in positive and negative ion modes, scanning range m / z 100-1500;

[0088] The capillary voltage for positive ions is 3.0 kV, and for negative ions it is 2.5 kV.

[0089] Tapered hole voltage 40V;

[0090] The temperature of the desolventizing gas is 400℃, and the flow rate of the desolventizing gas is 800L / h;

[0091] Collision energy 10-40 eV, data collected using MSE mode.

[0092] In step B3, the identification of endogenous differential metabolites is performed using the following criteria:

[0093] Qualitative analysis was performed using Progenesis QI software to match the Metlin and HMDB databases, combined with secondary mass spectrometry fragment information (match ≥85%). Quantitative analysis was performed using the external standard method (standard control, R). 2 ≥0.99).

[0094] The specific details of animal model construction and MSI imaging described in step C are as follows:

[0095] C1. Animal model construction: After feeding mice, human gastric cancer SGC-7901 cells were injected and inducing qi stagnation and blood stasis syndrome until the model was successfully established.

[0096] C2. Drug intervention and sample preparation: Mice that successfully developed the model were randomly divided into a drug administration group and a model control group. The drug administration group was administered the test drug by gavage, while the model control group was administered an equal volume of physiological saline by gavage. The drugs were administered continuously for 8 weeks, once a day. After the drug administration was completed, the mice were euthanized and dissected to obtain tumor tissue, stomach tissue, liver and kidney. The surface bloodstains were rinsed with physiological saline, the moisture was absorbed with filter paper, and the mice were frozen at -80℃ for 30 min. The tissue sections were then cut into 10 μm thick sections using a cryostat and mounted on conductive glass slides. The sections were stored at -20℃ for later use.

[0097] C3. MSI Imaging Detection: MSI tissue distribution data were obtained by detecting tissue sections using matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOFMSI) technology.

[0098] The specific details of animal model construction described in step C1 are as follows:

[0099] Male SPF-grade BALB / c nude mice, weighing 18-22g, were selected and acclimatized for 3 days (temperature 22-26℃, humidity 50%-60%, 12h light-dark cycle, free access to food and water).

[0100] Human gastric cancer SGC-7901 cells in logarithmic growth phase were harvested and their concentration adjusted to 1×10⁻⁶ cells using PBS buffer. 7 / mL, 0.2mL / mouse was subcutaneously injected into the back of the right forelimb near the axilla of a nude mouse;

[0101] Continue feeding for 2-3 weeks after inoculation, until the subcutaneous transplanted tumor reaches a volume of 200-300 mm.3 (Volume calculation formula: V=0.5×a×b) 2 When a is the major axis and b is the minor axis, the induction of qi stagnation and blood stasis syndrome begins: intraperitoneal injection of adrenaline 0.8 mg / kg daily, followed by immediate stimulation in a 0-4℃ ice bath environment for 15 min, for 3 consecutive days;

[0102] Evaluation criteria for successful model establishment: Mice exhibited lethargy, reduced activity, and a purplish-dark tongue; whole blood viscosity (high shear 50s) was also observed. -1 Low cut 5s -1 The levels of antibodies were more than 30% higher than those in normal nude mice, and the plasma viscosity was more than 20% higher.

[0103] The culture conditions for human gastric cancer SGC-7901 cells in step C1 are as follows:

[0104] RPMI-1640 medium containing 10% fetal bovine serum, 100 U / mL penicillin, and 100 μg / mL streptomycin was cultured in a 37°C, 5% CO2 incubator and passaged every 2-3 days.

[0105] The drug intervention and sample preparation described in step C2 are as follows:

[0106] Nude mice that had successfully developed the model were randomly divided into a drug administration group and a model control group, with 10 mice in each group.

[0107] The drug administration group was given the test drug by gavage at a dose of 20 g / kg / d, while the model control group was given an equal volume of physiological saline by gavage. The drug administration was carried out continuously for 8 weeks, once a day.

[0108] After administration, the mice were fasted for 12 hours but allowed free access to water. They were then anesthetized by intraperitoneal injection of 10% chloral hydrate (3 mL / kg), euthanized, and quickly dissected. Tumor tissue, stomach tissue, liver, and kidneys were separated. The surface bloodstains were rinsed with physiological saline, the moisture was blotted with filter paper, and the mice were frozen at -80℃ for 30 min. They were then cut into 10 μm thick sections using a cryostat, mounted on conductive glass slides, and stored at -20℃ for later use.

[0109] The specific details of the MSI imaging detection described in step C3 are as follows:

[0110] Matrix preparation: α-cyano-4-hydroxycinnamic acid (CHCA) was dissolved in acetonitrile-0.1% formic acid aqueous solution (1:1, v / v) to prepare a matrix solution of 10 mg / mL; the matrix was uniformly sprayed onto the surface of the tissue section using a matrix sprayer at a spray pressure of 0.3 MPa and a spray distance of 5 cm. After each spraying, the tissue section was dried at room temperature for 30 s, for a total of 5 spraying cycles.

[0111] Imaging and detection conditions: laser wavelength 337nm, laser frequency 1000Hz, scanning resolution 50μm, imaging range covering the entire tissue section, quality resolution ≥15000 (m / z 200), detection mode is reflection mode, m / z scanning range 100-1500, and the quality axis is calibrated using the external standard method (calibration materials: Angiotensin I, Glu-fibrinopeptide B).

[0112] In step C3, the MSI imaging data was analyzed using FlexImaging software. By setting a signal-to-noise ratio (S / N) ≥ 3.0, the signal peaks of the drug components were extracted to generate a pseudo-color image. ImageJ software was then used to quantitatively analyze the signal intensity of the imaging region. The signal intensity was positively correlated with the component concentration (calibration curve R). 2 ≥0.98).

[0113] The bioinformatics association analysis described in step D is as follows:

[0114] D1. Data Integration: Based on the qualitative and quantitative data and endogenous differential metabolite data obtained in step B and the MSI tissue distribution data obtained in step C, import them into Cytoscape software to construct a correlation network of "prototype drug components - secondary metabolites - endogenous differential metabolites".

[0115] D2. Target Association Analysis: Gastric cancer-related targets were retrieved from the GeneCards, OMIM, and DisGeNET databases. Potential targets corresponding to drug components were obtained from the SymMap and TCMSP databases. Venny software was used to screen drug-gastric cancer intersection targets. David 6.8 database was used to perform GO functional enrichment analysis (biological process BP, cellular component CC, molecular function MF) and KEGG pathway enrichment analysis on drug-gastric cancer intersection targets. The screening criterion was P<0.05.

[0116] D3. Screening of Core Active Ingredients: Core active ingredient groups are screened based on the following three core indicators:

[0117] D3.1 The concentration of the drug component in tumor tissue is ≥ 2.0 times the concentration in blood;

[0118] D3.2. The binding affinity (KD value) of the component to the core targets of gastric cancer (PRKACA, VEGFR-2, c-Met, ADRB2, NCOA2) is ≤1.0×10⁻⁶. -6 mol / L;

[0119] D3.3 The components can significantly regulate the expression of AKT, ERK1 / 2, PCNA or Caspase-3, which are related to the VEGF / VEGFR-2 or HGF / c-Met signaling pathway. The inhibition rate of PCNA, AKT and ERK1 / 2 protein expression is ≥30%, and the promotion rate of Caspase-3 protein expression is ≥30%.

[0120] In step D3, the binding affinity between the component and the target was detected using surface plasmon resonance (SPR) technology. The instrument used was a Biacore T200, the chip was a CM5, the running buffer was PBS-T (pH 7.4, containing 0.05% Tween-20), the flow rate was 30 μL / min, and the temperature was 25 °C.

[0121] In step D3, protein expression levels were detected using Western blotting. The specific steps are as follows:

[0122] Total protein was extracted from tumor tissue, and protein concentration was determined by BCA method. 30 μg of protein was loaded onto each well, followed by SDS-PAGE electrophoresis, membrane transfer, and blocking with 5% skim milk for 1 h. Primary antibody (PRKACA, VEGFR-2, etc., diluted 1:1000) was added and incubated overnight at 4°C. Secondary antibody (HRP-labeled, diluted 1:5000) was added and incubated at room temperature for 1 h. ECL chemiluminescence was performed, and the gray value of the bands was quantitatively analyzed using ImageJ software. The relative expression level was calculated with β-actin as an internal reference.

[0123] In step D3, the synergistic effect of the core active ingredient group was verified using the Chou-Talalay method. The co-existence index (CI) was calculated using CompuSyn software. CI < 1.0 indicates a synergistic effect, and CI < 0.8 indicates a significant synergistic effect.

[0124] The verification experiment used Zhi Pu Da Huang Tang as the research object.

[0125] I. Experimental Materials

[0126] Table 1 Experimental Materials

[0127]

[0128] II. Experimental Procedure

[0129] 2.1 Preparation of the test drug for Zhi Pu Da Huang Decoction

[0130] Weigh out 40g of Shenqu (medicated leaven), 30g of Xiangcha Cai (fragrant tea), 50g of Zhi Ke (fragrant orange peel), 40g of Zhi Dahuang (processed rhubarb), and 50g of Houpo (Magnolia officinalis) according to a weight ratio of 4:3:5:4:5. Place them in a Chinese medicine extraction tank, add 10 times the amount of purified water (1900mL), soak for 45min, heat to boiling, and decoct twice. The first decoction is for 1.5h, and the second decoction is for 1h. Combine the two decoctions, filter through a 400-mesh filter, and place the filtrate in a rotary evaporator. Concentrate under reduced pressure at a vacuum of -0.07MPa and a temperature of 55℃ to a crude drug concentration of 1.5g / mL. Then filter through a 0.22μm organic phase filter membrane, dispense, and store at 4℃ for later use.

[0131] 2.2 Poly-PK Multicomponent Metabolic Detection

[0132] (1) Study subjects: Twelve patients with stage II-III gastric cancer of qi stagnation and blood stasis type were selected (7 males and 5 females, aged 45-65 years, with a mean age of 56.3±5.2 years). At the same time, six healthy volunteers were selected (3 males and 3 females, aged 43-62 years, with a mean age of 54.5±4.8 years). Patients with liver and kidney dysfunction, cardiovascular and cerebrovascular diseases, other malignant tumors, and drug allergies were excluded.

[0133] (2) Sample collection: All subjects were given 200 mL of the test drug Zhipu Dahuang Decoction on an empty stomach. Urine samples of 5 mL were collected at 0 h before administration and at 1 h, 3 h, 6 h, 9 h, 12 h and 24 h after administration. The samples were placed in enzyme-free centrifuge tubes and frozen at -80℃.

[0134] (3) UPLC-Q-TOF / MS detection:

[0135] ① Sample pretreatment: Take 500 μL of urine sample, add an equal volume of acetonitrile, vortex for 3 min, centrifuge at 12000 r / min for 10 min, take the supernatant, filter through a 0.22 μm organic phase filter membrane, and wait for detection; the test drug sample is diluted 10 times and processed in the same way.

[0136] ② Detection parameters: The chromatographic column was a Waters ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.8 μm); the mobile phase A was acetonitrile, and the mobile phase B was 0.1% formic acid aqueous solution. The gradient elution program was: 0-5 min, 5%-20% A; 5-15 min, 20%-50% A; 15-25 min, 50%-80% A; 25-30 min, 80%-95% A; the flow rate was 0.3 mL / min; the column temperature was 35℃; the injection volume was 5 μL; the mass spectrometer used an ESI source, with simultaneous scanning in positive and negative ion modes, and a scan range of m / z 100-1500; the capillary voltage was 3.0 kV (positive ion) and 2.5 kV (negative ion); the cone voltage was 40 V; the desolvation gas temperature was 400℃; the desolvation gas flow rate was 800 L / h; the collision energy was 10-40 eV, and the data was acquired in MSE mode.

[0137] (4) Data processing: Raw data were imported into Progenesis QI software for peak alignment, noise reduction, and peak identification. The data were matched with the Metlin and HMDB databases and the secondary mass spectra of the standards (matching degree ≥85%). Five prototype components of the drug (acetic acid, naringenin, honokiol, aloe-emodin, and honokiol) and three secondary metabolites (emodin, norhonokiol, and naringenin) were identified. Quantification was performed using the external standard method, and the standard curve R was obtained. 2 All values ​​were ≥0.99, with a recovery rate of 85.2%-98.6% and RSD ≤5.0% (n=6).

[0138] (5) Screening for differentially expressed metabolites: Using healthy volunteers as controls, OPLS-DA analysis showed good separation between the two groups of samples (R0). 2 X=0.892, R 2 Y=0.915, Q 2 =0.876), 15 differentially expressed endogenous metabolites were screened out, including 8 upregulated metabolites (such as L-valine, oleic acid, and arachidonic acid) and 7 downregulated metabolites (such as citric acid, succinic acid, and glucose-6-phosphate). The specific results are shown in Table 2.

[0139] 2.3 Construction of a disease-symptom combined animal model and MSI imaging

[0140] (1) Cell culture: Human gastric cancer SGC-7901 cells were seeded in RPMI-1640 medium containing 10% fetal bovine serum, 100 U / mL penicillin and 100 μg / mL streptomycin, and cultured in a 37℃, 5% CO2 incubator. The cells were passaged every 2-3 days, and cells in the logarithmic growth phase were used for modeling.

[0141] (2) Animal model construction: After 3 days of acclimatization feeding, nude mice were subcutaneously injected with 1×10 70.2 mL of SGC-7901 cell suspension per animal was administered; 21 days post-inoculation, the xenograft reached a tumor volume of 268.5 ± 32.6 mm. 3 To induce qi stagnation and blood stasis syndrome: mice were intraperitoneally injected with 0.8 mg / kg of adrenaline daily, followed by an ice bath stimulation for 15 minutes, for 3 consecutive days. After model establishment, the high-shear viscosity (50 s) of whole blood in mice was measured. -1 The low shear viscosity (5s) is 5.82 ± 0.45 mPa·s. -1 The blood glucose level was 12.35±1.21 mPa·s, and the plasma viscosity was 1.86±0.15 mPa·s, which were significantly higher than those of normal nude mice (high shear 4.48±0.32 mPa·s, low shear 9.42±0.85 mPa·s, and plasma 1.55±0.12 mPa·s) (P<0.01). In addition, the mice showed symptoms such as lethargy and dark purple tongue, indicating successful modeling.

[0142] (3) Drug intervention: Nude mice that successfully modeled the disease were randomly divided into a drug administration group and a model control group, with 10 mice in each group. The drug administration group was given Zhipu Dahuang Decoction by gavage at a dose of 20g / kg / d, while the model control group was given an equal volume of physiological saline by gavage. The drug administration was carried out continuously for 8 weeks.

[0143] (4) MSI imaging detection:

[0144] ① Sample preparation: After administration, mice were anesthetized and euthanized. Tumor tissue, stomach tissue, liver and kidney were isolated, frozen sections (10μm) were prepared, mounted on conductive glass slides and stored at -20℃.

[0145] ② Matrix spraying: CHCA matrix solution (10mg / mL) was sprayed using a matrix sprayer at a spraying pressure of 0.3MPa and a spraying distance of 5cm. After each round of spraying, the solution was dried for 30s. A total of 5 rounds of spraying were performed.

[0146] ③ Imaging parameters: laser wavelength 337nm, laser frequency 1000Hz, scanning resolution 50μm, imaging range covering the entire tissue section, reflection mode, m / z scanning range 100-1500, external standard method for calibrating the mass axis.

[0147] ④ Data processing: The signal peaks of the drug components (S / N≥3.0) were extracted using FlexImaging software to generate pseudo-color images; Quantitative analysis using ImageJ software showed that honokiol, aloe-emodin, and acetic acid were significantly enriched in tumor tissue, with concentrations of 12.68±1.35 ng / mg, 8.95±0.92 ng / mg, and 15.32±1.56 ng / mg, respectively, which were 2.8 times, 2.3 times, and 3.1 times the blood concentrations, respectively. The specific results are shown in Table 3.

[0148] 2.4 Bioinformatics Association Analysis

[0149] (1) Screening of overlapping targets: 2867 gastric cancer-related targets were retrieved from databases such as GeneCards, 523 potential drug targets were obtained from databases such as SymMap, and 126 drug-gastric cancer overlapping targets were screened out using Venny 2.1.0.

[0150] (2) GO / KEGG enrichment analysis: David 6.8 database analysis showed that 126 overlapping targets were enriched in 186 GO-BP entries (such as cell proliferation regulation, apoptosis regulation, and angiogenesis regulation), 23 GO-CC entries (such as cell membrane, cytoplasm, and mitochondria), and 35 GO-MF entries (such as protein binding, kinase activity, and receptor binding); and enriched in 25 KEGG pathways (P<0.05), among which the core pathways were VEGF / VEGFR-2 signaling pathway, HGF / c-Met signaling pathway, and PI3K / AKT signaling pathway. The specific results are shown in Table 4.

[0151] (3) Screening of core active ingredient groups: combined with screening indicators (tumor enrichment concentration ≥ 2 times blood concentration, KD ≤ 1.0 × 10⁻⁶). -6 (mol / L, significantly regulates pathway protein expression), the core active ingredient group was screened out as honokiol, aloe-emodin, and acetic acid;

[0152] SPR analysis showed that the KD values ​​of all three substances, along with core targets such as PRKACA and VEGFR-2, were ≤5.0×10⁻⁶. -7 mol / L;

[0153] Western blot analysis showed that the combination of the three (weight ratio 2:1:3) significantly inhibited the expression of PCNA, AKT, and ERK1 / 2 proteins (inhibition rates of 42.3%, 38.5%, and 36.2%, respectively), and promoted the expression of Caspase-3 protein (promotion rate of 45.6%).

[0154] The Chou-Talalay method verification showed that the joint index CI of the three factors was 0.65 < 0.8, indicating a significant synergistic effect.

[0155] Subsequent CCK-8 assay confirmed that the inhibition rate of this core component group on the proliferation of human gastric cancer SGC-7901 cells was concentration-dependent, with an inhibition rate of 62.3% at a concentration of 200 μg / mL and an IC50 value of [missing value]. 50 The value was 108.2 μg / mL;

[0156] Flow cytometry analysis showed that after treatment with a concentration of 200 μg / mL for 48 h, the apoptosis rate reached 38.5%, which was significantly higher than that of the individual component treatment groups and the model control group (P < 0.01).

[0157] III. Experimental Results

[0158] Using the method of this invention, the core active ingredient group for anti-gastric cancer of qi stagnation and blood stasis type was successfully screened from Zhipu Dahuang Decoction: honokiol, aloe-emodin, and acetic acid. The synergistic effect was best when the weight ratio of the three was 2:1:3. It can inhibit the proliferation of gastric cancer cells and promote apoptosis by targeting PRKACA to regulate the VEGF / VEGFR-2 and HGF / c-Met signaling pathways, thereby exerting an anti-gastric cancer effect.

[0159] The specific experimental data are shown in the table below:

[0160] Table 2. Results of endogenous metabolite screening before and after drug administration in patients with gastric cancer of the Qi stagnation and blood stasis type.

[0161]

[0162] Table 3. Enrichment concentrations of drug components in different tissues (ng / mg, x±s, n=10)

[0163]

[0164] Table 4. Enrichment results of KEGG pathway at drug-gastric cancer intersection targets (first 10 targets)

[0165]

[0166] Table 5. Binding affinity of core active ingredient groups to core targets of gastric cancer (KD value, mol / L)

[0167]

[0168] Table 6. Effects of the core active ingredient group on the expression of proteins related to the VEGF / VEGFR-2 signaling pathway (x±s, n=3)

[0169]

[0170] 3.1 Results of synergistic effect verification of core active ingredient group:

[0171] The Chou-Talalay method was used to evaluate the synergistic effect of different combinations of magnolol, aloe-emodin, and acetic acid (1:1:1, 1:1:3, 2:1:2, 2:1:3, 3:1:2). The results showed that when the weight ratio of the three was 2:1:3, the synergistic index CI=0.65<0.8, indicating a significant synergistic effect.

[0172] Among other ratio combinations, 1:1:3 (CI=0.92) and 2:1:2 (CI=0.88) showed synergistic effects, but the synergistic effect was weaker than that of the 2:1:3 ratio.

[0173] No synergistic effect was observed in 1:1:1 (CI=1.05) and 3:1:2 (CI=1.12).

[0174] 3.2 Results of experiments on the anti-proliferation effects of the core active ingredient group on gastric cancer cells:

[0175] The inhibitory effect of the core active ingredient group (2:1:3) on the proliferation of human gastric cancer SGC-7901 cells was detected by the CCK-8 assay. The results showed that the inhibition rate of this ingredient group on SGC-7901 cells was concentration-dependent. The inhibition rate was 32.6% at a concentration of 50 μg / mL, 48.5% at a concentration of 100 μg / mL, and 62.3% at a concentration of 200 μg / mL. The IC50 value was [not specified in the original text]. 50 The value was 108.2 μg / mL.

[0176] 3.3 Experimental results of the core active ingredient group promoting apoptosis in gastric cancer cells:

[0177] Flow cytometry analysis showed that after 48 hours of treatment with the core active ingredient group (2:1:3, 200 μg / mL), the apoptosis rate of SGC-7901 cells was 38.5%, which was significantly higher than that of the groups treated alone with magnolol (22.3%), aloe-emodin (18.7%), acetic acid (12.5%), and the model control group (8.2%) (P < 0.01).

[0178] In summary, the core active ingredient group of magnolol, aloe-emodin, and acetic acid (weight ratio 2:1:3) screened in this invention can significantly inhibit the proliferation of gastric cancer cells and promote their apoptosis by targeting core targets such as PRKACA and VEGFR-2 and regulating the VEGF / VEGFR-2 and HGF / c-Met signaling pathways. Moreover, the three have a significant synergistic effect, which provides clear experimental evidence for the material basis of Zhipu Dahuang Decoction in treating gastric cancer of qi stagnation and blood stasis type, and also verifies the scientificity and reliability of this screening method.

Claims

1. A method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI, characterized in that, This method is based on the "formula-syndrome-disease" logic of traditional Chinese medicine, and integrates multi-component pharmacokinetic (Poly-PK) and mass spectrometry (MSI) technology. The specific steps are as follows: A. Pretreatment of Traditional Chinese Medicine Compound: Take the traditional Chinese medicine compound, and prepare the test drug through extraction and purification; B. Poly-PK multi-component metabolism assay: Urine samples were collected from subjects at multiple time points, and qualitative and quantitative data of the original drug component, secondary metabolites and endogenous metabolites of the test drug were detected using UPLC-QTOF / MS technology to screen for differential metabolites. C. Animal Model Construction and MSI Imaging: After constructing an animal model and administering drug intervention, biological samples are prepared; MSI imaging is performed on the biological samples to obtain MSI tissue distribution data; D. Bioinformatics association analysis: Integrate metabolic data, distribution data and target data to construct a multi-dimensional association network, clarify the mechanism of action of components through GO / KEGG enrichment analysis, and screen core active ingredient groups by combining multiple indicators.

2. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 1, characterized in that, The extraction process of the traditional Chinese medicine compound pretreatment described in step A is as follows: the traditional Chinese medicine compound is soaked in 8-12 times the amount of purified water for 30-60 minutes, then decocted 2-3 times for 1-2 hours each time. The decoctions are combined, filtered through a 400-mesh filter, and concentrated under reduced pressure using a rotary evaporator to a crude drug concentration of 1.0-2.0 g / mL. The solution is then filtered through a 0.22 μm organic phase filter membrane and stored at 4°C for later use. The reduced pressure concentration is specifically performed at a vacuum degree of -0.08 to -0.06 MPa and a temperature of 50-60°C.

3. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 1, characterized in that, The specific procedure for the Poly-PK multi-component metabolic assay described in step B is as follows: B1. Sample collection: Take 5 mL of urine samples from 0 h before administration and 1 h, 3 h, 6 h, 9 h, 12 h and 24 h after administration, respectively, place them in enzyme-free centrifuge tubes and freeze them in an ultra-low temperature freezer at -80℃. B2. UPLC-Q-TOF / MS detection: Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry was used to detect the test drug and urine samples to obtain qualitative and quantitative data of the drug's original chemical components, secondary metabolites and endogenous metabolites; B3. Screening of differential metabolites: The raw mass spectrometry data were imported into the software for preprocessing. Using healthy volunteers as controls, orthogonal partial least squares discriminant analysis was used to screen for endogenous differential metabolites before and after drug administration. The screening criteria were variable importance projection value >1.0, P<0.05 and Fold Change (FC) >2.0 or FC<0.

5.

4. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 3, characterized in that, In step B2, the specific detection conditions for UPLC-Q-TOF / MS detection are as follows: The UPLC detection conditions were as follows: the mobile phase consisted of acetonitrile phase A and 0.1% formic acid aqueous solution phase B; The gradient elution program was as follows: 0-5 min, 5%-20% A; 5-15 min, 20%-50% A; 15-25 min, 50%-80% A; 25-30 min, 80%-95% A; flow rate 0.3 mL / min. Column temperature 35℃; Injection volume: 5 μL; Mass spectrometry detection conditions: electrospray ionization (ESI) source, simultaneous scanning in positive and negative ion modes, scanning range m / z 100-1500; The capillary voltage for positive ions is 3.0 kV, and for negative ions it is 2.5 kV. Tapered hole voltage 40V; The temperature of the desolventizing gas is 400℃, and the flow rate of the desolventizing gas is 800L / h; Collision energy 10-40 eV, data collected using MSE mode.

5. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 1, characterized in that, The specific details of animal model construction and MSI imaging described in step C are as follows: C1. Animal model construction: After feeding mice, human gastric cancer SGC-7901 cells were injected and inducing qi stagnation and blood stasis syndrome until the model was successfully established. C2. Drug intervention and sample preparation: Mice that successfully developed the model were randomly divided into a drug administration group and a model control group. The drug administration group was administered the test drug by gavage, while the model control group was administered an equal volume of physiological saline by gavage. The drugs were administered continuously for 8 weeks, once a day. After the drug administration was completed, the mice were euthanized and dissected to obtain tumor tissue, stomach tissue, liver and kidney. The surface bloodstains were rinsed with physiological saline, the moisture was absorbed with filter paper, and the mice were frozen at -80℃ for 30 min. The tissue sections were then cut into 10 μm thick sections using a cryostat and mounted on conductive glass slides. The sections were stored at -20℃ for later use. C3. MSI Imaging Detection: MSI tissue distribution data were obtained by detecting tissue sections using matrix-assisted laser desorption / ionization time-of-flight mass spectrometry imaging technology.

6. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 5, characterized in that, The specific details of the MSI imaging detection described in step C3 are as follows: Matrix preparation: α-cyano-4-hydroxycinnamic acid was dissolved in acetonitrile-0.1% formic acid aqueous solution to prepare a matrix solution of 10 mg / mL; the matrix was uniformly sprayed onto the surface of the tissue section using a matrix sprayer at a spray pressure of 0.3 MPa and a spray distance of 5 cm. After each spraying, the tissue section was dried at room temperature for 30 s, for a total of 5 spraying cycles. Imaging and detection conditions: laser wavelength 337nm, laser frequency 1000Hz, scanning resolution 50μm, imaging range covering the entire tissue section, quality resolution ≥15000 (m / z 200), detection mode is reflection mode, m / z scanning range 100-1500, and the quality axis is calibrated using the external standard method.

7. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 1, characterized in that, The bioinformatics association analysis described in step D is as follows: D1. Data Integration: Based on the qualitative and quantitative data obtained in step B, the data on endogenous differential metabolites, and the MSI tissue distribution data obtained in step C, construct a correlation network of "prototype drug components - secondary metabolites - endogenous differential metabolites". D2. Target association analysis: Gastric cancer-related targets were retrieved from the database, and potential targets corresponding to drug components were obtained from the database. Drug-gastric cancer intersection targets were screened. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed on drug-gastric cancer intersection targets. The screening criterion was P<0.

05. D3. Screening of Core Active Ingredients: Core active ingredient groups are screened based on the following three core indicators: D3.1 The concentration of the drug component in tumor tissue is ≥ 2.0 times the concentration in blood; D3.2, The binding affinity of the component to the core target of gastric cancer is ≤1.0×10⁻⁶. -6 mol / L; D3.3 The components can significantly regulate the expression of AKT, ERK1 / 2, PCNA or Caspase-3, which are related to the VEGF / VEGFR-2 or HGF / c-Met signaling pathway. The inhibition rate of PCNA, AKT and ERK1 / 2 protein expression is ≥30%, and the promotion rate of Caspase-3 protein expression is ≥30%.

8. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 1, characterized in that: The core targets for gastric cancer described in step D3.2 include at least PRKACA, VEGFR-2, c-Met, ADRB2, and NCOA2.

9. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 1, characterized in that, The traditional Chinese medicine compound mentioned in step A is characterized in that the traditional Chinese medicine compound is a traditional Chinese medicine compound for gastric cancer of qi stagnation and blood stasis type, specifically including 3-5 parts of Shenqu (medicated leaven), 2-4 parts of Xiangchacai (fragrant tea), 3-6 parts of Zhike (immature bitter orange), 2-5 parts of prepared Dahuang (rhubarb), and 3-6 parts of Houpo (Magnolia officinalis).

10. The method for screening active ingredients of traditional Chinese medicine for gastric cancer using a combination of Poly-PK and MSI according to claim 9, characterized in that: It includes 4 parts Shenqu (medicated leaven), 3 parts Xiangcha Cai (fragrant tea), 5 parts Zhi Ke (tender orange peel), 4 parts Zhi Da Huang (processed rhubarb), and 5 parts Hou Po (Magnolia officinalis).

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