Metabolic marker for gastric cancer diagnosis or monitoring and screening method and application thereof

Abstract

The invention provides a metabolic marker for gastric cancer diagnosis or monitoring and a screening method and application thereof. Wherein the metabolic marker for diagnosing or monitoring the gastric cancer comprises at least one of taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl) propionic acid, indole-3-propionic acid, cholic acid, glycine ursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine and the like. The serum metabonomics research is carried out by adopting a large-scale clinical sample and a gastric cancer serum specific metabolome database, and a large number of specific metabolites related to diseases are obtained. Furthermore, the isotope internal standard corresponding to the metabolite is used for accurate qualitation and quantification, and a serum metabolism marker with high sensitivity and good specificity is searched for gastric cancer diagnosis. The 85 metabolites are adopted for gastric cancer diagnosis and analysis, and the method is simple, convenient, rapid, economical, relatively noninvasive and easy to popularize.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and in particular relates to a metabolic marker for gastric cancer diagnosis or monitoring, and a screening method and application thereof. Background Art

[0002] Gastric cancer is a common malignant tumor of the digestive system with a high mortality rate. According to the latest global cancer data statistics report for 2022 released by the World Health Organization's International Agency for Research on Cancer, gastric cancer is the fifth most common malignant tumor in the world and ranks fourth among the causes of cancer-related deaths worldwide.

[0003] Most gastric cancers will go through a series of precancerous lesions, including chronic gastritis, chronic atrophic gastritis, intestinal metaplasia and dysplasia, and eventually develop into gastric cancer. The risk of gastric cancer in patients with precancerous lesions is nearly three times that of patients without precancerous lesions. Early diagnosis of gastric precancerous lesions is of great significance for the prevention of gastric cancer. In recent years, under the combined effect of various factors such as the enhancement of people's health awareness and the improvement of social medical conditions, the overall incidence of gastric cancer has declined, but the mortality rate of gastric cancer remains high. Among them, the low diagnosis rate of early gastric cancer (E gastric cancer) is an important factor leading to the high mortality rate of gastric cancer. Due to the lack of awareness of early screening for tumors, the symptoms of precancerous and early cancer are not obvious, and the technical means of early screening for tumors are not perfect, most patients have developed to the advanced stage of gastric cancer when they are first diagnosed. The treatment of patients with advanced gastric cancer (A gastric cancer) is often ineffective and the prognosis is poor. The survey shows that the five-year survival rate of patients with E gastric cancer after surgery is over 90%, while the five-year survival rate of patients with A gastric cancer after surgery is only about 30%. Therefore, the timely detection and treatment of E gastric cancer must be taken seriously. However, the diagnosis of early gastric cancer is still mainly based on imaging, endoscopy, and pathological histology, all of which have certain limitations. Diagnosis based on imaging cannot accurately observe changes at the cellular level and is prone to miss key biological features; and invasive detection methods based on endoscopic biopsy, although highly accurate, bring pain to patients and have high requirements for physicians who implement this technology; non-invasive examination methods based on conventional serum tumor markers, such as gastric cancer markers carcinoembryonic antigen CEA, CA72-4, etc., have low diagnostic sensitivity and specificity, and it is difficult to become a specific marker for early screening of gastric cancer. Therefore, finding tumor markers with high sensitivity and specificity is of great significance for early diagnosis and treatment of gastric cancer patients. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a metabolic marker for gastric cancer diagnosis or monitoring, a screening method and application thereof, and screened out 85 markers with high sensitivity and specificity. Using at least one of the 85 markers for diagnosing gastric cancer is convenient and quick.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a metabolic marker for diagnosing or monitoring gastric cancer, wherein the metabolic marker is at least selected from taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine , triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenyl Guanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±) 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinolinepropionic acid, hexanoylcarnitine, carnitine C7:0, irbesartan, biliverdin, feces At least one of choline, trans-3-hydroxycotinine, propofol glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, N6-succinyladenosine, L-valine, L-isoleucine, L-threonine, L-glutamine, citric acid, 1-methylnicotinamide, creatine, L-lysine, nornicotine, ornithine, pyridoxolamine, palmitic acid, arachidonic acid, LysoPC (18:3), and LysoPC (20:3).

[0007] In the ROC curve evaluation method, the area under the ROC curve AUC value of a single metabolite marker in the present invention is 0.702 to 0.901. The performance of multiple metabolite groups is significantly better than that of a single metabolite, and the area under the ROC curve AUC value can reach up to 0.989, which can effectively diagnose gastric cancer patients.

[0008] In a second aspect, the present invention provides an application of the metabolic marker for diagnosing or monitoring gastric cancer in preparing a metabolite database, reagent product or kit for diagnosing or monitoring gastric cancer.

[0009] In a third aspect, the present invention provides a reagent product or a kit, comprising the standard substance of the metabolic marker for diagnosing or monitoring gastric cancer.

[0010] Preferably, the method further comprises extracting a solvent and / or an internal standard for enriching the metabolite marker.

[0011] In a fourth aspect, the present invention provides a method for screening metabolic markers for diagnosing or monitoring gastric cancer, comprising the following steps:

[0012] Obtaining test samples;

[0013] Spectral data were obtained by liquid chromatography tandem mass spectrometry detection analysis;

[0014] Construct a gastric cancer serum-specific metabolite database;

[0015] The machine learning random forest algorithm was used to analyze the metabolite integral data between gastric cancer group samples and non-gastric cancer group samples to screen out potential gastric cancer markers. The screened metabolites were identified as diagnostic markers for gastric cancer;

[0016] The metabolites were analyzed and their accuracy was verified by standards to determine the metabolic markers.

[0017] Preferably, in liquid chromatography tandem mass spectrometry, the mass spectrometry is selected from quadrupole mass spectrometry, time-of-flight mass spectrometry, ion hydrazine mass spectrometry or high-resolution orbital hydrazine mass spectrometry.

[0018] Preferably, in liquid chromatography tandem mass spectrometry, the mass spectrometry conditions and setting of the mass spectrometry qualitative and quantitative detection mode include: selecting an electrospray ion source, selecting an ion scanning mode according to the response of the target compound to be detected; selecting a multiple reaction monitoring method, and setting multiple reaction monitoring mode parameters.

[0019] Preferably, the gastric cancer sample group includes gastric cancer samples of different stages.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] (1) The present invention uses large-scale clinical samples and a gastric cancer serum-specific metabolome database to conduct serum metabolomics research and obtain a large number of specific metabolites related to the disease. The isotope internal standards corresponding to the above metabolites are further used for precise qualitative and quantitative analysis to find serum metabolite markers with high sensitivity and good specificity for gastric cancer diagnosis. The use of the above 85 metabolites for gastric cancer diagnosis and analysis is simple, rapid, economical and relatively non-invasive, and can be easily promoted widely. DETAILED DESCRIPTION

[0022] The present invention is further described in detail below in conjunction with specific embodiments so that those skilled in the art can understand the present invention more clearly.

[0023] The following embodiments are only used to illustrate the present invention, but are not limited to the scope of the present invention. Based on the specific embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work belong to the protection scope of the present invention.

[0024] In the examples of the present invention, unless otherwise specified, all raw material components are commercially available products well known to those skilled in the art; in the examples of the present invention, unless otherwise specified, the technical means used are conventional means well known to those skilled in the art. The key instrument information is shown in Table 1 below:

[0025] Table 1 Experimental instrument information

[0026] name model brand HPLC-MS / MS QTRAP 6500+ SCIEX HPLC-TOF-MS TripleTOF 6600 SCIEX Centrifuge 5424R Eppendorf Centrifugal Concentrator CentriVap LABCONCO Vortex mixer VORTEX-5 Kyllin-Be11

[0027] Example 1 Construction of Gastric Cancer Serum Specific Metabolite Ion Pair Database

[0028] S1, sample collection

[0029] After obtaining the patients' consent, this study collected peripheral venous blood serum from 50 healthy people, 95 patients with benign gastric diseases, 28 patients with stage I gastric cancer (early cancer), and 64 patients with stage II-IV gastric cancer (advanced cancer) from Hubei Provincial People's Hospital. Among them, healthy controls are healthy people without gastric diseases after physical examination; benign gastric diseases are patients with diseases including chronic gastritis, erosive gastritis, gastric ulcers, gastric polyps, benign gastric tumors, etc. after hospital examination; the diagnostic criteria for gastric cancer patients are confirmed by postoperative pathology. All samples had no history of other malignant tumors, no other major systemic diseases, and no history of chronic diseases with long-term medication. Samples from healthy people and patients with benign diseases were included as the non-gastric cancer group, and samples from patients with stage I gastric cancer were included as the gastric cancer group. The basic information of these research subjects is shown in Table 2:

[0030] Table 2 Basic information of research subjects in Example 1

[0031]

[0032]

[0033] Blood was collected in the early morning on an empty stomach. All serum samples were centrifuged and stored in a -80°C refrigerator. During the study, serum samples were taken out and thawed for subsequent analysis.

[0034] S2, sample pretreatment

[0035] Take out the samples from the -80℃ freezer and thaw on ice until there is no ice in each sample (subsequent operations are required to be performed on ice). After the sample is thawed, vortex for 10 seconds to mix, take 50μL of the sample and add it to the corresponding numbered centrifuge tube; add 300μL of pure methanol internal standard extract; vortex for 5 minutes, let it stand for 24 hours, and then centrifuge at 12000r / min and 4℃ for 10 minutes; draw 270μL of the supernatant and concentrate it for 24 hours; add 100μL of reconstitution solution (the volume ratio of acetonitrile and water is 1:1), and take 50μL of each sample to mix into the mix detection solution.

[0036] S3, database construction process

[0037] Liquid chromatography tandem mass spectrometry (LC-MS / MS) realizes the entire process from material separation by chromatography to material identification by mass spectrometry. Based on the broad targeted metabolomics approach, the above-mentioned mix test solution was used to establish a gastric cancer serum-specific metabolite ion pair database. The metabolite ion pairs mainly come from the following four sources: MIM-EPI acquisition, TOF acquisition, Myvi standard database, and gastric cancer literature metabolites. Among them, a total of 1,300 ion pairs were collected through the MIM-EPI detection mode, a total of 1,400 ion pairs were collected through the TOF detection mode, a total of 483 ion pairs were collected from the Myvi standard database, and 131 metabolites related to gastric cancer literature were collected. The ion pair information from all the above sources was summarized and deduplicated, and finally 3,203 gastric cancer serum-specific metabolite ion pairs were obtained.

[0038] Example 2

[0039] S1, sample collection

[0040] After obtaining the patients' consent, this study collected peripheral venous blood serum from 50 healthy people, 95 patients with benign gastric diseases, 28 patients with stage I gastric cancer (early cancer), and 64 patients with stage II-IV gastric cancer (advanced cancer) in Hubei People's Hospital. Among them, healthy controls are healthy people without gastric diseases after physical examination; benign gastric diseases are patients with diseases including chronic gastritis, erosive gastritis, gastric ulcers, gastric polyps, benign gastric tumors, etc. after hospital examination; the diagnostic criteria for gastric cancer patients are confirmed by postoperative pathology. All samples had no history of other malignant tumors, no other major systemic diseases, and no history of chronic diseases with long-term medication. Samples of healthy people and patients with benign diseases were included as the non-gastric cancer group, and samples of patients with stage I gastric cancer were included as the gastric cancer group. The basic information of these research subjects is shown in Table 2.

[0041] Table 2 Basic information of research subjects in Example 2

[0042]

[0043] Blood was collected in the early morning on an empty stomach. All serum samples were centrifuged and stored in a -80°C refrigerator. During the study, serum samples were taken out and thawed for subsequent analysis.

[0044] S2, serum extensive targeted metabolomics analysis

[0045] (1) Sample pretreatment

[0046] Take out the sample collected in step S1 from the -80℃ refrigerator, thaw on ice until there is no ice in the sample (subsequent operations are required to be performed on ice); after the sample is thawed, vortex for 10s to mix, take 50μL of the sample and add it to the corresponding numbered centrifuge tube; add 300μL of pure methanol internal standard extract (containing 100ppm concentration of L-phenylalanine internal standard); vortex for 5min, let stand for 24h, and then centrifuge at 12000r / min and 4℃ for 10min; take 270μL of the supernatant and concentrate for 24h; then add 100μL of a reconstituted solution composed of acetonitrile and water in a volume ratio of 1:1 for LC-MS / MS analysis. Take 20μL of each sample and mix it into a quality control sample (QC), and collect it once every 15 samples.

[0047] (2) Sample metabolite detection and analysis

[0048] Table 3 Experimental reagents

[0049] Compound CAS Number brand Methanol 67-56-1 Merck Acetonitrile 75-05-8 Merck Acetic acid 64-19-7 Aladdin L-Phenylalanine 63-91-2 isoreag

[0050] The liquid chromatography conditions were determined as follows:

[0051] Chromatographic column: Waters ACQUITY UPLC HSS T3 C18 1.8μm, 2.1mm*100mm; column temperature is 40℃; injection volume is 2μL.

[0052] Mobile phase: Phase A is an aqueous solution containing 0.1% acetic acid, and phase B is an acetonitrile solution containing 0.1% acetic acid. The elution gradient program is: 0 min, the volume ratio of phase A to phase B is 95:5; 11.0 min, the volume ratio of phase A to phase B is 10:90; 12.0 min, the volume ratio of phase A to phase B is 10:90; 12.1 min, the volume ratio of phase A to phase B is 95:5; 14.0 min, the volume ratio of phase A to phase B is 95:5 V / V. Flow rate 0.4 mL / min.

[0053] The mass spectrometry conditions were determined as follows:

[0054] The electrospray ionization (ESI) temperature was 500°C, the mass spectrometer voltage was 5500 V (positive) or -4500 V (negative), the ion source gas I (GS I) was 55 psi, the gas II (GS II) was 60 psi, the curtain gas (CUR) was 25 psi, and the collision-activated dissociation (CAD) parameter was set to high.

[0055] In a triple quadrupole (Qtrap), each ion pair is scanned and detected in MRM mode according to the optimized declustering potential (DP) and collision energy (CE).

[0056] The samples were analyzed and tested according to the determined liquid chromatography and mass spectrometry conditions: 20% of the samples from the non-gastric cancer group and the gastric cancer group were randomly selected, and the metabolomics method of enhanced ion scanning mass spectrometry (MIM-EPI) and time-of-flight mass spectrometry (TOF) combined with multi-reflection monitoring acquisition mode was used, and the local standard database was integrated to construct the gastric cancer plasma metabolite database.

[0057] The collected plasma samples were analyzed using liquid chromatography-mass spectrometry metabolomics methods and the constructed gastric cancer plasma metabolite database to obtain the original mass spectrometry data of each plasma sample.

[0058] (3) Spectral peak area preprocessing and integration

[0059] Based on the constructed database of gastric cancer serum-specific metabolites, the metabolites of the samples were qualitatively and quantitatively analyzed by mass spectrometry. Metabolites of different molecular weights can be separated by liquid chromatography. The characteristic ions of each substance were screened out by the multiple reaction monitoring mode (MRM) of the triple quadrupole, and the signal intensity (CPS) of the characteristic ions was obtained in the detector. The mass spectrometry file of the sample was opened with MultiQuant3.0.3 software to integrate and correct the chromatographic peaks. The peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. The S / N>5 was set, and the peaks with retention time offset not exceeding 0.2min were retained. Finally, all chromatographic peak area integral data were exported and saved.

[0060] (4) Experimental quality control

[0061] By overlapping and analyzing the total ion current graphs of mass spectrometry analysis of different quality control QC samples, the repeatability of metabolite extraction and detection, i.e., technical repetition, can be determined. The high stability of the instrument provides an important guarantee for the repeatability and reliability of the data. The CV value, or coefficient of variation, is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of data dispersion. The empirical cumulative distribution function (ECDF) can be used to analyze the frequency of CV of substances less than the reference value. The higher the proportion of substances with low CV values ​​in QC samples, the more stable the experimental data: the proportion of substances with CV values ​​less than 0.5 in QC samples is higher than 85%, indicating that the experimental data is relatively stable; the proportion of substances with CV values ​​less than 0.3 in QC samples is higher than 75%, indicating that the experimental data is very stable. At the same time, the changes in the CV value of the internal standard of L-phenylalanine during the detection process were monitored. The change in the CV value of the internal standard was less than 20%, indicating that the instrument had good stability during the detection process.

[0062] (5) Data processing and analysis

[0063] The peak area integration data were used to perform differential metabolite analysis between the two groups, and FDR < 0.05, FC > 1.5 or FC < 0.67 were set as the criteria for significant differences, and differential metabolites were screened as candidate metabolite markers for diagnosing gastric cancer. At the same time, the machine learning random forest (RF) algorithm was used to analyze the metabolite integration data between the two groups, and 2 / 3 of the gastric cancer patient samples and control serum sample data were used as training sets, and 1 / 3 as test sets. Decision tree modeling was performed on the training set, and then the predictions of multiple decision trees were combined. The final prediction results were obtained by scoring. This metabolite model can effectively diagnose gastric cancer patients. The above model was verified by the test set samples, and the screened metabolites became candidate metabolite markers. The union of the metabolites screened by the inter-group difference analysis and the metabolites screened by machine learning was used as a set of candidate metabolite markers for diagnosing gastric cancer.

[0064] (6) Serum metabolite profiling

[0065] The potential gastric cancer metabolite markers screened by the above analysis were inferred based on their retention time, primary and secondary mass spectra, and their molecular weight and molecular formula were compared with the spectrum information in the metabolite spectrum database to qualitatively identify the metabolites. Finally, the structure of the metabolite marker was verified by purchasing standard products and comparing their molecular weight, chromatographic retention time and corresponding multi-stage MS fragmentation spectrum.

[0066] In this example, 85 metabolites were screened to accurately distinguish between gastric cancer and non-gastric cancer patients. The specific information of the 85 metabolites and the diagnostic effect of individual metabolites on gastric cancer are shown in the following table:

[0067] Table 4 Relevant information of 85 potential gastric cancer markers screened in Example 1

[0068]

[0069]

[0070]

[0071] These metabolites can be used alone or as a combination.

[0072] Example 3: Establishment of a gastric cancer diagnostic model using serum targeted metabolomics

[0073] S1, sample collection

[0074] After obtaining the patients' consent, this study collected peripheral venous serum from 50 healthy people, 74 patients with benign diseases, and 102 patients with stage I gastric cancer at Beijing Friendship Hospital. Among them, the diagnostic criteria for gastric cancer patients were confirmed by postoperative pathology; the samples of the non-gastric cancer group included healthy people without gastric diseases after physical examination and patients with benign gastric diseases including chronic gastritis, erosive gastritis, gastric ulcer, gastric polyps, benign gastric tumors, etc. after hospital examination. All gastric cancer patients and non-gastric cancer group samples had no history of other malignant tumors, no other major systemic diseases, and no history of chronic diseases with long-term medication. The samples of healthy people and patients with benign diseases were included as the non-gastric cancer group, and the samples of patients with stage I gastric cancer were included as the gastric cancer group.

[0075] The basic information of these research subjects is shown in Table 5:

[0076] Table 5 Basic information of research subjects in Example 3

[0077]

[0078] Blood was collected in the early morning on an empty stomach. All serum samples were centrifuged and stored in a -80°C refrigerator. During the study, serum samples were taken out and thawed for subsequent analysis.

[0079] S2, sample metabolite detection and analysis

[0080] The main experimental reagents in this step are as follows:

[0081] Table 6 Main experimental reagents

[0082] Compound CAS Number brand Methanol 67-56-1 Merck Acetonitrile 75-05-8 Merck Acetic acid 64-19-7 Aladdin

[0083] (1) Sample pretreatment

[0084] Take out the sample collected in step S1 from the -80℃ refrigerator, thaw on ice until there is no ice in the sample (subsequent operations are required to be performed on ice); after the sample is thawed, vortex for 10s to mix; take 50μL of the project sample and add 150μL of the extract (the extract contains an isotope internal standard with a concentration of 100ppm), vortex for 3min, centrifuge at 12000rpm and 4℃ for 10min, and let stand overnight at -20℃ refrigerator; centrifuge at 12000rpm and 4℃ for 5min, take 170μL of the supernatant, transfer it to a 96-well plate in sequence, seal the plate after protein precipitation for LC-MS / MS analysis. Take 20μL of each sample and mix it into a quality control sample (QC), and collect it once every 15 samples.

[0085] (2) Determine the test conditions and conduct the test

[0086] In view of the differences in the properties of metabolite markers, targeted quantitative detection uses two methods, T3 column and Amide column, to separate metabolites to ensure the accuracy of metabolite quantification.

[0087] Determine the T3 column liquid chromatography conditions:

[0088] Column: Waters ACQUITY UPLC HSS T3 C18 1.8 μm,

[0089] 2.1mm*100mm; column temperature 40℃; injection volume 2μL.

[0090] Mobile phase: Phase A is 0.1% acetic acid solution, and phase B is 0.1% acetic acid in acetonitrile solution; elution gradient program: 0 min, the volume ratio of phase A to phase B is 95:5; 11.0 min, the volume ratio of phase A to phase B is 10:90; 12.0 min, the volume ratio of phase A to phase B is 10:90; 12.1 min, the volume ratio of phase A to phase B is 95:5; 14.0 min, the volume ratio of phase A to phase B is 95:5 V / V. Flow rate 0.4 mL / min.

[0091] Amide column HPLC conditions:

[0092] Column: Waters ACQUITY UPLC BEH Amide 1.7 μm,

[0093] 2.1mm*100mm; column temperature 40℃; injection volume 2μL.

[0094] Mobile phase: Phase A is an aqueous solution containing 20 mM ammonium formate and 0.4% ammonia water, and phase B is pure acetonitrile; elution gradient program: 0 min, the volume ratio of phase A to phase B is 10:90; 9.0 min, the volume ratio of phase A to phase B is 40:60; 10.0 min, the volume ratio of phase A to phase B is 60:40; 11.0 min, the volume ratio of phase A to phase B is 60:40; 11.1 min, the volume ratio of phase A to phase B is 10:90; 15.0 min, the volume ratio of phase A to phase B is 10:90. Flow rate 0.4 mL / min.

[0095] Mass spectrometry conditions: The mass spectrometry acquisition conditions of the T3 column and the Amide column were the same, mainly including: electrospray ionization (ESI) temperature of 500°C, mass spectrometry voltage of 5500V (positive), -4500V (negative), ion source gas I (GS I) 55psi, gas II (GS II) 60psi, curtain gas (curtain gas, CUR) 25psi, collision-activated dissociation (CAD) parameters set to high. In the triple quadrupole (Qtrap), each ion pair was scanned and detected in MRM mode according to the optimized declustering potential (DP) and collision energy (CE).

[0096] (3) Spectral peak area preprocessing and integration

[0097] MultiQuant 3.0.3 software was used to process the mass spectrometry data. The retention time and peak shape information of the standard were referred to, and the mass spectrometry peaks detected in different samples were integrated and calibrated to ensure the accuracy of qualitative and quantitative analysis.

[0098] All samples were qualitatively and quantitatively analyzed. The peak area (Area) of each chromatographic peak represented the relative content of the corresponding substance. Substituting it into the linear equation and calculation formula, the qualitative and quantitative analysis results of the analytes in all samples were finally obtained.

[0099] (4) Calculation of metabolite concentration

[0100] Prepare standard solutions of different concentrations of 0.01ng / mL, 0.05ng / mL, 0.1ng / mL, 0.5ng / mL, 1ng / mL, 5ng / mL, 10ng / mL, 50ng / mL, 100ng / mL, 200ng / mL, and 500ng / mL, and obtain the mass spectrometry peak intensity data of the corresponding quantitative signals of each concentration standard; use the external standard and internal standard concentration ratio (Concentration Ratio) of the corresponding metabolite as the horizontal axis and the external standard and internal standard peak area ratio (Area Ratio) as the vertical axis to draw the standard curve of different substances. Substitute the integrated peak area ratio of all samples detected into the linear equation of the standard curve for calculation, and further substitute it into the calculation formula for calculation. The dilution factor in MultiQuant 3.0.3 is set to 3, and the concentration value (ng / mL) obtained by substituting the integrated peak area ratio in the final sample into the standard curve is the content data of the substance in the actual sample.

[0101] (5) Experimental quality control

[0102] By overlapping and analyzing the total ion current graphs of mass spectrometry analysis of different quality control QC samples, the repeatability of metabolite extraction and detection, i.e., technical repetition, can be determined. The high stability of the instrument provides an important guarantee for the repeatability and reliability of the data. The CV value, or coefficient of variation, is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of data dispersion. The empirical cumulative distribution function (ECDF) can be used to analyze the frequency of CV of substances less than the reference value. The higher the proportion of substances with low CV values ​​in QC samples, the more stable the experimental data: the CV value of all substances in the QC samples is less than 0.3, indicating that the experimental data is relatively stable; the proportion of substances with CV values ​​less than 0.2 in the QC samples is higher than 90, indicating that the experimental data is very stable. At the same time, the changes in the CV value of the isotope internal standard during the detection process are monitored. The change in the CV value of the internal standard is less than 20%, indicating that the instrument has good stability during the detection process.

[0103] (6) Data processing and analysis

[0104] The metabolite concentrations between the gastric cancer patient group and the non-gastric cancer group were analyzed for significant differences, and FDR < 0.05, FC > 1.5 or FC < 0.67 were set as the significant difference criteria for differential metabolite screening. The differential metabolites screened were classified using a binary logistic regression algorithm to obtain the optimal classification metabolite combination and obtain a gastric cancer diagnostic model. The results of the change folds of individual metabolite markers are shown in Table 7:

[0105] Table 7 Metabolite change folds in gastric cancer patients vs non-gastric cancer patients

[0106]

[0107]

[0108]

[0109] The diagnostic model contains the following 85 metabolites: taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12 :0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, cocoa alkali, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinoline propionic acid, hexanoylcarnitine, carnitine C7:0, irbesartan, biliverdin, fecal bile 1-Hydroxycotinine, trans-3-hydroxycotinine, propofol glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, N6-succinyladenosine, L-valine, L-isoleucine, L-threonine, L-glutamine, citric acid, 1-methylnicotinamide, creatine, L-lysine, nornicotine, ornithine, pyridoxolamine, palmitic acid, arachidonic acid, LysoPC(18:3), LysoPC(20:3).

[0110] These 85 differential metabolites have strong ability to diagnose and distinguish gastric cancer and non-gastric cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance; when these 85 differential metabolites are used in combination for diagnosis, the AUC is further improved, and the AUC of 85 combined for diagnosing gastric cancer is 0.989. The results of single metabolite markers for gastric cancer diagnosis are shown in Table 8:

[0111] Table 8 AUC values ​​of individual metabolites for gastric cancer diagnosis

[0112]

[0113]

[0114]

[0115] Example 4: Construction of a gastric cancer diagnostic model using two serum metabolic markers This example has the same research subjects and detection and analysis methods as Example 3, except that any two serum metabolic markers, such as taurine and azelaic acid, or uridine and hypoxanthine, or L-tryptophan and hippuric acid, are used in the binary logistic regression modeling in step (6).

[0116] Any two differential metabolites have a strong ability to diagnose and distinguish gastric cancer and non-gastric cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance. Among them, the statistical data of the combined diagnosis of two metabolite markers are as follows: the AUC of taurine and azelaic acid combined to diagnose gastric cancer is 0.913; the AUC of uridine and hypoxanthine combined to diagnose gastric cancer is 0.915; the AUC of L-tryptophan and hippuric acid combined to diagnose gastric cancer is 0.912.

[0117] Example 5: Construction of a gastric cancer diagnostic model using 10 serum metabolic markers This example has the same research subjects and detection and analysis methods as Example 3, except that any 10 serum metabolic markers are used in the binary logistic regression modeling in step (6), such as taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid and glycoursodeoxycholic acid, or chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid and sphingosine.

[0118] Any 10 differential metabolites have a strong ability to diagnose and distinguish gastric cancer and non-gastric cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance. Among them, the statistical data of the combined diagnosis of 10 metabolic markers are as follows: the AUC value of taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid and glycoursodeoxycholic acid combined for the diagnosis of gastric cancer is 0.922; the AUC value of chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid and sphingosine combined for the diagnosis of gastric cancer is 0.913; the AUC value of N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanedioic acid and glycohyodeoxycholic acid combined for the diagnosis of gastric cancer is 0.920.

[0119] Example 6: Construction of a gastric cancer diagnostic model using 20 serum metabolic markers This example has the same research subjects and detection and analysis methods as Example 3, except that any 20 serum metabolic markers are used in the binary logistic regression modeling in step (6), such as taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid and sphingosine, or N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanediol, glycohyodeoxycholic acid acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z,9E,11Z,14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine and nicotinamide, or caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,1 3-Trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE(18:0 / 0:0), valsartan, and 2-quinolinepropionic acid.

[0120] Any 20 differential metabolites have strong ability to diagnose and distinguish gastric cancer and non-gastric cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance. The statistical data of the combined diagnosis of 20 metabolite markers are as follows:

[0121] The AUC of aminoethanesulfonic acid / taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid and sphingosine combined for the diagnosis of gastric cancer was 0.941.

[0122] The AUC of phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanedioic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine and nicotinamide combined for the diagnosis of gastric cancer was 0.939.

[0123] The AUC of caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxyoctadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androstenone glucuronide, LPE(18:0 / 0:0), valsartan and 2-quinolinepropionic acid for the diagnosis of gastric cancer was 0.938.

[0124] Example 7: Construction of a gastric cancer diagnostic model using 30 serum metabolic markers This example has the same research subjects and detection and analysis methods as Example 3, except that any 30 serum metabolic markers are used in the binary logistic regression modeling in step (6), such as taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanediolic acid, and glycohyodeoxycholic acid. Acid, or 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfuric acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,1 3-Trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE(18:0 / 0:0), valsartan, and 2-quinolinepropionic acid.

[0125] Any 30 differential metabolites have a strong ability to diagnose and distinguish gastric cancer patients from non-gastric cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance. The statistical data of the combined diagnosis of 30 metabolite markers are as follows:

[0126] The AUC of taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanedioic acid and glycohyodeoxycholic acid combined for the diagnosis of gastric cancer was 0.942.

[0127] 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfuric acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecanoic acid The AUC of the combined diagnosis of gastric cancer of enoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfate, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan and 2-quinolinepropionic acid was 0.968.

[0128] Example 8: Construction of a gastric cancer diagnostic model using 50 serum metabolic markers This example has the same research subjects and detection and analysis methods as Example 3, except that any 50 serum metabolic markers are used in the binary logistic regression modeling in step (6), such as taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, bile acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylethyl Acyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanedioic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z,9E,11Z,14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid , carnitine C10:0 and 3-hydroxytetradecanoic acid, or chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z,9E,11Z,14Z-eicosatetraenoic acid, di Docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE(18:0 / 0:0), valsartan, and 2-quinolinepropionic acid.

[0129] Any of the 50 differential metabolites has a strong ability to diagnose and distinguish gastric cancer and non-gastric cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance. Among them, the statistical results of the AUC value of the combined use of 50 differential metabolites for the diagnosis of gastric cancer are as follows:

[0130] Taurine, Azelaic Acid, Uridine, Hypoxanthine, L-Tryptophan, Hippuric Acid, 3-(3-Hydroxyphenyl)propionic Acid, Indole-3-propionic Acid, Cholic Acid, Glycoursodeoxycholic Acid, Chenodeoxycholic Acid, Deoxycholic Acid, Ursodeoxycholic Acid, L-Methionine, Corticosterone, 3-Methylxanthine, Indole-3-Carboxaldehyde, 1,7-Dimethylxanthine, Glycolithocholic Acid, Sphingosine, N-Phenylacetyl-L-Glutamine, Triiodothyronine, Proline, Carnitine C12:0, Carnitine C8:0, Dihydro-D-Sphingosine, Piperine, Phenoxyacetic Acid, Undecanoic Acid, Glycosyl The AUC value of deoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfuric acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0 and 3-hydroxytetradecanoic acid combined for the diagnosis of gastric cancer was 0.973.

[0131] Chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecandioic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid The AUC value of oxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE(18:0 / 0:0), valsartan and 2-quinolinepropionic acid combined for the diagnosis of gastric cancer was 0.951.

[0132] Example 9: Construction of a gastric cancer diagnostic model using 70 serum metabolic markers This example has the same research subjects and detection and analysis methods as Example 3, except that any 70 serum metabolic markers are used in the binary logistic regression modeling in step (6), such as taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, bile acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, bile acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L- -Methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanediol, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11 Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamic acid, (±) 12-hydroxy-5Z,8Z,10E,14Z-di Decatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinolinepropionic acid, hexanoylcarnitine, carnitine C7:0, irbesartan, biliverdin, stercobilin, trans-3-hydroxycotinine, propofol glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, and N6-succinyladenosine.

[0133] Any of the 70 differential metabolites has a strong ability to diagnose and distinguish gastric cancer and non-gastric cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance. The AUC of the combined use of the 70 differential metabolites in the diagnosis of gastric cancer is further improved to 0.987.

[0134] Example 10: Construction of a diagnostic model for serum targeted metabolomics to distinguish gastric cancer from healthy subjects

[0135] The samples of this example are from Example 3, 100 gastric cancer patients, and 100 healthy people and benign diseases. The metabolite detection and analysis method is the same as that of Example 3, and the following 85 metabolites are quantitatively detected, including: taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine , triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-dihydrocholic acid Phenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±) 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinolinepropionic acid, hexanoylcarnitine, carnitine C7:0, irbesartan, cholecalciferol Chlorophyll, stercobilin, trans-3-hydroxycotinine, propofol glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, N6-succinyladenosine, L-valine, L-isoleucine, L-threonine, L-glutamine, citric acid, 1-methylnicotinamide, creatine, L-lysine, nornicotine, ornithine, pyridoxolamine, palmitic acid, arachidonic acid, LysoPC(18:3), LysoPC(20:3).

[0136] Further preferred metabolite markers are p-tolylsulfate, docosahexaenoic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, salicylic acid, theophylline, γ-muricholic acid, carnitine C7:0, stercobilin, 13R-hydroxy-9Z,11E-octadecadienoic acid, N6-succinyladenosine, ornithine, pyridoxolamine, palmitic acid, and arachidonic acid. These metabolites undergo significant changes in gastric cancer patients, and the specific changes are shown in Table 9:

[0137] Table 9 Metabolite change folds in gastric cancer group VS non-gastric cancer group

[0138] Chinese name P value Fold change (FC) p-Tolylsulfate 0.022 0.232 Docosahexaenoic acid 0.002 0.179 Glycochenodeoxycholic acid 0.005 0.187 Glycyl-phenylalanine 0.038 3.343 Niacinamide 0.001 2.300 Salicylic acid 0.004 0.632 Theophylline 0.024 3.080 γ-Muricholic acid 0.004 2.008 Carnitine C7:0 0.016 0.648 Stercobilin 0.050 2.616 13R-Hydroxy-9Z,11E-octadecadienoic acid 0.000 0.222 N6-Succinyladenosine 0.005 1.570 Ornithine 0.002 0.601 Pyridoxamine 0.047 2.648 Palmitic acid 0.007 2.923 Arachidonic acid 0.005 0.179

[0139] These 16 differential metabolites have a strong ability to diagnose and distinguish early gastric cancer and non-gastric cancer patients individually, and when various metabolites are used in combination for diagnosis, the AUC is further improved. The AUC for diagnosing early gastric cancer is 0.792-0.969.

[0140] Example 11: Construction of a diagnostic model for distinguishing patients with stage I gastric cancer and benign gastric diseases using serum targeted metabolomics

[0141] The samples of this embodiment are derived from Example 2, 28 patients with stage I gastric cancer, and 95 patients with benign gastric diseases, including benign gastric diseases such as chronic gastritis, erosive gastritis, gastric ulcer, gastric polyps, and benign gastric tumors.

[0142] The metabolite detection and analysis method is the same as that in Example 3, and the following 85 metabolites are quantitatively detected, including: taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine , triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-dihydrocholic acid Phenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±) 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinolinepropionic acid, hexanoylcarnitine, carnitine C7:0, irbesartan, cholecalciferol Chlorophyll, stercobilin, trans-3-hydroxycotinine, propofol glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, N6-succinyladenosine, L-valine, L-isoleucine, L-threonine, L-glutamine, citric acid, 1-methylnicotinamide, creatine, L-lysine, nornicotine, ornithine, pyridoxolamine, palmitic acid, arachidonic acid, LysoPC(18:3), LysoPC(20:3).

[0143] Further preferred metabolite markers are deoxycholic acid, ursodeoxycholic acid, 3-methylxanthine, sphingosine, p-tolylsulfuric acid, docosahexaenoic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nornicotine, ornithine, pyridoxolamine, palmitic acid, and arachidonic acid. These metabolites undergo significant changes in gastric cancer patients, and the specific changes are shown in Table 10:

[0144] Table 10 Metabolite change folds in patients with gastric cancer VS patients with benign gastric diseases

[0145] Chinese name P-value Fold change (FC) Deoxycholic acid 0.042 2.096 Ursodeoxycholic acid 0.017 0.179 3-Methylxanthine 0.001 0.343 Sphingosine 0.006 0.591 p-Tolylsulfate 0.007 0.651 Docosahexaenoic acid 0.009 0.636 Glycochenodeoxycholic acid 0.046 0.284 Glycyl-phenylalanine 0.038 2.002 Nornicotine 0.001 0.501 Ornithine 0.000 0.601 Pyridoxamine 0.036 1.569 Palmitic acid 0.037 2.133 Arachidonic acid 0.000 0.650

[0146] These 13 differential metabolites have a strong ability to diagnose and distinguish patients with stage I gastric cancer and benign diseases individually, and when various metabolites are used in combination for diagnosis, the AUC is further improved, and the AUC for diagnosing early gastric cancer is 0.752-0.986.

[0147] Example 12: Construction of a diagnostic model for serum targeted metabolomics to distinguish between early gastric cancer and non-gastric cancer patients

[0148] The samples of this embodiment are derived from Embodiment 2 and Embodiment 3. The non-gastric cancer patient samples include 95 healthy people and 103 patients with benign gastric diseases in Embodiment 2 and Embodiment 3, a total of 198 cases; the early gastric cancer patients are 28 cases in Embodiment 2 and 102 cases in Embodiment 3, a total of 130 cases. Benign gastric diseases include benign gastric diseases such as chronic gastritis, erosive gastritis, gastric ulcer, gastric polyps, and benign gastric tumors.

[0149] The metabolite detection and analysis method is the same as that in Example 3, and the following 85 metabolites are quantitatively detected, including: taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine , triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-dihydrocholic acid Phenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±) 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinolinepropionic acid, hexanoylcarnitine, carnitine C7:0, irbesartan, cholecalciferol Chlorophyll, stercobilin, trans-3-hydroxycotinine, propofol glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, N6-succinyladenosine, L-valine, L-isoleucine, L-threonine, L-glutamine, citric acid, 1-methylnicotinamide, creatine, L-lysine, nornicotine, ornithine, pyridoxolamine, palmitic acid, arachidonic acid, LysoPC(18:3), LysoPC(20:3).

[0150] Further preferred metabolite markers are indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, ursodeoxycholic acid, 3-methylxanthine, sphingosine, p-tolylsulfuric acid, docosahexaenoic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, and theobromine. These metabolites undergo significant changes in patients with early gastric cancer, and the specific changes are shown in Table 11:

[0151] Table 11 Metabolite change folds in patients with early gastric cancer VS patients without gastric cancer

[0152] Chinese name P-value Fold change (FC) Indole-3-propionic acid 0.005 1.913 cholic acid 0.001 12.771 Glycoursodeoxycholic acid 0.026 0.525 Chenodeoxycholic acid 0.011 0.232 Ursodeoxycholic acid 0.004 0.379 3-Methylxanthine 0.007 0.524 Sphingosine 0.016 0.281 p-Tolylsulfate 0.007 0.602 Docosahexaenoic acid 0.009 0.616 Glycochenodeoxycholic acid 0.002 0.208 Glycyl-phenylalanine 0.004 3.202 γ-Muricholic acid 0.001 2.884 1,3-Diphenylguanidine 0.043 0.601 Lauric acid 0.032 9.678 Theobromine 0.036 1.933

[0153] These 15 differential metabolites have a strong ability to diagnose and distinguish early gastric cancer and non-gastric cancer patients individually, and when various metabolites are used in combination for diagnosis, the AUC is further improved, and the AUC for diagnosing early gastric cancer is 0.723-0.972.

[0154] Example 13: Construction of a diagnostic model for serum targeted metabolomics to distinguish early gastric cancer from healthy subjects

[0155] The samples of this embodiment are derived from Examples 2 and 3, 80 healthy human samples; the early gastric cancer patients are the patients with stage I gastric cancer in Examples 2 and 3, a total of 90 cases. The metabolite detection and analysis method is the same as that of Example 3, and the following 85 metabolites are quantitatively detected, including: taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, bile acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine , triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-dihydrocholic acid Phenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±) 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinolinepropionic acid, hexanoylcarnitine, carnitine C7:0, irbesartan, cholecalciferol Chlorophyll, stercobilin, trans-3-hydroxycotinine, propofol glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, N6-succinyladenosine, L-valine, L-isoleucine, L-threonine, L-glutamine, citric acid, 1-methylnicotinamide, creatine, L-lysine, nornicotine, ornithine, pyridoxolamine, palmitic acid, arachidonic acid, LysoPC(18:3), LysoPC(20:3).

[0156] Further preferred metabolite markers are taurine, hypoxanthine, L-tryptophan, sphingosine, p-tolylsulfate, docosahexaenoic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, deoxycholic acid, and L-methionine. These metabolites undergo significant changes in the body of patients with early gastric cancer, and the specific changes are shown in Table 12:

[0157] Table 12 Metabolite change folds in early gastric cancer patients VS healthy subjects

[0158] Chinese name P-value Fold change (FC) Taurine 0.011 2.6240168 Hypoxanthine 0.047 0.3662698 L-Tryptophan 0.040 0.4716718 Sphingosine 0.041 0.5296033 p-Tolylsulfate 0.037 0.6113507 Docosahexaenoic acid 0.012 0.6008531 Glycochenodeoxycholic acid 0.025 0.3641714 Glycyl-phenylalanine 0.036 2.7619774 Deoxycholic acid 0.045 2.933 L-Methionine 0.037 2.732

[0159] These 10 differential metabolites have a strong ability to diagnose and distinguish early gastric cancer from healthy people individually, and when various metabolites are used in combination for diagnosis, the AUC is further improved. The AUC for diagnosing early gastric cancer is 0.758-0.925.

[0160] Example 14 Detection Kit

[0161] This embodiment provides a detection kit prepared based on the above-mentioned metabolic markers, and the detection kit includes the following components:

[0162] Metabolic marker standards: taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C8:0, Dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12,13-trihydroxy -Octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinolinepropionic acid, hexanoylcarnitine, carnitine C7:0, irbesartan, biliverdin, stercobilin, trans-3-hydroxycotinine, propofol Glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, N6-succinyladenosine, L-valine, L-isoleucine, L-threonine, L-glutamine, citric acid, 1-methylnicotinamide, creatine, L-lysine, nornicotine, ornithine, pyridoxamine, palmitic acid, arachidonic acid, LysoPC (18:3), LysoPC (20:3), each standard is packaged separately or the standard mixed solution is packaged.

[0163] Serum sample metabolite extractants: Anhydrous methanol and 50% acetonitrile aqueous solution are used for sample preparation; 50% acetonitrile aqueous solution can be used as a solvent for dissolving standards.

[0164] Internal standard: L-phenylalanine.

[0165] Of course, when designing a test kit, it is not necessary to include all the above 85 markers. Only a few of them can be used, or a few or all of them can be combined with other markers. These standards can be packaged separately or in a mixture.

[0166] The detection kit provided in this embodiment can be used to diagnose or monitor gastric cancer.

[0167] Other raw materials or structures not specifically described in the present invention already exist in the prior art and can be directly purchased from the market.

[0168] The above are only preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A metabolic marker for diagnosing or monitoring gastric cancer, characterized in that: The metabolic marker is at least selected from taurine, azelaic acid, uridine, hypoxanthine, L-tryptophan, hippuric acid, 3-(3-hydroxyphenyl)propionic acid, indole-3-propionic acid, cholic acid, glycoursodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, ursodeoxycholic acid, L-methionine, corticosterone, 3-methylxanthine, indole-3-carboxaldehyde, 1,7-dimethylxanthine, glycolithocholic acid, sphingosine, N-phenylacetyl-L-glutamine, triiodothyronine, orthoamine, carnitine C12:0, carnitine C 8:0, dihydro-D-sphingosine, piperine, phenoxyacetic acid, undecanoic acid, glycohyodeoxycholic acid, 5-deoxy-5-methylthioadenosine, DL-3-phenyllactic acid, indolesulfonic acid, p-tolylsulfuric acid, 8-hydroxy-5Z, 9E, 11Z, 14Z-eicosatetraenoic acid, docosahexaenoic acid, β-hyodeoxycholic acid, glycochenodeoxycholic acid, glycyl-phenylalanine, nicotinamide, caffeine, salicylic acid, theophylline, γ-muricholic acid, 1,3-diphenylguanidine, lauric acid, theobromine, 9,12 ,13-trihydroxy-octadecenoic acid, carnitine C10:0, 3-hydroxytetradecanoic acid, menthol glucuronide, phenylsulfuric acid, tryptophanyl-glutamate, (±)12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, 2-(((2-ethylhexyl)oxy)carbonyl)benzoic acid, omeprazole sulfone, androsterone glucuronide, LPE (18:0 / 0:0), valsartan, 2-quinolinepropionic acid, caproylcarnitine, carnitine C7:0, irbesartan, biliverdin, stercobilin, trans-3 -hydroxycotinine, propofol glucuronide, 13R-hydroxy-9Z,11E-octadecadienoic acid, pregnanediol-3-glucuronide, deoxycholic acid 3-glucuronide, N6-succinyladenosine, L-valine, L-isoleucine, L-threonine, L-glutamine, citric acid, 1-methylnicotinamide, creatine, L-lysine, nornicotine, ornithine, pyridoxolamine, palmitic acid, arachidonic acid, LysoPC (18:3), LysoPC (20:3) at least one.

2. Use of the metabolic marker for diagnosing or monitoring gastric cancer according to claim 1 in preparing a metabolite database, reagent product or kit for diagnosing or monitoring gastric cancer.

3. A reagent product or a kit, characterized in that: A standard substance comprising the metabolic marker for diagnosing or monitoring gastric cancer as described in claim 1.

4. The reagent product or kit according to claim 3, characterized in that: The process also includes extracting a solvent and / or an internal standard that is enriched in the metabolite marker.

5. The method for screening metabolic markers for diagnosing or monitoring gastric cancer according to claim 1, characterized in that: The steps include: Obtaining test samples; Spectral data were obtained by liquid chromatography tandem mass spectrometry detection analysis; Construct a gastric cancer serum-specific metabolite database; The machine learning random forest algorithm was used to analyze the metabolite integral data between gastric cancer group samples and non-gastric cancer group samples, screen out potential gastric cancer markers, and identify the screened metabolites as diagnostic markers for gastric cancer; The metabolites are analyzed and their accuracy is verified by standards to determine the metabolic markers described in claim 1.

6. The method according to claim 5, characterized in that In liquid chromatography tandem mass spectrometry, the mass spectrometer is selected from quadrupole mass spectrometry, time-of-flight mass spectrometry, ion hydrazine mass spectrometry or high-resolution orbital hydrazine mass spectrometry.

7. The method according to claim 5, characterized in that In liquid chromatography tandem mass spectrometry, the mass spectrometry conditions and the setting of the mass spectrometry qualitative and quantitative detection mode include: selecting an electrospray ion source, selecting an ion scanning mode according to the response of the target compound to be detected; selecting a multiple reaction monitoring method, and setting the multiple reaction monitoring mode parameters.

8. The method according to claim 5, characterized in that The gastric cancer sample group includes gastric cancer samples of different stages.

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