Urine metabolism marker composition for distinguishing stomach non-cancerous diseases from stomach cancer and application of urine metabolism marker composition

Abstract

The invention relates to the technical field of biomedicine, in particular to a urine metabolism marker composition for distinguishing stomach non-cancerous diseases from stomach cancer and application of the urine metabolism marker composition. The urine metabolism marker composition for distinguishing the stomach non-cancerous diseases from the stomach cancer comprises cane sugar, indol sulfate, guanidinoacetic acid and carnosine. The urine metabolism marker composition provided by the invention has relatively high sensitivity, specificity, accuracy rate and precision rate in the aspect of distinguishing stomach non-cancerous diseases from stomach cancer, and provides a basis for early diagnosis of stomach cancer and selection of treatment methods. Meanwhile, stomach non-cancer diseases and stomach cancer are distinguished through urine metabolism markers, the diagnosis mode is non-invasive, samples are convenient to obtain, and the method is more suitable for stomach cancer disease risk screening of remote areas and large-scale people. The invention is expected to bring important breakthrough to early diagnosis and treatment of gastric cancer, and provides better medical service for patients.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a urine metabolic marker composition for distinguishing non-cancerous gastric diseases from gastric cancer and an application thereof. Background Art

[0002] Gastric cancer (GC) is a malignant tumor that originates from gastric mucosal epithelial cells. It ranks first among digestive tract malignant tumors and third among all malignant tumors. As one of the most common malignant tumors in China, the incidence and mortality of gastric cancer are both ranked third in my country. This is highly related to the living, eating habits and environment of Chinese residents. For example, long-term smoking, high-salt diet or frequent consumption of moldy food all lead to a significant increase in the incidence of gastric cancer. The symptoms of gastric cancer mainly include early satiety, obstruction and bleeding, but they mostly appear in the late stage of the disease. Once discovered, they are in the late stage of cancer, which reduces the cure rate and five-year survival rate of gastric cancer. Therefore, if accurate detection and diagnosis can be performed at the precancerous lesion stage, it will significantly reduce the incidence of gastric cancer, thereby reducing the number of people who die from gastric cancer.

[0003] The occurrence and development of gastric cancer is a long and continuous process, generally including chronic Helicobacter pylori gastritis, atrophic gastritis, intestinal metaplasia, and dysplasia, which eventually lead to the occurrence of gastric cancer. Gastric non-cancer diseases (GNCD) mainly include chronic gastritis, gastric ulcer, gastric polyps and gastric adenomas, which belong to one of the above processes of gastric cancer. There are no obvious symptoms in the early stage of gastric cancer, or non-specific symptoms such as upper abdominal discomfort and belching appear, which are often similar to the symptoms of chronic gastric diseases such as gastritis and gastric ulcer, and are easily ignored, resulting in the low early diagnosis rate of gastric cancer in my country. Gastric non-cancer diseases may be inflammatory reactions, or they may be in the early stages of the development of malignant tumors. The differential diagnosis of gastric non-cancer diseases can not only screen and diagnose gastric cancer early, but also reduce the overdiagnosis and treatment of gastric non-cancer diseases, which is helpful to attract attention, early response, early prevention, treatment and prognosis.

[0004] Therefore, there is an urgent need for an accurate diagnostic technology to distinguish non-cancerous gastric diseases from gastric cancer. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a urine metabolic marker composition and its application for distinguishing non-cancerous gastric diseases from gastric cancer, aiming to solve the problem of the lack of accurate diagnostic technology for distinguishing non-cancerous gastric diseases from gastric cancer.

[0006] The technical solution of the present invention is as follows:

[0007] In a first aspect of the present invention, a urine metabolite marker composition is provided for distinguishing non-cancerous gastric diseases from gastric cancer, wherein the urine metabolite marker composition comprises sucrose, indoxyl sulfate, guanidinoacetic acid and carnosine.

[0008] Optionally, the urine metabolic marker composition further comprises N-acetyl-L-glutamate, N-ω-acetylhistamine and thymidine.

[0009] Optionally, the urine metabolite marker composition further comprises hydroquinone, indole lactic acid, L-histidine and tryptamine.

[0010] Optionally, the urine metabolite marker composition consists of sucrose, hydroquinone, indole lactic acid, indoxyl sulfate, guanidinoacetic acid, L-histidine, carnosine, N-acetyl-L-glutamate, N-ω-acetylhistamine, thymidine and tryptamine.

[0011] The second aspect of the present invention provides a use of the urine metabolic marker composition of the present invention in the preparation of a reagent for distinguishing non-cancerous gastric diseases from gastric cancer.

[0012] Optionally, the non-cancerous gastric disease includes one of gastritis, gastric ulcer, gastric polyp and gastric adenoma.

[0013] The third aspect of the present invention provides a use of the urine metabolic marker composition of the present invention in the preparation of a kit for distinguishing non-cancerous gastric diseases from gastric cancer.

[0014] Optionally, the non-cancerous gastric disease includes one of gastritis, gastric ulcer, gastric polyp and gastric adenoma.

[0015] In a fourth aspect, the present invention provides a kit for distinguishing non-cancerous gastric diseases from gastric cancer, which comprises the urine metabolic marker composition for distinguishing non-cancerous gastric diseases from gastric cancer according to the present invention.

[0016] Optionally, the non-cancerous gastric disease includes one of gastritis, gastric ulcer, gastric polyp and gastric adenoma.

[0017] Beneficial effects: The urine metabolite marker composition provided by the present invention has high sensitivity, specificity, accuracy and precision in distinguishing non-cancerous gastric diseases from gastric cancer, providing a basis for the early diagnosis of gastric cancer and the selection of treatment methods. At the same time, the present invention distinguishes non-cancerous gastric diseases from gastric cancer through urine metabolite markers. This diagnostic method is non-invasive, easy to obtain samples, and more suitable for gastric cancer risk screening in remote areas and large populations. The present invention is expected to bring important breakthroughs in the early diagnosis and treatment of gastric cancer and provide better medical services for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Figure 3 is the multivariate ROC curve analysis of 11 important metabolic markers for distinguishing GNCD from GC in the modeling group.

[0019] Figure 2 Figure 3 is the multivariate ROC curve analysis of 11 important metabolic markers for distinguishing GNCD from GC in the validation group. DETAILED DESCRIPTION

[0020] The present invention provides a composition of urine metabolic markers for distinguishing non-cancerous gastric diseases from gastric cancer and its application. In order to make the purpose, technical scheme and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] Metabolomics is a qualitative and quantitative study of metabolites in biological systems. In recent years, with the rapid development of metabolomics technology, metabolomics has been widely used in disease screening. Metabolomics is based on liquid biological samples such as blood and urine, and conducts qualitative and quantitative analysis of small molecule metabolites, comprehensively reflects the subtle changes in physiology and pathology, screens metabolite markers related to disease development, and constructs disease diagnosis models for disease diagnosis and typing. Disease diagnosis using metabolomics methods has the advantages of non-invasiveness, convenient sample acquisition, high sensitivity and high specificity, and is more suitable for large-scale and remote areas with limited resources. Screening of people. Therefore, the present invention is based on metabolomics technology, by collecting urine samples, obtaining urine metabolomics data, and constructing a diagnostic model with high sensitivity and specificity to distinguish between non-cancerous diseases of the stomach and gastric cancer, providing a basis for the early diagnosis of gastric cancer and the selection of treatment methods.

[0022] An embodiment of the present invention provides a urine metabolite marker composition for distinguishing non-cancerous gastric diseases from gastric cancer, wherein the urine metabolite marker composition comprises sucrose, indoxyl sulfate, guanidinoacetic acid and carnosine.

[0023] Wherein, the non-cancerous gastric disease includes one of gastritis, gastric ulcer, gastric polyp and gastric adenoma.

[0024] The above-mentioned urine metabolic marker composition can accurately and reliably distinguish between non-cancerous gastric diseases and gastric cancer, and has high sensitivity, specificity, accuracy and precision in distinguishing non-cancerous gastric diseases from gastric cancer.

[0025] The above-mentioned metabolic markers are derived from urine. This diagnostic method is non-invasive, samples are easy to obtain, and are more suitable for gastric cancer risk screening in remote areas and large populations.

[0026] In one embodiment, the urine metabolite marker composition further comprises N-acetyl-L-glutamate, N-ω-acetylhistamine and thymidine.

[0027] That is, the urine metabolic marker composition includes sucrose, indoxyl sulfate, guanidinoacetic acid, carnosine, N-acetyl-L-glutamate, N-ω-acetylhistamine and thymidine.

[0028] The above-mentioned urine metabolic marker composition can accurately and reliably distinguish between non-cancerous gastric diseases and gastric cancer, and has high sensitivity, specificity, accuracy and precision in distinguishing non-cancerous gastric diseases from gastric cancer.

[0029] Further in one embodiment, the urine metabolite marker composition also includes hydroquinone, indole lactic acid, L-histidine and tryptamine.

[0030] That is, the urine metabolite marker composition includes sucrose, hydroquinone, indole lactic acid, indoxyl sulfate, guanidinoacetic acid, L-histidine, carnosine, N-acetyl-L-glutamate, N-ω-acetylhistamine, thymidine and tryptamine.

[0031] The above-mentioned urine metabolic marker composition can accurately and reliably distinguish between non-cancerous gastric diseases and gastric cancer, and has high sensitivity, specificity, accuracy and precision in distinguishing non-cancerous gastric diseases from gastric cancer.

[0032] In one embodiment, the urine metabolite marker composition consists of sucrose, hydroquinone, indole lactic acid, indoxyl sulfate, guanidine acetic acid, L-histidine, carnosine, N-acetyl-L-glutamate, N-ω-acetylhistamine, thymidine and tryptamine.

[0033] An embodiment of the present invention provides a use of the urine metabolic marker composition as described above in preparing a reagent for distinguishing non-cancerous gastric diseases from gastric cancer.

[0034] An embodiment of the present invention provides a use of the urine metabolic marker composition as described above in preparing a kit for distinguishing non-cancerous gastric diseases from gastric cancer.

[0035] An embodiment of the present invention provides a kit for distinguishing non-cancerous gastric diseases from gastric cancer, wherein the kit comprises the urine metabolic marker composition for distinguishing non-cancerous gastric diseases from gastric cancer as described above.

[0036] The present invention is further described in detail below through specific examples.

[0037] 1. Subjects

[0038] The inclusion and exclusion criteria for patients with non-cancerous gastric diseases and gastric cancer were as follows:

[0039] 1) Inclusion criteria: All subjects obtained written informed consent before the study, and were male or female aged ≥18 years; gastric cancer patients were diagnosed by the gold standard method of histopathological examination; patients were diagnosed as benign gastric diseases by biopsy / postoperative pathology or clinically diagnosed by comprehensive evaluation by clinicians. Only patients with non-cancerous gastric diseases such as gastritis, gastric polyps, and gastric adenomas were included.

[0040] 2) Exclusion criteria: pregnancy or lactation; emergency or rescue; history of malignant tumor or any anti-tumor treatment before sampling; patients with multiple primary malignant tumors at the same time.

[0041] A total of 347 urine samples were collected from two medical centers, including 146 urine samples from patients with gastric non-cancer disease (GNCD) and 201 urine samples from patients with gastric cancer (GC). The urine samples used for the modeling group were 110 in the GNCD group and 150 in the GC group; the urine samples used for the validation group were 36 in the GNCD group and 51 in the GC group (Table 1).

[0042] Table 1. Subjects

[0043] Gastric Non-Cancerous Diseases (GNCD) Group Gastric cancer (GC) group Modeling Group 110 150 Validation Group 36 51 total 146 201

[0044] 2. Urine metabolite detection

[0045] 1) Detection reagents

[0046] Methanol, acetonitrile, water, acetic acid, methyl tert-butyl ether of mass spectrometry grade purity, and formic acid of HPLC grade purity were purchased from Sigma-Aldrich, USA.

[0047] 2) Urine pretreatment

[0048] After taking out the urine sample from the -80℃ refrigerator and thawing, take 40μL of the urine sample into an extraction tube, add 400μL of pre-cooled methyl tert-butyl ether and methanol mixed extract (the volume ratio of methyl tert-butyl ether and methanol is 3:1), vortex and ultrasonically mix, then add 360μL of methanol-water mixed solution, vortex and centrifuge to separate the layers; take 300μL of the lower aqueous phase solution in the extraction tube, add 900μL of pre-cooled methanol solution thereto, take 1000μL of the supernatant after protein precipitation, spin dry, add 200μL of water to re-dissolve, and use the re-dissolved aqueous phase as the test solution for machine (LC-MS) detection.

[0049] 3) Test liquid detection

[0050] Using Waters ACQUTTY HSS T3 1.8 μm 2.1*100 mm column was used for small molecule separation; liquid chromatography and mass spectrometry used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively.

[0051] The mobile phase parameters are as follows: mobile phase A is an aqueous solution containing 0.1% formic acid; mobile phase B is an acetonitrile solution containing 0.1% formic acid; the separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, 13-18 minutes is 99% mobile phase B; wherein the above % are all volume percentages.

[0052] The mass spectrometry parameters are as follows: the mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each including positive and negative modes). The parameters used for Q Exactive are as follows: the resolution of the Full MS mode is 70,000, the scanning range is 100-1500 m / z, the AGC (automatic gain control) is 3E+6, and the Maximum IT is 200 milliseconds; in the Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometry is 17,500, the quadrupole window is 1.5 m / z, the AGC is 1E+5, the maximum ion injection time is 50 ms, and the HCD relative collision energy is 30 eV.

[0053] 3. Metabolomics data preprocessing and metabolite identification

[0054] 1) Metabolomics data preprocessing

[0055] (1) Extract peaks from the RAW format file downloaded from the mass spectrometer and convert it into a FeatureXML format file to reduce the dimension of the original mass spectrometry data and improve the signal-to-noise ratio; (2) Use the peak alignment algorithm of the OpenMS software to calibrate and align the retention time of the extracted peak format data between samples, thereby converting the mass spectrometry data into a data matrix; (3) Match and filter the isotope peaks in the data matrix obtained in step 2, and then replace abnormal data (such as 0, negative values, background noise, etc.) with vacant values; (4) Among all the characteristic peaks obtained in step 3, those with a detection rate of <80% are eliminated, and those with a detection rate of >80% are filled with the median value of the characteristic peak and 5% random noise (obeying the standard normal distribution); (5) In order to reduce the difference in metabolite concentrations between samples and make the data distribution more symmetrical, the Normalization Autoencoder (NormAE) is used for homogenization processing to remove systematic errors such as batch effects.

[0056] 2) Identification of metabolites

[0057] According to the original data, the spectral information of the primary parent ion (MS1) and secondary fragment ion (MS2) of the compound is obtained after software analysis, such as the mass-to-charge ratio (m / z) of the primary mass spectrum and the fragments of the secondary ion are matched with the spectral information of the primary and secondary metabolites in the public database to qualitatively identify the metabolites. Commonly used metabolite databases include the Human Metabolite Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), the Mass Spectrum Database (www.massbank.jp) and the Lipid Metabolite Database (Lipidmap, www.lipidmaps.org). Based on the metabolites identified in the relevant databases, the metabolites are finally verified according to the retention time, MS1, and MS2 mass spectrometry information when the standard is separated under the same chromatographic column and mass spectrometry conditions. The standard for metabolite identification is that the retention time is within 0.1min difference, and the theoretical value and measured value of the metabolite molecular weight are less than 10ppm.

[0058] 4. Data Analysis

[0059] 1) Screening of metabolic markers for distinguishing non-cancerous gastric diseases from gastric cancer

[0060] By performing metabolite detection on the above samples, a total of 313 metabolites were obtained after annotation. The data of the modeling group were subjected to lasso (Least Absolute Shrinkage and Selection Operator) regression, and a total of 11 differential metabolites were screened out (Table 2), which served as important metabolic markers to distinguish GNCD from GC.

[0061] Table 2. 11 important metabolic markers for distinguishing GNCD from GC

[0062]

[0063]

[0064] 2) Construction of a diagnostic model for distinguishing non-cancerous gastric diseases from gastric cancer

[0065] In order to verify the diagnostic effect of the 11 screened metabolic markers in distinguishing GNCD from GC, a multivariate ROC curve analysis was performed on the above 11 metabolic markers in the modeling group. Specifically, 3 / 4 of the sample data of the GNCD group and GC group in the modeling group were randomly used as training sets (training), and 1 / 4 was used as a test set (test) for learning, and the machine learning support vector machine (SVM) was used for random loop iteration 1000 times. By statistically calculating the average value of the final model accuracy, a diagnostic model for distinguishing gastric non-cancerous diseases from gastric cancer was constructed.

[0066] The ROC curve is a method to study the relationship between model sensitivity and specificity. It uses sensitivity as the ordinate and specificity as the abscissa. The evaluation basis is to compare the area under the curve (AUC). When the AUC is greater than 0.5, the closer the AUC is to 1, the better the model performance is, indicating a better diagnostic effect. If it is less than 0.5, it means that the model accuracy is poor. In addition to common parameters such as the receiver operating curve (ROC) and the area under the curve (AUC), the ROC classification prediction model also includes sensitivity, specificity, accuracy, and precision.

[0067] The sensitivity is:

[0068]

[0069] Specificity is:

[0070]

[0071] The accuracy is:

[0072]

[0073] The accuracy is:

[0074]

[0075] in,

[0076] TP (True positive): True positive, the number of samples that are actually positive examples that are correctly predicted as positive examples;

[0077] TN (Ture Negative): True negative, the number of samples that are actually negative examples but are correctly predicted as negative examples;

[0078] FP (False Positive): False positive, the number of samples that are actually negative examples but are mistakenly predicted as positive examples;

[0079] FN (False Negative): False negatives, the number of samples that are actually positive examples but are mistakenly predicted as negative examples;

[0080] The results are as follows Figure 1 As shown, AUC = 0.881 (sensitivity = 0.757, specificity = 0.893, accuracy = 0.815, precision = 0.903), the results show that the constructed diagnostic model has high diagnostic efficacy.

[0081] In addition, the diagnostic models of different combinations of metabolites were analyzed by ROC curves in the modeling group. Seven metabolites were used, sucrose, indoxyl sulfate, guanidinoacetic acid, carnosine, N-acetyl-L-glutamate, N-ω-acetylhistamine and thymidine, and four metabolites were used, sucrose, indoxyl sulfate, guanidinoacetic acid and carnosine. The results showed that when seven metabolites were used, AUC = 0.879 (sensitivity = 0.895, specificity = 0.704, accuracy = 0.815, precision = 0.810); when four metabolites were used, AUC = 0.838 (sensitivity = 0.649, specificity = 0.893, accuracy = 0.754, precision = 0.889). The above results show that the constructed diagnostic models have high diagnostic efficacy and clinical diagnostic significance.

[0082] 3) Validation of a diagnostic model for distinguishing non-cancerous gastric diseases from gastric cancer

[0083] In order to further verify the effectiveness of the diagnostic model for distinguishing gastric non-cancerous diseases from gastric cancer based on the modeling group data, the above diagnostic model was verified using the validation group data. A multivariate ROC curve analysis was specifically performed to evaluate the independent verification effect of the diagnostic model on unknown data sets other than the modeling group data set. After the validation group samples were placed in the diagnostic model constructed by the modeling group, the corresponding probability value (Probability) was output based on the detection data of the 11 important metabolic markers that distinguish GNCD from GC for each sample. Based on the probability value of each sample as the diagnostic threshold, a set of confusion matrices (including true positives, true negatives, false positives, and false negatives) was obtained. According to the formula, the sensitivity, specificity, accuracy, and precision can be calculated. According to the sensitivity and specificity values, a point can be marked in the ROC analysis diagram with sensitivity as the ordinate and 1 specificity as the abscissa. Similarly, when the probability value of each sample is used as the diagnostic threshold, multiple different points are obtained in the ROC analysis diagram. These points are linked to draw the ROC curve diagram ( Figure 2 ). Among them, the point in the upper left corner is the point with the best sensitivity and specificity, and the diagnostic threshold at this time is 0.5735.

[0084] As shown in the confusion matrix results in Table 3, in the diagnostic model constructed based on the above 11 metabolic markers, with a diagnostic threshold of 0.5735, among 51 gastric cancer patients, 37 were diagnosed as gastric cancer, and 14 were misdiagnosed as gastric non-cancerous diseases; among 36 patients with gastric non-cancerous diseases, 31 were correctly diagnosed, and 5 were misdiagnosed as gastric cancer. The ROC analysis results of the diagnostic model in the validation group are shown in Figure 3. Figure 2 As shown, based on the results of the confusion matrix, the sensitivity, specificity, precision and accuracy were calculated, and the results showed that AUC = 0.877 (sensitivity = 0.725, specificity = 0.861, accuracy = 0.782, precision = 0.881). The above results show that the diagnostic model constructed for distinguishing non-cancerous gastric diseases from gastric cancer also has a good diagnostic effect in the validation group.

[0085] Table 3. Confusion matrix of the diagnostic model for distinguishing gastric non-cancerous diseases from gastric cancer

[0086]

[0087]

[0088] In addition, the diagnostic model of different combinations of metabolite markers was verified in the validation group, and multivariate ROC curve analysis was performed. The results showed that the combination of the above 7 metabolite markers, sucrose, indoxyl sulfate, guanidinoacetic acid, carnosine, N-acetyl-L-glutamate, N-ω-acetylhistamine and thymidine, had an AUC of 0.882 (sensitivity = 0.647, specificity = 0.889, accuracy = 0.747, precision = 0.892) in the validation group; the combination of 4 metabolite markers, sucrose, indoxyl sulfate, guanidinoacetic acid and carnosine, had an AUC of 0.859 (sensitivity = 0.725, specificity = 0.861, accuracy = 0.782, precision = 0.881) in the validation group. The above results show that the diagnostic model constructed for distinguishing non-cancerous gastric diseases from gastric cancer also has a good diagnostic effect in the validation group.

[0089] In summary, the present invention is based on urine samples and uses metabolomics technology to detect small molecule metabolites in urine samples, and screens out a metabolic marker composition for distinguishing non-cancerous gastric diseases from gastric cancer. For the urine metabolic marker composition, a diagnostic model for distinguishing non-cancerous gastric diseases from gastric cancer is constructed through machine algorithm learning. The diagnostic model can accurately identify early gastric cancer, providing important help for the prevention of gastric cancer and reducing the incidence. At the same time, this method is non-invasive, and samples are easy to obtain, which is more suitable for gastric cancer risk screening in remote areas and large populations.

[0090] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A composition of urine metabolic markers for distinguishing non-cancerous gastric diseases from gastric cancer, characterized in that: The urine metabolic marker composition comprises sucrose, indoxyl sulfate, guanidinoacetic acid and carnosine.

2. The urine metabolic marker composition for distinguishing gastric non-cancerous diseases from gastric cancer according to claim 1, characterized in that: The urine metabolic marker composition also includes N-acetyl-L-glutamate, N-ω-acetylhistamine and thymidine.

3. The urine metabolic marker composition for distinguishing gastric non-cancerous diseases from gastric cancer according to claim 2, characterized in that: The urine metabolite marker composition also includes hydroquinone, indole lactic acid, L-histidine and tryptamine.

4. The composition of urine metabolic markers for distinguishing non-cancerous gastric diseases from gastric cancer according to claim 3, characterized in that: The urine metabolic marker composition consists of sucrose, hydroquinone, indole lactic acid, indoxyl sulfate, guanidinoacetic acid, L-histidine, carnosine, N-acetyl-L-glutamic acid, N-ω-acetylhistamine, thymidine and tryptamine.

5. Use of the urine metabolic marker composition according to any one of claims 1 to 4 in preparing a reagent for distinguishing non-cancerous gastric diseases from gastric cancer.

6. The use according to claim 5, characterized in that: The non-cancerous gastric disease includes one of gastritis, gastric ulcer, gastric polyp and gastric adenoma.

7. Use of the urine metabolic marker composition according to any one of claims 1 to 4 in preparing a kit for distinguishing non-cancerous gastric diseases from gastric cancer.

8. The use according to claim 7, characterized in that: The non-cancerous gastric disease includes one of gastritis, gastric ulcer, gastric polyp and gastric adenoma.

9. A kit for distinguishing non-cancerous gastric diseases from gastric cancer, characterized in that: A urine metabolic marker composition for distinguishing non-cancerous gastric diseases from gastric cancer comprising the composition described in any one of claims 1 to 4.

10. The kit for distinguishing gastric non-cancerous diseases from gastric cancer according to claim 9, characterized in that: The non-cancerous gastric disease includes one of gastritis, gastric ulcer, gastric polyp and gastric adenoma.

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