Plasma metabolic markers for gastric cancer detection and their applications

Abstract

The present invention relates to plasma metabolite markers for gastric cancer detection and their applications. Based on the metabolomics method, the present invention screened and obtained 15 metabolite markers that can distinguish between health, gastric non-cancer diseases and gastric cancer by mass spectrometry analysis of plasma samples of gastric cancer patients, gastric non-cancerous diseases and healthy controls. Multivariate multi-classification ROC curve analysis showed that the AUC value reached 0.852, and the 15 differential metabolite markers had good diagnostic efficacy in distinguishing health, gastric non-cancerous diseases and gastric cancer. Further selecting 11, 7 or 4 combinations from the 15 markers also had the same good diagnostic effect. And the above combination was further confirmed in the validation group. The metabolite combination provided by the present invention can accurately predict whether it is a healthy group, a patient with a gastric non-cancerous disease, or a patient with gastric cancer, which will help in the early prevention, treatment and prognosis of gastric cancer, etc., with good treatment effect and reduced treatment cost.

Description

Technical Field

[0001] The present invention relates to the field of metabolic marker analysis and detection, and in particular to plasma metabolic markers for gastric cancer detection and applications thereof. Background Art

[0002] Gastric cancer (GC) is one of the common malignant tumors, which refers to epithelial malignant tumors originating from the stomach. Gastric cancer can be affected by many factors, including various non-cancerous diseases of the stomach (such as gastric polyps, gastric adenomas, chronic gastritis and gastric ulcers), Helicobacter pylori infection, as well as bad eating habits, environment and genetic factors. Therefore, early diagnosis and treatment of gastric cancer will help reduce the mortality caused by gastric cancer, reduce treatment costs, and obtain better treatment effects.

[0003] Currently, there are many clinical examination methods for gastric cancer, mainly including electronic gastroscopy, imaging examination and tumor marker detection.

[0004] Electronic gastroscopy refers to the examination through gastroscopy, which can visually observe the specific situation of the patient's stomach. Once ulcers or atrophic gastritis are found in the stomach, further pathological examination can be used for diagnosis. It is also the gold standard for the diagnosis of gastric cancer. This is of great significance for the early diagnosis of gastric cancer and improving the survival rate of gastric cancer patients. However, as an invasive diagnostic method, electronic gastroscopy is currently less accepted by most patients, is not suitable for detection by the elderly, and requires a lot of manpower and material resources. Imaging examinations mainly include abdominal CT, abdominal ultrasound, and PET examinations, which can explain the changes in the disease from multiple angles such as morphological characteristics, density, and enhancement methods. Compared with gastroscopy, imaging examinations can quickly display information such as the size, shape, and metastasis of the lesion area. It is a non-invasive detection method that can be used as a diagnostic method for the initial diagnosis of cancer, which helps to diagnose the degree of differentiation, pathological type, TNM staging, and evaluation of chemotherapy efficacy of gastric cancer. However, it still needs to be combined with pathological examination in the end, so it can only be used as an auxiliary diagnostic method for gastric cancer. Tumor marker detection is widely used in clinical diagnosis, such as tumor markers such as CA72-4, CEA, and CA199. The combined diagnosis of these markers can improve accuracy and guide further examinations once gastric cancer is suspected. However, the test results of tumor markers are easily affected by factors such as the patient's diet, lifestyle, and medication before the test. Therefore, it is only a reference for cancer screening and cannot be used as a gold standard for cancer diagnosis. Especially for gastric cancer patients who need to evaluate the efficacy of chemotherapy, it is necessary to combine imaging examinations and clinical symptoms for comprehensive consideration.

[0005] As an emerging discipline, metabolomics mainly studies small molecule metabolites before and after the organism is disturbed (physiologically or pathologically), screens disease-related metabolic markers, and thus reveals its mechanism from a metabolic perspective. Metabolites can more comprehensively reflect subtle changes in physiology and pathology, and based on the advantages of high throughput and high sensitivity of metabolomics technology, the use of metabolomics methods for gastric cancer diagnosis is non-invasive and sample acquisition is more convenient. Therefore, metabolomics technology has immeasurable prospects in the screening and diagnosis of diseases such as cancer. Although it requires highly professional technical and equipment support, data analysis is complex, and the degree of standardization is relatively low, this method can improve the accuracy of diagnosis and has the potential to become a tool for early diagnosis and prognosis assessment.

[0006] The occurrence of gastric cancer is a process of continuous development of lesions, generally including chronic Helicobacter pylori gastritis, atrophic gastritis, intestinal metaplasia, to dysplasia, and finally leading to the occurrence of gastric cancer. Gastric non-cancer disease (GNCD) mainly includes chronic gastritis, gastric ulcer, gastric polyps and gastric adenomas. Gastritis refers to inflammation of the gastric mucosa, which is caused by infection, long-term use of nonsteroidal anti-inflammatory drugs (NSAIDs), autoimmune reaction and other reasons; gastric polyps refer to small protrusions on the gastric mucosa, which may be the result of ectopic gastric mucosa, or may be caused by inflammation, polyposis syndrome, etc., and are usually benign; gastric adenoma is an adenoma that occurs in the epithelial tissue of the gastric mucosa, which may be caused by abnormal proliferation of the gastric mucosa, and is usually benign. Gastritis, gastric polyps and gastric adenomas all belong to a certain process in the occurrence of gastric cancer, such as gastritis, as well as gastric polyps and gastric adenomas belong to the dysplasia stage. Current research mainly focuses on the analysis of metabolite differences between cancer and healthy individuals, and rarely involves non-cancerous gastric diseases, metabolic differences with gastric cancer, and in vitro diagnostic studies. Patients with non-cancerous gastric diseases, whether they are gastritis, gastric polyps, or gastric adenomas, are in the intermediate stage of gastric cancer development. The differential diagnosis of non-cancerous gastric diseases can not only screen and diagnose malignant tumors early, but also reduce the overdiagnosis and treatment of non-cancerous gastric diseases. Therefore, if there is a technology that can accurately predict whether a person is healthy, has a non-cancerous gastric disease, or has gastric cancer through a single test, it will help to attract early attention, early response, early prevention, symptomatic treatment, and prognosis.

[0007] The technical problem to be solved by the patent of this invention is to overcome the defects and shortcomings of the above-mentioned prior art, and to find a group of metabolic markers related to the occurrence and development of diseases ranging from health, non-cancerous gastric diseases to gastric cancer, which can be used to diagnose and distinguish healthy groups, patients with non-cancerous gastric diseases, and gastric cancer patients, which will help in early prevention, symptomatic treatment and prognosis, etc., improve treatment effects and reduce treatment costs. Summary of the invention

[0008] The purpose of the present invention is to provide a urine metabolic marker for diagnosing gastric cancer and its application in view of the shortcomings of the existing technology for diagnosing gastric cancer, especially the problem of inaccurate screening of non-cancerous diseases of the stomach.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0010] The invention discloses a plasma metabolic marker for diagnosing gastric cancer. The metabolic marker comprises pyroglutamic acid, glycochenodeoxycholic acid, hypoxanthine and phosphatidylcholine 34:2e.

[0011] Preferably, the metabolic markers further include: phosphatidylcholine 34:4, L-malic acid and D-galactosonic acid.

[0012] Preferably, the metabolic markers further include: N-formyl-L-methionine, dimethylphosphatidylethanolamine 36:2, L-ornithine and allantoic acid.

[0013] Preferably, the metabolic markers further include: L-valyl-L-alanine, phosphatidylcholine 38:6, phosphatidylcholine 36:2 and phosphatidylethanolamine 36:3p.

[0014] Preferably, the metabolite marker is a combination of metabolite markers.

[0015] The invention discloses a composition, which comprises: pyroglutamic acid, N-formyl-L-methionine, phosphatidylcholine 34:4, L-valyl-L-alanine, phosphatidylcholine 38:6, dimethylphosphatidylethanolamine 36:2, phosphatidylcholine 36:2, glycochenodeoxycholic acid, L-ornithine, L-malic acid, allantoic acid, hypoxanthine, D-galactosonic acid, phosphatidylethanolamine 36:3p and phosphatidylcholine 34:2e.

[0016] The invention discloses the use of the metabolic marker in preparing a reagent for diagnosing / differentiating gastric cancer and non-cancerous gastric diseases.

[0017] The invention discloses the use of the metabolic marker in preparing a kit for diagnosing / differentiating gastric cancer and non-cancerous gastric diseases.

[0018] Preferably, the sample used in the diagnostic process is selected from serum, plasma, blood or dried blood spots.

[0019] Preferably, the non-cancerous gastric disease is selected from gastritis, gastric polyps and gastric adenoma.

[0020] The invention discloses a kit for diagnosing gastric cancer, which comprises the metabolic marker.

[0021] Preferably, the kit further comprises a quality control substance or a standard substance.

[0022] Compared with the existing technology, the innovation of our invention lies in finding a group of metabolic markers related to the occurrence and development of gastric cancer, which will help monitor the cancer pathological process, help pay attention to the corresponding risks in advance, and detect, prevent and treat them as early as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1a . Linear correlation curve of the metabolic marker pyroglutamate in the modeling group.

[0024] Figure 1b .Linear correlation curve of the metabolic marker N-formyl-L-methionine in the modeling group.

[0025] Figure 1c .Linear correlation curve of the metabolite marker phosphatidylcholine 34:4 in the modeling group.

[0026] Figure 1d .Linear correlation curve of the metabolic marker L-valyl-L-alanine in the modeling group.

[0027] Figure 1e .Linear correlation curve of the metabolite marker phosphatidylcholine 38:6 in the modeling group.

[0028] Figure 1f .Linear correlation curve of the metabolite marker dimethylphosphatidylethanolamine 36:2 in the modeling group.

[0029] Figure 1g .Linear correlation curve of the metabolite marker phosphatidylcholine 36:2 in the modeling group.

[0030] Figure 1h .Linear correlation curve of the metabolic marker glycochenodeoxycholic acid in the modeling group.

[0031] Figure 1i .Linear correlation curve of the metabolic marker L-ornithine in the modeling group.

[0032] Figure 1j .Linear correlation curve of the metabolite marker L-malic acid in the modeling group.

[0033] Figure 1k .Linear correlation curve of the metabolic marker allantoic acid in the modeling group.

[0034] Figure 1l .Linear correlation curve of the metabolite marker hypoxanthine in the modeling group.

[0035] Figure 1m.Linear correlation curve of the metabolic marker D-galactosonic acid in the modeling group.

[0036] Figure 1n .Linear correlation curve of the metabolite marker phosphatidylethanolamine 36:3p in the modeling group.

[0037] Figure 1o .Linear correlation curve of the metabolite marker phosphatidylcholine 34:2e in the modeling group.

[0038] Figure 2 . Multivariate and multiclassification ROC curve analysis of 15 metabolic markers with high correlation with the occurrence and development of gastric cancer in the modeling group.

[0039] Figure 3 .Multivariate and multi-classification ROC curve analysis of 15 metabolic markers with high correlation with the occurrence and development of gastric cancer in the validation group. DETAILED DESCRIPTION

[0040] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0041] Example 1 Subject Condition and Sample Collection

[0042] Written informed consent was obtained from all subjects before participating in the study.

[0043] Inclusion and exclusion criteria were as follows:

[0044] Inclusion criteria for gastric cancer patients: (1) male or female aged ≥18 years; (2) patients diagnosed with primary gastric cancer by biopsy / postoperative pathology or by comprehensive evaluation by clinicians.

[0045] Exclusion criteria for gastric cancer patients: (1) pregnancy or lactation; (2) emergency or emergency treatment; (3) history of malignant tumor or any anti-tumor treatment before sampling; (4) concurrent multiple primary malignant tumors.

[0046] Inclusion criteria for patients with non-cancerous gastric diseases: (1) male or female aged ≥18 years; (2) confirmed by biopsy / postoperative pathology or clinically diagnosed as benign gastric diseases through comprehensive evaluation by clinicians, including but not limited to gastritis, gastric polyps, gastric adenoma, etc.

[0047] Exclusion criteria for patients with non-cancerous gastric diseases: (1) pregnancy or lactation; (2) emergency or emergency treatment; (3) history of malignant tumor or any anti-tumor treatment before sampling.

[0048] A total of 242 gastric cancer patients (GC), 178 gastric non-cancer disease patients (GNCD, including gastritis, gastric polyps and gastric adenoma patients) and 236 healthy controls (HC) were collected from two medical centers (Table 1). Plasma samples were collected in the early morning on an empty stomach and stored in a -80 degree refrigerator.

[0049] Example 2 Detection, identification and screening of small molecule metabolites in plasma samples and metabolic markers

[0050] 1. Screening of markers

[0051] 1) Reagents

[0052] Methanol, acetonitrile, water, acetic acid, isopropanol, and methyl tert-butyl ether of mass spectrometry grade purity, and formic acid and ammonium acetate of HPLC grade purity were purchased from Sigma-Aldrich, USA;

[0053] 2) Sample preparation

[0054] Take 100 μL of plasma, place it in 1000 μL of pre-cooled (methyl tert-butyl ether: methanol, volume ratio 3:1) solution, vortex mix the extracted blood sample, and obtain the sample extract; add 500 μL of (methanol: water, volume ratio 3:1) solution to the sample extract, sonicate, let stand, vortex and centrifuge to separate layers;

[0055] Organic phase: After the sample is layered, take 500 μL of the upper layer into a centrifuge tube, which is the organic phase; after the organic phase is dried, add 200 μL of (acetonitrile:isopropanol, volume ratio 3:1) and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube to mix, ultrasonically treat for 5 minutes, and then centrifuge the centrifuge tube at room temperature for 5 minutes (12000 rpm); take 180 μl of the supernatant from the centrifuge tube into a 2 mL glass injection vial, which is the organic phase material, and detect it by machine (LC-MS);

[0056] Aqueous phase: During sample stratification, take 400 μL of the lower aqueous phase to a centrifuge tube, and add 1100 μL of ice methanol to precipitate the protein; after the protein in the centrifuge tube is precipitated, centrifuge the centrifuge tube, take 1000 μL of the supernatant and transfer it to a new centrifuge tube, and dry it overnight; add 200 μL of water to the dried centrifuge tube and incubate it at room temperature for 15 minutes; after incubation, vortex the centrifuge tube, treat it with ultrasound for 5 minutes, and then centrifuge the centrifuge tube at room temperature for 5 minutes (12000 rpm); take 180 μL of the supernatant from the centrifuge tube to a 2 mL glass injection vial, which is the aqueous phase material, and detect it on the machine (LC-MS);

[0057] 3) Small molecule metabolite detection:

[0058] The organic phase used a Waters ACQUTTY UPLC ® BEH C8 1.7µm 2.1*100mm column, and the aqueous phase used a Waters ACQUTTY UPLC ® HSS T3 1.8µm 2.1*100mm column for small molecule separation; the liquid chromatography and mass spectrometer used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific);

[0059] The mobile phase parameters are as follows:

[0060] Organic phase-A was an aqueous solution containing 0.1% acetic acid and 0.1% ammonium acetate; mobile phase B was an acetonitrile-isopropanol (7:3 v / v) solution containing 0.1% acetic acid and 1% ammonium acetate, and the separation elution gradient was as follows: 55%-89% mobile phase B from 0 to 12 minutes, and 100% mobile phase B from 12 to 19.5 minutes;

[0061] Aqueous 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;

[0062] The mass spectrometry parameters are as follows:

[0063] The mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each including positive and negative modes). The parameters used by QExactive were as follows: Full MS mode resolution was 70,000, scan range was 100-1500 m / z, AGC was 3E+6, and Maximum IT was 200 ms; in Full MS / dd-MS2 mode, the secondary mass spectrometry resolution was 17,500, the quadrupole window was 1.5 m / z, AGC was 1E+5, the maximum ion injection time was 50 ms, and the HCD relative collision energy was 30 eV.

[0064] 4) Metabolomics data processing

[0065] First, the detection peaks were extracted from all mass spectra, and then baseline correction was applied to remove noise and retain the original signal peaks; the raw data were converted into central discrete data; then, the peaks in a single sample were compared with the retention times in the chromatogram for matching; the data set was further removed from the isotope peaks to obtain the final mass spectrum matrix data; in order to reduce the differences in metabolite concentrations between samples and make the data distribution more symmetrical, the Normalization Autoencoder (NormAE) was used for normalization processing;

[0066] 5) Identification of metabolites

[0067] Public databases such as the Human Metabolite Database (HMDB; www.hmdb.ca), the Metabolomics Database (Metlin; (https: / / metlin.scripps.edu), and the Mass Spectrum Database (http: / / www.massbank.jp / ) were used, as well as the primary and secondary chromatograms and mass spectra of the standards separated on the same chromatographic column; the retention time was within 0.1 min and the mass-to-charge ratio was less than 10 ppm, and the identification was matched with the database and the standard;

[0068] 6) Metabolomics data analysis

[0069] In the first step, the three groups of samples, HC, GNCD, and GC, were compared in pairs, and the significantly different metabolites were screened out to take the intersection and retain the metabolites that appeared together; in the second step, the above metabolites were subjected to linear correlation analysis in the three groups of samples, HC, GNCD, and GC, and the Pearson correlation coefficient was calculated, and the metabolite markers closely related to the occurrence and development of gastric cancer were selected through the Pearson correlation coefficient; in the third step, multivariate and multi-classification ROC curve analysis was performed on the metabolite markers closely related to the occurrence and development of gastric cancer;

[0070] 2. Results Analysis

[0071] 1) Subjects: The samples used in the modeling group and the validation group were our own actual samples collected from two medical centers, and the samples in the modeling group and the validation group were different samples. Specifically, the number of people in the modeling group was 174 plasma samples in the HC group, 136 plasma samples in the GNCD group, and 182 plasma samples in the GC group (Table 1); the number of people in the validation group was 62 plasma samples in the HC group, 42 plasma samples in the GNCD group, and 60 plasma samples in the GC group. The samples in the modeling group were used to complete the screening of metabolic markers and build the diagnostic model, and the samples in the validation group were used in the validation step.

[0072] Table 1. Subjects

[0073]

[0074] 2) The metabolomic data of the three modeling groups of HC, GNCD, and GC were compared pairwise, i.e., HC vs GC, HC vs GNCD, and GC vs GNCD. The significantly different metabolites were screened out with P value < 0.001, and then the intersection was taken, and finally 29 differential metabolites were obtained;

[0075] 3) For the above 29 differential metabolites, linear correlation analysis was performed in the three groups of samples in the modeling group HC, GNCD, and GC. The results showed that all differential metabolites showed positive or negative correlation, and all showed significant increase or decrease. Among them, we screened out 15 metabolic markers with |R|>0.2 ( Figure 1a-Figure 1o and Table 2).

[0076] Table 2. 15 metabolic markers associated with the development of gastric cancer

[0077] Metabolic Markers-Chinese Metabolic Markers-English HMDB |R| value Pyroglutamate Pyroglutamic acid HMDB0000267 0.308 N-Formyl-L-methionine N-Formyl-L-methionine HMDB0001015 0.213 Phosphatidylcholine 34:4 PC 34:4 #N / A 0.223 L-Valyl-L-Alanine L-Valyl-L-alanine HMDB0029120 0.235 Phosphatidylcholine 38:6 PC 38:6 #N / A 0.269 Dimethylphosphatidylethanolamine 36:2 dMePE 36:2 #N / A 0.230 Phosphatidylcholine 36:2 PC 36:2 #N / A 0.240 Glycochenodeoxycholic acid Glycochenodeoxycholic acid HMDB0000637 0.281 L-Ornithine L-Ornithine HMDB0000214 0.222 L-Malic Acid L-Malic acid HMDB0000156 0.228 Allantoic acid Allantoic acid HMDB0001209 0.214 Hypoxanthine Hypoxanthine HMDB0000157 0.416 D-galactosonic acid D-Galactonic acid HMDB0000565 0.209 Phosphatidylethanolamine 36:3p PE 36:3p #N / A 0.243 Phosphatidylcholine 34:2e PC 34:2e #N / A 0.272

[0078] Example 3 Construction of a diagnostic model for distinguishing between health, non-cancerous gastric diseases, and gastric cancer

[0079] In order to evaluate the effect of the screened metabolic markers in distinguishing health, non-cancerous gastric diseases, and gastric cancer, we once again performed multivariate multi-classification ROC analysis on these 15 metabolic markers that are highly correlated with the occurrence and development of gastric cancer. We randomly divided 3 / 4 of the modeling group sample data into a training set (train) and 1 / 4 into a test set (test). The training set was used to build and train the machine learning classification model, and the test set was used to evaluate the discriminative ability of the trained model. We used SVM to randomly cycle Diego 1000 times and constructed an evaluation model by statistically averaging the final model accuracy. The results showed that AUC = 0.852 ( Figure 2 ), indicating that the diagnostic model is efficient. The ROC curve is a method to study the relationship between model sensitivity and specificity, with sensitivity as the ordinate and 1-specificity as the abscissa. The evaluation basis is to compare the area under the curve (AUC). When AUC is greater than 0.5, the closer AUC is to 1, the better the model performance is, indicating that the diagnostic effect is better. If it is less than 0.5, it means that the accuracy of the model is poor.

[0080] Example 4: Construction of a diagnostic model for distinguishing health, gastric non-cancerous diseases, and gastric cancer using 11 metabolic markers

[0081] The research object of this embodiment is the same as that of embodiment 1, and the detection and analysis method of embodiment 2 is the same. Only when the diagnostic model is constructed by the SVM method, 11 metabolic markers, pyroglutamic acid, N-formyl-L-methionine, phosphatidylcholine 34:4, dimethylphosphatidylethanolamine 36:2, glycochenodeoxycholic acid, ornithine, L-malic acid, allantoic acid, hypoxanthine, D-galactosonic acid and phosphatidylcholine 34:2e are combined to construct the diagnostic model. The results show that AUC = 0.867, which has clinical diagnostic significance.

[0082] Example 5: Construction of a diagnostic model for distinguishing health, gastric non-cancerous diseases, and gastric cancer using seven metabolic markers

[0083] The research object of this embodiment is the same as that of Example 1, and the detection and analysis method of Example 2 is the same. Only when the gastric cancer diagnosis model is constructed by the SVM method, 7 metabolic markers, pyroglutamic acid, phosphatidylcholine 34:4, glycochenodeoxycholic acid, L-malic acid, hypoxanthine, D-galactosonic acid and phosphatidylcholine 34:2e are combined to construct the diagnosis model. The results show that AUC = 0.850, which has clinical diagnostic significance.

[0084] Example 6: Construction of a diagnostic model for distinguishing health, gastric non-cancerous diseases, and gastric cancer using four metabolic markers

[0085] The research object of this embodiment is the same as that of embodiment 1, and the detection and analysis method of embodiment 2 is the same. Only when the gastric cancer diagnosis model is constructed by the SVM method, four metabolic markers, pyroglutamic acid, glycochenodeoxycholic acid, hypoxanthine and phosphatidylcholine 34:2e, are combined to construct the diagnosis model. The results show that AUC = 0.823, which has clinical diagnostic significance.

[0086] Example 7: Model validation using validation group plasma samples

[0087] The research objects of this embodiment are the same as those of embodiment 1, and the detection and analysis methods of embodiment 2 are the same. In order to verify the effectiveness of the constructed diagnostic model, we put the validation group samples as unknown samples into the above-constructed diagnostic model for verification.

[0088] The diagnostic model constructed by combining 15 metabolic markers, pyroglutamic acid, N-formyl-L-methionine, phosphatidylcholine 34:4, L-valyl-L-alanine, phosphatidylcholine 38:6, dimethylphosphatidylethanolamine 36:2, phosphatidylcholine 36:2, glycochenodeoxycholic acid, L-ornithine, L-malic acid, allantoic acid, hypoxanthine, D-galactosonic acid, phosphatidylethanolamine 36:3p and phosphatidylcholine 34:2e, showed a validation result of AUC=0.859, ( Figure 3 );

[0089] The diagnostic model constructed by combining 11 metabolic markers, pyroglutamic acid, N-formyl-L-methionine, phosphatidylcholine 34:4, dimethylphosphatidylethanolamine 36:2, glycochenodeoxycholic acid, ornithine, L-malic acid, allantoic acid, hypoxanthine, D-galactosonic acid and phosphatidylcholine 34:2e, showed a validation result of AUC=0.870.

[0090] The diagnostic model constructed by combining 7 metabolic markers, pyroglutamic acid, phosphatidylcholine 34:4, glycochenodeoxycholic acid, L-malic acid, hypoxanthine, D-galactosonic acid and phosphatidylcholine 34:2e, showed an AUC of 0.860.

[0091] The diagnostic model constructed by combining four metabolic markers, pyroglutamate, glycochenodeoxycholic acid, hypoxanthine and phosphatidylcholine 34:2e, showed an AUC of 0.853.

[0092] The above results show that the prediction model we established also has a good diagnostic effect in the validation group.

[0093] The present invention illustrates the process method of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned process steps, that is, it does not mean that the present invention must rely on the above-mentioned process steps to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of the raw materials selected by the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A plasma metabolic marker for diagnosing gastric cancer, characterized in that: The metabolic markers include: pyroglutamic acid, glycochenodeoxycholic acid, hypoxanthine and phosphatidylcholine 34:2e.

2. The metabolic marker according to claim 1, characterized in that The metabolic markers also include: phosphatidylcholine 34:4, L-malic acid and D-galactosonic acid.

3. The metabolic marker according to claim 2, characterized in that The metabolic markers also include: N-formyl-L-methionine, dimethylphosphatidylethanolamine 36:2, L-ornithine and allantoic acid.

4. The metabolic marker according to claim 3, characterized in that The metabolic markers also include: L-valyl-L-alanine, phosphatidylcholine 38:6, phosphatidylcholine 36:2 and phosphatidylethanolamine 36:3p.

5. Use of the metabolic marker according to any one of claims 1 to 4 in the preparation of a reagent for diagnosing / differentiating gastric cancer and non-cancerous gastric diseases.

6. Use of the metabolic marker according to any one of claims 1 to 4 in preparing a kit for diagnosing / differentiating gastric cancer and non-cancerous gastric diseases.

7. The use according to any one of claims 5-6, characterized in that: The samples used in the diagnostic process are selected from serum, plasma, blood or dried blood spots.

8. The use according to any one of claims 5-6, characterized in that: The non-cancerous gastric disease is selected from gastritis, gastric polyps and gastric adenoma.

9. A kit for diagnosing gastric cancer, characterized in that: The kit comprises the metabolic marker according to any one of claims 1 to 4.

10. The kit according to claim 9, characterized in that The kit also includes a quality control substance or a standard substance.

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