Urine metabolic markers for gastric cancer detection and their applications

Abstract

The present invention relates to urine metabolite markers for gastric cancer detection and their applications. Based on the metabolomics method, the present invention detects and analyzes the metabolites of urine samples of gastric cancer patients, patients with non-cancerous gastric diseases and healthy controls, and screens 12 metabolite markers with high correlation with the development of gastric diseases. Multivariate multi-classification ROC curve analysis shows that the AUC value reaches 0.842, and the 12 differential metabolite markers have good diagnostic efficacy in distinguishing health, non-cancerous gastric diseases and gastric cancer. Further selecting 8 or 4 combinations from the 12 markers also has the same good diagnostic effect. And the above combination is further confirmed in the validation group. The metabolite combination provided by the present invention can accurately judge whether it is healthy, whether it has non-cancerous gastric diseases, and whether it has 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 urine metabolic markers for gastric cancer detection and applications thereof. Background Art

[0002] Gastric cancer (GC) is a malignant tumor that occurs inside the stomach, usually originating in the inner tissue of the gastric mucosa. The pathogenesis of gastric cancer is complex and may be related to multiple factors such as genetic factors, environmental factors, and infection. According to the latest global cancer burden data for 2020 released by the World Health Organization's International Agency for Research on Cancer (WHO IARC), there were 1.089 million new cases of gastric cancer and 768,000 deaths worldwide in 2020. Due to changes in dietary structure, increased work pressure, and Helicobacter pylori infection, gastric cancer tends to be younger, so screening and diagnosis of gastric cancer has become particularly important.

[0003] At present, clinical examination and diagnosis of gastric cancer include gastroscopy, imaging examination, blood marker detection, etc. 1) Gastroscopy is a method of directly observing the gastric mucosa. Through the gastroscope, the doctor can examine the inside of the stomach and look for tumors, ulcers or other abnormalities; however, it is an invasive examination method that requires inserting a soft gastroscope through the esophagus into the stomach, which may cause some discomfort, and smaller lesions or hidden parts such as some early lesions are easy to be missed; 2) Imaging examinations include CT, MRI, ultrasound, etc., among which CT scans can be used to assess the size and location of the tumor and whether it has spread to surrounding tissues or other organs; MRI: In some cases, MRI can also provide more detailed images, especially for soft tissue display; Ultrasound: Endoscopic ultrasound can provide more detailed images of the gastric wall structure, which is helpful to evaluate the depth of the tumor; however, there are problems of radiation exposure, low resolution, inability to identify smaller lesions in the early stage, and high cost; 3) Blood tumor marker tests such as CA72-4, CEA and CA199 are widely used in clinical diagnosis, but their sensitivity and specificity are not high, because the blood content of tumor markers is easily affected by factors such as the patient's diet intake, lifestyle and medication before testing. Therefore, it can only be used as an auxiliary examination method and cannot be used as the gold standard for cancer diagnosis. It still needs to be considered in combination with imaging examinations and clinical symptoms.

[0004] As an emerging discipline, metabolomics has been widely used in disease screening in recent years. Metabolomics is based on biological samples such as blood and urine to conduct qualitative and quantitative analysis of small molecule metabolites, screen metabolite markers related to disease development, and build disease diagnosis models for disease diagnosis and typing. Metabolites can more comprehensively reflect subtle changes in physiology and pathology, and based on the high throughput and high sensitivity of metabolomics technology, the use of metabolomics methods for gastric cancer diagnosis has the advantages of being non-invasive and more convenient to obtain samples.

[0005] The occurrence and development of gastric cancer is a continuous process, 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 polyp and gastric adenoma, which belong to one of the above processes of gastric cancer. Gastric cancer can occur in any part of the stomach, more than half of which occur in the gastric antrum, greater curvature, lesser curvature and anterior and posterior walls. There are no obvious symptoms in the early stage, or non-specific symptoms such as upper abdominal discomfort and belching appear. They are often similar to the symptoms of chronic gastric diseases such as gastritis and gastric ulcer, which are easily ignored, resulting in the low early diagnosis rate of gastric cancer in my country. Early gastric cancer is mostly asymptomatic or has only mild symptoms. When clinical symptoms are obvious, the lesion is already in the late stage. Therefore, we must be very vigilant about the early symptoms of gastric cancer and strengthen the early screening of gastric cancer to avoid delaying diagnosis and treatment. Current research mainly focuses on the metabolic differences between cancer and healthy individuals, and rarely involves the comparison of metabolic differences between non-cancerous gastric diseases and gastric cancer. Non-cancerous gastric diseases may be inflammatory reactions, or they may be in the early stages of development of malignant tumors. The differential diagnosis of non-cancerous gastric diseases can not only screen and diagnose gastric cancer early, but also reduce the overdiagnosis and treatment of non-cancerous gastric diseases, which helps to attract early attention, early response, early prevention, treatment and prognosis.

[0006] 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. Diagnosis of whether a patient has gastric cancer is certainly important, but being able to accurately predict the possibility of a patient suffering from early non-cancerous gastric diseases will help in early prevention, treatment and prognosis, etc., with better treatment effects and reduced treatment costs. Summary of the invention

[0007] The purpose of the present invention is to address the deficiencies in the prior art of gastric cancer diagnosis technology, especially the problem of inaccurate screening of non-cancerous gastric diseases, and to provide a metabolic marker with a high correlation with the development of gastric diseases from health, non-cancerous gastric diseases to gastric cancer, a urine metabolic marker for diagnosing gastric cancer and its application.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] The invention discloses a urine metabolic marker for diagnosing gastric cancer. The metabolic marker comprises glycylglycine, hippuric acid, 1-methylnicotinamide and guanidinoacetic acid.

[0010] Preferably, the metabolic markers further include: betaine, L-proline, N-cinnamoylglycine and thymine.

[0011] Preferably, the metabolic markers further include: L-serine, lactulose, dopamine and L-aspartyl-L-tryptophan.

[0012] Preferably, the metabolite marker is a combination of metabolite markers.

[0013] The invention discloses a composition, which comprises: glycylglycine, L-serine, hippuric acid, lactulose, betaine, L-proline, N-cinnamoylglycine, dopamine, L-aspartyl-L-tryptophan, 1-methylnicotinamide, thymine and guanidinoacetic acid.

[0014] The invention discloses the use of the metabolic marker in preparing a reagent for diagnosing / differentiating gastric cancer and non-cancerous gastric diseases.

[0015] Preferably, the sample used in the diagnostic process is selected from urine or body fluids.

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

[0017] Preferably, the non-cancerous gastric disease is selected from gastritis, gastric polyps and gastric adenoma.

[0018] Preferably, the sample used in the diagnostic process is selected from urine or body fluids.

[0019] The invention discloses a kit for diagnosing gastric cancer, characterized in that the kit comprises the metabolic marker.

[0020] The present invention discloses a kit for diagnosing gastric cancer, characterized in that the kit comprises the metabolite combination / composition.

[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 development of gastric diseases from health, non-cancerous gastric diseases to gastric cancer, which can be used to monitor the cancer pathological process, help to 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 metabolite marker 1-methylnicotinamide in the modeling group.

[0024] Figure 1b .Linear correlation curve of the metabolite marker L-proline in the modeling group.

[0025] Figure 1c .Linear correlation curve of the metabolite marker L-serine in the modeling group.

[0026] Figure 1d .Linear correlation curve of the metabolite marker dopamine in the modeling group.

[0027] Figure 1e .Linear correlation curve of the metabolite marker glycylglycine in the modeling group.

[0028] Figure 1f .Linear correlation curve of the metabolic marker guanidine acetate in the modeling group.

[0029] Figure 1g .Linear correlation curve of the metabolic marker N-cinnamoylglycine in the modeling group.

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

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

[0032] Figure 1j .Linear correlation curve of the metabolic marker betaine in the modeling group.

[0033] Figure 1k .Linear correlation curve of the metabolite marker thymine in the modeling group.

[0034] Figure 1l .Linear correlation curve of the metabolic marker L-aspartyl-L-tryptophan in the modeling group.

[0035] Figure 2 .Multivariate and multiclassification ROC curve analysis of 12 metabolic markers with high correlation with the development of gastric diseases in the modeling group.

[0036] Figure 3 .Multivariate and multiclassification ROC curve analysis of 12 metabolic markers with high correlation with the development of gastric diseases in the validation group. DETAILED DESCRIPTION

[0037] 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.

[0038] Example 1 Subject Condition and Sample Collection

[0039] Written informed consent was obtained from all subjects before the study. The inclusion and exclusion criteria for patients with non-cancerous gastric diseases and gastric cancer were as follows:

[0040] Inclusion criteria: (1) male or female aged ≥18 years; (2) patients with gastric cancer were diagnosed by the gold standard method of histopathological examination; (3) patients diagnosed as benign gastric diseases by biopsy / postoperative pathology or by comprehensive evaluation by clinicians, only patients with gastritis, gastric polyps, and gastric adenomas were included. Exclusion criteria: (1) pregnancy or lactation; (2) emergency or rescue; (3) history of malignant tumors or any anti-tumor treatment before sampling; (4) patients with multiple primary malignant tumors at the same time.

[0041] A total of 205 gastric cancer (GC) patients, 150 gastric non-cancer disease (GNCD) patients, and 236 healthy controls (HC) urine samples were collected from two medical centers. Urine samples were collected in the early morning on an empty stomach. All collected urine was centrifuged and stored in a -80 degree refrigerator. The samples were divided into a modeling group and a validation group, and the modeling group samples and the validation group samples were different samples, with the following specific distribution: the modeling group had 173 healthy control group (HC) urine samples, 107 gastric non-cancer disease (GNCD) urine samples, and 150 gastric cancer (GC) urine samples; the validation group included 63 healthy control group (HC) urine samples, 43 gastric non-cancer disease (GNCD) urine samples, and 55 gastric cancer (GC) urine samples (Table 1). First, statistical analysis was performed on the sample data of the modeling group to screen out metabolic markers with a high correlation with the development of gastric diseases, and to construct a diagnostic model that distinguishes between health, non-cancerous gastric diseases, and gastric cancer; then, the sample data of the validation group was used to verify the constructed diagnostic model.

[0042] Table 1. Subjects

[0043]

[0044] Example 2 Detection and identification of small molecule metabolites in urine samples

[0045] 1) Detection reagents: 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;

[0046] 2) Sample preparation: After thawing, take 40 μL of urine sample from the -80 degree refrigerator and put it into an extraction tube, add 400 μL of pre-cooled methyl tert-butyl ether and methanol mixed extract (methyl tert-butyl ether: methanol, volume ratio 3:1), vortex and ultrasonically mix, then add 360 μL of methanol-water mixed solution, vortex and centrifuge to separate the layers; take 300 μL of the lower aqueous phase solution in the extraction tube, add 900 μL of pre-cooled methanol solution to it, take 1000 μL of the supernatant after protein precipitation, spin dry, add 200 μL of water to re-dissolve, and use the aqueous phase polar substance LC-MS machine detection after re-dissolution;

[0047] 3) Detection of water-soluble small molecule metabolites: Waters ACQUTTY UPLC ® HSS T3 1.8µm 2.1*100mm column was used for small molecule separation; the liquid chromatography and mass spectrometer used ACQUITYUPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific);

[0048] The mobile phase parameters are as follows: 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;

[0049] 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), and the parameters used by Q Exactive were as follows: Full MS mode resolution was 70,000, the 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;

[0050] 4) Metabolomics data processing:

[0051] 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;

[0052] 5) Identification of metabolites: Use 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 / ), as well as the primary and secondary chromatograms and mass spectra of the standards separated on the same chromatographic column; when the retention time is within 0.1 min and the mass-to-charge ratio is less than 10 ppm, match with the database and the standard for identification.

[0053] Example 3. Screening of metabolic markers related to gastric disease development

[0054] 1) First, the metabolomic data of 173 HC samples, 107 GNCD samples, and 150 GC samples in the modeling group were compared pairwise, i.e., HC vs GC, HC vs GNCD, and GC vs GNCD, and the difference significance was analyzed. P The metabolites with differential expression value < 0.05 were then compared among the three groups of HC vs GC, HC vs GNCD, and GC vs GNCD. The metabolites that appeared in common among the three groups were retained by taking the intersection, and finally 16 significantly differential metabolites were obtained.

[0055] 2) For the above 16 differential metabolites, linear correlation analysis was performed in the three modeling groups HC, GNCD and GC. The results showed that most of the positively and negatively correlated metabolites showed significant increases or decreases. We screened out 12 metabolite markers with |R|>0.15 ( Figure 1a-1l and Table 2).

[0056] Table 2. 12 metabolic markers associated with the development of gastric diseases

[0057] Metabolic Markers-Chinese Metabolic Markers-English HMDB |R| value Glycylglycine Glycylglycine HMDB0011733 0.377 L-Serine L-Serine HMDB0000187 0.367 Hippuric acid Hippuric acid HMDB0000714 0.331 Lactose Lactulose HMDB0000740 0.290 Betaine Betaine HMDB0000043 0.261 L-Proline L-Proline HMDB0000162 0.253 N-Cinnamyl Glycine N-Cinnamoylglycine HMDB0011621 0.215 Dopamine Dopamine HMDB0000073 0.198 L-Aspartyl-L-Tryptophan L-Aspartyl-L-tryptophan HMDB0028764 0.201 1-Methylnicotinamide 1-Methylnicotinamide HMDB0000699 0.184 Thymine Thymine HMDB0000262 0.174 Guanidine acetic acid Glycocyamine HMDB0000128 0.161

[0058] Example 4 Multivariate multi-classification ROC curve analysis

[0059] In order to verify the diagnostic effect of the selected metabolites in distinguishing health, gastric non-cancerous diseases and gastric cancer, multivariate multi-classification ROC analysis was performed on the modeling group samples for 12 metabolites with high correlation with the development of gastric diseases. 3 / 4 of the modeling group sample data was randomly used as the training set (train), 1 / 4 as the test set (test), and SVM was randomly cycled 1000 times. By statistically calculating the average value of the final model accuracy, a diagnostic model for distinguishing health, gastric non-cancerous diseases and gastric cancer was constructed. The results showed that AUC = 0.842 ( Figure 2 ), the results showed that these 12 differential metabolites have good diagnostic efficacy in distinguishing health, non-cancerous gastric diseases and gastric cancer.

[0060] The ROC curve is a method to study the relationship between model sensitivity and specificity, with sensitivity as the vertical axis and 1-specificity as the horizontal axis. 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 a better diagnostic effect. If it is less than 0.5, it means that the model accuracy is poor.

[0061] Example 5 Multivariate multi-classification ROC curve analysis of different metabolic marker combinations

[0062] This embodiment has the same research object as that of embodiment 1 and the same detection and analysis method as that of embodiment 2, except that when constructing the diagnostic model using the SVM method, 8 metabolite markers, glycylglycine, hippuric acid, betaine, L-proline, cinnamylglycine, 1-methylnicotinamide, thymine and guanidinoacetic acid, and 4 metabolite markers, glycylglycine, hippuric acid, 1-methylnicotinamide and guanidinoacetic acid, were used respectively. The results showed that when 8 metabolite markers were used, AUC=0.845; when 4 metabolite markers were used, AUC=0.821, both of which had clinical diagnostic significance.

[0063] Example 6 Multivariate multi-classification ROC analysis using validation set data

[0064] Finally, using the validation group data, the above diagnostic model was validated and the results of multivariate and multiclassification ROC analysis were obtained;

[0065] Using the data from the modeling group, diagnostic models for distinguishing health, gastric non-cancerous diseases and gastric cancer were constructed with 12, 8 and 4 metabolic markers. Validation analysis was performed on additional validation group samples to construct multivariate and multi-classification ROC analysis.

[0066] When 12 metabolic markers, glycylglycine, L-serine, hippuric acid, lactulose, betaine, L-proline, N-cinnamylglycine, dopamine, L-aspartyl-L-tryptophan, 1-methylnicotinamide, thymine and guanidinoacetic acid, were used in combination, the results showed that AUC = 0.824 ( Figure 3 );

[0067] When eight metabolic markers, glycylglycine, hippuric acid, betaine, L-proline, cinnamylglycine, 1-methylnicotinamide, thymine, and guanidinoacetic acid, were used in combination, the results showed that AUC = 0.830;

[0068] When the combination of four metabolic markers, glycylglycine, hippuric acid, 1-methylnicotinamide, and guanidinoacetic acid, was used, the results showed that AUC = 0.812;

[0069] The above results show that the prediction model we established also has a good diagnostic effect in the validation group.

[0070] 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 urine metabolic marker for diagnosing gastric cancer, characterized in that: The metabolic markers include: glycylglycine, hippuric acid, 1-methylnicotinamide and guanidinoacetic acid.

2. The metabolic marker according to claim 1, characterized in that The metabolic markers also include: betaine, L-proline, N-cinnamoylglycine and thymine.

3. The metabolic marker according to claim 2, characterized in that The metabolic markers also include: L-serine, lactulose, dopamine and L-aspartyl-L-tryptophan.

4. Use of the metabolic marker according to any one of claims 1 to 3 in the preparation of a reagent for distinguishing between health, non-cancerous gastric diseases and gastric cancer.

5. The use according to claim 4, characterized in that The non-cancerous gastric disease is selected from gastritis, gastric polyps and gastric adenoma.

6. The use according to claim 4, characterized in that The sample used in the diagnostic process can be selected from urine, dried diaper or body fluids.

7. Use of the metabolic marker according to any one of claims 1 to 3 in preparing a kit for distinguishing between health, non-cancerous gastric diseases and gastric cancer.

8. The use according to claim 7, characterized in that The non-cancerous gastric disease is selected from gastritis, gastric polyps and gastric adenoma.

9. The use according to claim 7, characterized in that: The sample used in the diagnostic process can be selected from urine, dried diaper or body fluids.

10. A kit for diagnosing gastric cancer, characterized in that: The kit comprises the metabolic marker according to any one of claims 1 to 3.

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