CN118011003A - A biomarker composition for diagnosing gastric cancer and its application - Google Patents

Abstract

The invention discloses a biomarker composition for diagnosing gastric cancer and application thereof, and relates to the technical field of disease diagnosis. The composition is non-invasive when used for diagnosing gastric cancer, has higher specificity and sensitivity and high accuracy, can be suitable for urine and plasma biological samples, is convenient to obtain samples, has diversity in sample selection, is suitable for large-scale screening of common people, and is beneficial to formulating prevention and treatment strategies of gastric cancer.

Description

A biomarker composition for diagnosing gastric cancer and its application

Technical Field

The present invention relates to the technical field of disease diagnosis, and in particular to a biomarker composition for diagnosing gastric cancer and an application thereof.

Background technique

Gastric cancer (GC) is one of the common malignant tumors and refers to an epithelial malignant tumor that originates in the stomach. Currently, there are many clinical examination methods for gastric cancer, mainly including electronic gastroscopy, imaging examination and tumor marker detection.

Electronic gastroscopy refers to the use of gastroscopy to visually observe the specific conditions inside the patient's stomach. Once an ulcer or atrophic gastritis is 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 testing in the elderly, and requires a lot of manpower and material resources. Therefore, it is not suitable for large-scale screening of the general population.

Imaging examinations mainly include abdominal CT, abdominal ultrasound, and positron emission tomography (PET) examinations, which have high spatial resolution and powerful image post-processing technology support, and can explain the changes in the disease from multiple angles through morphological characteristics, density, and enhancement methods. Compared with electronic 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. It helps to diagnose the degree of differentiation, pathological type, TNM staging of gastric cancer, and evaluate the efficacy of chemotherapy. 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 CA72-4, CEA and CA199. However, at present, the sensitivity and accuracy of diagnosing gastric cancer by tumor markers are not high. In addition, the test results of tumor markers are easily affected by factors such as the patient's diet intake, living habits and drugs before the test. Therefore, it is only used as a reference for cancer screening and cannot be used to accurately screen and diagnose early gastric cancer, let alone as a gold standard for gastric 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.

Therefore, the prior art still needs to be improved and developed.

Summary of the invention

In view of the above-mentioned deficiencies in the prior art, the object of the present invention is to provide a biomarker composition for diagnosing gastric cancer and its application, aiming to solve the problem that the existing methods for diagnosing gastric cancer are either invasive or inaccurate.

The technical solution of the present invention is as follows:

In a first aspect of the present invention, a biomarker composition for diagnosing gastric cancer is provided, wherein the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid and arginine.

Optionally, the biomarker composition for diagnosing gastric cancer further comprises N-formyl-L-methionine.

Optionally, the biomarker composition for diagnosing gastric cancer further comprises suberic acid.

Optionally, the biomarker composition for diagnosing gastric cancer also includes 5'-methylthioadenosine.

Optionally, the biomarker composition for diagnosing gastric cancer further comprises 5-aminolevulinic acid.

Optionally, the biomarker composition for diagnosing gastric cancer further comprises pyroglutamate.

Optionally, the biomarker composition for diagnosing gastric cancer further includes at least one of 4-trimethylaminobutyric acid, L-tyrosine, desoleucine and acylcarnitine 10:1.

Optionally, the biomarker composition for diagnosing gastric cancer consists of guanidineacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid, pyroglutamic acid, 4-trimethylaminobutyric acid, L-tyrosine, deleucine and acylcarnitine 10:1.

The second aspect of the present invention provides a use of the biomarker composition for diagnosing gastric cancer as described above in the preparation of a product for diagnosing gastric cancer.

Optionally, the product is a reagent or a kit.

Optionally, the sample used by the product for diagnosing gastric cancer includes at least one of serum, plasma, blood, dried blood spots, urine, dried urine spots and body fluids.

Optionally, the kit includes quality control products or standards.

Beneficial effects: The biomarker composition for diagnosing gastric cancer described in the present invention includes guanidinoacetic acid and arginine. The composition is non-invasive when used for gastric cancer diagnosis, and has high specificity and sensitivity and high accuracy. Usually, the types of metabolites detected in urine and plasma are very different. For example, urea can usually only be detected in urine, while lipids and glucose can usually only be detected in blood. Even if some metabolites can be detected in both blood and urine, they may not necessarily become metabolic markers for gastric cancer diagnosis. The group of metabolite marker compositions provided by the present invention can be applied to both urine and plasma biological samples for the diagnosis of gastric cancer. Not only is the sample acquisition convenient, but the sample selection is also diverse, which is suitable for large-scale screening of the general population and helps to formulate prevention and treatment strategies for gastric cancer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a ROC curve diagram of urine samples of the modeling group in Example 3 of the present invention.

FIG. 2 is a ROC curve diagram of urine samples of the validation group in Example 3 of the present invention.

FIG3 is a ROC curve diagram of plasma samples in Example 7 of the present invention.

Detailed ways

The present invention provides a biomarker composition for diagnosing 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.

Unless otherwise defined, all technical terms and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

Metabolomics is an emerging discipline developed in the late 1990s and is widely used in clinical diagnosis, etiology and pathological mechanism research, and clinical medication guidance. Metabolomics can achieve high-throughput detection of small molecule metabolites in the body through standard prepared biological samples such as serum, plasma, and urine, and screen for significantly different metabolic markers by combining multivariate statistical analysis. A group of metabolic markers related to disease diagnosis and typing can be used for disease diagnosis and typing.

The metabolomics method is used for gastric cancer diagnosis. Metabolites with significant differences can be screened through changes in metabolites in the organism, which are used for the diagnosis and typing of gastric cancer, and have broad prospects in the study of biomarkers. Although the metabolomics method 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, is non-invasive, and sample acquisition is more convenient, and has the potential to become a tool for early diagnosis and prognosis evaluation. Therefore, the embodiment of the present invention screens out metabolites for diagnosing gastric cancer by a metabolomics method, and specifically provides a biomarker composition for diagnosing gastric cancer, wherein the biomarker composition for diagnosing gastric cancer includes guanidinoacetic acid and arginine. The composition is non-invasive for gastric cancer diagnosis, and has high specificity and sensitivity, high accuracy, and can be applied to urine and plasma biological samples. Not only is sample acquisition convenient, but sample selection is also diverse, suitable for large-scale screening of the general population, and helps to formulate prevention and treatment strategies for gastric cancer.

In some embodiments, the biomarker composition for diagnosing gastric cancer further comprises N-formyl-L-methionine. That is, the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine and N-formyl-L-methionine. Or the biomarker composition for diagnosing gastric cancer consists of guanidinoacetic acid, arginine and N-formyl-L-methionine.

In some embodiments, the biomarker composition for diagnosing gastric cancer further comprises suberic acid. That is, the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine and suberic acid, or the biomarker composition for diagnosing gastric cancer is composed of guanidinoacetic acid, arginine and suberic acid, or the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine, N-formyl-L-methionine and suberic acid, or the biomarker composition for diagnosing gastric cancer is composed of guanidinoacetic acid, arginine, N-formyl-L-methionine and suberic acid.

In some embodiments, the biomarker composition for diagnosing gastric cancer further comprises 5'-methylthioadenosine. That is, the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine, suberic acid and 5'-methylthioadenosine, or the biomarker composition for diagnosing gastric cancer consists of guanidinoacetic acid, arginine, suberic acid and 5'-methylthioadenosine; or the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid and 5'-methylthioadenosine, or the biomarker composition for diagnosing gastric cancer consists of guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid and 5'-methylthioadenosine.

In some embodiments, the biomarker composition for diagnosing gastric cancer further comprises 5-aminolevulinic acid. That is, the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine, suberic acid, 5'-methylthioadenosine and 5-aminolevulinic acid, or the biomarker composition for diagnosing gastric cancer is composed of guanidinoacetic acid, arginine, suberic acid, 5'-methylthioadenosine and 5-aminolevulinic acid, or the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine and 5-aminolevulinic acid, or the biomarker composition for diagnosing gastric cancer is composed of guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine and 5-aminolevulinic acid.

In some embodiments, the biomarker composition for diagnosing gastric cancer further comprises pyroglutamic acid. That is, the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid and pyroglutamic acid, or the biomarker composition for diagnosing gastric cancer is composed of guanidinoacetic acid, arginine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid and pyroglutamic acid, or the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid and pyroglutamic acid, or the biomarker composition for diagnosing gastric cancer is composed of guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid and pyroglutamic acid.

In some embodiments, the biomarker composition for diagnosing gastric cancer further comprises at least one of 4-trimethylaminobutyric acid, L-tyrosine, deleucine and acylcarnitine 10:1 (10:1 means that the side chain has 10 carbons and 1 double bond). That is, the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid, pyroglutamic acid and at least one of 4-trimethylaminobutyric acid, L-tyrosine, deleucine and acylcarnitine 10:1.

In some embodiments, the biomarker composition for diagnosing gastric cancer consists of guanidineacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid, pyroglutamic acid, 4-trimethylaminobutyric acid, L-tyrosine, deleucine and acylcarnitine 10:1.

In the above embodiments, each composition has high specificity and sensitivity when used for gastric cancer diagnosis, high accuracy, and can be applied to urine and plasma biological samples. Not only is the sample acquisition convenient, but the sample selection is also diverse, and it is suitable for large-scale screening of the general population.

The present invention also provides an application of the biomarker composition for diagnosing gastric cancer as described above in the present invention in the preparation of a product for diagnosing gastric cancer. Specifically, this embodiment provides an application of a reagent for detecting the biomarker composition in the preparation of a product for diagnosing gastric cancer.

In some embodiments, the product is a reagent or a kit. The reagent or kit has the advantages of high specificity, high sensitivity, high accuracy, and applicability to urine and plasma biological samples when used to diagnose gastric cancer, and has the potential to be used as a tool for early diagnosis and prognosis assessment of gastric cancer, achieving non-invasive diagnosis, and helping to formulate prevention and treatment strategies for gastric cancer.

In some embodiments, the sample used by the product for diagnosing gastric cancer includes at least one of serum, plasma, blood, dried blood spots, urine, dried diaper spots, and body fluids.

In some embodiments, the kit includes a control or standard.

The following describes it in detail through specific embodiments.

Example 1 Construction of gastric cancer urine specific metabolite ion pairs

(1) Sample collection

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

The inclusion and exclusion criteria for gastric cancer patients were as follows:

Inclusion criteria: (1) male or female aged ≥18 years; (2) patients diagnosed with primary gastric cancer by biopsy, postoperative pathological confirmation, or clinical evaluation by a clinician.

Exclusion criteria: (1) pregnancy or lactation; (2) emergency or rescue; (3) history of malignant tumor or any anti-tumor treatment before sampling; (4) concurrent multiple primary malignant tumors.

A total of 205 gastric cancer patients (GC, GC group) and 236 healthy controls (HC, HC group) were collected for urine samples (Table 1). Urine samples were collected in the early morning on an empty stomach. All collected urine was centrifuged and stored in a -80℃ refrigerator.

(2) Urine targeted metabolomics analysis

a. Analytical reagents

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

b. Sample preparation

The urine sample was taken out from the -80 °C refrigerator, thawed on ice, and vortexed for 10 s to mix. 40 μL of urine was taken and placed in 400 μL of pre-cooled mixed solution of methyl tert-butyl ether and methanol (the volume ratio of methyl tert-butyl ether and methanol was 3:1), and vortexed to obtain the sample extract;

Add 360 μL of a methanol-water mixture (methanol to water volume ratio of 3:1) to the sample extract, sonicate, let stand, vortex, and centrifuge to separate layers;

Take 300 μL of the lower layer liquid into a centrifuge tube, and add 900 μL of ice methanol to precipitate the protein; 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 for reconstitution, centrifuge at room temperature for 5 minutes (12000 rpm), take 180 μL of the supernatant into a 2 mL glass injection vial, which is the aqueous phase, and detect it on a machine (LC-MS, liquid chromatography-mass spectrometry).

c. Construction of ion pairs for metabolite detection

Based on public databases such as the metabolomics database (Metlin database (https://metlin.scripps.edu), mass spectrometry database (http://www.massbank.jp/), standard databases and literature, we collected ion pairs of urine-specific metabolites. In the GC group and HC group, 10 samples were randomly selected and mixed for testing, and finally a database of 2138 urine-specific metabolites was obtained.

Example 2 Targeted Detection of Gastric Cancer Urine Specific Metabolites

(1) Chromatographic and mass spectrometric detection conditions

The liquid chromatography detection conditions are as follows:

a. Instrument and column information: Waters ACQUTTY HSS T3 1.8μm2.1mm×100mmcolumn column for small molecule separation; the instrument used is Agilent 1290 liquid phase;

b. Mobile phase parameters are as follows: Mobile phase A is an aqueous solution containing 0.1% formic acid (the content of formic acid is by mass, and 0.1% formic acid in the mobile phase hereinafter refers to the mass content); 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;

The mass spectrometry parameters are as follows:

a. The mass spectrometer uses Agilent 6495 triple quadrupole;

b. Mass spectrometry data were collected in the scanning mode of Multiple Reaction Monitoring (MRM) mode (including positive and negative modes). The ionization mode used for mass spectrometry was an electrospray ionization (ESI) source with a spray voltage of 3000 V, a nebulizer pressure of 20 psi, and a sheath gas flow rate of 11 L/min. The collision energy (CE) voltage was optimized within the range of 5 ev to 80 ev based on the metabolite ion pair, and the dwell time was optimized within the range of 5 ms to 50 ms based on the intensity of the metabolite ion pair.

(2) MRM spectrum peak area preprocessing

Based on the urine-specific metabolite database constructed by ion pair MRM detection, targeted qualitative analysis is performed for the metabolite detection of urine. The sample preparation method is the same as the sample preparation method of Example 1. First, small molecule metabolites in urine are separated by chromatography, and the m/z value of a specific parent ion is detected by the MRM mode of a triple quadrupole, as well as the m/z value of the daughter ion falling from the parent ion under collision energy. The premise is that when the mass-to-charge ratio of this pair of parent and daughter ions matches the ion pair in the metabolite database, the signal intensity of the specific ion will be detected. Then, the mass spectrum is checked to ensure that the shape and signal quality of the ion peak are good, and noise removal or baseline correction is performed. Subsequently, peak identification is performed for each parent ion and daughter ion pair, including determining the position and shape of the peak, and after identifying the peak, the peak area of each peak is calculated, and finally all mass spectrum peak area integral data are derived for preservation.

Example 3 Screening of metabolic markers for gastric cancer diagnosis and construction of diagnostic model

(1) Subjects

The 236 urine samples of the HC group in Example 1 were divided into a modeling group and a verification group. At the same time, the 205 urine samples of the GC group in Example 1 were also divided into a modeling group and a verification group, and the urine samples of the modeling group were different from those of the verification group. Specifically, the number of people in the modeling group: 173 urine samples of the HC group and 150 urine samples of the GC group; the number of people in the verification group: 63 urine samples of the HC group and 55 urine samples of the GC group (as shown in Table 1). The screening of metabolic markers for gastric cancer diagnosis and the construction of the diagnostic model were both carried out using urine samples of the modeling group, and the urine samples of the verification group were used to finally verify the effectiveness of the model.

Table 1. Collection of urine samples from subjects

(2) Screening of metabolic markers for gastric cancer diagnosis

Based on the urine sample data of the modeling group (the urine sample data of the modeling group and the verification group were obtained by the test analysis method of Example 2), supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was performed on the HC group and the GC group using the peak area integration data, and then based on the VIP value of the OPLS-DA result>1, and the P value<0.05 as the significant difference as the intersection condition, 11 gastric cancer metabolic markers were screened out, as shown in Table 2 below, where the VIP (Variable important in the projection) value refers to the variable importance value.

Table 2. Eleven important metabolic markers distinguishing GC group from HC group

(3) Construction of gastric cancer diagnostic model

In order to verify the high efficiency of the 11 differential metabolites screened in distinguishing the HC group from the GC group, a multivariate ROC (receiver operating characteristic) curve analysis was performed. Three-quarters of the urine sample data of the modeling group were randomly used as training sets (training), and one-quarter were used as test sets (test) for learning. The support vector machine (SVM) was used to randomly cycle Diego 1000 times. The diagnostic model for distinguishing the HC group from the GC group was constructed by statistically calculating the average value of the final model accuracy. The ROC curve is a method for studying the relationship between model sensitivity and specificity. Sensitivity is used as the ordinate and 1-specificity is used as the abscissa. The evaluation basis is to compare the area under the ROC curve (AUC). When the AUC is greater than 0.5, the closer the 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.

The results showed that the AUC of the urine samples in the modeling group was 0.936 (sensitivity = 83.8%, specificity = 86.0%) (as shown in FIG1 ), indicating that the diagnostic model can effectively diagnose gastric cancer patients.

Furthermore, in order to verify the effectiveness of the constructed diagnostic model, the urine samples of the validation group were used as unknown samples in this embodiment, and were put into the diagnostic model constructed using the above 11 urine metabolic markers for verification. The verification results were: AUC = 0.906 (sensitivity = 81.8%, specificity = 82.5%) (as shown in Figure 2). The above results show that the diagnostic model established in this embodiment also has a good diagnostic effect in the validation group.

Example 4 Construction of a gastric cancer diagnostic model using seven urine metabolic markers

The research object of this embodiment is the same as that of embodiment 1, the detection and analysis method of embodiment 2 is the same, the setting method of the modeling group and the verification group is the same as that of embodiment 3, and the difference from embodiment 3 is that when the urine sample data of the modeling group is used to construct the gastric cancer diagnosis model by the SVM method: 7 urine metabolite markers, namely guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid and pyroglutamic acid are used. In the urine samples of the modeling group, the combination of guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5'-methylthioadenosine, 5-aminolevulinic acid and pyroglutamic acid combined to diagnose gastric cancer has an AUC of 0.928, a sensitivity of 87.2%, and a specificity of 88.6%, which has clinical diagnostic significance.

Furthermore, in order to verify the effectiveness of the constructed diagnostic model, the urine samples of the validation group were used as unknown samples in this embodiment and put into the diagnostic model constructed using the above 7 urine metabolite markers. The verification results were: AUC = 0.925, sensitivity = 88.5%, and specificity = 83.1%. The above results show that the diagnostic model established in this embodiment also has a good diagnostic effect in the validation group.

Example 5 Construction of a gastric cancer diagnostic model using four urine metabolic markers

This embodiment has the same research object as that of embodiment 1, the same detection and analysis method as that of embodiment 2, and the same setting method as that of the modeling group and the verification group as that of embodiment 3. The difference from embodiment 3 is that when constructing a gastric cancer diagnosis model using the SVM method using the urine sample data of the modeling group, four urine metabolite markers, namely guanidinoacetic acid, arginine, N-formyl-L-methionine and suberic acid, are used. In the urine samples of the modeling group, the combination of guanidinoacetic acid, arginine, N-formyl-L-methionine and suberic acid has an AUC of 0.894, a sensitivity of 86.8%, and a specificity of 80% for diagnosing gastric cancer, which has clinical diagnostic significance.

Furthermore, in order to verify the effectiveness of the constructed diagnostic model, the urine samples of the validation group were used as unknown samples in this embodiment and put into the diagnostic model constructed using the above four urine metabolite markers. The verification results were: AUC = 0.919, sensitivity = 88.5%, and specificity = 81.4%. The above results show that the diagnostic model established in this embodiment also has a good diagnostic effect in the validation group.

Example 6 Construction of a gastric cancer diagnostic model using two urine metabolic markers

This embodiment has the same research object as that of embodiment 1, the same detection and analysis method as that of embodiment 2, and the same setting method as that of the modeling group and the verification group as that of embodiment 3. The difference from embodiment 3 is that two urine metabolite markers, guanidinoacetic acid and arginine, are used when constructing a gastric cancer diagnosis model using the SVM method using urine sample data of the modeling group. In the urine samples of the modeling group, the AUC of guanidinoacetic acid and arginine combined for the diagnosis of gastric cancer is 0.891, the sensitivity is 82.5%, and the specificity is 82.5%, which has clinical diagnostic significance.

Furthermore, in order to verify the effectiveness of the constructed diagnostic model, the urine samples of the validation group were used as unknown samples in this embodiment and put into the diagnostic model constructed using the above two urine metabolite markers. The verification results were: AUC = 0.854, sensitivity = 76.4%, and specificity = 84.7%. The above results show that the diagnostic model established in this embodiment also has a good diagnostic effect in the validation group.

Example 7 Targeted detection of metabolic markers in plasma samples and diagnosis of gastric cancer

(1) Sample collection

All subjects obtained written informed consent before participating in the study. The inclusion and exclusion criteria for gastric cancer patients were as follows:

Inclusion criteria: (1) male or female aged ≥18 years; (2) patients diagnosed with primary gastric cancer by biopsy, postoperative pathological confirmation, or clinical evaluation by a clinician.

Exclusion criteria: (1) pregnancy or lactation; (2) emergency or rescue; (3) history of malignant tumor or any anti-tumor treatment before sampling; (4) concurrent multiple primary malignant tumors.

Plasma samples were collected from 242 gastric cancer patients and 236 healthy controls (Table 3), namely the GC group and the HC group. Plasma samples were collected in the early morning on an empty stomach. All collected plasma samples were stored in a -80℃ refrigerator.

Table 3. Plasma samples of subjects

Plasma samples HC group GC Group Number of people 236 242

(2) Sample preparation

The plasma sample was taken out from the -80°C freezer, thawed on ice, and vortexed for 10 seconds to mix. 100 μL of plasma was taken and placed in 1000 μL of pre-cooled methyl tert-butyl ether and methanol mixture (the volume ratio of methyl tert-butyl ether to methanol was 3:1), and vortexed to obtain the sample extract;

Add 500 μL of methanol-water mixture (methanol to water volume ratio of 3:1) to the sample extract, sonicate, let stand, vortex, and centrifuge to separate layers;

Take 400 μL of the lower layer liquid into a centrifuge tube, and add 1100 μL of ice methanol thereto 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 at room temperature for 15 minutes. Centrifuge for 5 minutes (12000 rpm), then take 180 μL of supernatant into a 2 mL glass injection vial, which is the aqueous phase and is detected by LC-MS.

(3) Targeted detection of metabolic markers for gastric cancer diagnosis

1) Liquid chromatography and mass spectrometry detection conditions

The liquid chromatography conditions were as follows:

a. Instrument and column information: Waters ACQUTTY HSS T3 1.8μm

2.1mm×100mm column for small molecule separation; the instrument used is Agilent 1290 liquid phase;

b. 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;

The mass spectrometry parameters are as follows:

a. The mass spectrometer uses Agilent 6495 triple quadrupole;

b. Mass spectrometry data were collected in MRM scanning mode (including positive and negative modes). The ionization mode used for mass spectrometry was ESI source, spray voltage was 3000 V, nebulization pressure was 20 psi, and sheath gas flow rate was 11 L/min. The collision energy voltage was optimized within 5 ev to 80 ev based on the metabolite ion pair, and the residence time was optimized within 5 ms to 50 ms based on the metabolite ion pair intensity.

2) Targeted detection of metabolic markers for gastric cancer diagnosis

The metabolite detection and analysis methods of this embodiment are the same as those of Example 2. The metabolite database constructed based on ion pair MRM detection is targeted at 11 gastric cancer metabolic markers as shown in Table 1, including suberic acid, N-formyl-L-methionine, 4-trimethylaminobutyric acid, 5'-methylthioadenosine, guanidinoacetic acid, arginine, L-tyrosine, deleucine, acylcarnitine 10:1, 5-aminolevulinic acid and pyroglutamic acid, and targeted qualitative detection is performed in plasma samples, and finally the mass spectrometry peak area integral data is exported and saved.

(4) Diagnosis of gastric cancer using 11 metabolic markers

In order to verify the diagnostic effect of the 11 metabolite markers in distinguishing the HC group from the GC group in plasma samples, a multivariate ROC curve analysis was performed. The results showed: AUC = 0.960, sensitivity = 93.4%, specificity = 81.4% (as shown in Figure 3).

The above results show that the above 11 metabolites can be detected in urine and plasma samples, and these metabolites can effectively distinguish the gastric cancer group from the healthy control group, showing excellent diagnostic efficacy. Therefore, unlike most existing diagnostic markers that are only applicable to a single biological sample, the biomarker composition for diagnosing gastric cancer provided by the present invention is applicable to urine and plasma biological samples, which is not only convenient for sample acquisition, but also has diversity in sample selection. At the same time, it is non-invasive when used to diagnose gastric cancer, and has high sensitivity and specificity, high accuracy, and is suitable for large-scale screening of the general population. It has the potential to become a tool for early diagnosis and prognosis evaluation of gastric cancer, and thus helps to formulate prevention and treatment strategies for gastric cancer.

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 (12)

1. A biomarker composition for diagnosing gastric cancer, characterized in that the biomarker composition for diagnosing gastric cancer comprises guanidinoacetic acid and arginine.

2. The biomarker composition for diagnosing gastric cancer according to claim 1, further comprising N-formyl-L-methionine.

3. The biomarker composition for diagnosing gastric cancer according to claim 1 or 2, further comprising suberic acid.

4. The biomarker composition for diagnosing gastric cancer according to claim 3, further comprising 5' -methylthioadenosine.

5. The biomarker composition for diagnosing gastric cancer according to claim 4, further comprising 5-aminolevulinic acid.

6. The biomarker composition for diagnosing gastric cancer according to claim 5, further comprising pyroglutamic acid.

7. The biomarker composition for diagnosing gastric cancer according to claim 6, further comprising at least one of 4-trimethylaminobutyric acid, L-tyrosine, norleucine and acyl carnitine 10:1.

8. The biomarker composition for diagnosing gastric cancer according to claim 7, consisting of guanidinoacetic acid, arginine, N-formyl-L-methionine, suberic acid, 5' -methylthioadenosine, 5-aminolevulinic acid, pyroglutamic acid, 4-trimethylaminobutyric acid, L-tyrosine, norleucine and acyl carnitine 10:1.

9. Use of a biomarker composition for diagnosing gastric cancer according to any of claims 1 to 8 in the manufacture of a product for diagnosing gastric cancer.

10. The use according to claim 9, wherein the product is a reagent or a kit.

11. The use according to claim 9, wherein the sample used by the product in the diagnosis of gastric cancer comprises at least one of serum, plasma, blood, dried blood flakes, urine, dried urine flakes and body fluid.

12. The use of claim 10, wherein the kit comprises a quality control or standard.