Screening method of metabolic marker for distinguishing stomach non-cancerous diseases and stomach cancer and application of screening method

Abstract

The invention provides a screening method of metabolic markers for distinguishing stomach non-cancerous diseases and stomach cancer and application of the screening method, and belongs to the technical field of in-vitro diagnosis. By analyzing clinical indexes and metabonomics data of blood samples of patients with stomach non-cancerous diseases and stomach cancer, 13 specific blood marker compositions related to stomach cancer are found, and the composition can be used for developing a non-invasive, non-invasive and high-accuracy diagnosis product for distinguishing stomach non-cancerous diseases and stomach cancer. Effective clinical treatment strategies and guidance are provided for early discovery, early diagnosis and early intervention of gastric cancer.

Description

Technical Field

[0001] The present invention belongs to the technical field of in vitro diagnosis, and in particular relates to a screening method for a metabolic marker for distinguishing non-cancerous gastric diseases from gastric cancer and an application thereof. Background Art

[0002] Gastric cancer (GC) refers to an epithelial malignant tumor that originates in the stomach. Since patients with early gastric cancer have no specific symptoms and signs, the rate of patients seeking medical treatment is low. Many patients are already in the late stage when they are discovered, and the overall survival rate is less than 50%. Therefore, early diagnosis and early treatment are of great significance to improving the treatment effect of gastric cancer and reducing the mortality rate of patients.

[0003] At present, common screening methods for gastric cancer mainly include gastroscopy, serum tumor markers and imaging examination (ultrasound examination) and other technologies. Among them, serum tumor markers, such as CA72-4, carcinoembryonic antigen (CEA), CA19-9, alpha-fetoprotein (AFP) and CA125 can be used for tumor development, efficacy evaluation and prognosis assessment, but serum tumor markers have limited screening value for early gastric cancer, and their specificity and sensitivity are low. Summary of the invention

[0004] The purpose of the present invention is to provide a screening method for metabolic markers for distinguishing non-cancerous gastric diseases from gastric cancer and its application.

[0005] The present invention provides a blood marker composition for distinguishing gastric non-cancerous diseases from gastric cancer. The blood marker composition comprises: a ratio of creatinine to creatine kinase concentration, a ratio of direct bilirubin to alpha-fetoprotein concentration, a ratio of direct bilirubin to creatine kinase concentration, a ratio of alpha-fetoprotein to carcinoembryonic antigen concentration, a ratio of alpha-fetoprotein to glycosylated serum protein concentration, a ratio of carcinoembryonic antigen to creatine kinase concentration, a ratio of carcinoembryonic antigen concentration to prothrombin time activity, a ratio of creatine kinase to glycosylated serum protein concentration, a ratio of prothrombin time activity to glycosylated serum protein concentration, a peak intensity ratio of mannose to triglyceride 54:7, a peak intensity ratio of mannose to phosphatidylcholine 38:5, a peak intensity ratio of mannose to ceramide d 40:2, and a peak intensity ratio of mannose to lysophosphatidylcholine 18:2.

[0006] The present invention also provides the use of a reagent or a kit for detecting the blood marker composition described in the above scheme in the preparation of a product for distinguishing non-cancerous gastric diseases from gastric cancer.

[0007] Preferably, the non-cancerous gastric disease includes one or more of gastritis, gastric polyps and gastric adenoma.

[0008] The present invention also provides the use of a reagent or a kit for detecting the blood marker composition described in the above scheme in the preparation of a gastric disease diagnosis product.

[0009] Preferably, the reagent or kit for detecting the blood marker composition according to claim 1 comprises a liquid chromatography-mass spectrometry detection reagent and a gas chromatography-mass spectrometry detection reagent.

[0010] The present invention also provides a method for screening a blood marker composition for distinguishing non-cancerous gastric diseases from gastric cancer, comprising the following steps:

[0011] The invention relates to collecting plasma samples from patients with non-cancerous gastric diseases and patients with gastric cancer respectively; separating an organic phase and an aqueous phase from the plasma; wherein the organic phase is non-polar metabolites and the aqueous phase is polar metabolites; obtaining polar metabolites and derivatized polar metabolites from the aqueous phase; using gas chromatography-mass spectrometry to detect the derivatized polar metabolites in the aqueous phase and using liquid chromatography-mass spectrometry to detect the polar metabolites in the aqueous phase and the non-polar metabolites in the organic phase respectively, to obtain metabolome detection data; performing preprocessing and metabolite identification on the metabolome detection data, to obtain metabolomics data; collecting blood routine tests and blood biochemical tests of the patients. The method comprises the following steps: measuring clinical indicators and obtaining clinical indicator data; performing data standardization processing on the metabolomics data and clinical indicator data, respectively, to obtain standardized metabolomics data and clinical indicator data; screening the standardized metabolomics data and clinical indicator data, respectively, to obtain preliminarily screened clinical indicator data and metabolome characteristic data; calculating the ratio of the composition between all possible two variables of clinical indicators and metabolome characteristics according to the preliminarily screened clinical indicator data and metabolome characteristic data, selecting the composition of the TOP 100 feature pairs according to the order of significance from small to large based on the P-value, and forming a data set with the clinical indicators and metabolome characteristics after the ratio is calculated; and using Lasso regression to screen out a blood marker composition for distinguishing gastric non-cancerous diseases from gastric cancer from the data set.

[0012] Preferably, the gas chromatography conditions for detecting the derivatized polar metabolites in the aqueous phase include: a chromatographic column of MS chromatographic column; carrier gas is high-purity helium; flow rate is 1 mL / min; heating program is: initial temperature 50°C, maintained for 2 minutes, continuously heated to 330°C at 15°C / min, maintained for 2 minutes; injection port temperature is: 280°C; split injection is adopted, and the split ratio is 10:1; mass spectrometry conditions for detecting derivatized polar metabolites in the aqueous phase include: electron ionization source; temperature is 250°C; electron energy is 70 eV; scanning range is 50-500 amu; detection voltage is 1.2 kV.

[0013] Preferably, the liquid chromatography conditions for detecting polar metabolites in the aqueous phase include: a chromatographic column of Waters ACQUTTY HSS T3 chromatographic column; the column temperature of the chromatographic column is 40°C; the mobile phase includes mobile phase A and mobile phase B, the mobile phase A is an aqueous solution containing 0.1% formic acid by volume, and the mobile phase B is an acetonitrile solution containing 0.1% formic acid by volume; the flow rate of the mobile phase is 0.4mL / min; the separation elution gradient is: 0min, 1% mobile phase B by volume; 13min, 70% mobile phase B by volume, 13.01min, 99% mobile phase B by volume; 18min, 99% mobile phase B by volume; 18.01min, 1% mobile phase B by volume; 22min, 1% mobile phase B by volume; the sample injection volume is 3μL; the temperature of the automatic sampler is 10°C.

[0014] Preferably, the liquid chromatography conditions for detecting the non-polar metabolites in the organic phase include: a chromatographic column of Waters ACQUTTY BEH C8; column temperature is 60°C; mobile phase includes mobile phase A and mobile phase B, mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate, and mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile / isopropanol is 7 / 3); the flow rate of the mobile phase is 0.4mL / min; the separation elution gradient is: 0-1min, 55% mobile phase B by volume; 4min, 75% mobile phase B by volume; 12min, 89% mobile phase B by volume; 12min, 100% mobile phase B by volume; 19.5min, 100% mobile phase B by volume; 19.51min, 55% mobile phase B by volume; 24min, 55% mobile phase B by volume; injection volume is 2μL; the temperature of the automatic sampler is 10°C.

[0015] Preferably, the mass spectrometry conditions for detecting polar metabolites in the aqueous phase and non-polar metabolites in the organic phase include: using full scan and data-dependent acquisition to obtain primary and secondary mass spectrometry data; the full scan mass spectrum range is 100-1500Da; the scanning range of the secondary scan mode is 100-310, 300-710 and 700-1500Da; using positive and negative ionization modes to collect data; automatic gain control (AGC) is 3E+6; Maximum IT is 200ms; the resolution of the first-level full scan is 70,000FWHM; in the second-level scanning mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5m / z, the automatic gain control (AGC) is 1E+5, the maximum ion injection time is 50ms, and the relative collision energy (HCD) is 30eV; the ion spray voltage is 3,500V in positive mode; the ion spray voltage is 3,000V in negative mode; the nebulizer is 20psi; the sheath gas temperature is 400℃, and the sheath gas flow rate is 10L / min.

[0016] The present invention provides a blood marker composition for distinguishing gastric non-cancerous diseases from gastric cancer, the blood marker composition comprising: a ratio of creatinine to creatine kinase concentration, a ratio of direct bilirubin to alpha-fetoprotein concentration, a ratio of direct bilirubin to creatine kinase concentration, a ratio of alpha-fetoprotein to carcinoembryonic antigen concentration, a ratio of alpha-fetoprotein to glycosylated serum protein concentration, a ratio of carcinoembryonic antigen to creatine kinase concentration, a ratio of carcinoembryonic antigen concentration to prothrombin time activity, a ratio of creatine kinase to glycosylated serum protein concentration, a ratio of prothrombin time activity to glycosylated serum protein concentration, a peak intensity ratio of mannose to triglyceride 54:7 (18:2 / 18:2 / 18:3), a peak intensity ratio of mannose to phosphatidylcholine 38:5 (18:1 / 20:4), a peak intensity ratio of mannose to ceramide d The peak intensity ratio of 40:2 (d18:2 / 22:0) and the peak intensity ratio of mannose to lysophosphatidylcholine 18:2. The present invention analyzes the clinical indicators and metabolomics data of blood samples of patients with non-cancerous gastric diseases and gastric cancer, and finds 13 specific blood marker compositions related to gastric cancer. It can be used to develop a non-invasive, non-invasive, and highly accurate diagnostic product for distinguishing non-cancerous gastric diseases from gastric cancer, and provides effective clinical treatment strategies and guidance for early detection, early diagnosis, and early intervention of gastric cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 This is a graph showing the results of a multivariate ROC curve analysis for distinguishing between a gastric non-cancerous disease group and a gastric cancer group based on a combination of 13 blood biomarkers in the modeling group;

[0019] Figure 2 The figure shows the result of multivariate ROC curve analysis for distinguishing gastric non-cancerous disease group from gastric cancer group based on the 13 blood biomarker combination in the validation group. DETAILED DESCRIPTION

[0020] The present invention provides a blood marker composition for distinguishing gastric non-cancerous diseases from gastric cancer, wherein the blood marker composition comprises: a ratio of creatinine to creatine kinase concentration (creatinine / creatine kinase), a ratio of direct bilirubin to alpha-fetoprotein concentration (direct bilirubin / alpha-fetoprotein), a ratio of direct bilirubin to creatine kinase concentration (direct bilirubin / creatine kinase), a ratio of alpha-fetoprotein to carcinoembryonic antigen concentration (alpha-fetoprotein / carcinoembryonic antigen), a ratio of alpha-fetoprotein to glycosylated serum protein concentration (alpha-fetoprotein / glycosylated serum protein), a ratio of carcinoembryonic antigen to creatine kinase concentration (carcinoembryonic antigen / creatine kinase), a ratio of carcinoembryonic antigen concentration to prothrombin time activity (carcinoembryonic antigen / coagulation time activity), and a ratio of creatine kinase concentration to creatine kinase concentration. The peak intensity ratios of mannose and triglyceride (mannose / triglyceride (18:2 / 18:2 / 18:3)), mannose and phosphatidylcholine (mannose / phosphatidylcholine (18:1 / 20:4)); the peak intensity ratios of mannose and ceramide (mannose / ceramide (d18:2 / 22:0)) and mannose and lysophosphatidylcholine (mannose / lysophosphatidylcholine (18:2)).

[0021] In the specific implementation process of the present invention, the blood marker composition consists of the following: the ratio of creatinine to creatine kinase concentration, the ratio of direct bilirubin to alpha-fetoprotein concentration, the ratio of direct bilirubin to creatine kinase concentration, the ratio of alpha-fetoprotein to carcinoembryonic antigen concentration, the ratio of alpha-fetoprotein to glycated serum protein concentration, the ratio of carcinoembryonic antigen to creatine kinase concentration, the ratio of carcinoembryonic antigen concentration to prothrombin time activity, the ratio of creatine kinase to glycated serum protein concentration, the ratio of prothrombin time activity to glycated serum protein concentration, the peak intensity ratio of mannose to triglyceride (18:2 / 18:2 / 18:3), the peak intensity ratio of mannose to phosphatidylcholine (18:1 / 20:4); the peak intensity ratio of mannose to ceramide (d18:2 / 22:0) and the peak intensity ratio of mannose to lysophosphatidylcholine (18:2).

[0022] In the present invention, the peak intensity is the relative concentration content.

[0023] In the present invention, the unit of creatinine concentration is: μmol / L; the unit of creatine kinase concentration is: U / L; the unit of direct bilirubin concentration is: μmol / L; the unit of alpha-fetoprotein concentration is: ng / ml; the unit of carcinoembryonic antigen concentration is: ng / ml; the unit of glycated serum protein concentration is: mmol / L; the unit of prothrombin time activity is: %.

[0024] In the present invention, taking phosphatidylcholine (18:1 / 20:4) as an example, it means that the phosphatidylcholine molecule contains two fatty acid chains, the first fatty acid chain is 18:1, and the second is 20:4; wherein, 18:1 means that there are 18 carbon atoms and 1 double bond on this chain; 20:4 means that there are 20 carbon atoms and 4 double bonds on this chain; taking ceramide (d18:2 / 22:0) as an example, it means sphingolipid lipids, d refers to the carbon chain of sphingosine containing two hydroxyl groups, wherein, d18:2 means that there are 18 carbon atoms and two double bonds on the sphingosine skeleton containing 2 hydroxyl groups, and 22:0 means that it is composed of a saturated fatty acid of 22 carbon atoms.

[0025] The present invention also provides the use of a product for detecting the blood marker composition described in the above scheme in the preparation of a product for distinguishing non-cancerous gastric diseases from gastric cancer.

[0026] In the present invention, the non-cancerous gastric diseases preferably include benign gastric diseases; in the specific implementation of the present invention, the non-cancerous gastric diseases include one or more of gastritis, gastric polyps and gastric adenomas.

[0027] The present invention also provides the use of a reagent or a kit for detecting the blood marker composition described in the above scheme in the preparation of a gastric disease diagnosis product; the gastric disease includes gastric cancer.

[0028] In the present invention, the reagents or kits for detecting the blood marker composition described in the above scheme include liquid chromatography-mass spectrometry detection reagents and gas chromatography-mass spectrometry detection reagents.

[0029] In the present invention, the product preferably includes a reagent or a kit.

[0030] In the present invention, the concentrations of creatinine, creatine kinase, direct bilirubin, alpha-fetoprotein, carcinoembryonic antigen and glycosylated serum protein and prothrombin time activity are preferably detected by clinical testing; the peak intensities of triglycerides (18:2 / 18:2 / 18:3), phosphatidylcholine (18:1 / 20:4), ceramide (d18:2 / 22:0) and lysophosphatidylcholine (18:2) are detected by liquid chromatography-mass spectrometry; and the peak intensity of mannose is detected by gas chromatography-mass spectrometry.

[0031] In the specific implementation process of the present invention, blood collection, analysis and preservation are carried out, and the steps are as follows: use purple-headed EDTA anticoagulant blood collection tubes and red-headed blood collection tubes to collect fasting blood samples from patients in the morning. For the blood sample collected by the purple-headed blood collection tube, gently invert the blood collection tube several times to ensure that the blood and the anticoagulant are fully mixed to avoid blood coagulation, and then centrifuge at 4°C and 3500rpm for 10 minutes to separate the plasma for routine blood analysis. After analysis, it is stored in a -80°C refrigerator for subsequent metabolomics testing. After the blood is collected by the red-headed blood collection tube, it is placed at room temperature for 30 minutes until the blood is completely coagulated to form a blood clot, and then centrifuged at 4°C and 3500rpm for 10 minutes to separate the serum for blood biochemical analysis.

[0032] In the present invention, the detection method of mannose, triglyceride (18:2 / 18:2 / 18:3), phosphatidylcholine (18:1 / 20:4), lysophosphatidylcholine (18:2) and ceramide (d18:2 / 22:0) comprises the following steps:

[0033] Plasma is collected from the patient to be tested, and an organic phase and an aqueous phase are separated from the plasma; wherein the organic phase is non-polar metabolites, and the aqueous phase is polar metabolites; polar metabolites and derivatized polar metabolites are obtained from the aqueous phase; gas chromatography-mass spectrometry is used to detect the derivatized polar metabolites in the aqueous phase, and liquid chromatography-mass spectrometry is used to detect the polar metabolites in the aqueous phase and the non-polar metabolites in the organic phase, respectively, to obtain metabolome detection data.

[0034] The present invention first collects plasma from a patient to be tested, and separates an organic phase and an aqueous phase from the plasma.

[0035] In the present invention, the plasma is preferably the plasma obtained after thawing of a frozen plasma sample. In the present invention, the frozen plasma sample is preferably stored at -80°C. In the present invention, the thawing is preferably thawing on ice; after the thawing, it is preferably further vortexed for 10 seconds. In the present invention, the step of separating the organic phase and the aqueous phase from the plasma preferably includes: mixing the plasma and the solvent, extracting to obtain an extract; adding a mixture of methanol and water to the extract, centrifuging, and separating the upper organic phase and the lower aqueous phase. In the specific implementation of the present invention, the volume ratio of the plasma to the solvent is 1:10; the solvent is a mixture of methyl tert-butyl ether and methanol; the volume ratio of methyl tert-butyl ether and methanol is 3:1. In the specific implementation of the present invention, based on the volume of plasma of 100 μL, the volume of the mixture of methanol and water added to the extract is 500 μL; the volume ratio of methanol and water is 3:1; the speed of the centrifugation is 12700 rpm, and the time is 5min.

[0036] After obtaining the aqueous phase, the present invention obtains polar metabolites and derivatized polar metabolites from the aqueous phase.

[0037] In the specific implementation process of the present invention, obtaining polar metabolites and derivatized polar metabolites from the aqueous phase includes the following steps: mixing the aqueous phase with ice methanol to precipitate protein; after protein precipitation, sequentially performing a first incubation and centrifugation, first collecting the first supernatant into an LC tube, then collecting the first supernatant into a GC tube, and spin-drying them respectively; adding water to the spin-dried LC tube sample to re-dissolve, sequentially incubating, vortex mixing, sonicating and centrifuging, collecting the second supernatant, and obtaining polar metabolites in the aqueous phase; dissolving the spin-dried GC tube sample in a methoxyamine hydrochloride (MeOX) solution, performing a second incubation, adding N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA), performing a third incubation, and centrifuging to obtain derivatized polar metabolites in the aqueous phase.

[0038] In the specific implementation of the present invention, the MeOX solution is prepared by the following method: 20 mg (±1 mg) of MeOX solid is weighed into an EP tube, 1 ml of anhydrous pyridine is added, ultrasonic treatment is performed for 5 min, and incubation is performed at 40° C. for 3 min.

[0039] In the specific implementation of the present invention, the volume ratio of the aqueous phase and ice methanol is 4:11; the aqueous phase and ice methanol are mixed in a centrifuge tube; the aqueous phase and ice methanol are mixed by vortex mixing; the temperature of the first incubation is 4°C; the time of the first incubation is 1h; the temperature of the centrifugation after the first incubation is 4°C; the speed of the centrifugation after the first incubation is 12700rpm; the time of the centrifugation after the first incubation is 10min; the device used for the spin drying is a vacuum concentrator (Speed-Vac). In the specific implementation of the present invention, the volume ratio of the first supernatant and the water used to re-dissolve the LC tube sample after spin drying is 5:1; the time of the incubation is 15min; the temperature of the incubation is room temperature; the time of ultrasound is 5min; the speed of the centrifugation after ultrasound is 12000rpm; the time of the centrifugation after ultrasound is 5min. In the specific implementation of the present invention, the volume ratio of the first supernatant liquid GC tube and methoxyamine hydrochloride (MeOX) solution is 35:1. In the specific implementation of the present invention, the temperature of the second incubation is 30°C; the time of the second incubation is 90 minutes; the rotation speed of the second incubation is 500 rpm; the temperature of the third incubation is 37°C; the time of the third incubation is 30 minutes; the rotation speed of the third incubation is 500 rpm.

[0040] After obtaining the organic phase, the present invention obtains non-polar metabolites from the organic phase.

[0041] In the present invention, obtaining non-polar metabolites from the organic phase preferably includes the following steps: after the organic phase is spin-dried, it is re-dissolved with a mixture of acetonitrile and isopropanol, incubated, vortexed, sonicated and centrifuged in sequence, and the supernatant is collected to obtain the non-polar metabolites of the organic phase.

[0042] In the specific implementation process of the present invention, the volume of the organic phase is 350 μL, the amount of the organic phase and the mixed solution of acetonitrile and isopropanol is 200 μL; the spin drying is carried out using a vacuum concentrator (Speed-Vac); the volume ratio of acetonitrile to isopropanol in the mixed solution of acetonitrile and isopropanol is 3:1; the incubation temperature is room temperature; the incubation time is 15 min; the ultrasonic time is 5 min; the centrifugation temperature is room temperature, the time is 5 min, and the rotation speed is 12000 rpm.

[0043] After obtaining the derivatized polar metabolites in the water phase, the present invention uses gas chromatography-mass spectrometry to detect the derivatized polar metabolites in the water phase.

[0044] In the specific implementation of the present invention, the derivatized polar metabolites in the aqueous phase are detected by GC-MS. In the specific implementation of the present invention, the gas chromatography conditions for detecting the derivatized polar metabolites in the aqueous phase include: a chromatographic column of MS chromatographic column, specification: Restek 30m*0.25mm*0.25μm; the system used is a gas chromatography system (7890B, Agilent); the carrier gas is high-purity helium; the flow rate is 1mL / min; the heating program is: initial temperature 50°C, maintained for 2min, 15°C / min continuously heated to 330°C, maintained for 2min; the injection port temperature is: 280°C; split injection is used, and the split ratio is 10:1. In the specific implementation process of the present invention, the mass spectrometry conditions for detecting the derivatized polar metabolites in the aqueous phase include: the system used is a time-of-flight mass spectrometry system (PEGASUS-BT, Leco); the electron ionization source (EI); the temperature is 250°C; the electron energy is 70eV; the scanning range is 50-500amu; the detection voltage is 1.2kV.

[0045] After obtaining the polar metabolites in the aqueous phase and the non-polar metabolites in the organic phase, the present invention uses liquid chromatography-mass spectrometry to respectively detect the polar metabolites in the aqueous phase and the non-polar metabolites in the organic phase.

[0046] In the present invention, the liquid chromatography-mass spectrometry is preferably high resolution liquid chromatography-mass spectrometry (UHPLC-MS).

[0047] In the specific implementation process of the present invention, the loading amount of the polar metabolites in the aqueous phase is 180 μL, which is loaded into a 2 mL glass injection bottle, and the polar metabolites in the aqueous phase are detected by a LC-MS machine.

[0048] In the specific implementation of the present invention, the liquid chromatography conditions for detecting polar metabolites in the aqueous phase include: the chromatographic column is Waters ACQUTTY HSS T3 chromatographic column, specification: 1.8μm2.1*100mm, system: ACQUITYUPLC I-Class liquid chromatography system (Waters), for small molecule separation; the column temperature is 40°C; the mobile phase includes mobile phase A and mobile phase B, the mobile phase A is an aqueous solution containing 0.1% formic acid by volume, and the mobile phase B is an acetonitrile solution containing 0.1% formic acid by volume; the flow rate of the mobile phase is 0.4mL / min; the separation elution gradient is: 0min, 1% mobile phase B by volume; 13min, 70% mobile phase B by volume, 13.01min, 99% mobile phase B by volume; 18min, 99% mobile phase B by volume; 18.01min, 1% mobile phase B by volume; 22min, 1% mobile phase B by volume; the sample injection volume is 3μL; the automatic sampler temperature is 10°C.

[0049] In the specific implementation process of the present invention, the loading amount of the non-polar metabolites of the organic phase is 180 μl of supernatant to a 2 mL glass injection vial, and the non-polar metabolites of the organic phase are detected by a LC-MS machine.

[0050] In the specific implementation of the present invention, the liquid chromatography conditions for detecting the non-polar metabolites in the organic phase include: the chromatographic column is Waters ACQUTTY BEH C8, specification: 1.7μm2.1*100mm, system: ACQUITYUPLC I-Class liquid chromatography system (Waters), for small molecule separation; column temperature: 60°C; mobile phases include mobile phase A and mobile phase B, wherein mobile phase A contains 0.1% by volume acetic acid and 1% by volume ammonium acetate in water; mobile phase B is an acetonitrile / isopropanol solution containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile to isopropanol is 7:3); the flow rate of the mobile phase is 0.4mL / min; the separation elution gradient is: 0-1min, 55% mobile phase B by volume; 4 min, 75% mobile phase B by volume; 12 min, 89% mobile phase B by volume; 12 min, 100% mobile phase B by volume; 19.5 min, 100% mobile phase B by volume; 19.51 min, 55% mobile phase B by volume; 24 min, 55% mobile phase B by volume; injection volume was 2 μL; autosampler temperature was 10°C.

[0051] In the specific implementation process of the present invention, the mass spectrometry conditions for detecting polar metabolites in the aqueous phase and detecting non-polar metabolites in the organic phase include: the system used is: Q-Exactive mass spectrometry system (Thermo Fisher Scientific), the MS instrument uses Orbitrap high-resolution mass spectrometry (Thermo Fisher) equipped with an electrospray ionization source (ESI); full scan and data-dependent acquisition are used to obtain primary and secondary mass spectrometry data; the full scan mass spectrum range is 100-1500Da; the scanning range of the secondary scan mode is 100-310, 300-710 and 700-1500Da; positive and negative ionization modes are used to collect data; automatic gain control (AGC) is 3E+6; Maximum IT is 200ms; the resolution of the first-level full scan is 70,000FWHM; in the second-level scanning mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5m / z, the automatic gain control (AGC) is 1E+5, the maximum ion injection time is 50ms, and the relative collision energy (HCD) is 30eV; the ion spray voltage is 3,500V in positive mode; the ion spray voltage is 3,000V in negative mode; the nebulizer is 20psi; the sheath gas temperature is 400℃, and the sheath gas flow rate is 10L / min.

[0052] The present invention also provides a method for screening a blood marker composition for distinguishing non-cancerous gastric diseases from gastric cancer, comprising the following steps:

[0053] Plasma samples were collected from patients with non-cancerous gastric diseases and patients with gastric cancer;

[0054] Separating an organic phase and an aqueous phase from plasma; wherein the organic phase is non-polar metabolites and the aqueous phase is polar metabolites;

[0055] Obtaining polar metabolites and derivatizing polar metabolites from aqueous phase;

[0056] Gas chromatography-mass spectrometry was used to detect the derivatized polar metabolites in the aqueous phase, and liquid chromatography-mass spectrometry was used to detect the polar metabolites in the aqueous phase and the non-polar metabolites in the organic phase, respectively, to obtain metabolome detection data;

[0057] Preprocessing the metabolomics detection data and identifying metabolites to obtain metabolomics data;

[0058] Collect clinical indicators of patients' blood routine and blood biochemical tests to obtain clinical indicator data;

[0059] Performing data standardization processing on the metabolomics data and clinical indicator data respectively to obtain standardized metabolomics data and clinical indicator data;

[0060] The standardized metabolomics data and clinical indicator data are screened respectively to obtain preliminary screened clinical indicator data and metabolomics characteristic data;

[0061] According to the clinical index data and metabolome feature data preliminarily screened, the ratio of the combination between all possible two variables of the clinical index and the metabolome feature is calculated respectively, and the combination of the top 100 feature pairs is selected from small to large according to the order of significance based on the P-value, and the clinical index and metabolome feature after the ratio is calculated are combined into a data set;

[0062] Lasso regression was used to screen out a blood marker combination for distinguishing non-cancerous gastric diseases from gastric cancer from the data set.

[0063] The present invention first collects plasma samples from patients with non-cancerous stomach diseases and patients with gastric cancer.

[0064] In the specific implementation of the present invention, the inclusion criteria of the patients with non-cancerous gastric diseases and gastric cancer are as follows: (1) age ≥ 18 years old; (2) gastric cancer patients are diagnosed by the gold standard method of histopathological examination; (3) patients diagnosed as benign gastric diseases by biopsy / postoperative pathology or clinically diagnosed by comprehensive evaluation of clinicians, only patients with gastritis, gastric polyps, and gastric adenomas are included. The exclusion criteria of patients with non-cancerous gastric diseases and gastric cancer are as follows: (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. Patient clinical data: When collecting plasma samples from subjects, the basic information and health status of the subjects are collected through questionnaires, including basic demographic characteristics, lifestyle, medical history, medication history, etc., and the specific clinical indicator data of the subjects are collected.

[0065] After obtaining the plasma, the present invention separates an organic phase and an aqueous phase from the plasma; wherein the organic phase is non-polar metabolites, the aqueous phase is polar metabolites, and polar metabolites and derivatized polar metabolites are obtained from the aqueous phase.

[0066] The present invention adopts gas chromatography-mass spectrometry to detect the derivatized polar metabolites in the water phase and adopts liquid chromatography-mass spectrometry to respectively detect the polar metabolites in the water phase and the non-polar metabolites in the organic phase to obtain metabolome detection data.

[0067] In the present invention, the separation of organic phase and water from plasma is the same as the above scheme, which will not be repeated here. The use of gas chromatography-mass spectrometry to detect the derivatized polar metabolites in the aqueous phase and the use of liquid chromatography-mass spectrometry to respectively detect the polar metabolites in the aqueous phase and the non-polar metabolites in the organic phase are the same as the above scheme, which will not be repeated here.

[0068] In the specific implementation of the present invention, the preprocessing of the metabolomics detection data includes GC-MS mass spectrum data preprocessing and LC-MS mass spectrum data preprocessing.

[0069] In the specific implementation process of the present invention, the GC-MS mass spectrometry data preprocessing includes the following steps: using LecoChromaTOF (version 5.4) to obtain the raw data of the GC-MS machine, using the Target Search in the Bioconductor software package of R language (version 4.0.3) to extract and align the peaks; based on the retention time of fatty acid methyl ester (FAME), it is converted into a retention index (RI), annotated using the Fiehn database, and the final identification of the metabolites is confirmed by manually comparing the chromatograms.

[0070] In the specific implementation process of the present invention, the LC-MS mass spectrometry data preprocessing includes the following steps: peak extraction, peak alignment, baseline correction, peak filtering, isotope peak removal, missing value filling, etc.; the present invention first extracts detection peaks and peak alignment from the mass spectrometry raw data, and then applies baseline correction to remove noise and retain the original signal peak; the raw data is converted into central discrete data, and at the same time, the isotope peaks in the mass spectrometry data are removed; then, the peak in a single sample is aligned with the retention time of the peak in the chromatogram of other samples, the isotope peak and the low-intensity adduct in the same analyte are removed, and the characteristic peak>50% missing values ​​are eliminated; the random forest chain equation (MICE Forest) is used to fill the missing values ​​to obtain the final mass spectrometry matrix data.

[0071] In the specific implementation process of the present invention, the identification of the metabolites includes the following steps: according to the original data, the spectral information of the compound parent ion and the secondary fragment ion is obtained after the software is parsed, and the metabolites are qualitatively identified by matching the spectral information of the primary and secondary metabolites in the database; based on the metabolites identified in the relevant database, the metabolites are finally verified according to the retention time, MS1, and MS2 mass spectrum information when the standard is separated under the same chromatographic column and mass spectrometry conditions; the standard for metabolite identification is that the retention time is within 0.1min difference and the metabolite molecular weight error is less than 10ppm. In the present invention, the spectral information includes the mass-to-charge ratio (m / z) of the primary mass spectrum and the fragments of the secondary ions. In the present invention, the database includes: HMDB ( www.hmdb.ca )、PubChem( https: / / pubchem.ncbi.nlm.nih.gov )、MassBank( http: / / www.massbank.jp )、MassBank ofNorth America( https: / / massbank.us ), Lipidmap ( https: / / www.lipidmaps.org ) and Lipidblast( https: / / fiehnlab.ucdavis.edu / projects / LipidBlast ).

[0072] The present invention collects clinical indicators of blood routine and blood biochemistry tests of patients to obtain clinical indicator data.

[0073] In the specific implementation of the present invention, the clinical indicators of the patient's blood routine and blood biochemical tests are shown in Table 1.

[0074] Table 1. Clinical indicators of blood routine and blood biochemical tests

[0075]

[0076]

[0077] After obtaining the metabolomics data and clinical indicator data, the present invention performs data standardization processing on the metabolomics data and clinical indicator data respectively to obtain standardized metabolomics data and clinical indicator data.

[0078] In the specific implementation of the present invention, the data standardization processing includes logarithmic change (Log-transformation) standardization processing, which is intended to eliminate the order of magnitude differences between different samples in the original data.

[0079] After obtaining the standardized clinical indicator data, the present invention performs screening from the standardized clinical indicator data to obtain the preliminarily screened clinical indicator data.

[0080] Univariate ROC is a method to examine the impact of a single feature on the performance of a classification model, and to explore the evaluation of a single feature on the diagnostic performance of the ROC model. In the specific implementation of the present invention, screening from the standardized clinical indicator data includes the following steps: performing univariate ROC analysis between patients with non-cancerous gastric diseases and patients with gastric cancer based on clinical indicators, and obtaining the area under the ROC curve (AUC) values ​​of all indicators. AUC value > 0.6 is used as a screening condition, and the clinical indicators preliminarily screened are retained.

[0081] After obtaining the standardized metabolomics data, the present invention performs screening from the standardized metabolomics data to obtain preliminarily screened metabolomics characteristic data.

[0082] Based on multivariate statistical analysis in the OPLS-DA model, the VIP value of all variables contributing to the model is obtained; the VIP value is used to measure the influence and explanatory power of the content of each metabolite component on the classification and discrimination of samples, and to assist in the screening of metabolite markers. In the specific implementation of the present invention, screening from standardized metabolomics data includes the following steps: based on the metabolomics data of the modeling group, an OPLS-DA model is constructed between patients with non-cancerous gastric diseases and patients with gastric cancer, and the VIP value of each metabolite contributing to the model is obtained. The intersection is taken based on VIP>1.5 and the AUC value>0.6 of the univariate ROC analysis result as the screening conditions, and the metabolomics feature data preliminarily screened is retained.

[0083] The present invention calculates the ratio of the combination of all possible two variables of clinical indicators and metabolomic characteristics based on the preliminary screened clinical indicator data and metabolomic characteristic data, selects the combination of the TOP 100 feature pairs from small to large according to the significance arrangement order based on the P-value, and forms a data set of clinical indicators and metabolomic characteristics after calculating the ratio.

[0084] In the specific implementation process of the present invention, the BiomarkerAnalysis module tool in the MetaboAnalyst website is used to calculate the composition ratios between all possible two variables of clinical indicators and metabolomics characteristics.

[0085] After obtaining the data set, the present invention uses Lasso regression to screen out a blood marker composition for distinguishing non-cancerous gastric diseases from gastric cancer from the data set.

[0086] The present invention uses Lasso regression to reduce the dimension of characteristic variables in the data set, and further screens out the optimal blood marker combination.

[0087] To further illustrate the present invention, a screening method for a metabolic marker for distinguishing non-cancerous gastric diseases from gastric cancer and its application provided by the present invention are described in detail below in conjunction with the accompanying drawings and examples, but they should not be construed as limiting the scope of protection of the present invention.

[0088] Example 1

[0089] Step 1: Test object and sample information

[0090] 1. Subject information:

[0091] 1) The inclusion criteria for patients with non-cancerous gastric diseases and gastric cancer are as follows:

[0092] (1) Male or female aged ≥18 years; (2) Patients with gastric cancer were diagnosed by the gold standard method of histopathological examination; (3) Patients were diagnosed with benign gastric diseases by biopsy / postoperative pathology or by comprehensive evaluation by clinicians. Only patients with gastritis, gastric polyps, and gastric adenomas were included.

[0093] 2) The exclusion criteria for patients with non-cancerous gastric diseases and gastric cancer are as follows:

[0094] (1) Pregnancy or lactation; (2) Emergency or rescue; (3) History of malignant tumor or any anti-tumor treatment before sampling; (4) Patients with multiple primary malignant tumors at the same time.

[0095] 2. Subject clinical data: When collecting plasma samples from subjects, we collected information about the subjects’ basic situation and health status through questionnaires, including basic demographic characteristics, lifestyle, medical history, medication history, etc. At the same time, we collected specific clinical index data of the subjects (Table 1) for subsequent statistical analysis and model construction.

[0096] Step 2: Plasma sample pretreatment and metabolite detection

[0097] 1. Pretreatment of plasma samples:

[0098] After the plasma sample was taken out of the -80°C freezer, it was thawed on ice and then vortexed for 10 seconds. Subsequently, 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 and methanol was 3:1), and vortexed to obtain a sample extract. Next, 500 μL of a mixture of methanol and water (the volume ratio of methanol and water was 3:1) was added to the sample extract, and the upper organic phase and the lower aqueous phase were separated by centrifugation at 12700 rpm for 5 minutes.

[0099] - Aqueous phase: Take 400 μL of aqueous phase from the lower layer to a centrifuge tube, add 1100 μL of ice methanol, and vortex to precipitate the protein; after protein precipitation, incubate at 4°C for 1 hour, then centrifuge at 12700 rpm for 10 minutes at 4°C, take 1000 μL of supernatant and transfer to the corresponding numbered LC tube, then take 350 μL of the lower layer liquid and transfer to the corresponding numbered GC tube, and then spin dry in a vacuum concentrator (Speed-Vac). 1) Add 200 μL of water to the dried LC tube sample to reconstitute it and incubate it at room temperature for 15 minutes; after incubation, vortex the centrifuge tube, perform ultrasound-assisted treatment (ultrasonic treatment at room temperature) for 5 minutes, and then centrifuge the centrifuge tube for 5 minutes at room temperature (12000 rpm). 180 μL of supernatant was taken from the centrifuge tube and transferred to a 2 mL glass injection bottle, which was a polar metabolite and was detected by LC-MS; 2) The dried GC tube sample was dissolved in 10 μL of methoxyamine hydrochloride (MeOX) solution, vortexed for 10 seconds, and the centrifuge tube was centrifuged at 12700 rpm for 1 minute at room temperature; it was transferred to a heating mixer, set at 30°C, 500 rpm, and incubated for 90 minutes; 90 μL of N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA) solution was added, and it was transferred to a heating mixer, set at 37°C, 500 rpm and incubated for 30 minutes; the centrifuge tube was centrifuged at 12700 rpm for 1 minute at room temperature, and 85 μL of the upper supernatant was transferred to the corresponding injection bottle and detected by GC-MS;

[0100] -Organic phase: 350 μL of organic phase was taken from the upper layer and transferred to a centrifuge tube, and then dried using a vacuum concentrator (Speed-Vac). The dried sample was reconstituted with 200 μL of acetonitrile / isopropanol (v / v, 3:1) and incubated at room temperature for 15 min; after incubation, the centrifuge tube was vortexed and treated with ultrasound (ultrasonic treatment at room temperature) for 5 min, and then the centrifuge tube was centrifuged at room temperature for 5 min (12000 rpm); 180 μL of supernatant was taken from the centrifuge tube and transferred to a 2 mL glass injection vial, which was a non-polar substance and was detected by LC-MS.

[0101] 2. High-throughput gas chromatography-mass spectrometry (GC-MS) detection:

[0102] -GC-MS parameters: After derivatization, the sample is injected into a The MS column (Restek 30m*0.25mm*0.25μm) was used as a gas chromatography system (7890B, Agilent) and a time-of-flight mass spectrometry system (PEGASUS-BT, Leco) for detection. High-purity helium was used as the carrier gas with a flow rate of 1mL / min. The gas chromatography temperature program was as follows: initial temperature 50℃, maintained for 2min, and then continuously heated to 330℃ at 15℃ / min and maintained for 2min. The main parameters of the injection port were: 280℃, split injection (10:1). The main parameters of the mass spectrometry were: electron ionization source (EI), temperature 250℃, electron energy 70eV; scanning range 50-500amu, detection voltage 1.2kV.

[0103] 3.UHPLC-MS detection:

[0104] (1) Polar metabolite chromatographic parameters: Polar metabolite analysis was performed using Waters ACQUTTY HSS T3 chromatographic column (1.8μm 2.1*100mm) was used for small molecule separation, and the column temperature was 40℃; the liquid chromatography and mass spectrometer used ACQUITYUPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively; the mobile phase composition was as follows: mobile phase A was an aqueous solution containing 0.1% formic acid; mobile phase B was an acetonitrile solution containing 0.1% formic acid, and the flow rate was 0.4mL / min; the separation elution gradient was as follows: 0min, 1% mobile phase B; 13min, 70% mobile phase B, 13.01min, 99% mobile phase B; 18min, 99% mobile phase B; 18.01min, 1% mobile phase B; 22min, 1% mobile phase B. The sample injection volume was 3μL, and the autosampler temperature was 10℃;

[0105] (2) Non-polar metabolite chromatography parameters: Lipid metabolite analysis using Waters ACQUTTY A BEHC8 (1.7 μm 2.1*100 mm) column was used for small molecule separation, and the column temperature was 60 °C. The liquid chromatography and mass spectrometer used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively. The mobile phase composition was as follows: mobile phase A was water; mobile phase B was acetonitrile / isopropanol (70 / 30, V / V), both containing 0.1% acetic acid and 1% ammonium acetate, and the flow rate was 0.4 mL / min. The separation elution gradient was as follows: 0-1 min, 55% mobile phase B; 4 min, 75% mobile phase B; 12 min, 89% mobile phase B; 12 min, 100% mobile phase B; 19.5 min, 100% mobile phase B; 19.51 min, 55% mobile phase B; 24 min, 55% mobile phase B; the injection volume was 2 μL, and the autosampler temperature was 10 °C.

[0106] (3) LC-MS mass spectrometry parameters: Full scan and data-dependent acquisition (DDA) were used to obtain primary (MS1) and secondary mass spectrometry (MS2) data for both polar and non-polar metabolites. The full scan mass spectrum range was 100-1500 Da. The secondary scan mode (Full MS / dd-MS2) scan range was 100-310, 300-710 and 700-1500 Da. The MS instrument used an Orbitrap high-resolution mass spectrometer (Thermo Fisher) equipped with an electrospray ionization source (ESI), and data were collected in positive and negative ionization modes. The specific parameters are as follows: automatic gain control (AGC) is 3E+6, Maximum IT is 200ms, the first-level full scan (Full scan) resolution is 70,000FWHM (@200m / z), in the second-level scan mode (Full MS / dd-MS2), the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5m / z, the automatic gain control (AGC) is 1E+5, the maximum ion injection time is 50ms, the relative collision energy (HCD) is 30eV, the ion spray voltage is 3,500V in positive mode and 3,000V in negative mode, the nebulizer is 20psi, the sheath gas temperature is 400℃, and the sheath gas flow rate is 10L / min.

[0107] Step 3: Metabolomics data preprocessing and metabolite identification

[0108] 1. Metabolomics data preprocessing:

[0109] (1) GC-MS mass spectrometry data preprocessing: Leco ChromaTOF (version 5.4) was used to obtain the raw data from the GC-MS machine, and the peaks were extracted and aligned using the Target Search in the Bioconductor software package in the R language (version 4.0.3). The retention time of fatty acid methyl esters (FAME) was converted into retention index (RI), and annotation was performed using the Fiehn database. The final identification of the metabolites was confirmed by manually comparing the chromatograms.

[0110] (2) LC-MS mass spectrometry data preprocessing: The metabolomics data preprocessing steps include peak extraction, peak alignment, baseline correction, peak filtering, isotope peak removal, missing value filling, etc. First, the detection peaks are extracted and aligned from the mass spectrometry raw data, and then the baseline correction is applied to remove the noise and retain the original signal peak; the raw data is converted into central discrete data, and at the same time, the isotope peaks in the mass spectrometry data are removed; then, the peaks in a single sample are aligned with the retention times of the peaks in the chromatograms of other samples, the isotope peaks and low-intensity adducts of the same analyte are removed, and the characteristic peaks with >50% missing values ​​are eliminated; the random forest chain equation (MICE Forest) is used to fill the missing values ​​to obtain the final mass spectrometry matrix data.

[0111] 2. Identification of metabolites:

[0112] The original data is analyzed by software to obtain the spectral information of the compound parent ion and secondary fragment ion, such as the mass-to-charge ratio (m / z) of the primary mass spectrum and the fragments of the secondary ion. The metabolites are qualitatively identified by matching the spectral information of the primary and secondary metabolites in the database. Commonly used metabolite databases include HMDB ( www.hmdb.ca )、PubChem( https: / / pubchem.ncbi.nlm.nih.gov )、MassBank( http: / / www.massbank.jp )、MassBank of NorthAmerica( https: / / massbank.us ), and the lipid database Lipidmap ( https: / / www.lipidmaps.org ) and Lipidblast( https: / / fiehnlab.ucdavis.edu / projects / LipidBlast ); Based on the metabolites identified in the relevant database, the metabolites are finally verified according to the retention time, MS1, and MS2 mass spectrometry information of the standard when separated under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are that the retention time is within 0.1min difference and the molecular weight error of the metabolite is less than 10ppm.

[0113] Step 4: Diagnostic method for distinguishing non-cancerous gastric diseases from gastric cancer based on plasma marker composition

[0114] 1. Subject information

[0115] A total of 323 plasma samples were collected from subjects through two medical centers. Among them, there were 87 cases of non-cancerous gastric diseases and 235 cases of gastric cancer. The samples in the modeling group and the validation group are different samples. The specific distribution is as follows: there are 243 plasma samples in the modeling group, including 66 patients with non-cancerous gastric diseases and 177 patients with gastric cancer; there are 80 plasma samples in the validation group, including 21 patients with non-cancerous gastric diseases and 59 patients with gastric cancer (Table 2). Plasma samples were collected in the early morning on an empty stomach, and all collected plasma samples were stored in a -80°C refrigerator.

[0116] Table 2. Sample information

[0117]

[0118]

[0119] 2. Blood marker composition screening method:

[0120] (1) Data standardization: Log-transformation was performed on clinical index data and metabolomics data to eliminate the order of magnitude differences between different samples in the original data and obtain standardized data.

[0121] (2) Screening of clinical indicator blood markers: Univariate ROC is a method to examine the impact of a single feature on the performance of a classification model, and to explore the evaluation of a single feature on the diagnostic performance of the ROC model. Based on clinical indicators, univariate ROC analysis was performed between the gastric non-cancer disease group and the gastric cancer group samples in the modeling group to obtain the area under the ROC curve (AUC) values ​​of all indicators. The univariate ROC analysis AUC value > 0.6 was used as a screening condition, and a total of 15 clinical indicators were screened between the gastric non-cancer disease group and the gastric cancer group in the modeling group.

[0122] (3) Screening of metabolite blood markers: First, based on the multivariate statistical analysis in the OPLS-DA model, the VIP values ​​of all variables contributing to the model were obtained. The VIP value was used to measure the influence and explanatory power of the content of each metabolite component on the classification and discrimination of samples, which assisted in the screening of metabolite markers. Therefore, based on the metabolomics data of the modeling group, an OPLS-DA model was constructed between the gastric non-cancer disease group and the gastric cancer group to obtain the VIP value of each metabolite contributing to the model. VIP>1.5 and univariate ROC analysis AUC value>0.6 were used as screening conditions to take the intersection. Finally, a total of 18 metabolomics features were screened between the gastric non-cancer disease group and the gastric cancer group in the modeling group.

[0123] (4) Blood marker composition ratio: Based on the screening of 15 clinical indicators and 18 metabolome features, the BiomarkerAnalysis module tool in the MetaboAnalyst website was used to calculate the composition ratio between all possible two variables of 15 clinical indicators and 18 metabolome features, respectively, according to the clinical indicators and metabolome features. Based on the P-value, the composition of the top 100 feature pairs was selected from small to large according to the order of significance. The 115 clinical indicators and 118 metabolome features after the ratio calculation, a total of 233 feature variable data, were combined into one data set. Subsequently, the final blood marker composition was screened out using Lasso regression.

[0124] (5) Lasso regression screening of blood marker combinations: Lasso regression was used to reduce the dimension of the above 233 feature variables and further screen out the best blood marker combinations. Finally, a total of 13 blood marker combinations were screened out for constructing diagnostic models for gastric non-cancerous diseases and gastric cancer. The 13 blood marker compositions included 9 clinical indices (creatinine / creatine kinase, direct bilirubin / alpha-fetoprotein, direct bilirubin / creatine kinase, alpha-fetoprotein / carcinoembryonic antigen, alpha-fetoprotein / glycosylated serum protein, carcinoembryonic antigen / creatine kinase, carcinoembryonic antigen / prothrombin time activity, creatine kinase / glycosylated serum protein, prothrombin time activity / glycosylated serum protein) and 4 metabolomic features (mannose / triglyceride (18:2 / 18:2 / 18:3), mannose / phosphatidylcholine (18:1 / 20:4), mannose / ceramide (d18:2 / 22:0), mannose / lysophosphatidylcholine (18:2)) (Table 3).

[0125] Table 3. Composition of 13 blood markers for distinguishing gastric non-cancer disease group from gastric cancer group

[0126]

[0127] 3. Establish a diagnostic model for non-cancerous gastric diseases and gastric cancer

[0128] In order to evaluate the diagnostic effect of the 13 screened plasma marker combinations in distinguishing gastric non-cancerous diseases from gastric cancer, a multivariate ROC analysis was performed based on the 13 blood marker combinations. Specifically, 3 / 4 samples were randomly selected from the modeling group sample data as training sets (training), and the remaining 1 / 4 samples were used as test sets (test). Among them, the training set is used to construct and train the machine learning classification model, and the remaining samples are used as the test set to verify the discriminative ability of the trained model. The SVM is randomly iterated 1000 times, and a diagnostic model for gastric non-cancerous diseases and gastric cancer is constructed by statistically calculating the average value of the final model accuracy. The results showed AUC = 0.932 (specificity: 82.4%, sensitivity: 75.0%), indicating that the diagnostic model for gastric non-cancerous diseases and gastric cancer constructed based on the 13 blood marker combinations has a high diagnostic ability, see. Figure 1 .

[0129] Generally speaking, 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 AUC value of the area under the curve. When AUC is greater than 0.5, the closer it is to 1, the better the model performance and the better the judgment effect; if it is less than 0.5, it means that the performance of the model is close to random guessing, the discrimination ability is poor, and the accuracy is poor.

[0130] In addition to common parameters such as the receiver operating curve (ROC) and the area under the curve (AUC), the ROC classification prediction model also includes sensitivity and specificity.

[0131] The sensitivity is:

[0132]

[0133] Specificity is:

[0134]

[0135] in,

[0136] TP (True positive): True positive, the number of samples that are actually positive examples that are correctly predicted as positive examples;

[0137] TN (Ture Negative): True negative, the number of samples that are actually negative examples but are correctly predicted as negative examples;

[0138] FP (False Positive): False positive, the number of samples that are actually negative examples but are mistakenly predicted as positive examples;

[0139] FN (False Negative): False negatives, the number of samples that are actually positive examples but are mistakenly predicted as negative examples.

[0140] 4. External Validation of Diagnostic Models for Gastric Non-Cancerous Diseases and Gastric Cancer

[0141] In order to further verify the predictive effect of the gastric non-cancerous disease and gastric cancer diagnostic model constructed based on the combination of 13 blood markers, the validation group data set was used as an unknown sample and placed in the gastric non-cancerous disease and gastric cancer diagnostic model constructed using the modeling group to evaluate the independent verification effect of the model on the unknown data set other than the modeling group data set. Based on the metabolomics data of 13 blood markers, each sample will output a corresponding probability value (Probability). Based on the probability value of each sample as the diagnostic threshold, a set of confusion matrices (including true positives, true negatives, false positives, and false negatives) is obtained, and the sensitivity and specificity can be calculated according to the formula. In the ROC analysis diagram with sensitivity as the ordinate and 1-specificity as the abscissa, the values ​​corresponding to different sensitivities and 1-specificities have corresponding points. Similarly, when the probability value of each sample is used as the diagnostic threshold, multiple different points can be obtained from the ROC analysis diagram. By connecting these points, an ROC curve diagram will be drawn, see. Figure 2 .

[0142] Among them, the point in the upper left corner is the point with the best sensitivity and specificity, and the diagnostic threshold at this time is 0.5799. As shown in the confusion matrix results in the following table (Table 4), in the prediction model constructed based on the above 13 metabolic markers, with 0.5799 as the diagnostic threshold, among the 59 gastric cancer patients, 44 were diagnosed as gastric cancer, and 15 were misjudged as gastric non-cancer disease group; among the 21 gastric non-cancer disease patients, 18 were diagnosed as gastric non-cancer disease group, and 3 were misjudged as gastric cancer group; the ROC analysis results of the diagnostic model in the validation group are shown as follows: Figure 2 As shown, the sensitivity and specificity were calculated based on the confusion matrix results, and the results showed that AUC = 0.917 (specificity: 85.7%, sensitivity: 74.6%), indicating that the diagnostic model for gastric non-cancerous diseases and gastric cancer established by the screened 13 blood markers combination has good predictive ability, see Figure 2 .

[0143] Table 4. Confusion matrix of the diagnostic model for distinguishing gastric non-cancerous diseases from gastric cancer

[0144] Gastric cancer Non-cancerous stomach diseases Gastric cancer, N=59 44(TP) 15(FN) Non-cancerous gastric diseases, N=21 3(FP) 18(TN)

[0145] 5. Comparison of the predictive efficacy of blood marker combination and serum tumor markers

[0146] The serum tumor markers alpha-fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19-9, carbohydrate antigen 12-5 and carbohydrate antigen 72-4 were compared with the 13 blood marker compositions screened in the present invention in terms of diagnostic effect in distinguishing gastric non-cancerous diseases from gastric cancer. Table 5 shows the results of univariate and multivariate ROC curve analysis of 13 blood marker compositions and serum tumor markers based on the modeling group and validation group samples to distinguish gastric non-cancerous disease groups from gastric cancer groups. The results showed that compared with the ROC diagnostic model established based on the 13 blood marker compositions, the serum tumor markers had poor diagnostic effect in distinguishing gastric non-cancerous disease groups from gastric cancer groups. Therefore, the diagnostic model established based on the 13 blood marker compositions can significantly improve the diagnostic effect of distinguishing gastric non-cancerous disease groups from gastric cancer groups.

[0147] Table 5. ROC analysis results of 13 blood marker combinations and serum tumor markers in distinguishing gastric non-cancerous diseases from gastric cancer

[0148]

[0149] Compared with the prior art, the present invention not only uses metabolome characteristics, but also incorporates blood clinical indicators through routine blood tests and blood biochemical tests, which more comprehensively reflects the physiological and pathological state of the body than other methods, and reduces the possibility of bias in the results. In addition, the ratio of blood clinical indicators and the ratio of metabolome characteristics are also included. This method can enrich the number of characteristics, improve the probability of blood marker screening, and more comprehensively reflect the state of the body than considering blood clinical indicators and metabolome characteristics alone. Therefore, the use of multi-dimensional blood biomarkers and the ratio characteristics between the compositions can more accurately distinguish between non-cancerous gastric diseases and gastric cancer, which helps to improve the sensitivity and specificity of diagnosis, thereby providing patients with earlier treatment and management measures and reducing possible misdiagnosis and missed diagnosis.

[0150] Although the above embodiment describes the present invention in detail, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A blood marker composition for distinguishing gastric non-cancerous diseases from gastric cancer, characterized in that: The blood marker composition comprises: a ratio of creatinine to creatine kinase concentration, a ratio of direct bilirubin to alpha-fetoprotein concentration, a ratio of direct bilirubin to creatine kinase concentration, a ratio of alpha-fetoprotein to carcinoembryonic antigen concentration, a ratio of alpha-fetoprotein to glycosylated serum protein concentration, a ratio of carcinoembryonic antigen to creatine kinase concentration, a ratio of carcinoembryonic antigen concentration to prothrombin time activity, a ratio of creatine kinase to glycosylated serum protein concentration, a ratio of prothrombin time activity to glycosylated serum protein concentration, a peak intensity ratio of mannose to triglyceride 54:7, a peak intensity ratio of mannose to phosphatidylcholine 38:5, a peak intensity ratio of mannose to ceramide d 40:2, and a peak intensity ratio of mannose to lysophosphatidylcholine 18:

2.

2. Use of a reagent or a kit for detecting the blood marker composition according to claim 1 in the preparation of a product for distinguishing non-cancerous gastric diseases from gastric cancer.

3. The use according to claim 1, characterized in that: The non-cancerous gastric diseases include one or more of gastritis, gastric polyps and gastric adenoma.

4. Use of a reagent or a kit for detecting the blood marker composition according to claim 1 in the preparation of a gastric disease diagnosis product.

5. The use according to any one of claims 2 to 4, characterized in that: The reagents or kits for detecting the blood marker composition according to claim 1 include liquid chromatography-mass spectrometry detection reagents and gas chromatography-mass spectrometry detection reagents.

6. A method for screening a blood marker composition for distinguishing gastric non-cancerous diseases from gastric cancer, characterized in that: The following steps are involved: Plasma samples were collected from patients with non-cancerous gastric diseases and patients with gastric cancer; Separating an organic phase and an aqueous phase from plasma; wherein the organic phase is non-polar metabolites and the aqueous phase is polar metabolites; Obtaining polar metabolites and derivatizing polar metabolites from aqueous phase; Gas chromatography-mass spectrometry was used to detect the derivatized polar metabolites in the aqueous phase, and liquid chromatography-mass spectrometry was used to detect the polar metabolites in the aqueous phase and the non-polar metabolites in the organic phase, respectively, to obtain metabolome detection data; Preprocessing the metabolomics detection data and identifying metabolites to obtain metabolomics data; Collect clinical indicators of patients' blood routine and blood biochemical tests to obtain clinical indicator data; Performing data standardization processing on the metabolomics data and clinical indicator data respectively to obtain standardized metabolomics data and clinical indicator data; The standardized metabolomics data and clinical indicator data are screened respectively to obtain preliminary screened clinical indicator data and metabolomics characteristic data; According to the clinical index data and metabolome feature data preliminarily screened, the ratio of the combination between all possible two variables of the clinical index and the metabolome feature is calculated respectively, and the combination of the top 100 feature pairs is selected from small to large according to the order of significance based on the P-value, and the clinical index and metabolome feature after the ratio is calculated are combined into a data set; Lasso regression was used to screen out a blood marker combination for distinguishing non-cancerous gastric diseases from gastric cancer from the data set.

7. The screening method according to claim 6, characterized in that The gas chromatography conditions for detecting the derivatized polar metabolites in the aqueous phase include: The chromatographic column is -5Sil MS chromatographic column; carrier gas is high-purity helium; flow rate is 1mL / min; heating program is: initial temperature 50℃, hold for 2min, continuously heat to 330℃ at 15℃ / min, hold for 2min; injection port temperature is: 280℃; split injection is adopted, split ratio is 10:1; The mass spectrometry conditions for detecting the derivatized polar metabolites in the aqueous phase include: electron ionization source; temperature of 250° C.; electron energy of 70 eV; scanning range of 50 to 500 amu; and detection voltage of 1.2 kV.

8. The screening method according to claim 6, characterized in that The liquid chromatography conditions for detecting polar metabolites in the aqueous phase include: the chromatographic column is Waters ACQUTTY HSS T3 chromatographic column; the column temperature of the chromatographic column is 40°C; the mobile phase includes mobile phase A and mobile phase B, the mobile phase A is an aqueous solution containing 0.1% formic acid by volume, and the mobile phase B is an acetonitrile solution containing 0.1% formic acid by volume; the flow rate of the mobile phase is 0.4mL / min; the separation elution gradient is: 0min, 1% mobile phase B by volume; 13min, 70% mobile phase B by volume, 13.01min, 99% mobile phase B by volume; 18min, 99% mobile phase B by volume; 18.01min, 1% mobile phase B by volume; 22min, 1% mobile phase B by volume; the sample injection volume is 3μL; the temperature of the automatic sampler is 10°C.

9. The screening method according to claim 6, characterized in that The liquid chromatography conditions for detecting the non-polar metabolites in the organic phase include: the chromatographic column is Waters ACQUTTY BEH C8; column temperature is 60°C; mobile phase includes mobile phase A and mobile phase B, mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate, and mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile / isopropanol is 7 / 3); the flow rate of the mobile phase is 0.4mL / min; the separation elution gradient is: 0-1min, 55% mobile phase B by volume; 4min, 75% mobile phase B by volume; 12min, 89% mobile phase B by volume; 12min, 100% mobile phase B by volume; 19.5min, 100% mobile phase B by volume; 19.51min, 55% mobile phase B by volume; 24min, 55% mobile phase B by volume; injection volume is 2μL; the temperature of the automatic sampler is 10°C.

10. The screening method according to claim 6, characterized in that The mass spectrometry conditions for detecting polar metabolites in the aqueous phase and non-polar metabolites in the organic phase include: full scan and data-dependent acquisition to obtain primary and secondary mass spectrometry data; full scan mass spectrum range of 100 to 1500 Da; secondary scan mode scanning range of 100 to 310, 300 to 710 and 700 to 1500 Da; positive and negative ionization modes are used to collect data; automatic gain control (AGC) is 3E+6; Maximum IT is 200ms; the resolution of the first-level full scan is 70,000FWHM; in the second-level scanning mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5m / z, the automatic gain control (AGC) is 1E+5, the maximum ion injection time is 50ms, and the relative collision energy (HCD) is 30eV; the ion spray voltage is 3,500V in positive mode; the ion spray voltage is 3,000V in negative mode; the nebulizer is 20psi; the sheath gas temperature is 400℃, and the sheath gas flow rate is 10L / min.

Images