Application of differentially expressed serum protein glycoform as biological detection marker for helicobacter pylori negative gastric cancer diagnosis

Abstract

The invention relates to application of serum protein glycoforms, in particular to application of differentially expressed serum protein glycoforms as biological detection markers for helicobacter pylori negative gastric cancer diagnosis, and aims to overcome the defects of insufficient research on HpNGC and lack of high-accuracy biological detection markers at present. According to the invention, differential expression of seroglycoprotein glycoforms of HV, HpNAG and HpNGC patients is analyzed by using a lectin chip technology, and it is verified that the differentially expressed seroglycoprotein glycoforms can be used as a biological detection marker for helicobacter pylori negative gastric cancer diagnosis.

Description

Technical Field

[0001] The present invention relates to the application of serum protein glycoforms, and specifically to the application of differentially expressed serum protein glycoforms as a biomarker for the diagnosis of Helicobacter pylori-negative gastric cancer. Background Art

[0002] Gastric cancer is one of the most common malignant tumors globally. According to the statistics of the World Health Organization, since 2018, the number of new cases of gastric cancer globally has exceeded 1 million per year, accounting for 5.7% of cancer diagnosis cases.

[0003] Most patients with gastric cancer (GC) are detected with Helicobacter pylori (Hp) infection at the time of diagnosis. Currently, methods such as 13C / 14C urea test, rapid urease test, and fecal antigen detection are commonly used. Excluding Hp infection is initially diagnosed as Helicobacter pylori-negative gastric cancer (HpNGC). Using only one method for detection often results in false-negative results. The reasons are as follows: Atrophic gastritis (AG) is a chronic disease mainly characterized by gastric gland atrophy or intestinal metaplasia (IM) of the gastric mucosa, and is an important precursor lesion for the development of gastric cancer. Long-term Hp infection of the gastric mucosa can lead to AG and IM, and the gastric mucosa in the state of atrophy or intestinal metaplasia will automatically clear Hp. Such a situation is a suspected past Hp infection, and usually, the precursor lesion is diagnosed as Helicobacter pylori-negative atrophic gastritis (HpNAG). As AG progresses to the GC stage, both pepsinogen I (PGI) and pepsinogen II (PGII) synthesized by oxyntic gland cells may decrease, and the decrease level of PGI is significantly higher than that of PGII. When PGI ≤ 70 U / ml or PGI / II ≤ 3.0, it indicates that the patient is in the AG stage and has a suspected past Hp infection.

[0004] Theoretically, when strictly defining HpNGC, three situations should be excluded using the existing detection methods, namely current Hp infection, past Hp infection, and suspected past Hp infection in GC patients. However, in actual research, clinically, non-invasive, highly sensitive, and highly specific detection methods are often preferred first, and not all detection methods are usually used. In addition, there are relatively few clinical data available for analysis in the current research on HpNGC, so a standard definition and diagnostic criteria have not yet been formed.

[0005] HpNGC patients account for 0.42 - 5.4% of gastric cancer patients, with poor prognosis and low postoperative survival rate. However, current research on HpNGC is insufficient, lacking highly accurate biological detection markers. Summary of the Invention

[0006] The object of the present invention is to solve the deficiencies of current insufficient research on HpNGC and the lack of highly accurate biological detection markers, and to provide the application of differentially expressed serum protein glycoforms as biological detection markers for the diagnosis of Helicobacter pylori-negative gastric cancer.

[0007] Inventive Concept

[0008] Protein glycosylation is an important post-translational modification, and its products are involved in biological processes such as molecular recognition, protein transport, regulation, and inflammation. In fact, abnormal protein glycosylation is related to the pathogenesis of various diseases, such as cancer, inflammation, and congenital diseases. The present invention takes the serum proteins of Helicobacter pylori-negative healthy volunteers (HV), HpNAG patients, and HpNGC patients as the research objects, uses lectin microarray technology to analyze the possibility of serum glycoprotein glycoforms becoming biological detection markers for HpNGC patients, and constructs a diagnostic model in combination with machine learning algorithms to provide auxiliary support for the screening and monitoring of HpNGC and HpNAG patients.

[0009] The main reasons for the present invention to select serum as the sample to be detected are as follows: First, serum contains a rich proteome, and the content of its components can reflect the current state of the body; Second, blood flows dynamically throughout the body, and serum samples can be collected multiple times in small amounts, so as to detect the dynamic progress of patients in terms of time dimension and disease lesion dimension, and thus find the most effective diagnosis and treatment methods; Third, currently, for the research on serum, the collection methods and analysis means are relatively mature, so using serum as the detection carrier will have relatively high data reliability; Fourth, the collection method is simple, non-invasive, and low-cost.

[0010] To achieve the above object, the technical solution provided by the present invention is as follows:

[0011] The application of differentially expressed serum protein glycoforms as biological detection markers for the diagnosis of Helicobacter pylori-negative gastric cancer.

[0012] Further, the differential expression is:

[0013] Compared with HV,

[0014] the Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) sugar chain structures recognized by ECA,

[0015] The anti-A and anti-B human blood group and the sugar chain structures of Terminal with GalNAc and Gal recognized by SJA,

[0016] The sugar chain structure of Galα1-3(Fucα1-2)Gal (blood group B antigen) recognized by EEL,

[0017] The sugar chain structures of Fucα1-3(Galβ1-4)GlcNAc and Fucα1-6GlcNAc (core fucose) recognized by AAL,

[0018] The sugar chain structures of αGalNAc, αGal and anti-A and B recognized by GSL-I,

[0019] The sugar chain structures of Gal, T antigen and blood group H recognized by PTL-II,

[0020] The sugar chain structures of (GalNAc)n and blood-group A recognized by SBA,

[0021] The sugar chain structure of Galβ1-3GalNAcα-Ser / Thr (T antigen) recognized by ACA,

[0022] The sugar chain structures of Multivalent Sia and (GlcNAc)n recognized by WGA,

[0023] The sugar chain structure of Fucα1-2Galβ1-4GlcNAc recognized by UEA-I,

[0024] The sugar chain structures of Galβ1-4GlcNAc, Galβ1-3GlcNAc recognized by MAL-I,

[0025] The sugar chain structures of β-D-GlcNAc, (GlcNAcβ1-4)n, Galβ1-4GlcNAc, GalNAc and tri / tetra-antennary N-glycan recognized by DSA,

[0026] Significantly up-regulated in HpNGC;

[0027] The sugar chain structure of Galβ1-3GalNAcα-Ser / Thr (T) recognized by Jacalin,

[0028] The sugar chain structures of High-Man and Man5-GlcNAc2-Asn recognized by HHL,

[0029] The structure of the biantennary complex-type N-glycan sugar chain with galactose on the outer side recognized by PHA-E,

[0030] The sugar chain structures of Fucα1-2Galβ1-4GlcNAc and anti-H blood group specificity recognized by LTL,

[0031] The sugar chain structures of α-D-Man, Fucα1-6GlcNAc and α-D-Glc recognized by LCA,

[0032] The sugar chain structures of β-Gal, Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) recognized by RCA120,

[0033] The sugar chain structures of Terminal GlcNAc, Manα1-6(Manα1-3)Man and High-Mannose recognized by ConA,

[0034] The sugar chain structures of α-D-Glc, Fucα1-6GlcNAc and α-D-Man recognized by PSA,

[0035] The expression was significantly down-regulated in HpNGC.

[0036] Furthermore, the differential expression is as follows:

[0037] Compared with HpNAG,

[0038] The sugar chain structures of anti-A and anti-B human blood group and Terminal with GalNAc and Gal recognized by SJA,

[0039] The sugar chain structures of β-Gal, Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) recognized by RCA120,

[0040] The sugar chain structures of Gal, T antigen and blood group H recognized by PTL-II,

[0041] The sugar chain structures of tri / tetra-antennary N-glycan, β-D-GlcNAc, (GlcNAcβ1-4)n, Galβ1-4GlcNAc and GalNAc recognized by DSA,

[0042] Blood-group A, GalNAcα1-3Gal, (GalNAc)n and α- or β-linked terminal GalNAc glycan structures recognized by SBA,

[0043] α-D-Glc and α-D-Man glycan structures recognized by PSA,

[0044] Fucα1-2Galβ1-4Glc(NAc) glycan structure recognized by UEA-I,

[0045] High-Mannose and Manα1-3Man glycan structures recognized by GNA,

[0046] Terminal GalNAc and Galβ1-3GalNAc glycan structures recognized by BPL,

[0047] Galβ1-4GlcNAc and Galβ1-3GlcNAc glycan structures recognized by MAL-I,

[0048] Upregulated expression in HpNGC;

[0049] High-Man, Man5-GlcNAc2-Asn and Manα1-6Man glycan structures recognized by HHL,

[0050] Terminal with GalNAcα / β1-3 / 6Gal glycan structure recognized by WFA,

[0051] GlcNAc and agalactosylated tri / tetra antennary glycans glycan structures recognized by GSL-II,

[0052] Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal, Siaα2-3 and Siaα2-3GalNAc glycan structures recognized by MAL-II,

[0053] Bisecting GlcNAc and biantennary complex-type N-glycan without outer Gal glycan structures recognized by PHA-E,

[0054] Galβ1-3GalNAcα-Ser / Thr(T) glycan structure recognized by PNA,

[0055] Fucα1-2Galβ1-4GlcNAc, Fucα1-3(Galβ1-4)GlcNAc and anti-H blood group specificity glycan structures recognized by LTL,

[0056] (GlcNAc)n and high mannose-type N-glycans glycan structures recognized by LEL,

[0057] αGalNAc, αGal and anti-A and B glycan structures recognized by GSL-I,

[0058] GalNAcα1-3((Fucα1-2))Gal (blood group A antigen), Tn antigen and αGalNAc glycan structures recognized by DBA,

[0059] α-D-Man, Fucα1-6GlcNAc and α-D-Glc glycan structures recognized by LCA,

[0060] Trimers and tetramers of GlcNAc and core (GlcNAc) of N-glycan glycan structures recognized by STL,

[0061] Down-regulated in HpNGC.

[0062] Meanwhile, the application of differentially expressed serum protein glycotypes as biomarkers for the diagnosis of Helicobacter pylori-negative gastritis is also provided.

[0063] Furthermore, the differential expression is as follows:

[0064] Compared with HV,

[0065] Galβ1-4GlcNAc (type II) and Galβ1-3GlcNAc (type I) glycan structures recognized by ECA,

[0066] Terminal with GalNAcα / β1-3 / 6Gal glycan structures recognized by WFA,

[0067] GlcNAc and agalactosylated tri / tetra antennary glycans glycan structures recognized by GSL-II,

[0068] The sugar chain structures of Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal, Siaα2-3 and Siaα2-3GalNAc recognized by MAL-II,

[0069] The sugar chain structures of GalNAcα1-3Galβ1-3 / 4Glc, GalNAc and GalNAcα1-3Gal recognized by PTL-I,

[0070] The sugar chain structure of Galβ1-3GalNAcα-Ser / Thr(T) recognized by PNA,

[0071] The sugar chain structures of Fucα1-3(Galβ1-4)GlcNAc and Fucα1-6GlcNAc(core fucose) recognized by AAL,

[0072] The sugar chain structures of GalNAc and Galβ1-3GalNAc recognized by MPL,

[0073] The sugar chain structures of (GlcNAc)n and high mannose-type N-glycans recognized by LEL,

[0074] The sugar chain structures of αGalNAc and αGal anti-A and B recognized by GSL-I,

[0075] The sugar chain structures of Tn antigen, GalNAcα1-3((Fucα1-2))Gal(blood group A antigen) and αGalNAc recognized by DBA,

[0076] The sugar chain structures of sugar chain T antigen, Gal and blood group H recognized by PTL-II,

[0077] The sugar chain structures of High-Mannose, Manα1-6Man recognized by NPA,

[0078] Up-regulated expression in HpNAG;

[0079] The sugar chain structures of GlcNAcβ1-3-GalNAcα-Ser / Thr(Core3), GalNAcα-Ser / Thr(Tn) and Galβ1-3GalNAcα-Ser / Thr(T) recognized by Jacalin,

[0080] The sugar chain structures of Galβ1-3GlcNAc(type I), Galβ1-4GlcNAc(type II) and β-Gal recognized by RCA120,

[0081] Terminal GlcNAc, Manα1-6(Manα1-3)Man and High-Mannose glycan structures recognized by ConA,

[0082] Multivalent Sia and (GlcNAc)n glycan structures recognized by WGA,

[0083] High-Mannose and Manα1-3Man glycan structures recognized by GNA,

[0084] Galβ1-3GalNAcα-Ser / Thr (T antigen) glycan structure recognized by ACA,

[0085] Down-regulated expression in HpNAG.

[0086] Meanwhile, the application of differentially expressed serum protein glycotypes as biomarkers for the diagnosis of Helicobacter pylori-negative gastric cancer and Helicobacter pylori-negative gastritis is also provided.

[0087] Furthermore, the differential expression is as follows:

[0088] Terminal with GalNAcα / β1-3 / 6Gal glycan structure recognized by WFA,

[0089] GlcNAc and agalactosylated tri / tetra antennary glycans glycan structures recognized by GSL-II,

[0090] Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal and Siaα2-3GalNAc glycan structures recognized by MAL-II,

[0091] Galβ1-3GalNAcα-Ser / Thr (T) glycan structure recognized by PNA,

[0092] αGalNAc, Tn antigen and GalNAcα1-3((Fucα1-2))Gal (blood group A antigen) glycan structures recognized by DBA,

[0093] (GlcNAc)n and high mannose-type N-glycans glycan structures recognized by LEL,

[0094] αGalNAc and αGal, anti-A and B glycan structures recognized by GSL-I,

[0095] STL recognizes the sugar chain core (GlcNAc) of N-glycan, trimers and tetramers of GlcNAc, and oligosaccharide structures containing GlcNAc and MurNAc.

[0096] It is lowly expressed between HV and HpNAG and highly expressed between HpNAG and HpNGC.

[0097] RCA120 recognizes the sugar chain structures of Galβ1-3GlcNAc (type I), Galβ1-4GlcNAc (type II), and β-Gal.

[0098] WGA recognizes the sugar chain structures of Multivalent Sia and (GlcNAc)n.

[0099] It is highly expressed between HV and HpNAG and lowly expressed between HpNAG and HpNGC.

[0100] Advantages of the present invention:

[0101] The present invention uses lectin chip technology to analyze the differential expression of serum glycoprotein glycoforms in HV, HpNAG patients, and HpNGC patients, and verifies that the differentially expressed serum glycoprotein glycoforms can be used as biological detection markers for the diagnosis of Helicobacter pylori-negative gastric cancer. Description of the Drawings

[0102] Figure 1 It is a schematic diagram of a lectin chip array;

[0103] Figure 2 It is a scanning result diagram of a lectin chip;

[0104] Figure 3 It is a standard curve for protein concentration determination by BCA;

[0105] Figure 4 It is an HCA diagram of serum glycoprotein glycoforms of HV and HpNGC;

[0106] Figure 5 It is an HCA diagram of serum glycoprotein glycoforms of HpNAG and HpNGC;

[0107] Figure 6 It is an HCA diagram of serum glycoprotein glycoforms of HV and HpNAG;

[0108] Figure 7 It is an HCA diagram of serum glycoprotein glycoforms of HV, HpNAG, and HpNGC;

[0109] Figure 8 Results of Student's t test for the glycan structures bound by 12 lectins that were significantly upregulated in HV and HpNGC serum samples; successively the glycan structures recognized by ECA, SJA, EEL, AAL, GSL-I, PTL-II, SBA, ACA, DSA, WGA, UEA-I, and MAL-I;

[0110] Figure 9 Results of Student's t test for the glycan structures bound by 8 lectins that were significantly downregulated in HV and HpNGC serum samples; successively the glycan structures recognized by Jacalin, HHL, PHA-E, LTL, LCA, RCA120, ConA, and PSA;

[0111] Figure 10 Results of the principal component analysis of 20 lectins in HV and HpNGC serum samples;

[0112] Figure 11 Results of Student's t test for the glycan structures bound by 10 lectins that were significantly upregulated in HpNAG and HpNGC serum samples; successively the glycan structures recognized by SJA, RCA120, PTL-II, DSA, SBA, PSA, UEA-I, GNA, MAL-I, and BPL;

[0113] Figure 12 Results of Student's t test for the glycan structures bound by 12 lectins that were significantly downregulated in HpNAG and HpNGC serum samples; successively the glycan structures recognized by HHL, WFA, GSL-II, MAL-II, PHA-E, PNA, LTL, LEL, GSL-I, DBA, LCA, and STL;

[0114] Figure 13 Results of the principal component analysis of 22 lectins in HpNAG and HpNGC serum samples;

[0115] Figure 14 Results of Student's t test for the glycan structures bound by 14 lectins that were significantly upregulated in HV and HpNAG serum samples; successively the glycan structures recognized by ECA, WFA, GSL-II, MAL-I, PTL-I, PNA, AAL, MPL, LEL, GSL-I, DBA, STL, PTL-II, and NPA;

[0116] Figure 15Results of Student's t test for sugar chain structures bound by 6 lectins with significantly downregulated expression in HV and HpNAG serum samples; successively the sugar chain structures recognized by Jacalin, RCA120, ConA, ACA, WGA, and GNA;

[0117] Figure 16 Principal component analysis results of 20 lectins in HV and HpNAG serum samples;

[0118] Figure 17 Results of Student's t test for sugar chain structures bound by 8 lectins with significantly upregulated expression in HV, HpNAG, and HpNGC serum samples; successively the sugar chain structures recognized by WFA, GSL-II, MAL-II, PNA, LEL, GSL-I, DBA, and STL;

[0119] Figure 18 Results of Student's t test for sugar chain structures bound by 2 lectins with significantly downregulated expression in HV, HpNAG, and HpNGC serum samples; respectively the sugar chain structures recognized by WGA and RCA120;

[0120] Figure 19 Principal component analysis results of 10 lectins in HV, HpNAG, and HpNGC serum samples. Detailed implementation method

[0121] In the present invention, 146 clinical serum samples are used to compare the differentially expressed glycoforms of serum proteins in HV, HpNAG patients and HpNGC patients by using lectin chip technology and T - test method, cluster analysis and principal component analysis. It is studied that the differentially expressed serum protein glycoforms can be used as biological detection markers for the diagnosis of HpNGC and / or HpNAG.

[0122] I. Preparation work

[0123] 1.1. Main reagents and consumables

[0124] Table 1 Main reagents and consumables

[0125] Material Name Experimental Use Reagent, Supplier Chip Substrate As a Carrier for Spotting Lectins Gold Seal (U.S) 37 Kinds of Lectins Preparation of Lectin Chip Vector Lab (U.S) 384-Well Plate Chip Spotting Plate Genetix (U.K) Chip Incubation Box Fix Samples and Lectin Chip Bio-Rad (U.S) Fluorescent Dry Powder (Cy3 / Cy5) Fluorescently Labeled Serum Samples Amerhsma (U.S) Protease Inhibitor Prevent Degradation of Serum Proteins Sigma-Aldrich (U.S) Sephadex G-25 Column Isolate Fluorescently-Bound Proteins GE Healthcare (U.S) Bradford Reagent Determine Protein Concentration Sigma-Aldrich (U.S) Bovine Serum Albumin Chip Blocking and Chip Quality Control Calbiochem (GER) Hydroxylamine Hydrochloride Terminate Fluorescent Labeling Sigma-Aldrich (U.S) Glycine Block and Incubate Chip Sigma-Aldrich (U.S) Tween-20 Wash Chip Sigma-Aldrich (U.S) 0.22μm Filter Membrane and 0.45μm Filter Membrane Sterilize Samples and Buffers Millipore (U.S)

[0126] 1.2. Solution preparation

[0127] 1) Lectin spotting solution: Used for spotting lectin chips, prepared according to the instructions of 37 lectins. Specifically: Weigh the lectin, add the corresponding monosaccharide and bovine serum albumin (BSA), dissolve it in phosphate buffered saline (PBS) buffer of the corresponding pH, and add Na + and Mg + to maintain the activity of the binding site. After filtering the solution with a 2.22 μm filter membrane, store it at -80 °C. All 37 lectins are dissolved in phosphate buffered saline of different pH values according to this method.

[0128] 2) 10% GPTS solution: Used for preparing epoxidized glass substrates. Add 450 μL of glacial acetic acid to the measured 30 mL of tetramethyldivinyldisilane (GPTS), and make up the volume to 300 mL with absolute ethanol.

[0129] 3) Lectin chip blocking buffer: Used to block the remaining activated groups on the lectin chip. The preparation method is: Weigh 0.2 g of BSA and 0.75 g of glycine (Gly), dissolve them in 1 mL of 10×PBS with a pH of 7.4, add 5 μL of Tween-20, and make up the volume to 10 mL. After the blocking buffer is prepared, filter it with a 0.22 μm filter membrane and store it in a -20 °C refrigerator for later use.

[0130] 4) Lectin chip incubation buffer: Used for incubating samples and lectin chips. The preparation method is: Weigh 0.3 g of BSA and 1.125 g of Gly, dissolve them in 1.5 mL of 10×PBS with a pH of 7.4, add Tween-20 (7.5 μL) and then make up the volume. After filtering the solution with a 2.22 μm filter membrane, store it in a -20 °C environment.

[0131] 5) 10×PBS: Used for preparing the lectin chip washing solution. The preparation method is: Weigh 39.3 g of Na 2 HPO 4 , 2.4 g of KH 2 PO 4 and 80 g of NaCl, dissolve them in 1 L of ultrapure water, adjust the pH to 7.4 with HCl, and store at room temperature.

[0132] 6) 1×PBS: Used for washing lectin chips. The preparation method is: Dilute it to a 1× concentration with 450 mL of sterile water and 50 mL of 10×PBS.

[0133] 7) 1×PBST: Used for washing lectin chips. The preparation method is: Add 1 mL of 0.2% Tween-20 to 500 mL of PBS.

[0134] 8) 4M Hydroxylamine: Used to terminate fluorescence labeling. Preparation method: Weigh hydroxylamine hydrochloride (2.779 g), dissolve it and make up the volume. After filtering the solution with a 2.22 μm filter membrane, store it at -4°C.

[0135] 9) Cy3 Fluorescent Dye: Used to label samples. According to the instructions, add dimethyl sulfoxide (DMSO) to dissolve the Cy3 fluorescent dry powder. Usually, 50 mg of Cy3 fluorescent dry powder is dissolved in 6 mL of DMSO, react in the dark on a shaker for 30 minutes, and dispense 100 μL of Cy3 fluorescent dye into each 250 μL centrifuge tube, store at -20°C in the refrigerator for later use.

[0136] 10) Sodium Carbonate Solution: Used for Cy3 fluorescent labeling of proteins. Preparation method: Weigh an appropriate amount of Na 2 CO 3 Dissolve it with ultrapure water to a final concentration of 0.1 mol / L, adjust the pH to 9.3 using NaHCO 3 solution, filter it with a 0.22 μm diameter filter membrane, and store it at -4°C in the refrigerator for later use.

[0137] II. Test Procedure

[0138] 2.1. Sample Collection and Processing

[0139] 1) Collect about 1 ml of venous blood for later use.

[0140] 2) Place the collected venous blood at 4°C for 30 minutes, centrifuge the blood at 5000 r / min for 10 minutes to separate the layers.

[0141] 3) Aspirate the upper serum after layering, add 5 μL of protease inhibitor, dispense into two tubes, and store at -80°C for later use.

[0142] 2.2. Serum Sample Quantification and Fluorescent Labeling

[0143] 2.2.1. Use a BCA kit to measure the serum protein concentration by the Bradford method

[0144] 1) Prepare an appropriate amount of working solution according to the number of samples, mix solutions A and B (50:1), and let it stand at room temperature to obtain the BCA working solution.

[0145] 2) Dilute the BSA standard to a final concentration of 0.5 mg / mL and let it stand at room temperature.

[0146] 3) Add the diluted BSA standard to the 96-well plate in gradients of 0, 2, 4, 6, 8, 12, 16, 20 μL respectively, and make up to 20 μL with PBS.

[0147] 4) Add the sample to be tested into a 96-well plate (20 μL), and finally add the prepared BCA working solution (200 μL) into the wells containing the sample to be tested and the BSA standard. Place the 96-well plate on a shaker at 37 °C for 30 minutes, and measure the absorbance of the sample to be tested or the standard in each well using an ELISA reader at an excitation wavelength of 562 nm.

[0148] 2.2.2. Cy3 Fluorescent Labeling of Serum Protein and Quantification

[0149] Mix 100 μL of 0.1 M NaHCO 3 buffer and 10 μL of serum sample, then add 5 μL of Cy3 fluorescence. Place it on a shaker and react for 1 h (protected from light). After the reaction, add 20 μL of 4 M hydroxylamine hydrochloride to the sample to terminate the fluorescence labeling reaction (the termination step is carried out on ice). Use a high-speed centrifuge to centrifuge the sample on the wall of the centrifuge tube and let it stand for column chromatography. After the fluorescence binds to the serum protein, when passing through the Sephadex G25 desalting column, it will pass through the column faster than the free fluorescence and the protein that has not bound the fluorescence. Therefore, collect the first pink liquid that flows out, which is the Cy3 fluorescently labeled protein. Quantify the Cy3 fluorescently labeled protein using a Nano-drop, record its concentration, and store the sample at -20 °C protected from light.

[0150] 2.3. Lectin Chip Experiment

[0151] Spot 37 kinds of lectins, BSA negative control, and Marker positive control on the chip substrate to obtain a lectin chip. Store it in a 4 °C refrigerator for later use. Then perform the lectin chip experiment through the following steps:

[0152] 1) Take out the chip stored in the 4 °C refrigerator and mark the serial number by engraving on the lower right corner of the side with the lectin spotted. Place it in a 37 °C vacuum drying oven for 30 minutes to warm up. Then place the chip in an alcohol solution for 10 seconds briefly to fix the lectin.

[0153] 2) Wash the chip: Set the shaker speed to 85 r / min, place the chip in a 1×PBST washing tank and wash twice, then in a 1×PBS washing tank and wash twice, and spin dry and seal for later use (pass through alcohol once before spin drying).

[0154] 3) Place the large-area sealing cover glass in the hybridization groove on the hybridization box. The side of the cover glass with the rubber ring faces up as the container for the sealing solution. Add 600 μL of 1× sealing buffer, then cover the lectin chip (the side with the lectin spotted faces down to react with the sealing solution). Clamp the hybridization box with a clip and gently pat until no immobile small bubbles are visible to the naked eye and the sealing solution flows freely.

[0155] 4) Place the hybridization box in a constant temperature rotating hybridizer and block the reaction at 37°C for 1 hour.

[0156] 5) After the blocking reaction is completed, clean the chip (same steps as above).

[0157] 6) Prepare the incubation system

[0158] The incubation system is 120 μL in total, consisting of 90 μL incubation buffer (80 μL 1.5×, 8 μL 4M hydroxylamine, 2 μL 10% Tween-20), Cy3 fluorescently labeled protein, and ultrapure water. After calculating the sample loading volume, add the sample according to the loading volume, and then add pure water to make up the incubation system to 120 μL.

[0159] The sample loading volume is calculated as follows:

[0160] ① Use the BSA standard concentration and its corresponding absorbance in step 2.2.1 to draw a linear regression graph, with the horizontal axis being the concentration of the BSA standard (mg / mL) and the vertical axis being the absorbance of the mixed solution at 562nm (A562 value), and obtain the following: Figure 3 The standard curve and the corresponding calculation formula y=0.7773x+0.143, R 2 =0.9982, R 2 >0.99, so the data obtained are linearly correlated and have strong reliability.

[0161] ② Substitute the absorbance of the sample to be tested into the calculation formula to calculate the initial concentration of the sample.

[0162] ③ After the sample to be tested goes through step 2.2.2., the Cy3 fluorescently labeled protein concentration is obtained. Based on the initial concentration and the Cy3 fluorescently labeled protein concentration, the loading amount during the lectin chip incubation process is calculated.

[0163] 7) Add the incubation system to the small cover glass, tighten the clamp, and tap until the bubble-free liquid is in a free-flowing state. Incubate the reaction at 37°C with constant rotation for 1 hour under the same closed reaction conditions.

[0164] 8) After the incubation is completed, the chip is cleaned in the same way, dried and stored at room temperature away from light for subsequent scanning.

[0165] 9) Clean the large area sealing cover glass and the small area incubation cover glass: First, use detergent water to wash the phosphate buffer bound to the glass slide, and then put it in an ultrasonic instrument for 5 minutes; then rinse it with ultrapure water to wash away the detergent and binding substances on the surface of the glass slide, and then put it in an ultrasonic instrument for 5 minutes. Finally, put it in alcohol for 5 minutes, take it out and spin dry, and store it at room temperature for the next experiment.

[0166] Repeat steps 2.1 - 2.3 to separately process 146 serum samples from 67 healthy individuals without Helicobacter pylori infection, 49 patients with atrophic gastritis, and 30 gastric cancer patients, obtaining lectin microarray data corresponding to the 146 test samples.

[0167] 2.4. Experimental Results and Data Processing

[0168] 2.4.1. Scan the lectin microarray using a dedicated chip scanner in the laboratory

[0169] Arrange all lectin microarrays as Figure 1 shown to form a lectin microarray array, then place the lectin microarray array into the chip scanner with the side spotted with lectin facing down. Turn on each switch of the scanner in sequence, open the chip scanner software (GenePix software), and set parameters such as the excitation light (532nm) and laser intensity. First, roughly scan the chip to adjust the position, and then precisely scan the target area to obtain the experimental result image of the lectin microarray, as Figure 2 shown, and save it.

[0170] 2.4.2. Data Analysis

[0171] Simultaneously analyze the experimental result image of the lectin microarray using GenePixPro software, Excel software, and the lectin normalization software GraphPad Prism software 8.0. Specifically:

[0172] 1) Open the picture in GenePixPro software, circle the points corresponding to the 37 lectins, BSA negative control, and Marker positive control on the chip, obtain data through the Analysis button, and import it into Excel for further analysis.

[0173] 2) Calculate the normalized signal values of the 37 lectins: Select the two columns of data, F532 Median - B532Median and B532 SD, for a single lectin, divide the two, and if the resulting value is greater than 1.5, it is a valid fluorescence value, and if it is less than 1.5, it is classified as zero to obtain the signal value of a single lectin. Calculate the signal values corresponding to the 37 lectins in sequence. After taking the median of the signal values of a single lectin, calculate the ratio to the total sum of the lectin signal values. The resulting value is the normalized signal value (Normalized Fluorescent Intensity, NFI) of a single lectin. Calculate the NFI of the 37 lectins in sequence.

[0174] Repeat steps 1) and 2) for the lectin microarrays of the 146 serum samples in the experimental result image, thereby obtaining the normalized signal values of the 37 lectins corresponding to the 146 serum samples.

[0175] 3) Analyze the normalized signal values corresponding to HV, HpNAG, and HpNGC using GraphPad Prism software 8.0, and compare them using the Student's t test.

[0176] 2.4.3. Serum Protein Glycoforms of Helicobacter pylori-Negative Healthy Volunteers, Atrophic Gastritis, and Gastric Cancer Patients

[0177] Using the normalized signal values of 37 lectins corresponding to the serum samples of 67 healthy people without Helicobacter pylori infection, 49 patients with atrophic gastritis, and 30 patients with gastric cancer, calculate the mean fluorescence signal value and its standard deviation (SD value) of the 37 lectins for the three groups of samples. As shown in Table 2.

[0178] Table 2 Summary Table of Lectin Chip Results

[0179]

[0180]

[0181] III. Model Establishment

[0182] Take the normalized signal values of 37 lectins in 3 groups of samples (67 cases of HV, 49 cases of HpNAG, and 30 cases of HpNGC) as candidate variables (input features) of the model. Based on the training set data, use the eXtreme Gradient Boosting (XGB) algorithm, K-Nearest Neighbor (KNN) algorithm, Support Vector Machines (SVM) algorithm, Random Forest (RF) algorithm, Logistic Regression (LR) algorithm, and Multilayer Perceptron (MLP) algorithm to construct 6 diagnostic models respectively, and optimize the parameters to improve the model classification effect. Finally, use the test set data to verify the classification effect of the 6 models.

[0183] IV. Result Analysis

[0184] 4.1. Differences in Serum Protein Glycan Structures between Helicobacter pylori-Negative Healthy Volunteers and Gastric Cancer Patients

[0185] Hierarchical cluster analysis (HCA) was performed on the normalized signal values of 67 serum samples in the HV group and 30 serum samples in the HpNGC group to visualize the data, allowing for an intuitive observation of the similarities and differences in the glycoprotein glycoforms of each serum sample between the two groups. Similarity indicates that there is no significant differential expression of the sugar chain structures bound by one or several lectins between the two groups, while difference indicates that the sugar chain structures bound by one or several lectins are significantly differentially expressed between the two groups. From Figure 4 the HCA results shown, it can be observed that there are differences in the serum protein glycoforms among different samples. When looking between groups, the samples in the HV group and the HpNGC group mostly cluster together, indicating that the two groups of samples can be well distinguished.

[0186] Through HCA, it was found that there were obvious differences in the sugar chain structures bound by certain lectins between the two groups of samples. After analyzing the two groups of samples using the Student's t test method with GraphPad Prism 8 software, the analysis results showed that compared with HV, there were 20 sugar chain structures bound by lectins that were differentially expressed in HpNGC, among which 12 sugar chain structures bound by lectins were significantly upregulated, such as Figure 8As shown, it includes the Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) glycan structures recognized by ECA, the anti-A and anti-B human blood group and the glycan structures with Terminal GalNAc and Gal recognized by SJA, the Galα1-3(Fucα1-2)Gal (blood group B antigen) glycan structure recognized by EEL, the Fucα1-3(Galβ1-4)GlcNAc and Fucα1-6GlcNAc (core fucose) glycan structures recognized by AAL, the αGalNAc, αGal and anti-A and B glycan structures recognized by GSL-I, the Gal, T antigen and blood group H glycan structures recognized by PTL-II, the (GalNAc)n and blood-group A glycan structures recognized by SBA, the Galβ1-3GalNAcα-Ser / Thr (T antigen) glycan structure recognized by ACA, the Multivalent Sia and (GlcNAc)n glycan structures recognized by WGA, the Fucα1-2Galβ1-4GlcNAc glycan structure recognized by UEA-I, the Galβ1-4GlcNAc, Galβ1-3GlcNAc glycan structures recognized by MAL-I, and the β-D-GlcNAc, (GlcNAcβ1-4)n, Galβ1-4GlcNAc, GalNAc and tri / tetra-antennary N-glycan glycan structures recognized by DSA. As Figure 9As shown, the expression of 8 kinds of lectin-binding glycans decreased significantly, including glycan structures such as Galβ1-3GalNAcα-Ser / Thr(T) recognized by Jacalin, High-Man and Man5-GlcNAc2-Asn recognized by HHL, the bi-antennary complex N-glycan structure with galactose on the outside recognized by PHA-E, Fucα1-2Galβ1-4GlcNAc and anti-H blood group specificity glycan structures recognized by LTL, α-D-Man, Fucα1-6GlcNAc and α-D-Glc glycan structures recognized by LCA, β-Gal, Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) glycan structures recognized by RCA120, Terminal GlcNAc, Manα1-6(Manα1-3)Man and High-Mannose glycan structures recognized by ConA, α-D-Glc, Fucα1-6GlcNAc and α-D-Man glycan structures recognized by PSA.

[0187] Twenty kinds of lectin-recognized differentially expressed glycan structures between HV and HpNGC were screened out. Subsequently, principal component analysis (PCA analysis) was performed on the 20 lectins. The results are as Figure 10 shown. The two groups of samples were separated from each other, with only a small part overlapping, and the data was reliable.

[0188] 4.2. Differences in serum protein glycan structures of patients with Helicobacter pylori-negative atrophic gastritis and gastric cancer

[0189] Hierarchical cluster analysis (HCA) was performed on the lectin microarray experimental data of 49 serum samples in the HpNAG group and 30 serum samples in the HpNGC group. The results are as Figure 5 shown.

[0190] The HCA results showed that there were obvious differences in the glycan structures recognized by some lectins between the two groups of samples. The Student's t test was used to analyze the two groups of samples with GraphPad Prism 8 software. The analysis results showed that compared with HpNAG, there were differentially expressed glycan structures recognized by 22 lectins. Among them, the expression of glycan structures recognized by 10 lectins increased significantly, such as Figure 11As shown, it includes the anti-A and anti-B human blood group recognized by SJA and the sugar chain structures of Terminal with GalNAc and Gal, the β-Gal, Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) sugar chain structures recognized by RCA120, the Gal, T antigen and blood group H sugar chain structures recognized by PTL-II, the tri / tetra-antennary N-glycan, β-D-GlcNAc, (GlcNAcβ1-4)n, Galβ1-4GlcNAc and GalNAc and sugar chain structures recognized by DSA, the blood-group A, GalNAcα1-3Gal, (GalNAc)n and α- or β-linked terminal GalNAc sugar chain structures recognized by SBA, the α-D-Glc and α-D-Man sugar chain structures recognized by PSA, the Fucα1-2Galβ1-4Glc(NAc) sugar chain structure recognized by UEA-I, the High-Mannose and Manα1-3Man sugar chain structures recognized by GNA, the Terminal GalNAc and Galβ1-3GalNAc sugar chain structures recognized by BPL, and the Galβ1-4GlcNAc and Galβ1-3GlcNAc sugar chain structures recognized by MAL-I. As Figure 12As shown, the expression of 12 kinds of sugar chains bound by lectins was significantly reduced, including sugar chain structures such as High-Man, Man5-GlcNAc2-Asn, and Manα1-6Man recognized by HHL, the sugar chain structure of Terminal with GalNAcα / β1-3 / 6Gal recognized by WFA, the sugar chain structure of GlcNAc and agalactosylated tri / tetraantennary glycans recognized by GSL-II, Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal, Siaα2-3, and Siaα2-3GalNAc sugar chain structures recognized by MAL-II, the sugar chain structures of Bisecting GlcNAc and biantennary complex-type N-glycan with outer Gal recognized by PHA-E, the sugar chain structure of Galβ1-3GalNAcα-Ser / Thr(T) recognized by PNA, Fucα1-2Galβ1-4GlcNAc, Fucα1-3(Galβ1-4)GlcNAc, and anti-H blood group specificity sugar chain structures recognized by LTL, the sugar chain structures of (GlcNAc)n and high mannose-type N-glycans recognized by LEL, the sugar chain structures of αGalNAc, αGal, and anti-A and B recognized by GSL-I, GalNAcα1-3((Fucα1-2))Gal (blood group A antigen), Tn antigen, and αGalNAc sugar chain structures recognized by DBA, the sugar chain structures of α-D-Man, Fucα1-6GlcNAc, and α-D-Glc recognized by LCA, and the sugar chain structures of trimers and tetramers of GlcNAc and core (GlcNAc) of N-glycan recognized by STL.

[0191] Twenty-two sugar chain structures with differential expression recognized by lectins between HpNAG and HpNGC were screened out. Subsequently, PCA analysis was performed on the 22 lectins, and the results are as Figure 13 shown. The two groups of samples were separated from each other, with only a small part overlapping, indicating the reliability of the data.

[0192] 4.3. Differences in Serum Protein Sugar Chain Structures between Helicobacter pylori-Negative Healthy Volunteers and Patients with Atrophic Gastritis

[0193] HCA analysis was performed on 67 serum samples in the HV group and 49 serum samples in the HpNAG group. From Figure 6The HCA analysis results showed that there were differences in the serum protein glycoforms between the HV group and the HpNAG group samples. Specifically, the samples from the two groups could be clustered well, indicating that the discrimination effect between the two groups of samples was relatively good.

[0194] The HCA analysis results showed that there were obvious differences in the sugar chain structures bound by some lectins between the two groups of samples. The Student's t test was used to analyze the two groups of samples with GraphPad Prism 8 software. The analysis results showed that compared with HV, there were differential expressions of sugar chain structures bound by 20 lectins. Among them, the expressions of sugar chain structures bound by 14 lectins were significantly increased, such as Figure 14 shown, including the Galβ1-4GlcNAc (type II) and Galβ1-3GlcNAc (type I) sugar chain structures recognized by ECA, the Terminal with GalNAcα / β1-3 / 6Gal sugar chain structure recognized by WFA, the GlcNAc and agalactosylated tri / tetra antennary glycans sugar chain structure recognized by GSL-II, the Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal, Siaα2-3 and Siaα2-3GalNA sugar chain structures recognized by MAL-II, the GalNAcα1-3Galβ1-3 / 4Glc, GalNAc and GalNAcα1-3Gal sugar chain structures recognized by PTL-I, the Galβ1-3GalNAcα-Ser / Thr (T) sugar chain structure recognized by PNA, the Fucα1-3(Galβ1-4)GlcNAc and Fucα1-6GlcNAc (core fucose) sugar chain structures recognized by AAL, the GalNAc and Galβ1-3GalNAc sugar chain structures recognized by MPL, the (GlcNAc)n and high mannose-type N-glycans sugar chain structures recognized by LEL, the αGalNAc and αGalanti-Aand B sugar chain structures recognized by GSL-I, the Tn antigen, GalNAcα1-3((Fucα1-2))Gal (blood group Aantigen) and αGalNAc sugar chain structures recognized by DBA, the sugar chain T antigen, Gal and blood groupH sugar chain structures recognized by PTL-II, the High-Mannose, Manα1-6Man sugar chain structure recognized by NPA. The expressions of sugar chain structures bound by 6 lectins were significantly decreased, such as Figure 15As shown, it includes sugar chain structures such as GlcNAcβ1-3-GalNAcα-Ser / Thr (Core3) recognized by Jacalin, GalNAcα-Ser / Thr (Tn), and Galβ1-3GalNAcα-Ser / Thr (T); sugar chain structures such as Galβ1-3GlcNAc (type I), Galβ1-4GlcNAc (type II), and β-Gal recognized by RCA120; terminal GlcNAc, Manα1-6(Manα1-3)Man, and High-Mannose sugar chain structures recognized by ConA; Multivalent Sia and (GlcNAc)n sugar chain structures recognized by WGA; High-Mannose and Manα1-3Man sugar chain structures recognized by GNA; and Galβ1-3GalNAcα-Ser / Thr (T antigen) sugar chain structure recognized by ACA.

[0195] Twenty sugar chain structures recognized by lectins with differential expression between HV and HpNAG were screened out. Subsequently, PCA analysis was performed on the 20 lectins, and the results are as Figure 16 shown, indicating a relatively high separation coefficient and a low overlap degree between the two groups of samples, and the data is reliable.

[0196] 4.4. Differences in serum protein sugar chain structures among Helicobacter pylori-negative healthy volunteers, atrophic gastritis, and gastric cancer patients

[0197] A total of 146 serum samples (67 HV, 49 HpNAG, and 30 HpNGC) were analyzed simultaneously, and HCA analysis was performed on the lectin chip results of the three groups of samples to visualize the data. According to Figure 7 the HCA results shown, it can be observed that the serum glycoprotein glycoforms of the three groups of samples are all different. From the perspective of between groups, the aggregation degree of each group of samples is relatively high, indicating that the three groups of samples can be well distinguished.

[0198] The HCA analysis results found that there were obvious differences in the sugar chain structures bound by some lectins among the three groups of samples. Subsequently, the Student's t test was used to analyze the three groups of samples using GraphPad Prism8 software. The analysis results showed that there were significant differences in the sugar chain structures recognized by 10 lectins among HV, HpNAG, and HpNGC. There were 8 lectins that recognized sugar chain structures, such as Figure 17As shown, it includes the GalNAcα / β1-3 / 6Gal glycan structures recognized by WFA, the GlcNAc and agalactosylated tri / tetra antennary glycans glycan structures recognized by GSL-II, the Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal and Siaα2-3GalNAc glycan structures recognized by MAL-II, the Galβ1-3GalNAcα-Ser / Thr(T) glycan structure recognized by PNA, the αGalNAc, Tn antigen and GalNAcα1-3((Fucα1-2))Gal (blood group A antigen) glycan structures recognized by DBA, the (GlcNAc)n and high mannose-type N-glycans glycan structures recognized by LEL, the αGalNAc and αGal, anti-A and B glycan structures recognized by GSL-I, and the glycan core (GlcNAc) of N-glycan, trimers and tetramers of GlcNAc and oligosaccharide containing GlcNAc and MurNAc glycan structures were lowly expressed between HV and HpNAG and highly expressed between HpNAG and HpNGC. The Galβ1-3GlcNAc (type I), Galβ1-4GlcNAc (type II) and β-Gal glycan structures recognized by RCA120 and the Multivalent Sia and (GlcNAc)n glycan structures recognized by WGA were highly expressed between HV and HpNAG and lowly expressed between HpNAG and HpNGC. The results are as Figure 18 shown.

[0199] PCA analysis was performed on the differentially expressed glycan structures recognized by 10 lectins. The analysis results are as Figure 19 shown. All three groups of samples were well separated, with only a small part overlapping, and the data was reliable.

[0200] 4.5. Model evaluation

[0201] According to the results verified by the test set data, the ROC curve of the model constructed by the KNN algorithm was the smoothest, and the recall rate, precision rate, sensitivity and specificity were all higher than those of the other 5 models. Therefore, the comprehensive effect of the model was the most excellent and could be used as a potential method for the clinical diagnosis of HpNGC.

Claims

1. Use of differentially expressed serum protein glycotypes as a biomarker for the diagnosis of Helicobacter pylori-negative gastric cancer.

2. The use according to claim 1, wherein, the differential expression is: compared with HV, the Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) glycan structures recognized by ECA, the anti-A and anti-B human blood group and the glycan structures terminated with GalNAc and Gal recognized by SJA, the Galα1-3(Fucα1-2)Gal (blood group B antigen) glycan structure recognized by EEL, the Fucα1-3(Galβ1-4)GlcNAc and Fucα1-6GlcNAc (core fucose) glycan structures recognized by AAL, the αGalNAc, αGal and anti-A and B glycan structures recognized by GSL-I, the Gal, T antigen and blood group H glycan structures recognized by PTL-II, the (GalNAc)n and blood-group A glycan structures recognized by SBA, the Galβ1-3GalNAcα-Ser / Thr (T antigen) glycan structure recognized by ACA, the Multivalent Sia and (GlcNAc)n glycan structures recognized by WGA, the Fucα1-2Galβ1-4GlcNAc glycan structure recognized by UEA-I, the Galβ1-4GlcNAc, Galβ1-3GlcNAc glycan structures recognized by MAL-I, the β-D-GlcNAc, (GlcNAcβ1-4)n, Galβ1-4GlcNAc, GalNAc and tri / tetra-antennary N-glycan glycan structures recognized by DSA, are significantly up-regulated in HpNGC; the Galβ1-3GalNAcα-Ser / Thr (T) glycan structure recognized by Jacalin, the High-Man and Man5-GlcNAc2-Asn glycan structures recognized by HHL, the biantennary complex-type N-glycan glycan structure with galactose on the outer side recognized by PHA-E, the Fucα1-2Galβ1-4GlcNAc and anti-H blood group specificity glycan structures recognized by LTL, the α-D-Man, Fucα1-6GlcNAc and α-D-Glc glycan structures recognized by LCA, the β-Gal, Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) glycan structures recognized by RCA120, Terminal GlcNAc, Manα1-6(Manα1-3)Man and High-Mannose glycan structures recognized by ConA, α-D-Glc, Fucα1-6GlcNAc and α-D-Man glycan structures recognized by PSA, are significantly down-regulated in HpNGC.

3. The application according to claim 1, characterized in that, the differential expression is: compared with HpNAG, anti-A and anti-B human blood group and Terminal with GalNAc and Gal glycan structures recognized by SJA, β-Gal, Galβ1-3GlcNAc (type I) and Galβ1-4GlcNAc (type II) glycan structures recognized by RCA120, Gal, T antigen and blood group H glycan structures recognized by PTL-II, tri / tetra-antennary N-glycan, β-D-GlcNAc, (GlcNAcβ1-4)n, Galβ1-4GlcNAc and GalNAc and glycan structures recognized by DSA, blood-group A, GalNAcα1-3Gal, (GalNAc)n and α-or β-linked terminal GalNAc glycan structures recognized by SBA, α-D-Glc and α-D-Man glycan structures recognized by PSA, Fucα1-2Galβ1-4Glc(NAc) glycan structure recognized by UEA-I, High-Mannose and Manα1-3Man glycan structures recognized by GNA, Terminal GalNAc and Galβ1-3GalNAc glycan structures recognized by BPL, Galβ1-4GlcNAc and Galβ1-3GlcNAc glycan structures recognized by MAL-I, are up-regulated in HpNGC; High-Man, Man5-GlcNAc2-Asn and Manα1-6Man glycan structures recognized by HHL, Terminal with GalNAcα / β1-3 / 6Gal glycan structure recognized by WFA, GlcNAc and agalactosylated tri / tetra antennary glycans glycan structure recognized by GSL-II, Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal, Siaα2-3 and Siaα2-3GalNAc glycan structures recognized by MAL-II, The glycan structures of bisecting GlcNAc and biantennary complex-type N-glycan with outer Gal recognized by PHA-E, The glycan structure of Galβ1-3GalNAcα-Ser / Thr (T) recognized by PNA, The glycan structures of Fucα1-2Galβ1-4GlcNAc, Fucα1-3(Galβ1-4)GlcNAc and anti-H blood group specificity recognized by LTL, The glycan structures of (GlcNAc)n and high mannose-type N-glycans recognized by LEL, The glycan structures of αGalNAc, αGal and anti-A and B recognized by GSL-I, The glycan structures of GalNAcα1-3((Fucα1-2))Gal (blood group A antigen), Tn antigen and αGalNAc recognized by DBA, The glycan structures of α-D-Man, Fucα1-6GlcNAc and α-D-Glc recognized by LCA, The glycan structures of trimers and tetramers of GlcNAc and core (GlcNAc) of N-glycan recognized by STL, Are down-regulated in HpNGC.

4. Application of differentially expressed serum protein glycotypes as a biomarker for the diagnosis of Helicobacter pylori-negative gastritis.

5. The application according to claim 4, Characterized in that, The differential expression is: Compared with HV, The glycan structures of Galβ1-4GlcNAc (type II) and Galβ1-3GlcNAc (type I) recognized by ECA, The glycan structure of Terminal with GalNAcα / β1-3 / 6Gal recognized by WFA, The glycan structures of GlcNAc and agalactosylated tri / tetra antennary glycans recognized by GSL-II, The glycan structures of Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal, Siaα2-3 and Siaα2-3GalNA recognized by MAL-II, The glycan structures of GalNAcα1-3Galβ1-3 / 4Glc, GalNAc and GalNAcα1-3Gal recognized by PTL-I, The glycan structure of Galβ1-3GalNAcα-Ser / Thr (T) recognized by PNA, The glycan structures of Fucα1-3(Galβ1-4)GlcNAc and Fucα1-6GlcNAc (core fucose) recognized by AAL, The glycan structures of GalNAc and Galβ1-3GalNAc recognized by MPL, The sugar chain structures of (GlcNAc)n and high mannose-type N-glycans recognized by LEL, The sugar chain structures of αGalNAc and αGal anti-A and B recognized by GSL-I, The sugar chain structures of Tn antigen, GalNAcα1-3((Fucα1-2))Gal (blood group A antigen) and αGalNAc recognized by DBA, The sugar chain structures of T antigen, Gal and blood group H recognized by PTL-II, The sugar chain structures of High-Mannose and Manα1-6Man recognized by NPA, Up-regulated expression in HpNAG; The sugar chain structures of GlcNAcβ1-3-GalNAcα-Ser / Thr (Core3), GalNAcα-Ser / Thr (Tn) and Galβ1-3GalNAcα-Ser / Thr (T) recognized by Jacalin, The sugar chain structures of Galβ1-3GlcNAc (type I), Galβ1-4GlcNAc (type II) and β-Gal recognized by RCA120, The sugar chain structures of terminal GlcNAc, Manα1-6(Manα1-3)Man and High-Mannose recognized by ConA, The sugar chain structures of Multivalent Sia and (GlcNAc)n recognized by WGA, The sugar chain structures of High-Mannose and Manα1-3Man recognized by GNA, The sugar chain structure of Galβ1-3GalNAcα-Ser / Thr (T antigen) recognized by ACA, Down-regulated expression in HpNAG.

6. Application of differentially expressed serum protein glycotypes as biological detection markers for the diagnosis of Helicobacter pylori-negative gastric cancer and Helicobacter pylori-negative gastritis.

7. The application according to claim 6, characterized in that, the differential expression is: The sugar chain structure of Terminal with GalNAcα / β1-3 / 6Gal recognized by WFA, The sugar chain structures of GlcNAc and agalactosylated tri / tetra antennary glycans recognized by GSL-II, the sugar chain structures of Siaα2-3Galβ1-3GalNAc, Siaα2-3Galβ1-4Glc(NAc) / Glc, Siaα2-3Gal and Siaα2-3GalNAc recognized by MAL-II, The sugar chain structure of Galβ1-3GalNAcα-Ser / Thr (T) recognized by PNA, The sugar chain structures of αGalNAc, Tn antigen and GalNAcα1-3((Fucα1-2))Gal (blood group A antigen) recognized by DBA, The sugar chain structures of (GlcNAc)n and high mannose-type N-glycans recognized by LEL, The sugar chain structures of αGalNAc, αGal, anti-A and B recognized by GSL-I, The sugar chain structures of the sugar chain core (GlcNAc) of N-glycan, trimers and tetramers of GlcNAc and oligosaccharide containing GlcNAc and MurNAc recognized by STL, Low expression between HV and HpNAG, high expression between HpNAG and HpNGC; The sugar chain structures of Galβ1-3GlcNAc (type I), Galβ1-4GlcNAc (type II) and β-Gal recognized by RCA120, The sugar chain structures of Multivalent Sia and (GlcNAc)n recognized by WGA, high expression between HV and HpNAG, low expression between HpNAG and HpNGC.

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