Identification and prediction method based on serum N-glycan expression

Abstract

The invention discloses an identification and prediction method based on serum N-glycan expression, which comprises the following steps: collecting a serum sample, and extracting glycoprotein in the serum sample to obtain a serum protein solution; the glycoprotein is coupled with the formylated solid phase resin; sialic acid at the tail end of glycan is subjected to protection modification through ethyl acetate esterification and carbodiimide coupling; cutting by using PNGase F to obtain N-glycan on the protein connected with the solid-phase resin; analyzing a serum N-glycan structure expression mode by using MALDI-TOF-MS (Matrix-Assisted Laser Desorption / Ionization Time of Flight Mass Spectrometry-Mass Spectrometry-Mass Spectrometry-Mass Spectrometry-Mass Spectrometry-Mass Spectrometry-Mass Spectrometry-Mass Spectrometry-Mass Spectrometry), calculating a ratio of an under-peak area of glycan peaks with different mass An A.NG algorithm is constructed, and the serum sample is evaluated by calculating the ratio AUC of the relative abundance of up-regulated N-glycan to the relative abundance of down-regulated N-glycan. The method is stable in result and good in repeatability, and has the potential of accurate individualized detection, treatment and monitoring in the future.

Description

Technical Field

[0001] The invention belongs to the technical field of biomolecule analysis, and in particular relates to an identification and prediction method based on serum N-glycan expression. Background Art

[0002] Glycosylation is an important and complex type of protein post-translational modification. The exploration of biological markers or drug target development based on glycosylation has been widely carried out. Serum is a common and easily accessible biological sample. The study of abnormal glycosylation of serum proteins has important potential for diagnosing diseases and evaluating prognosis. At present, the research and clinical application of abnormal glycosylation of serum proteins in patients with lung adenocarcinoma have made certain progress. Some specific types of glycoproteins have been shown to be abnormally expressed in advanced lung adenocarcinoma and involved in tumor progression, but research in related fields is still relatively scarce and limited. Specifically, there is a lack of consistency in the glycoproteomics methods used to identify and verify abnormal glycosylation of lung adenocarcinoma, and there are differences in the methods used in different studies, which increases the uncertainty of the results. Secondly, most of the current studies focus on patients who have been diagnosed with lung adenocarcinoma, ignoring the transformation from benign lung nodules to lung cancer, and therefore cannot accurately reflect the application value of abnormal glycosylation for early cancer screening. In addition, other factors in serum (such as infection, inflammation, etc.) may affect the accuracy and reliability of glycosylation markers of lung adenocarcinoma, leading to missed diagnosis or misdiagnosis, which means that the experimental results need to be verified by larger samples.

[0003] Therefore, it is necessary to enrich serum N-glycans based on solid-phase chemoenzymatic methods and perform glycan expression abundance analysis, based on which a new predictive model that can be used for early screening and differential diagnosis of lung adenocarcinoma is constructed. Summary of the invention

[0004] The purpose of the present invention is to provide an identification and prediction method based on serum N-glycan expression to solve the above problems.

[0005] A technical solution of the present invention is:

[0006] A method for identifying and predicting serum N-glycan expression, comprising the steps of:

[0007] (1) collecting serum samples, extracting proteins from the serum samples, and obtaining serum protein solutions;

[0008] (2) coupling the protein to an aldehyde-modified solid phase resin;

[0009] (3) Protecting and modifying the sialic acid at the end of the glycan by ethyl acetate esterification and carbodiimide coupling;

[0010] (4) using PNGase F to cleave and obtain N-glycans on the protein attached to the solid phase resin;

[0011] (5) Using MALDI-TOF-MS to analyze the expression pattern of serum N-glycan structure, the ratio of the area under the peak of glycans with different mass / charge ratios to the area under the peak of internal standard DP7 was calculated to obtain the relative expression abundance of N-glycans with different structures;

[0012] (6) The A.NG algorithm was constructed to evaluate serum samples by calculating the AUC ratio of the relative abundance of up-regulated N-glycans to down-regulated N-glycans.

[0013] Furthermore, in step (2), coupling the protein with the aldehyde-modified solid phase resin is specifically performed by: placing the solid phase resin in a centrifuge tube, washing the solid phase resin with a protein binding buffer, adding the serum protein solution, then adding deionized water and a protein binding buffer to the centrifuge tube, and reacting in a constant temperature mixer; then adding sodium cyanoborohydride, and continuing to react in the constant temperature mixer; and washing the solid phase resin.

[0014] Furthermore, the temperature of the constant temperature mixer is 25° C. and the rotation speed is 1200 rpm.

[0015] Furthermore, the washing of the solid phase resin is specifically as follows: washing the solid phase resin with a PBS solution, adding a PBS buffer solution and sodium cyanoborohydride to the solid phase resin, and continuing the reaction; washing the solid phase resin with a Tris-HCl solution, adding a Tris-HCl solution and sodium cyanoborohydride to the solid phase resin, and reacting at room temperature, and washing the solid phase resin with a sodium chloride solution and deionized water.

[0016] Furthermore, in step (3), the protective modification of the sialic acid at the end of the polysaccharide by ethyl acetate esterification and carbodiimide coupling is specifically as follows: the solid phase resin is washed with anhydrous ethanol, EDC-HCl solution and HOBt solution are added and vortexed to mix, and then the mixed solution is continued to react; the solid phase resin is washed with formic acid solution, acetonitrile solution, sodium chloride solution and deionized water respectively; p-toluidine solution is prepared with hydrochloric acid, and the p-toluidine solution is mixed with EDC solution and concentrated hydrochloric acid to obtain a p-toluidine and EDC mixed solution; then the p-toluidine and EDC mixed solution is added to the solid phase resin, mixed, and incubated at room temperature; the solid phase resin is washed with sodium chloride solution and deionized water.

[0017] Furthermore, the concentration of the formic acid solution is 10%, the concentration of the acetonitrile solution is 10%, and the concentration of the concentrated hydrochloric acid is 37%.

[0018] Furthermore, in step (4), the use of PNGase F to cut and obtain N-glycans on the protein attached to the solid phase resin is specifically as follows: adding ammonium bicarbonate solution and PNGase F to the solid phase resin, mixing, and reacting to obtain an N-glycan solution; collecting the solution containing N-glycans by centrifugation, washing the solid phase resin with acetonitrile, and collecting by centrifugation, and mixing with the previously collected N-glycan solution.

[0019] Further, in step (5), the analysis of serum N-glycan structure expression pattern using MALDI-TOF-MS, calculating the ratio of the area under the peak of glycan peaks with different mass / charge ratios to the area under the peak of internal standard DP7, and obtaining the relative expression abundance of N-glycans with different structures is specifically as follows: dropping the peptide calibration standard solution, DP7 solution and the N-glycan solution on the surface of the MALDI target plate, and standing at room temperature until the sample is completely dried and crystallized; dropping the DHB matrix on the surface covered with the dried peptide calibration standard solution and N-glycan sample crystals; setting the MALDI-TOF-MS mass spectrometer. The MALDI target plate was placed in the MALDI-TOF-MS mass spectrometer, and the MALDI-TOF-MS mass spectrometer was calibrated by peptide calibration standards. After the calibration, the N-glycan sample was tested. After obtaining the MALDI mass spectrometer data, the N-glycan expression spectrum was obtained by analyzing the FlexAnalysis software, and the ratio of the area under the peak of each mass / charge ratio glycan peak to the area under the peak of the internal standard DP7 was calculated as its relative expression abundance, and the N-glycan structure and composition of each mass / charge ratio were annotated by GlycoWorkbench software.

[0020] Furthermore, in step (6), the A.NG algorithm is constructed to evaluate serum samples by calculating the ratio of the relative abundance of up-regulated N-glycans to down-regulated N-glycans, AUC, to specifically: identify 7 up-regulated N-glycans and 9 down-regulated N-glycans in lung cancer serum proteins; use the peak areas of the 7 up-regulated N-glycans and 9 down-regulated N-glycans to calculate the A.NG scores of the normal control group, patients with benign lung nodules, patients before early lung adenocarcinoma surgery, and patients with advanced lung adenocarcinoma; construct receiver operating characteristic curves of the A.NG scores of five groups, namely, the healthy control group and patients with benign lung nodules, the healthy control group and patients before early lung adenocarcinoma surgery, the healthy control group and patients with advanced lung adenocarcinoma, the benign lung nodules and patients before early lung adenocarcinoma surgery, and the early lung adenocarcinoma surgery and patients with advanced lung adenocarcinoma; calculate the A.NG algorithm score of the serum sample, and determine whether the patient from whom the serum sample is derived has lung adenocarcinoma and the stage of lung cancer based on the score.

[0021] Furthermore, when A.NG≤0.272, it is a healthy person; when 0.272<A.NG≤0.326, it is a patient with benign lung nodules or early lung adenocarcinoma; when 0.326<A.NG≤0.539, it is a patient with early lung adenocarcinoma; and when A.NG>0.539, it is a patient with advanced lung adenocarcinoma.

[0022] The present invention provides an identification and prediction method based on serum N-glycan expression, which has the advantages of:

[0023] (1) This method is used to enrich and analyze glycans, and the results are stable and reproducible. It can also reflect the individual differences of each patient and has the potential for precise individualized treatment in the future;

[0024] (2) The A.NG algorithm constructed by this method using glycosylation expression patterns can be used as an effective marker for early differentiation and staging of benign and malignant lung cancer, helping to achieve sensitive, rapid and accurate diagnosis of early lung cancer, facilitating early diagnosis of lung cancer, which is crucial for treatment and prognosis;

[0025] (3) The glycosylation expression pattern of lung cancer patients in this method may be related to disease prognosis. The A.NG algorithm has the potential to achieve prognostic assessment and treatment response monitoring of patients through quantification and analysis of serum glycans. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative labor.

[0027] Figure 1 It is a schematic diagram of the specific N-glycan analysis of serum protein of lung cancer patients and the A.NG algorithm construction process in the identification and prediction method based on serum N-glycan expression of the present invention;

[0028] Figure 2 It is a schematic diagram of predicting whether a patient from a serum sample has lung adenocarcinoma and its stage according to the A.NG algorithm in a method for identifying and predicting based on serum N-glycan expression of the present invention;

[0029] Figure 3 It is a schematic diagram of the comparison of the A.NG scores of N-glycans in the serum of the normal control group, benign lung nodules, early lung adenocarcinoma preoperatively and advanced lung adenocarcinoma patients in the identification and prediction method based on serum N-glycan expression described in the present invention;

[0030] Figure 4This is a schematic diagram of the sensitivity and specificity comparison of the ROC curves of A.NG algorithm for distinguishing the presence or absence of lung adenocarcinoma and lung adenocarcinoma of different stages in the identification and prediction method based on serum N-glycan expression described in the present invention. DETAILED DESCRIPTION

[0031] The present invention is based on the solid phase chemoenzymatic method to perform enrichment and identification analysis of serum protein N-glycans. First, serum samples from patients with lung tumors (benign and malignant) and healthy controls are collected, proteins in the serum samples are extracted and coupled to the aldehyde-modified solid phase resin, and then the sialic acid at the end of the glycan is protected and modified by ethyl acetate esterification and carbodiimide coupling; then, PNGase F (N-glycosidase F) is used to cut and obtain the N-glycans on the protein attached to the resin; then, MALDI-TOF-MS (matrix-assisted laser desorption ionization time-of-flight mass spectrometry) is used to analyze the expression pattern of serum N-glycans (with 1mM DP7 as the detection internal standard), and the relative expression abundance of N-glycans with different structures is obtained by calculating the ratio of the area under the peak of glycan peaks with different mass / charge ratios to the area under the peak of the internal standard DP7; finally, the A.NG algorithm is constructed, that is, by calculating the upregulated N-glycans (UpNG, up-regulated N-glycans) and the downregulated N-glycans (DownNG, down-regulated N-glycans) The ratio of the relative abundance of N-glycans and AUC was used to evaluate whether the source of the serum sample was likely to have lung adenocarcinoma and to predict the tumor stage of the patient from whom the sample was derived.

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more clearly understood, the technical solutions of the present invention are further described below in conjunction with the accompanying drawings and embodiments. However, the present invention is not limited to the embodiments listed, and should also include any other known changes within the scope of the rights claimed by the present invention.

[0033] First, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] Secondly, the present invention is described in detail using structural schematic diagrams, etc. When describing the embodiments of the present invention in detail, for the sake of convenience, the schematic diagrams will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional space of length, width and depth should be included.

[0035] Example 1

[0036] See also Figure 1 , Figure 1 Schematic diagram of the serum protein-specific N-glycan analysis and A.NG algorithm construction process of lung cancer patients in a method for identification and prediction based on serum N-glycan expression according to the present invention. Figure 1 As shown, the steps of implementing the identification prediction method based on serum N-glycan expression are:

[0037] 1. Collect serum samples, extract proteins from the serum samples, and obtain serum protein solutions;

[0038] 2. coupling the protein to an aldehyde-modified solid phase resin;

[0039] 3. Protect and modify the sialic acid at the end of the glycan by ethyl acetate esterification and carbodiimide coupling;

[0040] 4. Use PNGase F to cut and obtain the N-glycans on the protein attached to the solid phase resin;

[0041] 5. Use MALDI-TOF-MS to analyze the expression pattern of serum N-glycan structure, calculate the ratio of the area under the peak of glycans with different mass / charge ratios to the area under the peak of internal standard DP7, and obtain the relative expression abundance of N-glycans with different structures;

[0042] 6. Construct the A.NG algorithm to evaluate serum samples by calculating the AUC ratio of the relative abundance of up-regulated N-glycans to down-regulated N-glycans.

[0043] In this embodiment, the above steps are specifically as follows:

[0044] (1) Sign the informed consent form, collect serum from subjects, and measure serum protein concentration by BCA method;

[0045] (2) Take 200 μL AminoLink TM Coupling Resin In an empty centrifugal column tube, wash the solid phase resin (AminoLink) twice with 1x protein binding buffer and add a total of 500 μg of serum protein solution;

[0046] (3) Add deionized water to the empty centrifuge column tube to make up the volume to 425 μL, then add 50 μL of 10x protein binding buffer, and react in a constant temperature mixer at 25°C and 1200 rpm for 4 hours;

[0047] (4) Add 25 μL of 1 M sodium cyanoborohydride to the mixture of the solution and the solid phase resin, and continue the reaction at 25° C. and 1200 rpm in a constant temperature mixer for 4 hours;

[0048] (5) The solid phase resin was washed three times with 1x PBS solution, and then 475 μL 1x PBS buffer and 25 μL 1 M sodium cyanoborohydride were added to the solid phase resin, and the reaction was continued for 4 hours;

[0049] (6) Wash the resin three times with 1 M Tris-HCl solution (pH = 7.6), add 475 μL 1 M Tris-HCl solution (pH = 7.6) and 25 μL 1 M sodium cyanoborohydride to the solid phase resin and react at room temperature for 30 minutes, and wash the solid phase resin three times with 1 M sodium chloride solution and deionized water;

[0050] (7) First, wash the solid phase resin with anhydrous ethanol, add 200 μL of EDC-HCl solution and 200 μL of HOBt solution and vortex mix, then continue to react the mixed solution at 37°C for 1 hour;

[0051] (8) Washing the solid phase resin with 10% (volume ratio) formic acid solution, 10% (volume ratio) acetonitrile solution, 1 M sodium chloride solution and deionized water, respectively, and repeating for 3 times;

[0052] (9) Prepare a 1M p-toluidine solution with a final concentration of 1M using 1M hydrochloric acid, take 400 μL of the 1M p-toluidine solution, mix it with 50 μL of EDC solution and 16 μL of 37% (volume ratio) concentrated hydrochloric acid; then add 400 μL of the p-toluidine and EDC mixed solution to the solid phase resin, mix well, and incubate at room temperature for 4 hours;

[0053] (10) Wash the solid phase resin three times with 1 M sodium chloride solution and deionized water;

[0054] (11) Add 200 μL of 25 mM ammonium bicarbonate solution and 0.5 μL of N-glycosidase F to the solid phase resin, mix well, and react at 37°C overnight to obtain an N-glycan solution;

[0055] (12) centrifuging and collecting the solution containing N-glycans, washing the solid phase resin twice with 100 μL of 10% acetonitrile, centrifuging and collecting, and mixing with the previously collected N-glycan solution;

[0056] (13) 0.5 μL of peptide calibration standard solution, 1 μL of 1 M DP7 solution (internal standard), and 1 μL of N-glycan solution were dropped onto the surface of the MALDI target plate and allowed to stand at room temperature until the sample was completely dried and crystallized;

[0057] (14) DHB matrix (ACN / H 2 O / TFA (50:50:0.1, v / v / v) containing 20 μg / μL DHB) was dropped onto the surface of the crystals covered with the dried peptide calibration standard solution and N-glycan sample;

[0058] (15) Setting the MALDI-TOF-MS mass spectrometer instrument parameters, selecting the positive ion mode as the ionization mode, setting the laser repetition rate to 1 kHz, the laser power to 40%, the MS1 scanning range to 900-6000 m / z, and each spectrum accumulated by 1000 excitations;

[0059] (16) placing the MALDI target plate into a mass spectrometer, calibrating the MALDI-TOF-MS mass spectrometer using peptide calibration standards, and testing the N-glycan samples after the calibration. Each sample should be tested at least 4 times;

[0060] (17) After obtaining the MALDI mass spectrometry data, the N-glycan expression spectrum was obtained by analyzing with FlexAnalysis software, and the ratio of the area under the peak of each mass / charge ratio glycan peak to the area under the peak of the internal standard DP7 was calculated as its relative expression abundance. The N-glycan structure and composition of each mass / charge ratio were annotated with GlycoWorkbench software;

[0061] (18) identified 7 upregulated and 9 downregulated N-glycans in lung cancer serum proteins;

[0062] (19) Please refer to Figure 3 , Figure 3 The figure is a schematic diagram of the comparison of the A.NG scores of the serum N-glycans of the normal control group, benign lung nodules, early lung adenocarcinoma preoperative patients and late lung adenocarcinoma patients in the identification and prediction method based on serum N-glycan expression described in the present invention. Figure 3 As shown, the peak areas of 7 up-regulated N-glycans and 9 down-regulated N-glycans were used to calculate the A.NG scores of normal control group, patients with benign pulmonary nodules, patients with early lung adenocarcinoma preoperatively, and patients with advanced lung adenocarcinoma. It was found that the A.NG values ​​showed an increasing trend from normal control to advanced lung adenocarcinoma (p<0.05). The fold changes of the average A.NG values ​​of patients with benign pulmonary nodules, patients with early lung adenocarcinoma preoperatively, and patients with advanced lung adenocarcinoma relative to the normal control group were 1.15, 1.54, and 1.86, respectively;

[0063] (20) Please refer to Figure 4 , Figure 4 The figure is a schematic diagram showing the comparison of the sensitivity and specificity of the ROC curve of A.NG algorithm for distinguishing the presence or absence of lung adenocarcinoma and different stages of lung adenocarcinoma in the identification and prediction method based on serum N-glycan expression described in the present invention. Figure 4 As shown, the receiver operating characteristic (ROC) curves of A.NG scores in five groups were constructed, including healthy control group and patients with benign pulmonary nodules, healthy control group and patients with early lung adenocarcinoma before surgery, healthy control group and patients with advanced lung adenocarcinoma, benign pulmonary nodules and patients with early lung adenocarcinoma before surgery, and patients with early lung adenocarcinoma before surgery and advanced lung adenocarcinoma;

[0064] (21) Calculate the A.NG algorithm score of the serum sample, and determine whether the patient from which the serum sample originated has lung adenocarcinoma based on the score. Figure 2 , Figure 2 Schematic diagram of predicting whether a patient from a serum sample has lung adenocarcinoma and its stage according to the A.NG algorithm in a method for identifying and predicting based on serum N-glycan expression according to the present invention. Figure 2 As shown, A.NG≤0.272 represents healthy people, 0.272<A.NG≤0.326 represents benign lung nodules or early (stage I-II) lung adenocarcinoma patients, 0.326<A.NG≤0.539 represents early (stage I-II) lung adenocarcinoma patients, and A.NG>0.539 represents advanced lung adenocarcinoma patients.

[0065] Please continue reading Figure 3 and Figure 4 ,like Figure 3 and 4 As shown in the figure, by analyzing the expression of N-glycans in the serum of normal control group, patients with benign lung nodules, early lung adenocarcinoma preoperatively and patients with advanced lung adenocarcinoma, it was found that the A.NG score showed an increasing trend from the healthy control population to patients with advanced lung cancer. The results showed that the A.NG score is helpful in distinguishing whether patients have lung adenocarcinoma and the stage of lung adenocarcinoma.

[0066] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying and predicting serum N-glycan expression, characterized in that: Includes steps: (1) collecting serum samples, extracting proteins from the serum samples, and obtaining serum protein solutions; (2) coupling the protein to an aldehyde-modified solid phase resin; (3) Protecting and modifying the sialic acid at the end of the glycan by ethyl acetate esterification and carbodiimide coupling; (4) using PNGase F to cleave and obtain N-glycans on the protein attached to the solid phase resin; (5) Using MALDI-TOF-MS to analyze the expression pattern of serum N-glycan structure, the ratio of the area under the peak of glycans with different mass / charge ratios to the area under the peak of internal standard DP7 was calculated to obtain the relative expression abundance of N-glycans with different structures; (6) The A.NG algorithm was constructed to evaluate serum samples by calculating the AUC ratio of the relative abundance of up-regulated N-glycans to down-regulated N-glycans.

2. The method for identifying and predicting serum N-glycan expression according to claim 1, characterized in that: In step (2), the protein is coupled to the aldehyde-modified solid phase resin by: placing the solid phase resin in a centrifuge tube, washing the solid phase resin with a protein binding buffer, adding the serum protein solution, then adding deionized water and a protein binding buffer to the centrifuge tube, and reacting in a constant temperature mixer; then adding sodium cyanoborohydride, and continuing to react in the constant temperature mixer; and washing the solid phase resin.

3. The method for identification and prediction based on serum N-glycan expression according to claim 2, characterized in that: The temperature of the constant temperature mixer is 25°C and the rotation speed is 1200 rpm.

4. The method for identification and prediction based on serum N-glycan expression according to claim 2, characterized in that: The washing of the solid phase resin is specifically as follows: washing the solid phase resin with a PBS solution, adding a PBS buffer solution and sodium cyanoborohydride to the solid phase resin, and continuing the reaction; washing the solid phase resin with a Tris-HCl solution, adding a Tris-HCl solution and sodium cyanoborohydride to the solid phase resin, and reacting at room temperature, and washing the solid phase resin with a sodium chloride solution and deionized water.

5. The method for identification and prediction based on serum N-glycan expression according to claim 1, characterized in that: In step (3), the protective modification of the sialic acid at the end of the polysaccharide by ethyl acetate esterification and carbodiimide coupling is specifically as follows: washing the solid phase resin with anhydrous ethanol, adding EDC-HCl solution and HOBt solution and vortex mixing, and then continuing to react the mixed solution; The solid phase resin is washed with formic acid solution, acetonitrile solution, sodium chloride solution and deionized water respectively; a p-toluidine solution is prepared with hydrochloric acid, and the p-toluidine solution is evenly mixed with an EDC solution and concentrated hydrochloric acid to obtain a p-toluidine and EDC mixed solution; the p-toluidine and EDC mixed solution is then added to the solid phase resin, mixed evenly, and incubated at room temperature; the solid phase resin is washed with a sodium chloride solution and deionized water.

6. The method for identification and prediction based on serum N-glycan expression according to claim 5, characterized in that: The concentration of the formic acid solution is 10%, the concentration of the acetonitrile solution is 10%, and the concentration of the concentrated hydrochloric acid is 37%.

7. The method for identification and prediction based on serum N-glycan expression according to claim 1, characterized in that: In step (4), the method of using PNGase F to cut and obtain N-glycans on the protein attached to the solid phase resin is specifically as follows: adding ammonium bicarbonate solution and PNGase F to the solid phase resin, mixing, and reacting to obtain an N-glycan solution; collecting the solution containing N-glycans by centrifugation, washing the solid phase resin with acetonitrile, and collecting by centrifugation, and mixing with the previously collected N-glycan solution.

8. The method for identification and prediction based on serum N-glycan expression according to claim 1, characterized in that: In step (5), the analysis of serum N-glycan structure expression pattern using MALDI-TOF-MS, calculating the ratio of the area under the peak of glycan peaks with different mass / charge ratios to the area under the peak of internal standard DP7, and obtaining the relative expression abundance of N-glycans with different structures is specifically as follows: dropping peptide calibration standard solution, DP7 solution and the N-glycan solution on the surface of MALDI target plate, and standing at room temperature until the sample is completely dried and crystallized; dropping DHB matrix on the surface covered with the dried peptide calibration standard solution and N-glycan sample crystals; setting up MALDI-TOF-MS mass spectrometer Parameters: placing the MALDI target plate into the MALDI-TOF-MS mass spectrometer, calibrating the MALDI-TOF-MS mass spectrometer with a peptide calibration standard, and testing the N-glycan sample after the calibration; after obtaining the MALDI mass spectrometry data, using FlexAnalysis software to analyze and obtain the N-glycan expression spectrum, calculating the ratio of the area under the peak of each mass / charge ratio glycan peak to the area under the peak of the internal standard DP7 as its relative expression abundance, and using GlycoWorkbench software to annotate the N-glycan structure and composition of each mass / charge ratio.

9. The method for identification and prediction based on serum N-glycan expression according to claim 1, characterized in that: In step (6), the A.NG algorithm is constructed to evaluate serum samples by calculating the ratio of the relative abundance of up-regulated N-glycans to down-regulated N-glycans AUC, specifically: identifying 7 up-regulated N-glycans and 9 down-regulated N-glycans in lung cancer serum proteins; using the peak areas of the 7 up-regulated N-glycans and the 9 down-regulated N-glycans to calculate the A.NG scores of the normal control group, patients with benign lung nodules, patients with early lung adenocarcinoma preoperatively, and patients with advanced lung adenocarcinoma; The receiver operating characteristic curves of A.NG scores were constructed for five groups, namely healthy control group and patients with benign pulmonary nodules, healthy control group and patients before surgery for early lung adenocarcinoma, healthy control group and patients with advanced lung adenocarcinoma, benign pulmonary nodules and patients before surgery for early lung adenocarcinoma, and patients before surgery for early lung adenocarcinoma and patients with advanced lung adenocarcinoma. The A.NG algorithm score of serum samples was calculated, and the score was used to determine whether the patient from whom the serum sample was derived had lung adenocarcinoma and the stage of lung cancer.

10. The method for identification and prediction based on serum N-glycan expression according to claim 9, characterized in that: When A.NG≤0.272, it is a healthy person; when 0.272<A.NG≤0.326, it is a patient with benign lung nodules or early lung adenocarcinoma; when 0.326<A.NG≤0.539, it is a patient with early lung adenocarcinoma; when A.NG>0.539, it is a patient with advanced lung adenocarcinoma.

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