High-throughput one-stop glycomics and glycoproteomics sample pretreatment platform

Abstract

The invention belongs to the technical field of biological analysis, and relates to a glycomics and glycoproteomics sample analysis technology, in particular to a high-throughput and one-stop sample pretreatment platform for glycomics and glycoproteomics research. The platform integrates key technologies of enzymolysis, enrichment, derivatization and the like, absorbent cotton is used as an economical and efficient enrichment material, efficient one-stop operation is realized by using a 96-well plate, the sample loss can be effectively reduced, and the reaction efficiency is improved. The platform has high selectivity and recovery rate, and can be successfully applied to breast cancer serum samples to identify biomarkers related to lesion degrees. The platform can process multiple samples at the same time, adapts to multiple enrichment processes, meets wide research requirements, can improve the experiment efficiency and optimize the data quality according to the analysis requirements of glycopeptides and carbohydrate chains in complex biological samples, and provides a comprehensive solution for glycomics and glycoproteomics research.

Description

Technical Field

[0001] This invention belongs to the field of bioanalysis technology, specifically to glycomics and glycoproteomics sample analysis techniques. Specifically, it relates to a high-throughput, one-stop sample pretreatment platform for glycomics and glycoproteomics research. This platform improves experimental efficiency and optimizes data quality for the analysis of glycopeptides and sugar chains in complex biological samples. Background Art

[0002] Literature has documented that glycosylation, a key post-translational protein modification, plays a crucial role in cell adhesion, metabolism, and signal transduction. Aberrant glycosylation of glycoproteins is closely linked to the development of numerous diseases, making glycoprotein glycosylation analysis a hot topic in proteomics research and one with significant clinical applications. Despite significant advances in glycoprotein profiling, the analysis of glycosylated proteins in complex samples remains challenging. This is primarily due to the low relative abundance of glycopeptides after hydrolysis, their easy obscuration by non-glycosylated peptides, and their high heterogeneity. Therefore, developing effective glycopeptide enrichment and separation strategies is crucial for facilitating the analysis of glycosylated proteins in complex samples. In particular, structural and functional investigations of N / O-glycopeptides and N / O-glycans are crucial for understanding their roles in biological processes. Research has shown that existing glycomics and glycoproteomics approaches face numerous challenges, such as inefficient sample preparation, high experimental costs, and a lack of high-throughput and diverse methods. Furthermore, further research is needed on biomarkers for diseases such as breast cancer to improve the accuracy of disease diagnosis and treatment. Therefore, there is an urgent need for an efficient and innovative platform to improve the processing efficiency of N / O-glycopeptides and N / O sugars and enrich the research of disease-related biomarkers.

[0003] Based on the current state of the art, the inventors of this application intend to provide a high-throughput, one-stop sample pretreatment platform for glycomics and glycoproteomics research. This platform improves experimental efficiency and optimizes data quality for the analysis of glycopeptides and glycans in complex biological samples. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-throughput one-stop glycomics and glycoproteomics sample pretreatment platform based on the current state of the art to meet the needs of the biomedical research field for efficient processing and analysis of N / O-glycopeptides and N / O sugars.

[0005] The objectives of the present invention are: 1) Improve efficiency: by developing a high-throughput pre-treatment platform that can process multiple samples in a short period of time, thereby saving time and resources and accelerating glycomics and glycoproteomics research; 2) Reduce costs: use easily accessible and economical cotton wool as enrichment materials to reduce experimental costs and enable more researchers to access this technology; 3) Provide a comprehensive solution: by providing a variety of enrichment processes suitable for glycopeptides of different types and structures, meet diverse research needs and promote the development of the field of glycomics and glycoproteomics; 4) Discover biomarkers: provide methods for evaluating different types and stages of liver diseases, which will help to diagnose and monitor these diseases more accurately, thereby improving clinical decision-making and treatment. In summary, the present invention aims to provide a comprehensive tool for the biomedical science and medical communities to promote the further development of glycomics and glycoproteomics research and contribute to the fields of health management and disease diagnosis.

[0006] In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solutions:

[0007] Provided is a high-throughput, one-stop mass spectrometry glycoproteomics and glycomics sample pretreatment platform. This platform integrates key technologies such as enzymatic hydrolysis, enrichment, and derivatization, utilizes cotton wool as an economical and efficient enrichment material, and employs 96-well plates for efficient, one-stop operation, effectively reducing sample loss and improving reaction efficiency. The platform can process multiple samples simultaneously and adapt to a variety of enrichment processes. This platform can improve experimental efficiency and optimize data quality for the analysis of glycopeptides and sugar chains in complex biological samples.

[0008] Specifically,

[0009] The high-throughput one-stop mass spectrometry glycoproteomics and glycomics sample pretreatment platform of the present invention mainly includes the following parts:

[0010] (1) Sample input part, used to add samples containing glycoproteins into a 96-well plate, with a certain amount of sample added to each well, such as 2 μl of serum;

[0011] (2) Enzymatic hydrolysis, which is used to enzymatically hydrolyze the sample under certain temperature and time conditions to obtain glycoprotein hydrolysis products, namely glycopeptides and peptides (or sugars and proteins); different types of enzymes can be used in the enzymatic hydrolysis, such as trypsin, chymotrypsin, endoglycosidase, etc., which can be selected according to different samples and purposes. Different temperature and time parameters can be set for the enzymatic hydrolysis, such as 37-50 degrees Celsius, 2-24 hours, etc., and optimized according to different enzymes and samples;

[0012] (3) The enrichment part is used to fully mix the absorbent cotton with the hydrolyzate, so that the glycopeptides or sugar chains are adsorbed by the absorbent cotton, while the peptides and other impurities are eluted, thereby achieving the enrichment of glycopeptides and sugar chains; different mixing and elution conditions can be set in the enrichment part, such as mixing time, mixing intensity, type and amount of eluent, etc., and optimized according to different samples and purposes;

[0013] (4) The transfer part is used to transfer the enriched absorbent cotton to another 96-well plate, add a certain amount of eluent to each well, elute the glycopeptides and sugar chains from the absorbent cotton, and obtain the eluent of glycopeptides and sugar chains; the transfer part can set different transfer and elution conditions, such as elution time, elution speed, type and amount of eluent, etc., and optimize according to different samples and purposes;

[0014] (5) Analysis: For mass spectrometry analysis of glycopeptides, different types of mass spectrometers can be used, such as electrospray ionization (ESI) mass spectrometers, ion trap mass spectrometers, time-of-flight mass spectrometers (TOF), etc., according to different samples and purposes. Different mass spectrometry parameters can be set in the analysis, such as voltage, flow rate, scan range, resolution, fragmentation energy, etc., and optimized according to different samples and purposes;

[0015] (6) Analysis: Mass spectrometry analysis of sugar chains. Different types of mass spectrometers can be used for analysis. This experiment mainly uses matrix-assisted laser desorption ionization (MALDI) mass spectrometers, which are selected according to different samples and purposes.

[0016] (7) Data processing is used to process mass spectrometry data and identify and quantify the composition and structure of glycopeptides and sugar chains. Different types of software and algorithms can be used in the data processing section. Software commonly used for sugar chain results, such as GlycoWorkbench and GlycoMod, and software commonly used for glycopeptide results, such as Byonic and pGlyco, can be selected based on different samples and purposes. Different data processing parameters can be set in the data processing section, such as mass error, matching degree, confidence level, false positive rate, etc., and optimized according to different samples and purposes.

[0017] More specifically,

[0018] The present invention provides a high-throughput one-stop mass spectrometry glycoproteomics and glycomics sample pretreatment platform, which uses the following operating steps:

[0019] (1) Add a sample containing glycoprotein to a 96-well plate, add a certain amount of sample to each well, then add a certain amount of enzymatic hydrolysis solution, and enzymatically hydrolyze the sample under certain temperature and time conditions to obtain glycoprotein hydrolysis products, namely glycopeptides and peptides;

[0020] (2) Add a certain amount of absorbent cotton to each well and mix the absorbent cotton with the hydrolyzate thoroughly, so that the glycopeptides and sugar chains are adsorbed to the absorbent cotton, while the peptides and other impurities are eluted, thereby achieving the enrichment of glycopeptides and sugar chains;

[0021] (3) The enriched cotton wool is transferred to another 96-well plate, and a certain amount of eluent is added to each well to elute the glycopeptides and sugar chains from the cotton wool to obtain an eluate of glycopeptides and sugar chains;

[0022] (4) The eluate is divided into two parts, one for mass spectrometry analysis of glycopeptides and the other for mass spectrometry analysis of sugar chains;

[0023] (5) For the eluate used for mass spectrometry analysis of glycopeptides, directly perform mass spectrometry analysis to identify and quantify the composition and structure of glycopeptides;

[0024] (6) For the eluate used for mass spectrometry analysis of sugar chains, a certain amount of enzymatic hydrolysis solution is first added to a 96-well plate, and the sugar chains are enzymatically hydrolyzed under certain temperature and time conditions to obtain the hydrolysis products of the sugar chains, namely monosaccharides and oligosaccharides;

[0025] (7) adding a certain amount of derivatization reagent to each well, and derivatizing the monosaccharides and oligosaccharides under certain temperature and time conditions to obtain derivatized monosaccharides and oligosaccharides;

[0026] (8) The derivatized monosaccharides and oligosaccharides are subjected to mass spectrometry analysis to identify and quantify the composition and structure of the sugar chains.

[0027] In the present invention,

[0028] For glycoproteomics sample pretreatment, the following steps are used:

[0029] (1) A sample containing glycoprotein is added to a 96-well plate, a certain amount of sample is added to each well, and then a certain amount of trypsin enzymatic solution is added. The sample is enzymatically hydrolyzed under certain temperature and time conditions to obtain the hydrolyzed products of the glycoprotein, namely, glycosylated peptides and non-glycosylated peptides;

[0030] (2) Fill each well of the device with a certain amount of absorbent cotton in advance, balance the absorbent cotton with pre-prepared buffer solution A, and then activate the absorbent cotton with pre-prepared buffer solution B;

[0031] (3) The balanced and activated cotton wool is fully mixed with the hydrolyzate, so that the glycopeptides are adsorbed on the cotton wool, while the non-glycosylated peptides and other impurities (such as salts, reaction reagents, etc.) are eluted, thereby achieving the enrichment of glycosylated peptides;

[0032] (4) Adding a certain amount of eluent to each well to elute the glycopeptide from the cotton wool to obtain a glycopeptide eluate; (5) Freeze-drying the eluate and using it for mass spectrometry analysis to identify and quantify the composition and structure of the glycopeptide;

[0033] In the present invention, the following steps are used for the pretreatment of glycomic samples:

[0034] (1) Add a sample containing glycoprotein to a 96-well plate, add a certain amount of sample to each well, then add a certain amount of enzymatic hydrolysis solution, and hydrolyze the sample under certain temperature and time conditions to obtain the hydrolysis products of glycoprotein, namely sugar chains and proteins;

[0035] (2) Fill each well of the device with a certain amount of absorbent cotton in advance, balance the absorbent cotton with pre-prepared buffer solution A, and then activate the absorbent cotton with pre-prepared buffer solution B;

[0036] (3) The balanced and activated cotton wool is fully mixed with the hydrolyzate, so that the sugar chains are adsorbed on the cotton wool, and the deglycosylated proteins and other impurities (such as salts, reaction reagents, etc.) are eluted, thereby achieving the enrichment of sugar chains;

[0037] (4) Add a certain amount of eluent to each well to elute the sugar chains from the absorbent cotton, obtain the sugar chain eluate and freeze-dry it;

[0038] (5) adding a certain amount of derivatization reagent to each well, and derivatizing the sugar chains under certain temperature and time conditions to obtain derivatized sugar chains;

[0039] (6) The derivatized sugar chains are subjected to mass spectrometry analysis to identify and quantify the composition and structure of the sugar chains.

[0040] In the present invention, the sample is a biological sample containing glycoprotein, such as serum, plasma, urine, saliva, cerebrospinal fluid, tissue, cell, etc.

[0041] In the present invention, the enzymatic hydrolysis solution is a solution containing one or more enzymes capable of hydrolyzing glycoproteins, such as trypsin, chymotrypsin, endoglycosidase, and the like.

[0042] In the present invention, the buffer solutions A and B for balancing and activating the absorbent cotton are as follows: the buffer solution A is 0.1% (V / V) TFA; and the buffer solution B is 80% (V / V) ACN 0.1% TFA.

[0043] In the present invention, the absorbent cotton is a fibrous material that can absorb glycopeptides and sugar chains, such as cotton, cotton balls, etc.

[0044] In the present invention, the eluent is a solution capable of eluting glycopeptides and sugar chains from absorbent cotton, such as a 0.1% trifluoroacetic acid aqueous solution.

[0045] In the present invention, the enzymatic hydrolysis solution is a solution containing one or more enzymes capable of hydrolyzing proteins or sugar chains. For example, the enzymatic hydrolysis solution for glycoproteins is Trypsin (pancreatin), and the enzymatic hydrolysis solution for sugars is PNGase F glycosidase.

[0046] In the present invention, the derivatization reagent is a reagent that can undergo derivatization reaction with monosaccharides and oligosaccharides, such as methylamine hydrochloride.

[0047] In the present invention, the mass spectrometer is an instrument capable of performing mass spectrometry analysis on glycopeptides and sugar chains, such as an electrospray ionization (ESI) mass spectrometer, a matrix-assisted laser desorption ionization (MALDI) mass spectrometer, an ion trap mass spectrometer, a time-of-flight mass spectrometer (TOF), and the like.

[0048] In the present invention, the software and algorithms used are those capable of processing mass spectrometry data, identifying and quantifying the composition and structure of glycopeptides and sugar chains, such as Byonic, Byologic, Proteome Discoverer, GlycoWorkbench, GlycoMod, etc.

[0049] In the present invention, the method for searching for disease-related glycoprotein biomarkers is as follows, comprising the following steps:

[0050] (1) Collecting biological samples containing glycoproteins from the serum of patients with the disease and normal controls, with each sample being approximately 20 μl;

[0051] (2) using the high-throughput one-stop mass spectrometry glycoproteomics and glycomics sample preprocessing platform of the present invention to process and analyze the collected biological samples to obtain mass spectrometry data of glycopeptides and sugar chains;

[0052] (3) Processing the mass spectrometry data to screen out glycopeptides and glycans with significant differences as candidate glycoprotein biomarkers;

[0053] (4) Validate candidate glycoprotein biomarkers and use statistical methods to evaluate their sensitivity and specificity in distinguishing patients with the disease from normal controls, as well as their effectiveness in predicting disease severity and metastatic potential;

[0054] (5) Identify disease-related glycoprotein biomarkers and, based on the validation results, select glycopeptides and sugar chains with high sensitivity, high specificity, and high predictive ability as disease-related glycoprotein biomarkers.

[0055] In the present invention, a diagnosis and prediction model for the deterioration of liver diseases is established based on a machine learning method: five N-glycans are used as serum diagnostic sugar group markers to distinguish severe breast cancer disease groups (carcinoma in situ and carcinoma in situ with invasion) from benign control groups (healthy people and benign breast fibroids). A logistic regression model is established on the training set (70% of the grouped samples); and the test set (30% of the grouped samples) is used for model evaluation. The ROC results are as follows: Figure 3 The model's AUC was 0.90. The experimental results demonstrate that the five N-glycans described can serve as serum diagnostic markers for distinguishing severe breast cancer (carcinoma in situ and carcinoma in situ with invasion) from benign controls (healthy individuals and benign breast fibroids). This biomarker combination exhibits high sensitivity, specificity, and accuracy. This provides important evidence for further clinical research and offers new insights into the diagnosis and treatment of severe liver diseases.

[0056] Technical effects of the present invention:

[0057] (1) Provides a high-throughput pre-processing platform: This platform can complete the pre-processing of 400 samples within 2 hours, with an average processing time of only 0.3 minutes per sample. This significantly improves the efficiency and scale of glycoproteomics and glycomics research.

[0058] (2) Optimization of the enrichment process: The present invention optimizes the entire enrichment process, enabling enzymatic hydrolysis before glycopeptide enrichment and enzymatic hydrolysis and derivatization before sugar chain identification to be completed in the same plate; this one-stop processing method reduces sample transfer, reduces sample loss, and significantly saves processing time.

[0059] (3) Use and efficiency of cotton wool: Using cotton wool to enrich glycosylated peptides and sugar chains has low material cost, high selectivity and high recovery rate. This method can effectively separate glycopeptides and sugar chains from complex samples, thereby improving the sensitivity and accuracy of mass spectrometry analysis.

[0060] (4) Wide range of applications: The high-throughput platform of the present invention can simultaneously detect N-glycopeptides, O-glycopeptides, as well as N- and O-glycochains. The platform is suitable for the study of glycoproteomics and glycomics and has broad application prospects.

[0061] (5) Clinical Application Example: The method of the present invention was applied to serum samples from breast cancer patients, successfully achieving the enrichment and detection of glycopeptides and sugar chains in 176 clinical samples. This study identified a set of biomarkers that can be used to predict the progression of breast cancer, providing valuable information for the early diagnosis and treatment of breast cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1This is a schematic diagram and a physical picture of the high-throughput one-stop mass spectrometry glycoproteomics and glycomics sample pretreatment platform of the present invention; it mainly includes: sample input part; enzymatic hydrolysis part; enrichment part; transfer part; analysis part; analysis part; data processing part.

[0063] Figure 2 This is an example flow chart of the application of the present invention in serum samples of breast cancer patients.

[0064] Figure 3 This is a diagram of the biomarker mining results in serum samples of breast cancer patients according to the present invention. DETAILED DESCRIPTION

[0065] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] Example 1: This example uses the high-throughput, one-stop mass spectrometry-based glycoproteomics and glycomics sample preprocessing platform of the present invention to process and analyze serum samples from 176 breast cancer patients to identify glycoprotein biomarkers associated with breast cancer. The specific steps are as follows:

[0067] (1) Serum samples (approximately 20 μL each) were collected from 88 breast cancer patients and 88 normal controls.

[0068] (2) Serum samples were added to 96-well plates, with 2 μL of sample added to each well. Two plates were required in total.

[0069] (3) adding 38 μL of PNGase enzyme buffer solution and enzymatically hydrolyzing the sample at 37°C for 16 hours to obtain glycoprotein hydrolysates, namely sugars and proteins;

[0070] (4) The enzymatically hydrolyzed sample was diluted to 20 times its original volume with binding buffer solution B (80% (v / v) ACN 0.1% TFA), and the hydrolyzate was thoroughly mixed with cotton wool to allow the sugar chains to be adsorbed and bound to the cotton wool, while proteins and other impurities were eluted, thereby achieving the enrichment of glycopeptides and sugar chains;

[0071] (5) Transfer the enriched cotton wool to another 96-well plate, add 80 μL of binding buffer B to each well, and wash away the protein and other impurities from the cotton wool. After washing, only the cotton wool bound to the sugar chains is obtained.

[0072] (6) After washing, the cotton wool was transferred to another 96-well plate, and 50 μL of x elution buffer A (0.1% TFA) was added to each well to elute the previously bound sugar chains from the cotton wool. After freeze-drying, the sugar chains were used for methylation derivatization of the sialic acid portion of the sugar chains.

[0073] (7) The specific derivatization conditions are as follows: 15 μL of 5 M methylamino chloride (in DMSO) and 15 μL of 1 M PyAOP (in DMSO:4-NMM = 70:30 (V / V)) were added, and the mixture was incubated at room temperature for 1 hour. 500 μL of binding buffer B was added to terminate the reaction, and then the solution was treated by the cotton-HILIC enrichment method described above to remove excess reagents. The derivatized N-glycan was obtained;

[0074] (8) The derivatized N-glycan was subjected to mass spectrometry analysis using a MALDI-TOF mass spectrometer with a laser energy of 50%, a scan range of 1000-4000 m / z, and a resolution of 10,000 to identify and quantify the composition and structure of the sugar chains;

[0075] Example 2: Breast cancer-related N-glycan differential analysis and machine learning

[0076] (1) The mass spectrometry data were processed using GlycoWorkbench software and the GlycoMod algorithm, with the mass error set to 0.1 Da, the matching degree set to 90%, the confidence level set to 95%, and the false positive rate set to 1%, to screen out glycoprotein biomarkers with high predictive ability.

[0077] (2) R (version 4.2.2) was used to compare the significant differences of N-glycopeptides between different groups: T-test was used to analyze the significant differences in the relative contents of all glycopeptides in the two disease groups, and N-glycopeptides with significant differences (p < 0.05) between the two groups were screened as candidate N-glycopeptide biomarkers.

[0078] (3) Machine learning modeling analysis (python, version 3.10): To further reduce the feature variables, we considered: 1. the significant difference between N-glycan and the two diseases, 2. the correlation between N-glycan and clinical information, 3. the relative abundance of N-glycan in the two diseases, and 4. the contribution of N-glycan in distinguishing the two diseases. After these points, the feature variables were screened and a biomarker combination of five N-glycans was finally selected. To evaluate the diagnostic performance of this biomarker combination of five N-glycans, a logistic regression model was constructed and evaluated using indicators such as sensitivity, specificity, accuracy, and area under the ROC curve (AUC).

[0079] The results showed that the expression levels of N-glycan in the serum of 176 breast cancer patients were detected by mass spectrometry. The relative expression levels of the five N-glycans in the two diseases were as follows: Figure 3As shown in the box plot, the relative contents of the five N-glycans in the severe breast cancer disease group (carcinoma in situ and carcinoma in situ with invasion) were significantly upregulated compared with the benign control group (healthy people and benign breast fibroids) (P<0.05).

Claims

1. A high-throughput one-stop mass spectrometry glycoproteomics and glycomics sample pretreatment platform, characterized by: It mainly includes the following parts: (1) Sample input part, used to add samples containing glycoproteins into the 96-well plate; (2) Enzymatic hydrolysis, which is used to enzymatically hydrolyze the sample under certain temperature and time conditions to obtain glycoprotein hydrolysates, namely glycopeptides and peptides or sugars and proteins; (3) The enrichment part is used to fully mix the absorbent cotton with the hydrolyzate, so that the glycopeptides or sugar chains are adsorbed on the absorbent cotton, while the peptides and other impurities are eluted, thereby achieving the enrichment of glycopeptides and sugar chains; (4) Transfer part, used to transfer the enriched absorbent cotton to another 96-well plate, add a certain amount of eluent to each well, elute the glycopeptides and sugar chains from the absorbent cotton, and obtain the eluate of glycopeptides and sugar chains; (5) Analysis: For mass spectrometry analysis of glycopeptides, different types of mass spectrometers are used according to different samples and purposes; (7) Data processing part, which is used to process mass spectrometry data, identify and quantify the composition and structure of glycopeptides and sugar chains.

2. The sample pretreatment platform according to claim 1, characterized in that: The following steps were used for glycoproteomics sample pretreatment: (1) A sample containing glycoprotein is added to a 96-well plate, a certain amount of sample is added to each well, and then a certain amount of trypsin enzymatic solution is added. The sample is enzymatically hydrolyzed under certain temperature and time conditions to obtain the hydrolyzed products of the glycoprotein, namely, glycosylated peptides and non-glycosylated peptides; (2) Fill each well of the device with a certain amount of absorbent cotton in advance, balance the absorbent cotton with pre-prepared buffer solution A, and then activate the absorbent cotton with pre-prepared buffer solution B; (3) The balanced and activated cotton wool is fully mixed with the hydrolyzate, so that the glycopeptides are adsorbed on the cotton wool, while the non-glycosylated peptides and other impurities (such as salts, reaction reagents, etc.) are eluted, thereby achieving the enrichment of glycosylated peptides; (4) Add a certain amount of eluent to each well to elute the glycopeptide from the absorbent cotton to obtain the glycopeptide eluate; (5) The eluate is lyophilized and used for mass spectrometry analysis to identify and quantify the composition and structure of the glycopeptides.

3. The sample pretreatment platform according to claim 1, characterized in that: The following steps were used for glycomic sample pretreatment: (1) Add a sample containing glycoprotein to a 96-well plate, add a certain amount of sample to each well, then add a certain amount of enzymatic hydrolysis solution, and hydrolyze the sample under certain temperature and time conditions to obtain the hydrolysis products of glycoprotein, namely sugar chains and proteins; (2) Fill each well of the device with a certain amount of absorbent cotton in advance, balance the absorbent cotton with pre-prepared buffer solution A, and then activate the absorbent cotton with pre-prepared buffer solution B; (3) The balanced and activated cotton wool is fully mixed with the hydrolyzate, so that the sugar chains are adsorbed on the cotton wool, and the deglycosylated proteins and other impurities (such as salts, reaction reagents, etc.) are eluted, thereby achieving the enrichment of sugar chains; (4) Add a certain amount of eluent to each well to elute the sugar chains from the absorbent cotton, obtain the sugar chain eluate and freeze-dry it; (5) adding a certain amount of derivatization reagent to each well, and derivatizing the sugar chains under certain temperature and time conditions to obtain derivatized sugar chains; (6) The derivatized sugar chains are subjected to mass spectrometry analysis to identify and quantify the composition and structure of the sugar chains.

4. The sample pretreatment platform according to claim 2 or 3, characterized in that: The sample is a biological sample containing glycoprotein, selected from serum, plasma, urine, saliva, cerebrospinal fluid, tissue or cell.

5. The sample pretreatment platform according to claim 2 or 3, characterized in that: The enzymatic hydrolysis solution is a solution containing one or more enzymes capable of hydrolyzing glycoproteins, selected from trypsin, chymotrypsin or endoglycosidase.

6. The sample pretreatment platform according to claim 2 or 3, characterized in that: The buffer solution A for balancing and activating the absorbent cotton is 0.1% (V / V) TFA; the buffer solution B is 80% (V / V) ACN 0.1% TFA; the absorbent cotton is a fibrous material cotton or cotton ball that can adsorb glycopeptides and sugar chains.

7. The sample pretreatment platform according to claim 2 or 3, characterized in that: The eluent is a solution capable of eluting glycopeptides and sugar chains from absorbent cotton, and is selected from a 0.1% trifluoroacetic acid aqueous solution.

8. The sample pretreatment platform according to claim 5, characterized in that: The enzymatic hydrolysis solution for glycoprotein samples is Trypsin, and the enzymatic hydrolysis solution for sugar samples is PNGase F glycosidase.

9. The sample pretreatment platform according to claim 1, 2 or 3, characterized in that: The derivatization reagent is methylamine hydrochloride, a reagent capable of undergoing derivatization reaction with monosaccharides and oligosaccharides.

10. The sample pretreatment platform according to claim 1, characterized in that: The mass spectrometer is an instrument capable of performing mass spectrometry analysis on glycopeptides and sugar chains, and is selected from an electrospray ionization (ESI) mass spectrometer, a matrix-assisted laser desorption ionization (MALDI) mass spectrometer, an ion trap mass spectrometer or a time-of-flight (TOF) mass spectrometer.

11. The sample pretreatment platform according to claim 1, characterized in that: The software and algorithm used in the processing platform are software and algorithms that can process mass spectrometry data, identify and quantify the composition and structure of glycopeptides and sugar chains, and are selected from Byonic, Byologic, Proteome Discoverer, GlycoWorkbench, or GlycoMod.

12. The sample pretreatment platform according to claim 1, 2 or 3, characterized in that: The following steps were used to identify disease-associated glycoprotein biomarkers: (1) Collecting biological samples containing glycoproteins from the serum of patients with the disease and normal controls, with each sample being approximately 20 μl; (2) using the sample preprocessing platform to process and analyze the collected biological samples to obtain mass spectrometry data of glycopeptides and sugar chains; (3) Processing the mass spectrometry data to screen out glycopeptides and glycans with significant differences as candidate glycoprotein biomarkers; (4) Validate candidate glycoprotein biomarkers and use statistical methods to evaluate their sensitivity and specificity in distinguishing patients with the disease from normal controls, as well as their effectiveness in predicting disease severity and metastatic potential; (5) Identify disease-related glycoprotein biomarkers and, based on the validation results, select glycopeptides and sugar chains with high sensitivity, high specificity, and high predictive ability as disease-related glycoprotein biomarkers.

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