CN113030305A

CN113030305A - Construction method and application of physiological abundance range of N-glycopeptide of healthy people

Info

Publication number: CN113030305A
Application number: CN202110228407.XA
Authority: CN
Inventors: 秦伟捷; 董航言; 李圆圆; 尚诗婷
Original assignee: BEIJING PROTEOME RESEARCH CENTER
Current assignee: BEIJING PROTEOME RESEARCH CENTER; Academy of Military Medical Sciences AMMS of PLA
Priority date: 2021-03-02
Filing date: 2021-03-02
Publication date: 2021-06-25
Anticipated expiration: 2041-03-02
Also published as: CN113030305B

Abstract

The invention provides a method for constructing a physiological abundance range of N-glycopeptide of a healthy population, and provides a method for screening potential markers of abnormal N-glycopeptide of a disease based on the constructed physiological abundance range of the N-glycopeptide of the healthy population. The invention and the construction of the physiological abundance range of the urine N-glycopeptide provide a new method and thought for the function and mechanism research and the clinical biomarker screening based on the urine glycoproteomics.

Description

Construction method and application of physiological abundance range of N-glycopeptide of healthy people

Technical Field

The invention particularly relates to a construction method of a physiological abundance range of N-glycopeptide of healthy people and application thereof.

Background

N-glycosylation modification is a ubiquitous post-translational modification of proteins and is closely associated with a variety of cellular processes, such as molecular recognition and signal transduction, cell adhesion, immune response, and apoptosis. Abnormal N-glycosylation modifications often cause neurodegenerative diseases, diabetes, nephropathy, tumors, and inflammatory diseases. Therefore, the deep investigation of the expression level of the N-glycopeptide has significant clinical reference significance for disease prevention, diagnosis, staging and curative effect tracking evaluation.

Urine is a common biological sample for disease biomarker discovery, physical health status monitoring, and clinical diagnosis. The change of urine proteome from glomerular filtration and urinary system secretion can reflect the physiological and pathological states of human body. Urine is widely studied because of its completely non-invasive, repeatable sampling, and lower biological complexity, and its easy analysis. However, the physiological abundance of glycoproteome in urine fluctuates due to differences among individuals and changes of physiological conditions, and a construction method for the physiological abundance range of N-glycopeptide in urine of large-scale healthy people is not available at present. Therefore, it is difficult to determine whether the differences of N-glycopeptides found in clinical disease biomarker studies are due to normal physiological fluctuations, inter-individual differences or disease-induced changes, which poses a great challenge to later-stage large-scale sample validation and limits the research of tumor candidate marker screening in practical samples.

Disclosure of Invention

Aiming at the problems and limitations existing in the background technology, the technical problem to be solved by the invention is how to construct a physiological abundance range of N-glycopeptide of a healthy population covering individual difference and physiological fluctuation conditions, and based on the constructed range, abnormal N-glycopeptide of different tumor patient populations or abnormal N-glycoprotein corresponding to the abnormal N-glycopeptide is screened out to be used as a potential marker of corresponding tumors.

The invention aims to provide a method for constructing the physiological abundance range of N-glycopeptide of healthy people, which comprises the steps of measuring the quantitative value of the N-glycopeptide in a physiological sample of the healthy people in a large scale and constructing the physiological abundance range of the N-glycopeptide of the healthy people covering individual difference and physiological fluctuation conditions.

In the method, the physiological abundance range of the N-glycopeptide of the healthy population covering individual differences and physiological fluctuation conditions is constructed by adopting a non-standard quantitative method, carrying out median normalization on the measured quantitative value of the N-glycopeptide, filling the missing quantitative value by adopting a random value lower than 1% quantile, and establishing the upper limit and the lower limit of the physiological range to obtain the physiological abundance range of the N-glycopeptide of the healthy population.

In the above method, the physiological abundance range of N-glycopeptides of the healthy population is defined as the sum of the median of the quantitative values of the N-glycopeptides in the physiological samples of the healthy population and the difference between the upper quartile and the lower quartile which is 1.5 times the quantitative value of the N-glycopeptides in the physiological samples of the healthy population, and the physiological abundance range of each N-glycopeptide is defined as the difference between the median of the quantitative values of the N-glycopeptides in the physiological samples of the healthy population and the upper quartile and the lower quartile which is 1.5 times the quantitative value of the N-glycopeptides in the physiological samples of the healthy population, i.e. the physiological abundance range of each N-glycopeptide is defined as (Q2-1.5(Q3-Q1), Q2+1.5(Q3-Q1)), wherein Q1, Q2, and Q3 are the upper quart.

In the above method, the biological sample is derived from urine.

In the above method, the determination of the quantitative value comprises the steps of: precipitating with acetone, extracting urine protein, reducing and alkylating, performing peptide digestion, enriching N-glycopeptide by hydrophilic interaction chromatography, cutting off N-sugar chain by peptide N-glycylamidase, desalting, drying, and performing mass spectrum identification.

In the above method, the hydrophilic interaction chromatography adopts HILIC filler with particle size of 1.5-5 μm, and optionally HILIC filler with particle size of 5 μm.

In the above method, the hydrophilic interaction chromatography is performed by using TFA and H₂O, ACN buffer solution.

The invention also aims to provide a method for screening potential markers of abnormal N-glycopeptides of diseases based on the constructed physiological abundance range of the N-glycopeptides of the healthy population, which comprises the following steps:

s1, determining the quantitative value of N-glycopeptide in physiological samples of a plurality of tumor patients;

s2, comparing the quantitative value of each N-glycopeptide identified by each patient physiological sample with the physiological abundance range of the N-glycopeptide of the healthy population constructed based on the construction method, wherein the N-glycopeptide of which the quantitative value is out of the range is the abnormal N-glycopeptide of the patient physiological sample, and obtaining all the abnormal N-glycopeptides of the patient physiological sample;

s3, integrating the abnormal N-glycopeptide information of each patient physiological sample of the tumor, constructing an abnormal N-glycopeptide association matrix of the tumor patient population, defining that more than 70% of physiological samples are abnormal as the standard of population level abnormal N-glycopeptide, and screening the abnormal N-glycopeptide of the tumor patient population or the abnormal N-glycoprotein corresponding to the abnormal N-glycopeptide as a potential marker of the tumor.

In the above method, the biological sample is derived from urine.

In the above method, the hydrophilic interaction chromatography is carried out by using HILIC filler with particle size of 1.5-5 μm.

The invention also provides a method for typing tumor patients in the physiological abundance range of the N-glycopeptide of the healthy population, which is constructed based on the construction method, and the method comprises the following steps:

y1, determining the quantitative value of N-glycopeptide in physiological samples of a plurality of tumor patients;

y2, comparing the quantitative value of each N-glycopeptide identified by each physiological sample of the patient with the physiological abundance range of the N-glycopeptide of the healthy population constructed based on the construction method, wherein the N-glycopeptide of which the quantitative value is out of the range is the abnormal N-glycopeptide of the physiological sample of the patient, and obtaining all the abnormal N-glycopeptides of the physiological sample of the patient;

y3, and N-glycopeptide abnormally expressed in more than 20% of the physiological samples of the selected patients, and the differences among the physiological samples of the tumor patients are examined for molecular typing.

The invention also provides an analysis device, which comprises the following modules:

w1, data storage module: the data storage module is configured to store the physiological abundance range value of each N-glycopeptide of the healthy population, and preferably, the physiological abundance range value is determined by the construction method;

w2, data receiving module: the data receiving module is configured to receive quantitative values of a plurality of N-glycopeptides in a physiological sample of each of a plurality of patients suffering from a same disease, preferably a same tumor;

w3, data comparison module: the data comparison template is configured to receive quantitative values of a plurality of N-glycopeptides in the physiological samples of the plurality of patients sent by the data receiving module, and call physiological abundance ranges of healthy people corresponding to the N-glycopeptides from the data storage module for comparison;

w4, judgment module: the judging module is configured to receive the comparison result sent by the data comparing module, judge the comparison result according to a preset judging condition, judge the N-glycopeptide meeting the preset judging condition as the abnormal N-glycopeptide of the patient suffering from the disease or candidate as the abnormal N-glycopeptide of the patient suffering from the disease, judge the N-glycopeptide not meeting the preset judging condition as the abnormal N-glycopeptide of the patient not suffering from the disease or candidate as the abnormal N-glycopeptide of the patient suffering from the disease, and output the judging result; the predetermined judgment condition is that the quantitative value of the N-glycopeptide exceeds the physiological abundance range of the healthy population corresponding to the N-glycopeptide.

In the above analysis device, the device further comprises a disease potential marker screening module, and the data storage module further stores a screening standard, wherein the screening module receives the determination result from the determination module, screens out abnormal N-glycopeptide of the disease patient population or abnormal N-glycoprotein corresponding to the abnormal N-glycopeptide as a potential marker of the disease according to the screening standard, and the screening standard is that more than 70% of physiological samples show abnormality as population level abnormal N-glycopeptide.

In the above analysis apparatus, the apparatus further includes a molecular typing module, where the molecular typing module receives the determination result from the determination module, selects N-glycopeptides that are abnormally expressed in more than 20% of the physiological samples of the patients, and associates the abnormally expressed N-glycopeptides with physiological activities by using a consistent clustering algorithm to perform molecular typing.

The present invention also provides a computer-readable storage medium storing a computer program for performing the steps of:

receiving quantitative values for a plurality of N-glycopeptides in a physiological sample of each of a plurality of patients having the same disease;

comparing the quantitative values of the plurality of N-glycopeptides in the physiological samples of the plurality of patients with the physiological abundance range of the healthy population corresponding to each N-glycopeptide to obtain a comparison result;

judging the comparison result according to a preset judgment condition, judging that the N-glycopeptide meeting the preset judgment condition is or is not a candidate of the abnormal N-glycopeptide of the patient suffering from the disease, judging that the N-glycopeptide not meeting the preset judgment condition is not or is not a candidate of the abnormal N-glycopeptide of the patient suffering from the disease, and outputting the judgment result; the predetermined judgment condition is that the quantitative value of the N-glycopeptide exceeds the physiological abundance range of the healthy population corresponding to the N-glycopeptide.

In the above readable storage medium, the computer program further includes the steps of:

and screening abnormal N-glycopeptides of the disease patient population or abnormal N-glycoprotein corresponding to the abnormal N-glycopeptides as the potential marker of the disease according to the screening standard of the disease potential marker of the judgment result, wherein the screening standard is that more than 70 percent of physiological samples show abnormality as population level abnormal N-glycopeptides.

The above-mentioned readable storage medium, the computer program further comprising the steps of:

selecting N-glycopeptides abnormally expressed in more than 20% of physiological samples of patients, and performing molecular typing by associating the abnormally expressed N-glycopeptides with physiological functions by adopting a consistent clustering algorithm.

The invention also provides the application of the construction method, the physiological abundance range of the N-glycopeptide of the healthy people constructed by the construction method, the screening method, the computing device or the typing method in tumor research.

The method can construct the physiological abundance range of the N-glycopeptide of the healthy population covering individual difference and physiological fluctuation conditions, and can screen abnormal N-glycopeptide of different tumor patient populations as a potential marker based on the constructed range. The invention and the construction of the physiological abundance range of the urine N-glycopeptide provide a new method and thought for the function and mechanism research and the clinical biomarker screening based on the urine glycoproteomics.

Drawings

FIG. 1 is a main component analysis chart of urine samples of lung cancer patients and healthy people in example 1 according to example 2 of the present invention.

FIG. 2 is a graph showing the abnormal distribution of N-glycopeptide in lung cancer patients in accordance with example 2 of the present invention.

FIG. 3 is a functional analysis chart of the lung cancer potential markers in example 2 of the present invention.

FIG. 4 is a chart of molecular typing analysis of a sample before treatment of a lung cancer patient according to example 2 of the present invention.

FIG. 5 is a graph showing the analysis of the difference in function between type 1 and type 3 of samples before treatment of lung cancer patients in example 2 of the present invention.

Detailed Description

The present invention is described in further detail below with reference to specific embodiments, which are given for the purpose of illustration only and are not intended to limit the scope of the invention. The examples provided below serve as a guide for further modifications by a person skilled in the art and do not constitute a limitation of the invention in any way.

The experimental procedures in the following examples are conventional unless otherwise specified. Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.

Trypsin (Trypsin): promoga (USA), # V5111.

Peptide N-glycoamidase (PNGase F): new England Biolabs (UK), # P0704S.

ACN: acetonitrile, Merck (Germany), 1.00030.4008.

TFA: trifluoroacetic Acid, Sigma-Aldrich (USA), Sigma-T6508 Trifluoroacetic.

In the examples below, 50 lung cancer patients fall between the ages of 20-90 years. 291 healthy volunteers aged between 20-90 years.

Preliminary experiment 1, particle size optimization (1.5 μm, 3 μm, 5 μm)

And (3) activating the filler: weighing HILIC filler and peptide fragment (5mg:80 μ g)HILIC fillers of the same particle size (1.5 μm, 3 μm, 5 μm) each 5mg were activated by adding 100. mu.L of 0.5% (v/v) TFA solution to the filler for 10min, adding 0.5% (v/v) FA solution for 30min, and washing 3 times with 100. mu.L of 0.5% (v/v) FA. Then adding Bindingbuffer 1FA: H₂O, 100 mu L of ACN (5:15:80, v/v/v) is activated for 10min to 1h, 100 mu L of Binding buffer is added into the peptide fragment according to the filler, Binding buffer 1(5mg:100 mu L), 100 mu L of HILIC filler mixed solution is added into the hot-dried peptide fragment, the mixture is shaken at 30 ℃ for 2h,

cleaning: the samples were all transferred to fill 2 layers C₈Repeatedly retaining in tip head of the membrane for 3 times,

and (3) elution: then, the tube was replaced to elute the N-glycopeptide after washing 3 times with 80. mu.L of Bindingbuffer 1. Adding 0.5% (v/v) FA into tip head for eluting for 3 times, wherein the eluent FA is FA: H₂ACN (0.5:94.5:5, v/v/v) was eluted 1 time at 80. mu.L each.

As a result: the HILIC filler with the particle size of 5 mu m is adopted for enrichment, and higher identification quantity of N-glycoprotein and N-glycopeptide is shown. Therefore, 5 μm is screened for a better particle size and the next condition optimization is carried out.

Pre-experiment 2, buffer optimization (FA and TFA systems)

5mg of a HILIC filler having a particle size of 5. mu.m was weighed out based on the filler-peptide fragment (5mg: 80. mu.g), and 100. mu.L of a 0.5% (v/v) TFA solution was added to the filler to activate for 10min, and a 0.5% (v/v) FA solution was added to activate for 30min, and washed 3 times with 100. mu.L of 0.5% (v/v) FA. After that, 2 treatments were set:

treatment 1: binding buffer 1FA: H by FA system₂O ACN (5:15:80, v/v/v) for 10min-1 h. Then, 100 μ L of HILIC filler mixed liquor is added into the balanced peptide fragment, and then the peptide fragment is incubated for 2h at 30 ℃ with shaking. tip head filled with 2 layers of C₈Membrane-trapped HILIC packing with Bindingbuffer 1FA: H₂O ACN (5:15:80, v/v/v) 80. mu.L was washed 3 times, and then the elution was carried out after replacing the EP tube. 0.5% FA eluted 3 times first, followed by FA: H₂ACN (0.5:94.5:5, v/v/v) was washed 1 time, 80. mu.L each.

And (3) treatment 2: TFA System Bindingbuffer 2TFA: H₂O ACN (1:19:80, v/v/v) for 10min-1 h. Then, 100 μ L of HILIC filler mixed liquor is added into the balanced peptide fragment, and then the peptide fragment is incubated for 2h at 30 ℃ with shaking. tip head filled with 2 layers of C₈Membrane-trapped HILIC packing with Bindingbuffer 2TFA: H₂O ACN (1:19:80, v/v/v) 80. mu.L was washed 3 times, and then the elution was carried out after changing the EP tube. 80 μ L of eluent 2TFA H₂O ACN (1:79:20, v/v/v), elution was performed 3 times.

As a result: the average identified amount and average selectivity of N-glycoprotein and N-glycopeptide under the TFA system are higher than those of the FA system. The coverage rate is higher, and the method is more stable. The following examples therefore use the TFA system Binding buffer 2TFA H₂O ACN (1:19:80, v/v/v), eluent TFA H₂O:ACN(1:79:20,v/v/v)。

Preliminary experiment 3 same healthy person five consecutive days relevance investigation

Selecting one healthy young volunteer, collecting morning urine for 5 days continuously, extracting urine protein, performing enzymolysis, and removing 80 μ g peptide segment, and drying at 45 deg.C for enrichment. To 5mg of HILIC filler (particle size 5 μm), 100 μ L of 0.5% (v/v)) TFA was added for activation for 10min, followed by 100 μ L of 0.5% (v/v)) FA for activation for 30min, followed by TFA: H₂And (3) balancing the filler by using an ACN (1:19:80, v/v/v) buffer solution for 10min-1h, adding 100 mu L of filler mixed solution into the hot dried peptide fragment, and incubating for 2h at 30 ℃ with shaking. Filling pipette tip with two layers C₈Membranes, three HILIC packings, 80. mu.L TFA: H₂Washing with ACN (1:19:80, v/v/v) buffer solution three times, replacing centrifuge tube, and using TFA H₂ACN (1:79:20, v/v/v) was eluted three times and dried by heating at 45 ℃. After hot drying, 20 mu L of 25mmol/L ABC/H is used₂ ¹⁸And O redissolving the peptide fragment, measuring the pH value to be about 7.5, adding 100U PNGase F, and carrying out enzyme digestion at 37 ℃ for 16 h. Activation of layer 3C with 80. mu.L of 100% (v/v) ACN, 50% (v/v) ACN and 0.1% (v/v) TFA, respectively₁₈Membrane, 3.5. mu.L of the enzyme cut was mixed with 80. mu.L of 0.1% (v/v) FA for desalting, and the remainder was stored in a refrigerator at-80 ℃. Samples were pipetted into tip heads 3 times in duplicate, syringe pressed, 80 μ L0.1% (v/v) FA washed 3 times, and eluted 2 times with 50% ACN/0.1% FA (v/v). And (4) drying at 45 ℃ and performing mass spectrum identification.

As a result: the fluctuation range is between 0.854 and 0.942, and has higher correlation, which indicates that the physiological fluctuation of urine N-glycoprotein group is more stable in a short time in the same healthy person.

Example 1 construction of physiological abundance Range of N-glycopeptides for healthy population

1. The quantitative values of N-glycopeptides in physiological samples of healthy population were determined on a large scale (previously examined on the N-glycoproteome identification accumulation curve of 40 healthy persons, in which the accumulation identification amounts of N-glycoprotein and N-glycosylation site tended to saturate after the number of samples reached 14 and 20, respectively).

The quantitative value of N-glycopeptide in a physiological sample is measured for 291 healthy volunteers, urine protein is extracted after acetone precipitation, peptide fragment enzyme digestion is carried out after reductive alkylation, N-glycopeptide is enriched by a hydrophilic interaction chromatography (HILIC) method, N-glycopeptide is cut off by peptide N-glycyase (PNGase F), and mass spectrum identification is carried out after desalting and heat drying. The specific method comprises the following steps:

1.1 extraction of urine protein by acetone precipitation

The physiological sample to be tested is first middle morning urine, 20mL of urine is transferred and centrifuged at the speed of 3000g for 30min and 12000g for 30min at the temperature of 4 ℃, after supernatant liquid is taken, 3 times of volume of acetone precooled in advance (precooled at the temperature of minus 20 ℃ for 4 hours in advance) is added into the supernatant liquid, and the mixture is placed into a refrigerator at the temperature of minus 20 ℃ for precipitation for 4 hours. After removal, the tube was centrifuged at 12000g for 30min at 4 ℃ to collect the precipitate, and 400. mu.L of 8mol/L Urea (UA) was added thereto to dissolve the urine protein. Ultrasonic working for 1s, interval 1s, working for 30 times, dissolving precipitate, centrifuging, and collecting supernatant.

1.2 Ultrafiltration tube assisted digestion

The extracted urine protein was transferred to a 30kD ultrafiltration tube and centrifuged at 14000 g. Then, Dithiothreitol (DTT) is added into 8mol/L UA as a solvent to keep the final concentration of Dithiothreitol (DTT) at 10mmol/L, denaturation reaction is carried out for 4h at 37 ℃, and 200 mu L of 50mmol/L Iodoacetamide (IAA) is added into an ultrafiltration tube and incubated for 40min at room temperature in a dark place. Adding 200 mu L of 50mmol/L Ammonium Bicarbonate (ABC) solution (pH 8.4), washing and replacing the pH of the system, adding a corresponding volume of Trypsin (0.5mg/mL) according to the mass ratio of Trypsin to protein (1:100), adding enzyme twice, performing enzyme digestion for 2h at 37 ℃ for the first time, and performing enzyme digestion for 14h for the second time. And collecting peptide fragments after enzyme digestion, and determining the concentration. 80 μ g of the peptide fragment was removed and dried by heat for enrichment.

1.3 HILIC Filler activation and enrichment of N-glycopeptides

Each sample scaleTaking 5mg HILIC filler with the particle size of 5 μm, adding 100 μ L buffer solution 1 for primary activation for 10min, then adding 100 μ L buffer solution 2 for secondary activation for 30min, balancing the filler with 100 μ L buffer solution 3 for 30min, uniformly mixing 80 μ g peptide fragment dried in the step 1.2 with 100 μ L buffer solution 3, and oscillating and incubating for 2h at 30 ℃. 2-layer C filled with pipette tips (tip heads)₈After 3 times of retention of the sample by the membrane, the membrane was washed 3 times with 80. mu.L of buffer 3, and the EP tube was replaced to elute the N-glycopeptide 3 times with 80. mu.L of buffer 4 each time. After drying by heat, the peptide fragment was reconstituted with 20. mu.L of 25mmol/L buffer 5 and the pH was determined to be after 7-8. Adding 100U PNGase F, and performing enzyme digestion in water bath at 37 ℃ for 16h to cut off sugar chains. After the enzyme digestion, 80 μ L of 100% (v/v) ACN, 50% (v/v) ACN and 0.1% (v/v) FA are used to activate layer 3C₁₈Membrane, 3.5. mu.L of the digested material was mixed with 80. mu.L of 0.1% (v/v) FA and desalted, the remainder was retained at-80 ℃. First, the sample was repeatedly transferred 3 times into tip heads, the syringe was pressed, 80. mu.L of 0.1% (v/v) FA was washed 3 times, and buffer 6 was eluted 2 times. Drying at 45 ℃, and carrying out mass spectrum identification analysis to obtain the quantitative value of the N-glycopeptide.

The composition of the buffer 1 is trifluoroacetic acid (TFA): H₂O(0.5:99.5,v/v)。

The buffer solution 2 consists of Formic Acid (FA): H₂O(0.5:99.5,v/v)。

The buffer 3 is composed of TFA H₂O:ACN(1:19:80,v/v/v)。

The buffer 4 has the composition of TFA H₂O:ACN(1:79:20,v/v/v)。

The composition of the buffer solution 5 is 25mmol/L ABC (H)₂ ¹⁸O), the pH is about 7.5.

The buffer solution 6 has the composition of TFA H₂O:ACN(0.1:49.9:50,v/v/v)。

The mass spectrum identification and analysis method comprises the following steps: LC-MS/MS: the mobile phases A and B were an aqueous solution containing 0.1% (v/v) FA and an acetonitrile solution containing 0.1% (v/v) FA, respectively. By C₁₈The sample was eluted as a reversed phase capillary column (120 mm. times.1.9 μm) for 90min at a flow rate of 0.6. mu.L/min. The elution gradient is 8-60 min, and the concentration of B is 10-30%; 60-79 min, 30% -42% of B; 79-80 min, 42-95% B; 80-85 min, 95% B. Acquiring a spectrogram in a positive ion mode, wherein the mass-to-charge ratio range of primary mass spectrum scanning is 300-1400The resolution was 70000. The secondary mass spectral resolution was 17500, the isolation window (m/z) was 3, and the dynamic exclusion time was 15 s.

Mass spectral data were retrieved in the Maxquant 1.2.5.8 software using Uniprot _ human (2015.7, 20207 tape peptide) as the protein database. The search parameter settings are as follows: the proteolytic enzyme is trypsin, the maximum number of the missed cuts of the glycoprotein identification is two, and the maximum number of the modifications on the peptide fragment is set to be five. Variable modifications include protein N-terminal acetylation, methionine oxidation modification, and deamidation¹⁸O site modification, and immobilization modification includes cysteine alkylation modification. The false positive rate (FDR) of the protein and PSM was 1%.

2. Constructing the physiological abundance range of the N-glycopeptide of the healthy population covering individual difference and physiological fluctuation conditions

In the quantitative analysis, the logarithm value of a single N-glycopeptide quantitative value is taken as an abscissa, and the distribution proportion of different N-glycopeptide quantitative values is taken as an ordinate, so that the abundance distribution condition of the N-glycopeptide is obtained. Carrying out median normalization on the quantitative values of the N-glycopeptide of the urine of the 291 healthy volunteers measured in the step 1 by adopting a non-standard quantitative method, filling missing quantitative values by adopting random values lower than 1% quantile, and setting the upper limit and the lower limit of a physiological range: the upper limit of the physiological abundance of each N-glycopeptide is the sum of the differences between the median of the quantitative values of the N-glycopeptides in the physiological samples of all healthy people and the upper quartile number which is 1.5 times of the quantitative values of the N-glycopeptides, and the lower limit of the physiological abundance of each N-glycopeptide is the difference between the median of the quantitative values of the N-glycopeptides in the physiological samples of all healthy people and the upper quartile number which is 1.5 times of the quantitative values of the N-glycopeptides, namely the physiological abundance range of each N-glycopeptide is (Q2-1.5(Q3-Q1) and Q2+1.5(Q3-Q1)), wherein Q1, Q2 and Q3 are the upper quartile number, the median number and the lower quartile number of the quantitative values of the physiological abundance of the N-.

Example 2 screening of potential markers for abnormal N-glycopeptide in patients with lung cancer

S1, the quantitative value of N-glycopeptide in the pre-treatment physiological sample (morning urine) of 50 lung cancer patients was measured, and the measurement method was referred to step 1 in example 1.

3133N-glycopeptides were identified from pre-treatment samples of 50 lung cancer patients, together with 291 healthy human samples from example 1. As shown in fig. 1, the lung cancer sample can be clearly distinguished from the healthy human sample by principal component analysis.

S2, comparing the quantitative value of each N-glycopeptide identified by each physiological sample of the patient with the physiological abundance range of the N-glycopeptide of the healthy population constructed in the embodiment 1, wherein the N-glycopeptide of which the quantitative value is out of the range is the abnormal N-glycopeptide of the physiological sample of the patient, and obtaining all the abnormal N-glycopeptides of the physiological sample of the patient;

s3, integrating the abnormal N-glycopeptide information of the physiological samples of the 50 lung cancer patients, constructing an abnormal N-glycopeptide association matrix of a lung cancer patient population, defining that more than 70% of the physiological samples are abnormal as the standard of the population level abnormal N-glycopeptide, screening the abnormal N-glycopeptide of the lung cancer patient population, and taking the screened abnormal N-glycopeptide or the N-glycoprotein corresponding to the abnormal N-glycopeptide as a potential marker.

FIG. 2 shows the abnormal distribution of N-glycopeptide in the pre-treatment samples of lung cancer patients, wherein over 70% (over 35) of lung cancer patients have over 25N-glycopeptides with abnormal population expression compared with healthy people.

And performing GO annotation and GO term significance enrichment analysis on the cell location, biological process and function of the N-glycoprotein corresponding to the abnormal N-glycopeptide by using a Uniprot and DAVID database.

The 25 potential markers found were analyzed for function and the results are shown in fig. 3, showing that the 25 potential marker functions include two classes, G1 and G2, where the function of G1 is primarily related to metabolism and biosynthesis and G2 is related to the functions of NTRK3 and interleukin signaling.

According to the above method, an analysis device is provided, the analysis device comprising the following modules:

The device also comprises a disease potential marker screening module, and the data storage module also stores screening standards, wherein the screening module receives the judgment result from the judgment module, screens out abnormal N-glycopeptide of the disease patient population or abnormal N-glycoprotein corresponding to the abnormal N-glycopeptide as a potential marker of the disease according to the screening standards, and the screening standards indicate that more than 70% of physiological samples are abnormal as population level abnormal N-glycopeptide.

A computer-readable storage medium is developed according to the above method, the computer-readable storage medium storing a computer program for performing the steps of:

Example 3 screening of N-glycopeptides that were abnormal in 20% of lung cancer patient samples

Molecular typing studies were performed using pre-treatment samples (morning urine) of 50 lung cancer patients: the method comprises the following specific steps:

and (3) detecting the difference among the samples of the lung cancer patients by using 740N-glycopeptides which are abnormally expressed in more than 20 percent of the total samples so as to carry out molecular typing.

The results are shown in FIG. 4: using a consensus clustering algorithm, these samples can be divided into four subtypes.

In FIG. 5, 91 key N-glycopeptides were selected from 740N-glycopeptides, which clearly distinguished type 1 from type 3 (C1 and C3). Wherein the column directions are, from left to right, C1, C2, C3 and C4, respectively. The row directions are respectively G6, G2, G1, G4, G3 and G5 from top to bottom. The G1 panel is mainly associated with plasma lipoprotein and IGF functions. Among the groups of G2, it is mainly associated with immune function, and the expression of N-glycopeptide of type 3 is significantly reduced compared with that of type 1. The expression of N-glycopeptides of G3 and G5, type 3 is significantly higher than that of type 1, and G3 and G5 are respectively related to vitamin metabolism and EGFR signal.

The device also comprises a molecular typing module, wherein the molecular typing module receives the judgment result from the judgment module, selects the N-glycopeptide abnormally expressed in more than 20% of the physiological samples of the patients, and associates the abnormally expressed N-glycopeptide with the physiological activity by adopting a consistency clustering algorithm to perform molecular typing.

In the above readable storage medium, the computer program further includes the steps of: selecting N-glycopeptides abnormally expressed in more than 20% of physiological samples of patients, and performing molecular typing by associating the abnormally expressed N-glycopeptides with physiological functions by adopting a consistent clustering algorithm.

In conclusion, the method can construct and obtain the physiological abundance range of the N-glycopeptide of the healthy population covering individual difference and physiological fluctuation conditions, and can screen abnormal N-glycopeptide of different tumor patient populations as potential markers based on the constructed range. The invention and the construction of the physiological abundance range of the urine N-glycopeptide provide a new method and thought for the function and mechanism research and the clinical biomarker screening based on the urine glycoproteomics.

The present invention has been described in detail above. It will be apparent to those skilled in the art that the invention can be practiced with equivalent parameters and conditions within a wide range without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with reference to specific embodiments, it will be appreciated that the invention can be further modified. In summary, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The use of some of the essential features is possible within the scope of the claims attached below.

Claims

1. A method for constructing a physiological abundance range of N-glycopeptide of a healthy population is characterized by comprising the steps of measuring N-glycopeptide quantitative values in physiological samples of the healthy population in a large scale and constructing the physiological abundance range of the N-glycopeptide of the healthy population covering individual differences and physiological fluctuation conditions.

2. The method of claim 1, wherein the physiological abundance range of N-glycopeptides of healthy population is constructed by normalizing the measured N-glycopeptide quantitation values by a non-standard quantitation method, filling missing quantitation values by a random value below 1% quantile, wherein the upper physiological abundance limit of each N-glycopeptide is the sum of the median of the quantitation values of the N-glycopeptide in the physiological samples of the healthy population and the difference between the median of the quantitation values of the N-glycopeptide in the physiological samples of the healthy population and 1.5 times the upper quartile, and the lower physiological abundance limit of each N-glycopeptide is the difference between the median of the quantitation values of the N-glycopeptide in the physiological samples of the healthy population and the difference between the median of the N-glycopeptide in the physiological samples of the healthy population and 1.5 times the upper quartile, i.e., the physiological abundance range of each N-glycopeptide is (Q2-1.5) (Q3-Q1), q2+1.5(Q3-Q1)), wherein Q1, Q2, Q3 are the upper quartile, the middle quartile and the lower quartile, respectively, of the quantitative value of the physiological abundance of the N-glycopeptide.

3. The method of any one of claims 1-2, wherein the biological sample is derived from urine.

4. The method of claim 3, wherein the step of determining the quantitative value comprises the steps of: precipitating with acetone, extracting urine protein, reducing and alkylating, performing peptide digestion, enriching N-glycopeptide by hydrophilic interaction chromatography, cutting off N-sugar chain by peptide N-glycylamidase, desalting, drying, and performing mass spectrum identification; the hydrophilic interaction chromatography adopts HILIC filler with particle size of 1.5-5 μm, TFA and H₂O, ACN buffer solution.

5. The method for screening the potential marker of abnormal N-glycopeptide of the disease based on the physiological abundance range of the N-glycopeptide of the healthy population constructed by the method of any one of claims 1 to 4, which is characterized by comprising the following steps:

s2, comparing the quantitative value of each N-glycopeptide identified by each physiological sample of the patient with the physiological abundance range of the N-glycopeptide of the healthy population constructed based on the method of any one of claims 1 to 4, wherein the N-glycopeptide of which the quantitative value falls outside the range is the abnormal N-glycopeptide of the physiological sample of the patient, and obtaining all the abnormal N-glycopeptides of the physiological sample of the patient;

6. The method for typing tumor patients based on the physiological abundance range of N-glycopeptides of healthy people constructed by any one of claims 1 to 4, comprising the following steps:

y2, comparing the quantitative value of each N-glycopeptide identified by each physiological sample of the patient with the physiological abundance range of the N-glycopeptides of the healthy population constructed based on the method of any one of claims 1 to 4, wherein the N-glycopeptides with the quantitative values falling outside the range are abnormal N-glycopeptides of the physiological sample of the patient, and obtaining all abnormal N-glycopeptides of the physiological sample of the patient;

7. An analysis device, characterized in that the analysis device comprises the following modules:

w1, data storage module: the data storage module is configured to store a physiological abundance range value of each N-glycopeptide of the healthy population, preferably the physiological abundance range value is determined by the method of any one of claims 1 to 4;

8. The device of claim 7, wherein the device further comprises a disease potential marker screening module and the data storage module further stores screening criteria,

the screening module receives the judgment result from the judgment module, screens out abnormal N-glycopeptide of the disease patient population or abnormal N-glycoprotein corresponding to the abnormal N-glycopeptide as a potential marker of the disease according to the screening standard, and the screening standard is that more than 70% of physiological samples show abnormality as population level abnormal N-glycopeptide.

9. The device of claim 7, further comprising a molecular typing module, wherein the molecular typing module receives the determination result from the determining module, selects N-glycopeptides that are abnormally expressed in more than 20% of the physiological samples, and associates the abnormally expressed N-glycopeptides with physiological activities by using a consistent clustering algorithm to perform molecular typing.

10. A computer-readable storage medium storing a computer program for performing the steps of: