CN111366633A - Lung benign disease screening and evaluating product based on saliva specific glycoprotein sugar chain structure and application - Google Patents
Lung benign disease screening and evaluating product based on saliva specific glycoprotein sugar chain structure and application Download PDFInfo
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- G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
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- G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites
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Abstract
The invention discloses a product for screening and evaluating lung benign diseases (COPD) related based on saliva specific glycoprotein sugar chain structures and application thereof, provides a product for screening, early diagnosis, risk evaluation, drug screening and/or curative effect evaluation of lung benign diseases aiming at saliva samples, detects the expression level change of specific glycoprotein sugar chain structures (4GlcNAc, Sia α 2-3Gal, Gal β 1-3GlcNAc, Sia α 2-3) in the saliva samples and whether the specific glycoprotein sugar chain structures contain 10 specific N-sugar chains, and takes the corresponding lectin with the specific binding recognition function as MAL-I which can be independently used as a reagent for recognizing the saliva specific glycoprotein sugar chain structures to prepare related medical products.
Description
Technical Field
The invention relates to determination of benign lung disease (COPD) markers based on saliva-specific glycoproteins, and related products and applications thereof.
Background
Primary bronchogenic carcinoma, lung cancer for short, is a malignant tumor originating from the bronchial mucosa or glands. Lung cancer is currently the most common of all deaths worldwide due to malignant tumors. Moreover, its incidence is increasing year by year at a rate of about 5%, and it is expected that by 2020, the number of deaths due to lung cancer will reach 50% of the total number of deaths due to malignant tumor. Lung cancer has become a major health problem and social problem to be solved urgently in various countries around the world, especially in developing countries. The national sampling survey conducted in 2010 shows that the death rate of lung cancer is increased to 30.83/10 ten thousand, wherein 41.34/10 ten thousand for men and 19.84/10 ten thousand for women, the lung cancer death rate accounts for 22.70 percent of the total death rate related to tumors, and the lung cancer death rate is 1 st, and the lung cancer death rate is 40.98/10 ten thousand and the lung cancer death rate is in the top and is obviously higher than that in rural areas (26.93/10 ten thousand) in urban population in China.
Most lung cancers are currently classified into two broad categories based on their degree of differentiation, morphological characteristics and biological characteristics: non-small cell lung cancer (NSCLC) and Small Cell Lung Cancer (SCLC). The former mainly includes Adenocarcinoma (ADC) and squamous cell carcinoma (abbreviated as squamous carcinoma, SCC). Among them, ADC has the highest incidence, accounting for about 50% of lung cancer; SCC is the once most common type of lung cancer, now accounting for approximately 1/3% of lung cancers, while small cell lung cancer accounts for only 15% of lung cancers.
Although the treatment of lung cancer has been advanced in recent years, the prognosis of lung cancer is still not ideal as a whole, and the 5-year survival rates of non-small cell lung cancer and small cell lung cancer are only 14% and 4%, respectively. The main reason for this is that lung cancer is hidden, and there are usually no obvious symptoms in the early stage, and most lung cancer patients are in the advanced stage when the diagnosis is confirmed, and the chance of operative cure is lost. Therefore, early screening and diagnosis of lung cancer are crucial to improve survival rate and prolong survival time of lung cancer patients.
Due to the atypical nature of early symptoms of lung cancer, such as paroxysmal dry cough, hemoptysis or blood-carrying sputum, low fever, chest discomfort and decreased endurance, it is difficult to distinguish them from benign diseases of the lung, and the final diagnosis of lung cancer requires biopsy and histopathology. Therefore, it is necessary to further find out the markers in saliva that can distinguish the benign diseases in the lung.
A number of studies in recent years have shown that: with the development of tumors, abnormal changes of glycoprotein glycosylation are accompanied in body fluids such as tumor tissues, serum and saliva of patients. However, current studies have found 1939 proteins in human saliva and 3020 proteins in human plasma, with only 27% of the saliva proteome coinciding with the plasma proteome. Among the 177 potential biomarkers of proteins found to be associated with cardiovascular disease by plasma proteomics, only 40% of the proteins were also present in the salivary proteome; of the 1058 proteins listed as potential cancer-associated biomarkers, only 34% of the proteins were present in the salivary proteome. This indicates that many biomarkers in blood circulation are not identifiable in saliva. In addition, even the same protein is usually present in significantly lower amounts in saliva than in serum. Therefore, in practice, there is no necessary connection between the two, and researchers are often required to go through a lot of experiments and analyses to judge whether there is a possibility of proteome coincidence.
Disclosure of Invention
With respect to the screening, diagnosis and assessment of benign diseases of the lung (COPD), etc., the inventors have determined the following conclusions through a number of experiments and analyses:
one, the sugar chains recognized by MAL-I (4GlcNAc, Sia α 2-3Gal, Gal β 1-3GlcNAc, Sia α 2-3) were higher in saliva of benign lung disease patients than those of healthy volunteers (COPD group 63.5%, HV group 57.3%) and lung small cell carcinoma patients (SCLC group 57.9%), but lower than those of lung adenocarcinoma patients and lung squamous carcinoma patients (ADC group 67.5%, SCC group 87.1%).
Two, the following 10N-sugar chains m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060, m/z3253.152 were present only in saliva of patients with COPD, but not in saliva of patients with HV, ADC, SCC, SCLC.
From the above conclusions, the following application schemes have been derived:
in a first aspect, a lectin is used as a reagent for identifying a sugar chain structure of a saliva-specific glycoprotein to construct a related product, wherein the lectin is MAL-I, and is used for screening, early diagnosis, risk assessment, drug screening and/or curative effect assessment of Benign Pulmonary Disease (BPD); the related products are kits, devices, operable systems and/or combinations thereof, and prompt the following detection basis:
if one or any combination of m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060, m/z3253.152 among N-sugar chains recognized by lectin MAL-I is present in the saliva sample, it indicates that the body of the saliva sample is a benign lung disease patient.
Here, the specific form of the "prompt detection basis" is not limited, and for example: the detection basis is recorded in the attached product specification; if software is involved, the detection basis can also be embodied by a corresponding algorithm.
In a second aspect, use of a unit for identifying a specific sugar chain, which identifies the following 10N-sugar chains (which can be concluded by judging "presence", "absence"), for constructing a product for screening for Benign Pulmonary Disease (BPD), early diagnosis, risk assessment, drug screening and/or efficacy assessment based on a saliva sample: m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060, m/z3253.152 or any combination thereof.
Here, the identification unit may be an executable software code module, which identifies whether one or any combination of the following 10N-sugar chains exists, and thus can identify whether the body of the saliva sample is a benign lung disease patient; accordingly, the product may be a control device (for example, which can cause the sugar chain analysis apparatus to perform recognition of a specific sugar chain as instructed) connected to the sugar chain analysis apparatus (for example, a mass spectrometer), an information output device (for example, which outputs a list of sugar chains with a flag according to the sugar chain analysis apparatus), a combination thereof, and the like. With the development of sugar chain recognition technology, other specific forms of recognition units may also occur.
In a third aspect, a system for pulmonary benign disease (COPD) screening, early diagnosis, risk assessment, drug screening and/or efficacy assessment, for a saliva sample, the system comprising:
A. lectin MAL-I;
B. an affinity chromatography solid phase carrier for coupling with lectin MAL-I;
C. a reagent and a device for separating N-sugar chains;
D. a mass spectrometer for displaying m/z values corresponding to the respective N-sugar chains;
E. a label, module or processor for identifying one or any combination of the following 10N-sugar chains from the mass spectrogram to derive a determination of whether one or any combination of the following 10N-sugar chains is contained: m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060, m/z 3253.152.
The affinity chromatography solid phase carrier for coupling the lectin MAL-I is Fe3O4Magnetic microparticles, sepharose, sephadex or glass microspheres.
In a fourth aspect, an intelligent terminal includes a processor and a program memory, and when some part of programs stored in the program memory is loaded by the processor, the following steps are executed:
acquiring N-sugar chain information identified by lectin MAL-I in a saliva sample;
it was judged whether or not one or any combination of the following 10N-sugar chains m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060, m/z3253.152 was present.
If the saliva sample exists, prompt information indicating that the saliva sample body is a benign lung disease patient is output.
Here, the intelligent terminal may communicate with a sugar chain analysis device (e.g., a mass spectrometer), may also be connected to a mobile storage medium, or the like, to acquire N-sugar chain information recognized by the lectin MAL-I in the saliva sample; it is also possible to combine the judgment part with the acquisition part, i.e., directly acquire/judge whether there is information on one or any combination of N-sugar chains m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060 and m/z3253.152 in the saliva sample.
In a fifth aspect, a computer readable storage medium stores a computer program which, when loaded by a processor, performs the steps described above.
By adopting the scheme of the invention, whether the subject is a benign lung disease patient can be quickly and accurately identified according to the saliva sample.
Drawings
FIG. 1 is a scattergram analysis of the results of lectin MAL-I in saliva samples tested by example using lectin chips. In the figure, the ordinate represents the normalized fluorescence intensity NFI corresponding to the lectin on the lectin chip, the horizontal line in the figure represents the comparison between the two groups shown at both ends, P is P-Value obtained from one-way anova, and P <0.001 indicates that the difference is extremely significant. HV: healthy volunteers; COPD: benign lung disease patients, ADC: lung adenocarcinoma patients, SCC: squamous cell lung carcinoma patients, SCLC patients.
FIGS. 2 to 6 show the mass spectra of the N-sugar chain structure of MAL-I isolated glycoprotein from saliva samples of HV, COPD, ADC, SCC and SCLC, respectively.
Detailed Description
Previous studies by the applicant found that the benign disease of lung, i.e., the glycoprotein glycoform of lung, is significantly different from healthy volunteers, lung adenocarcinoma patients, squamous lung carcinoma patients and small lung cell carcinoma patients, and the sugar chain recognized by MAL-I is significantly different from HV, COPD, ADC, SCC and SCLC (fig. 1). Based on this, we designed and completed the following experiment.
The following detailed description is provided for the verification experiments and analysis of the present application, and the specific development process of the inventors is not limited thereto.
The first research method comprises the following steps:
a lectin magnetic particle compound is prepared by coupling lectin MAL-I on the surface of a ferroferric oxide nano magnetic particle to enrich specific glycoprotein in each group of saliva mixed samples, N-sugar chains on the specific glycoprotein are separated by a PNGase F enzyme cutting method, Mass Spectrum identification is carried out on the sugar chains by Matrix-Assisted Laser ionization Time of Flight Mass spectrometry (Matrix-Assisted Laser ionization-Time of Flight-Mass Spectrum, MALDI-TOF-MS), and sugar chain structures are conjectured, so that saliva specific glycoprotein sugar chain spectrums identified by the lectin MAL-I, healthy volunteers and benign lung disease patients and lung cancer patients are obtained, and differences are contrastively analyzed.
1.1 saliva sample Collection and pretreatment
Saliva samples of healthy volunteers, benign lung disease patients, adenocarcinoma lung patients, squamous carcinoma lung patients and small cell lung cancer patients adopted in the experiment are strictly subject to ethical examination and approval (Human Research Ethics committes (HRECs)) of the first subsidiary hospital of the northwest university and the west-ampere transportation university. All volunteers donated saliva samples, along with clinicians assisting in sampling guidance, were informed, consented and highly coordinated to the study work, completing collection of saliva samples under uniform sampling requirements. The concrete requirements are as follows: the sample donor needs to be free from diabetes, organs except the lung should be free from chronic diseases such as inflammation and tumor, the donor needs to be determined not to eat within 3 hours before saliva is collected and not to take medicines within 24 hours when sampling, then the donor needs to rinse the mouth three times by using clean sterile physiological saline (0.9% NaCl) to ensure the oral hygiene of the donor and no food residues, the tongue tip of the donor is propped against the palate and the saliva sample naturally secreted under the tongue is collected into a 2mL centrifuge tube, and 10 mu L Protease Inhibitor (Protease Inhibitor Cocktail, Sigma-Aldrich, U.S. A) is immediately added and temporarily stored in ice bath. Saliva samples were co-collected under clinician direction this example: among them, 30 healthy volunteers (HV, n ═ 30), 34 benign lung disease patients (COPD, n ═ 34), 49 lung adenocarcinoma patients (ADC, n ═ 49), 55 lung squamous cell carcinoma patients (SCC, n ═ 55), 22 lung small cell carcinoma patients (SCLC, n ═ 22), and the specific sample information is shown in table 1.
The method comprises the steps of collecting saliva within 12 hours, subpackaging the saliva into centrifuge tubes according to 1mL, adding 1 × PBS to supplement the saliva to 1mL if the quantity of the saliva is less than 1mL, centrifuging for 10,000g × 15min, carefully sucking supernate, adding protease inhibitor according to the quantity of 10 mu L protease inhibitor of 1mg salivary protein after the concentration of the saliva is measured by a micro nucleic acid protein measuring instrument (Nano-drop), then measuring 50 mu g of each saliva sample according to the quantity, mixing, respectively obtaining saliva mixed samples of healthy volunteers and breast disease groups (BD), and measuring the concentration of the mixed samples by using a BCA protein quantitative kit (Biyuntian biotechnology, Shanghai, China).
TABLE 1 saliva sample information for diagnosis of benign diseases of the lung
Age (mean + -SD)
1.2 preparation of lectin MAL-I magnetic microparticle Complex
2mg of epoxidised modified Fe3O4Adding the magnetic particles into a 2mL centrifuge tube, adding 1mL absolute ethyl alcohol, repeatedly reversing for 2min, placing on a magnetic separator to make the magnetic particles fully adsorbed on the bottom surface and the side surface of the centrifuge tube, pouring off the ethyl alcohol, repeating the steps for 5 times, and fully cleaning the magnetic particles. The washed magnetic particles are rinsed by 1mL of coupling buffer solution, and after 3 times of rinsing, 600 mu L of 0.5mg/mL lectin MAL-I solution (MAL-I is dissolved in the coupling buffer solution) is added and fixed on a shaking table for shaking reaction at room temperature for 6 hours to ensure that the coupling is fully performed. And (3) after the coupling is finished, placing the centrifugal tube on a magnetic separation frame for separation, removing liquid, taking down the centrifugal tube, adding 1mL of coupling buffer solution to wash the MAL-I-magnetic particle complex, repeatedly reversing to resuspend the MAL-I-magnetic particle complex, placing the MAL-I-magnetic particle complex on the magnetic separation frame for separation after the MAL-I-magnetic particle complex is fully suspended, removing the coupling buffer solution, and repeatedly washing for 3 times so as to fully remove unbound lectin MAL-I and obtain the lectin MAL-I magnetic particle complex.
1.3 isolation of glycoprotein recognized by lectin MAL-I
First, the lectin magnetic microparticle complex was washed 3 times for 3min using a binding buffer. And (3) complementing the volume of the salivary protein to 500 mu L by using a binding buffer solution, uniformly mixing, adding the mixture into the lectin magnetic particle complex, reversely and uniformly mixing, and placing the mixture into a shaking table to incubate for 3h at 25 ℃ with shaking. After incubation, magnetic field separation was performed, the supernatant was discarded, and the magnetic particles were washed with washing buffer 5 times for 3min each time. After the washing, magnetic field separation is carried out, the supernatant is discarded, 400 mu L of washing buffer solution is added into the magnetic particles, the mixture is inverted and mixed evenly, and then the mixture is put into a shaking table and is shaken and eluted for 1h at 25 ℃. Separating by magnetic field, collecting supernatant, repeating the previous step once, mixing the two collected supernatants, and determining the concentration of the eluted protein by BCA.
1.4 isolation of N-sugar chains
(1) 400 ug of the glycoprotein separated by MAL-I-magnetic complex was added with 8-fold volume of 9M Urea/1MNH4HCO3Uniformly mixing the solution, and then carrying out oscillation reaction at 37 ℃ for 1h to denature glycoprotein;
(2) adding 100mM DTT solution mother liquor with the volume of 5% into the protein solution to ensure that the final concentration of DTT is 5mM, uniformly mixing, standing at room temperature for 1h, and reducing disulfide bonds in the protein;
(3) cooling the protein solution to room temperature, adding 5% volume of 200mM IAM solution mother liquor into the protein solution to ensure that the final concentration of IAM is 10mM, and reacting for 30min in a shaking way at room temperature in a dark place;
(4) transferring the protein solution into a 10K ultrafiltration centrifugal tube, centrifuging at 10000g room temperature for 15min, discarding the filtrate, and adding 400 μ L of 40mM NH into the tube4HCO3And (5) blowing, beating and uniformly mixing the solution, and repeating the steps for 5 times. Supplementing the volume to 500 mu L, adding 3 mu L PNGaseF glycosidase, mixing uniformly, placing in a shaking table at 37 ℃ to shake and incubate overnight, and carrying out enzyme digestion on N-glycan on glycoprotein;
(5)10000g for 10min, and collecting filtrate. Then 200. mu.L of ultrapure water was added to the tube, and after mixing, the filtrate was collected by centrifugation again, and the two filtrates were mixed and dried by a freeze dryer.
1.5 desalting and purifying sugar chains
(1) Pre-cleaning: the Hypercarb SPE column (50mg) was removed and 3mL of 1M NaOH solution, 3mL of ultrapure water, 3mL of 30% acetic acid, and 3mL of ultrapure water were added to the column in this order;
(2) balancing: to the column were added 3mL of 50% ACN/0.1% TFA, followed by 3mL of 5% ACN/0.1% TFA solution;
(3) loading: to the lyophilized N-glycan was added 500. mu.L of 0.1% TFA and vortexed. Adding the dissolved N-glycan into a column, collecting filtrate, loading the filtrate again, and repeating the loading for 3 times;
(4) cleaning: 3mL of ultrapure water and 3mL of 5% ACN/0.1% TFA were sequentially added to the column;
(5) and (3) elution: 400 μ L of 50% ACN/0.1% TFA was added to the column, the filtrate was collected and repeated once, and the two collected filtrates were combined and lyophilized using a freeze dryer.
1.6 Mass spectrometric analysis of N-glycans
(1) Adding 20 μ L sugar chain solution into lyophilized N-glycan, blowing and beating until sugar chain is completely dissolved, and centrifuging after vortex;
(2) sampling 2 μ L of the dissolved sugar chain on a target plate with MTP Anchor chip 384 points by using a pipette, vacuum-drying, sampling 2 μ L of the sugar chain in situ of the target plate, and vacuum-drying;
(3) applying 1 μ L of 20mg/ml DHB matrix solution to the crystallized sugar chain sample, and vacuum drying;
(4) and (3) putting the target plate on a machine, and identifying the polysaccharide in a reflection positive ion mode.
1.7 Mass spectrometric data analysis
Opening mass spectrum original data by using flexAnalysis software, selecting mass spectrum peaks in the range of m/z 1000-: the molecular weight and the charge state of the precursor ions are selected, the tolerance of the precursor ions is 1, the tolerance of the fragment ions is 0.5, no chemical derivatization exists, and no reduction end modification exists. And drawing secondary fragments and primary mass spectrum results.
Method for calculating relative abundance of sugar chains: the signal values of all sugar chains are added as denominator (N), and the signal value (N) of a single sugar chain is divided by N, i.e., N/N is the relative abundance of a single sugar chain.
II, research results:
a total of 286 kinds of signal peaks (SNR > 6) were identified by MALDI-TOF-MS, and 90 kinds of sugar chain signal peaks were resolved, among which 76 kinds of N-sugar chain peaks recognized by MAL-I, as shown in Table two. 23, 22, 29 and 24 MAL-I specific recognition N-sugar chains are identified in HV and COPD groups, ADC, SCC and SCLC groups respectively, and the relative abundances are 57.2%, 63.5%, 67.5%, 87.1% and 57.9% respectively.
TABLE 2 MAL-I-recognized sugar chains isolated from five groups of samples
And (4) supplementary notes: blue (dark) square: n-acetylglucosamine; red triangle: fucose; green (dark) circle: mannose; yellow (light) circle: galactose; yellow (light) square: n-acetylgalactosamine; purple diamond shape: sialic acid; ND represents that no corresponding sugar chain was detected in the sample.
Of which 10N-sugar chains were present only in the saliva samples of the COPD group. The method comprises the following specific steps:
m/z1567.563、m/z 1707.658、m/z 1730.636、m/z 1762.626、m/z 1866.661、m/z2031.763、m/z 2580.930、m/z 2865.043、m/z 2938.060、m/z 3253.152。
of which 6N-sugar chains were present only in saliva samples of the HV group. The method comprises the following specific steps:
m/z 1590.565、m/z 1704.608、m/z 2021.731、m/z 2266.858、m/z 2921.031、m/z2943.026。
of these, 7N-sugar chains were present only in the saliva samples of the ADC group. The method comprises the following specific steps:
m/z 1589.545、m/z 1792.623、m/z 1875.673、m/z 1990.737、m/z 2286.799、m/z2342.744、m/z 2496.861。
of these, 21N-sugar chains were present only in saliva samples of SCC group. The method comprises the following specific steps:
m/z 1501.529、m/z 1641.599、m/z 1787.657、m/z 1979.708、m/z 2088.785、m/z2168.784、m/z 2190.766、m/z 2205.766、m/z 2328.846、m/z 2350.804、m/z 2418.853、m/z2440.835、m/z 2449.805、m/z 2570.898、m/z 2573.948、m/z 2594.910、m/z 2772.957、m/z2805.967、m/z 2828.983、m/z 2864.009、m/z 3423.222。
of these, 6N-sugar chains were present only in saliva samples of the SCLC group. The details are as follows
m/z 1600.573、m/z 1647.586、m/z 1844.679、m/z 2020.711、m/z 2800.004、m/z3074.11。
Therefore, it can be finally found that: the saliva sample is detected, and only one or any combination of the following 10N-sugar chains (m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060 and m/z 3253.152) is needed to be detected, so that the results of screening, early diagnosis, risk assessment, drug screening and/or curative effect assessment of benign lung diseases can be made.
Claims (6)
1. Use of a lectin alone as a reagent for saliva-specific glycoprotein carbohydrate chain structure recognition to construct a product of interest, wherein: the lectin is MAL-I, and is used for screening, early diagnosis, risk assessment, drug screening and/or curative effect assessment of benign lung diseases (COPD); the related products are kits, devices, operable systems and/or combinations thereof, and prompt at least one of the following detection criteria:
if one or any combination of the following 10N-sugar chains among the sugar chains recognized by the lectin MAL-I is present in the saliva sample, the saliva sample is indicated to be mainly a lung benign disease patient;
m/z1567.563、m/z 1707.658、m/z 1730.636、m/z 1762.626、m/z 1866.661、m/z2031.763、m/z 2580.930、m/z 2865.043、m/z 2938.060、m/z 3253.152。
2. use of a recognition unit for specific sugar chains for the construction of a product for use in lung benign disease (COPD) screening, early diagnosis, risk assessment, drug screening and/or efficacy assessment based on saliva samples, characterized in that: the recognition unit recognizes one or any combination of the following sugar chains m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060, m/z 3253.152.
3. A system for pulmonary benign disease (COPD) screening, early diagnosis, risk assessment, drug screening and/or efficacy assessment for a saliva sample, the system comprising:
A. lectin MAL-I;
B. an affinity chromatography solid phase carrier for coupling with lectin MAL-I;
C. a reagent and a device for separating N-sugar chains;
D. a mass spectrometer for displaying m/z values corresponding to the respective N-sugar chains;
E. a label, module or processor for identifying any one or any combination of the following 10N-sugar chains according to the mass spectrogram to obtain a determination result of whether one or any combination of the following 10N-sugar chains is contained;
m/z1567.563、m/z 1707.658、m/z 1730.636、m/z 1762.626、m/z 1866.661、m/z2031.763、m/z 2580.930、m/z 2865.043、m/z 2938.060、m/z 3253.152。
4. the system of claim 3, wherein: the affinity chromatography solid phase carrier for coupling the lectin MAL-I is Fe3O4Magnetic microparticles, sepharose, sephadex or glass microspheres.
5. An intelligent terminal comprising a processor and a program memory, characterized in that: when some part of the program stored in the program memory is loaded by the processor, the following steps are executed:
acquiring N-sugar chain information identified by lectin MAL-I in a saliva sample;
judging whether one or any combination of the following N-sugar chains m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060 and m/z3253.152 exists;
if present, outputting a prompt indicating that the body of the saliva sample is a benign lung disease patient (COPD).
6. A computer-readable storage medium storing a computer program, characterized in that: the computer program when loaded by a processor performs the steps of:
acquiring N-sugar chain information identified by lectin MAL-I in a saliva sample;
judging whether one or any combination of the following N-sugar chains m/z1567.563, m/z1707.658, m/z1730.636, m/z1762.626, m/z1866.661, m/z2031.763, m/z 2580.930, m/z2865.043, m/z2938.060 and m/z3253.152 exists;
if present, outputting a prompt indicating that the body of the saliva sample is a benign lung disease patient (COPD).
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