CN111048149A - Application of specific lectin combination in construction of lung benign disease/lung cancer identification tool based on glycoprotein carbohydrate chain in saliva - Google Patents

Abstract

The application provides an application of a specific lectin combination in construction of a test tool for identifying benign lung diseases/lung cancers based on salivary glycoprotein sugar chains. The specific lectin combination is selected from five lectins SNA, ACA, ConA, LCA and Jacalin; the specific lectin combination is obviously and differentially expressed in the benign lung disease sample compared with the lung cancer sample; and a model is established:

Description

Application of specific lectin combination in construction of lung benign disease/lung cancer identification tool based on glycoprotein carbohydrate chain in saliva

Technical Field

The invention relates to application of a specific lectin combination in construction of a lung benign disease/lung cancer identification test tool based on salivary glycoprotein sugar chains.

Background

According to the international cancer research center (IARC) "2018 global cancer statistics", one fifth of men and one sixth of women worldwide will suffer from cancer during their lifetime, and one eighth of men and one eleventh of women die from this disease. This report provides an estimate of the morbidity and mortality of 36 cancers in 185 countries worldwide. The report shows that the number of new cases in this year will reach 1810 thousands, and the number of deaths will reach 960 thousands. The number of newly added cases and death cases four years ago is 1410 ten thousand and 820 ten thousand respectively. Lung cancer is one of the leading causes of death in men and women, with the highest incidence of lung cancer in north america, northern europe, western europe, etc., and in china, australia, new zealand, etc.

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.

Glycosylation is one of the most common post-translational modifications of proteins, and studies have found that more than about half of mammalian proteins are glycosylated. Glycosylation, as an important post-translational modification, can affect the solubility, folding, positioning and conformational maintenance of proteins, regulate the function and degradation of proteins; molecule-to-molecule, molecule-to-cell, and cell-to-cell interactions may also be mediated. A large number of studies indicate that protein glycosylation, as an important post-translational modification, can change in the processes of tumorigenesis, infiltration and metastasis and has corresponding functional effects. The change in glycosylation modification mainly includes two aspects: 1) changes in glycosylation binding sites on polypeptide chains; 2) change of oligosaccharide chain structure. Therefore, the differentially expressed protein glycosylation subtype can become a novel tumor marker for early diagnosis of tumor and can also become a novel target for biological targeted therapy of tumor.

With the development of scientific technology, the diversity and sensitivity of detection technology are continuously improved, and in recent years, saliva as a clinical sample has been widely used in drug level monitoring, disease condition monitoring and efficacy evaluation of various diseases such as aids, autoimmune diseases, alcoholic cirrhosis, cystic fibrosis, diabetes, cardiovascular diseases, caries and other diseases. The literature and past research work of the applicant show that saliva is rich in N-linked glycoproteins and O-linked glycoproteins, and the change of the glycosylation of the saliva proteins has high relevance to the occurrence and development of diseases. Disease-related biomarkers can be found from the changed glycoglycoproteins for detection, and new technologies and new methods based on saliva detection are gradually becoming a direction for the development of noninvasive clinical diagnosis in the future.

It is important to note that although there have been studies on the identification of lung cancer and its typing and staging based on glycoprotein sugar chains in serum samples, some factors in the blood can reach salivary glands with the circulation of body fluids, or enter the oral cavity with the secretion of saliva or cause changes in the transcriptional profile of genes in saliva, causing changes in the abundance or species of certain proteins, thereby reflecting changes in the levels of some proteins in the blood. However, it has been found that 1939 proteins are present in human saliva and 3020 proteins are present in human plasma, and that only 27% of the saliva proteome coincides 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.

The lectin chip is capable of detecting the change of the glycoprotein sugar chain structure and the connection mode in a sample at high flux by the specific binding of the lectin probe fixed on the chip and the glycoprotein sugar chain in the sample, is one of the most effective analysis tools for researching the change of the glycoprotein sugar chain structure, and contributes to the development of new methods for diagnosing and monitoring diseases.

Binary Logistic stepwise regression analysis and ROC curve analysis (receiver operating regression) are often found in agricultural statistical analysis, financial management and other aspects, and are also found in the direction of disease diagnosis at present.

Disclosure of Invention

The invention aims to provide a scheme for rapidly and accurately identifying benign lung disease/lung cancer without damage sampling.

The present application, through extensive experimentation and analysis, ultimately establishes that a specific lectin combination for a saliva sample can be used to determine whether a subject lung disease patient has lung cancer (or just benign lung disease); moreover, a diagnostic model of Lung Cancer (LC) is constructed, and the diagnostic ability of the lung cancer is evaluated through ROC curve analysis.

The application scheme is summarized as follows:

in a first aspect, the use of specific lectin combinations to construct a test tool for the identification of benign lung disease/lung cancer based on the glycophorin chains. The specific lectin combination is selected from five lectins SNA, ACA, ConA, LCA and Jacalin; the specific lectin combination is significantly differentially expressed in benign lung disease samples compared to lung cancer samples. Specifically, reference may be made to tables 2 to 4.

The test tool can be a lectin chip, a kit or a lectin detection intelligent terminal.

In a second aspect, an intelligent terminal for identifying benign lung disease/lung cancer based on sialoglycoprotein sugar chains. The system comprises a processor and a program memory, wherein when a program stored in the program memory is loaded by the processor, the following steps are executed:

obtaining lectin test results of saliva samples, wherein the lectin test results represent expression levels of glycoprotein sugar chains corresponding to lectins SNA, ACA, ConA, LCA and Jacalin;

calculating a detection value of the following model LC (model LC) based on the lectin test result;

outputting and displaying the calculated detection value, and prompting an identification conclusion; if the detection value is more than or equal to 0.097, the saliva sample belongs to the main body of the Lung Cancer (LC) patient, otherwise, if the detection value is less than 0.097, the saliva sample belongs to the Lung Cancer (LC) patient. The "prompt discrimination conclusion" as used herein may be a conclusion of directly outputting whether lung cancer or lung benign disease is present, or may be a conclusion of only providing a detection value, a reference value and a discrimination basis, or both.

The specific form of the intelligent terminal can be a special self-service terminal device, and can also be a mobile phone, a tablet personal computer and the like which are commonly used by common users.

In a third aspect, a human-computer interaction device includes a display screen, and the display screen sequentially displays the following interfaces during the operation of the human-computer interaction device:

an input interface for lectin test results of the saliva sample; the lectin test results represent the expression levels of glycoprotein sugar chains corresponding to the lectins SNA, ACA, ConA, LCA, and Jacalin;

an output interface of lung cancer identification information; the lung cancer identification information comprises a reference value 0.097 of a model LC (model LC) and a detection value and/or identification conclusion; the model lc (model lc) is:

if the detection value is more than or equal to 0.097, the saliva sample belongs to the main body of the Lung Cancer (LC) patient, otherwise, if the detection value is less than 0.097, the saliva sample belongs to the Lung Cancer (LC) patient.

The specific form of the man-machine interaction device can be a special self-service terminal device, and can also be a mobile phone, a tablet personal computer and the like which are commonly used by common users. The input interface may also contain advance prompts, confirmation information (for example, asking the user to confirm identity information, detection purpose, etc.). The input interface of the lectin test result of the saliva sample can be an input item for directly presenting the several kinds of lectins, or can be an input item for presenting each kind of related lectins in sequence; the specific man-machine interaction mode can be modified in various ways and can be set according to actual needs.

In the human-computer interaction device, the screen display content of the display screen can be obtained by running a predetermined program through a processor and a memory in the human-computer interaction device for data processing, or can be obtained by connecting a remote server (running the predetermined program on the server for data processing) for data transmission.

In a fourth aspect, a system for identifying benign lung disease/lung cancer based on sialoglycoprotein sugar chains, comprising:

A. a device for obtaining an expression level of a specific glycoprotein sugar chain structure of a saliva sample, the specific glycoprotein sugar chain structure corresponding to the aforementioned specific lectin combination;

B. the intelligent terminal is used for identifying benign lung diseases/lung cancers based on the glycoprotein chain of saliva.

In the system, the device for obtaining the expression level of the specific glycoprotein sugar chain structure of the saliva sample and the intelligent terminal for identifying benign lung diseases/lung cancers based on the salivary glycoprotein sugar chain can be integrated medical diagnosis comprehensive devices, can also be discrete devices, and even does not have any signal connection (for example, lectin test results of saliva samples can be automatically taken and sent by medical staff, patients and the like).

The device for obtaining the expression level of the specific glycoprotein sugar chain structure of the saliva sample comprises a lectin chip, an incubation box and a biochip scanning system, wherein the specific lectin combination is arranged on the lectin chip.

By adopting the scheme of the invention, whether the tested lung disease patient has lung cancer (or only benign lung disease) can be quickly and accurately identified according to the saliva sample.

Drawings

Fig. 1, 2 and 3 are scattergram analyses of lectin-differentially expressed results using 190 saliva samples (HV, n-30; COPD, n-34; ADC, n-49; SCC, n-55; SCLC, n-22) 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 represents the comparison between the two groups shown at the two ends, P is P-Value obtained from one-way variance analysis, generally P <0.05 indicates a significant difference, P <0.01 indicates a very significant difference, and P <0.001 indicates a very significant difference. HV: saliva of healthy volunteers, COPD: lung benign disease patient saliva, ADC: lung adenocarcinoma patient saliva, SCC: squamous cell lung carcinoma patient saliva, SCLC: saliva of a patient with small cell lung cancer.

Fig. 4, fig. 5, fig. 6, fig. 7 and fig. 8 are schematic diagrams of building diagnostic models of PUD, LC, ADC, SCC, SCLC based on 37 lectins, respectively, and evaluating their diagnostic abilities by ROC curve analysis. PUD: lung disease, LC: lung cancer, ADC: lung adenocarcinoma, SCC: squamous lung carcinoma, SCLC: small cell lung cancer.

Detailed Description

The following detailed description is provided for the relevant validation experiments and analyses, and the specific development process of the inventors is not limited thereto.

Screening of differential sugar chain structure of saliva protein of patients with HV, COPD, ADC, SCC and SCLC

The research method comprises the following steps:

1.1 saliva sample Collection and pretreatment

Saliva samples from healthy volunteers and patients with benign lung disease, adenocarcinoma and squamous carcinoma of the lung and small cell carcinoma used in this experiment were strictly approved by ethical examination (HumanResearch Ethics Committees (HRECs)) in the first subsidiary hospital of the northwest university and the western 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, other organs except the lung should be free from chronic diseases such as inflammation and tumor, and the donor needs to be sure not to eat within 3 hours before saliva is collected and take medicines within 24 hours when sampling, then gargle three times with 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. A total of 190 saliva samples were collected under clinician guidance: among them, 30 healthy volunteers (HV, n-30), 34 benign patients of lung (COPD, n-34), 49 patients of lung adenocarcinoma (ADC, n-49), and 55 patients of lung squamous carcinoma (SCC, n-45). Specific sample information of 22 cases (SCC, n-22) of small cell lung cancer patients is shown in table 1.

Collecting saliva within 12 hours, subpackaging saliva into centrifuge tubes according to 1mL, adding 1 XPBS to complement to 1mL if the quantity is less than 1mL, centrifuging for 10,000g × 15min, carefully sucking supernatant, measuring the concentration by a micro nucleic acid protein analyzer (Nano-drop), adding protease inhibitor according to the quantity of 1mg saliva protein to 10 μ L protease inhibitor, mixing uniformly, and subpackaging at-80 ℃ for storage.

TABLE 1 lectin chip for diagnosis of pulmonary diseases and saliva sample information constructed by diagnosis model

1.2 fluorescent labeling and quantitation of Individual salivary proteins

Taking 100 μ g of saliva sample quantified by Nano-drop, adding 0.1mol/L Na in equal volume₂CO₃/NaHCO₃pH9.3 buffer, 1mg: 120. mu.L of Cy3 fluorescent dry powder was dissolved in DMSO and 5. mu.L of fluorescent solution was added to the sample and incubated at room temperature for 3 hours, during which the sample was kept strictly protected from light and kept shaking. After the reaction, 10. mu.L of 4M hydroxylamine solution was added to the sample, reacted for 5 minutes on ice, and after the excess free fluorescence was sufficiently blocked, the protein sample was separated using Sephadex G-25 molecular sieve gel column. And (3) collecting the fluorescent sample by using a new 1.5mL centrifuge tube, quantifying, protecting the fluorescence-labeled sample from light to prevent the quenching of the fluorescent group, and storing at-20 ℃.

1.3 lectin chip example by example detection of differences in salivary protein sugar chain expression

The research adopts a lectin chip which is designed by functional glycomics laboratories of northwest university and consists of 37 lectins, and can recognize and combine common N-sugar chain and O-sugar chain structures. The specific preparation process of the chip is described in YannanQin et al. Four arrays are spotted on each substrate, and the four arrays on the chip are mutually independent through adhesive tapes on the matched cover plates to form mutually closed chambers, so that each chip can simultaneously detect 4 different protein samples.

Before the lectin chip assay samples were performed, the chip was removed from 4 ℃, vacuum-warmed at 37 ℃ for 30min, then washed 5min × 2 times with 1 × PBST on a horizontal shaking shaker at 70rpm, and then washed 5min × 2 times with 1 × PBS to sufficiently wash free lectin not coupled to the slide. After washing, the residual PBS was spun off using a small glass slide centrifuge. Before loading, 120 μ L of lectin chip blocking solution was added to each array region of the incubation box to block the epoxy groups on the blank surface of the slide to reduce the signal value on the back of the slide during fluorescence scanning. After the incubation box is sealed, the reaction is carried out for 1h at 37 ℃ in a constant-temperature incubation box, after the sealing is finished, 1 XPBST is cleaned for 5min multiplied by 2 times, 1 XPBS is cleaned for 5min multiplied by 2 times, after the drying is carried out, protein sample incubation systems (80 mu L agglutinin chip incubation liquid, 8 mu L4M hydroxylamine, 2 mu L0.1% Tween-20, 4 mu g Cy3 labeled saliva protein samples and ultrapure water are added into each array area of the incubation box to complement the final volume to 120 mu L) are added into the array areas of the incubation box, the incubation is carried out for 3h at 37 ℃, after the reaction is finished, 1 XPBST is cleaned for 5min multiplied by 2 times, 1 XPBS is cleaned for 5min multiplied by 2 times, the drying is carried out. The final data reading of the lectin chip is realized by using a Genepix 4000B chip scanner manufactured by Axon, and the scanning parameters are set as follows: excitation wavelength 532nm, PMT power 70% and excitation intensity 100%.

1.4 lectin chip data analysis

The quantification process of the lectin chip fluorescence signal was performed by GenePix Pro (4000B) software, and data obtained by data extraction for each array included: the net difference FI (fluorescence intensity) obtained by subtracting the background signal from the probe signal, the standard deviation SD (Standard development) of the background, and the like. In the analysis process, firstly, judging the validity of the point data according to the FI/SD of each point, taking the point with the FI/SD being more than or equal to 1.5 as valid data, calculating the standard normalized fluorescence signal value NFI (normalized fluorescence intensity) of each probe, namely dividing the mean FI value of each probe by the sum of the FI values of 14 detection probes, and expressing the sum as follows by using a formula: NFI_x＝Median FI_x/(Median FI₁+Median FI₂+Median FI₃+…+Median FI₁₄) From this, NFI for 37 lectin probes per array were obtained and used for statistical analysis.

The statistical Analysis is mainly to perform One-factor Analysis of variance (One-Way ANOVA Analysis) between groups on four groups of data of HV, COPD, ADC, SCC and SCLC by using GraphPad Prism 6.0, and the specific method is to record the data into a "Column" Analysis program according to groups, and select a Scatter diagram "Scatter & SD" and the like according to the drawing requirements. The "Analyze" analysis program was then entered to compare pairwise across groups and report the significance of the difference, P-Value.

The research results are as follows:

2.1 lectin chip analysis of the differences in salivary glycoprotein sugar chain expression in benign Lung diseases and Lung cancer patients

190 saliva samples are detected by a lectin chip array one by one, each NFI value is obtained through chip data processing, each average NFI result can visually display the combination condition of each saliva sample and each probe through the average NFI calculated by each group of data classes, and the dispersion degree of each lectin on the saliva sample detection can be evaluated according to the standard deviation SD.

TABLE 2 RATION values (first part)

TABLE 3 RATION values (second part)

TABLE 4 RATION values (third part)

2.2 screening of salivary differential glycoprotein sugar chains in benign pulmonary disease and Lung cancer patients

Comparing each group pairwise with each other according to the average NFI values to obtain Ratio values among comparison groups, and defining that Ratio >1.500 represents that sugar chains recognized by the lectin are highly expressed in the comparison among the groups, and Ratio <0.667 represents that the sugar chains recognized by the lectin are less expressed in the comparison among the groups, screening out differentially expressed lectins through the comparison among the groups, analyzing example data corresponding to a lectin probe through statistics, judging the differential significance of the sugar chain structures recognized by the lectin in healthy volunteers, benign lung patients and lung cancer patients through single factor variance analysis, and comparing the results with those of fig. 1-3, wherein a series of statistics can visually reflect the differential significance and discrete degree of comparison among the groups, and comparing the sugar chain structures with other glycoprotein structures in saliva according to the result of single factor variance analysis among the groups, such as that the sugar chain structures recognized by the specifically recognized part of glycoprotein structures are differentially expressed by the lectin groups, such as specifically recognized Man α -6(Man α -3), Galinc 633, Galinc 3, and the differential expression of sugar chains recognized part of glycoprotein structures among the GSC, SCC, GSC, SCC, and SCC can be significantly reduced compared with other groups (GSH-3).

Second, 37 kinds of agglutinin are utilized to construct diagnosis models of PUD, LC, ADC, SCC and SCLC

The research method comprises the following steps:

saliva samples (COPD, n-34; ADC, n-49; SCC, n-55; SCLC, n-22) and 30 healthy volunteer saliva samples were used from 160 patients with benign lung disease and different types of lung cancer. The research expects to realize accurate identification of lung adenocarcinoma ADC, lung squamous carcinoma SCC and small cell lung cancer SCLC from healthy volunteers HV and lung benign disease COPD patients through chip data of the lectin. Thus, the ROC curve analysis was first performed on 37 lectins to estimate their diagnostic and identification capabilities for the sample. Normally, the discrimination model is considered to have certain discrimination capability when the area under the ROC curve line reaches 0.70, but whether the discrimination is used for discrimination of HV/(ADC, SCC, SCLC), COPD/(ADC, SCC, SCLC) or discrimination between three lung cancer diseased groups, the ROC-AUC of a single candidate lectin except HHL in the SCLC/(COPD, ADC, SCC) discrimination respectively reaches more than 0.7, and the AUC values of the rest lectins are all lower than the standard of 0.70, which indicates that only the single lectin is not enough for discrimination of lung diseases, lung cancer or saliva samples of different lung cancer types. Therefore, the diagnostic model is constructed by the binary stepwise Logistic regression method in the research, so that the sample identification capability of the lectin is improved.

Since the previous experimental data showed significant differences between the HV group data and the other four groups of data, a model pud (model pud) was first constructed to preliminarily determine whether the patient had lung disease.

There are 7 types of lectins constituting the Model PUD, including PHA-E + L, PWM, ConA, RCA120, SJA, and PTL-I, Jacalin, and AUC values of all other lectins except the Model PUD are less than 0.7, so that it is not diagnostic. The AUC value of Model PUD is 0.725, cut-off value is 0.913 (the detection value is more than or equal to 0.913 for PUD patients, otherwise, the detection value is less than 0.913 for healthy people), the sensitivity is 0.608, and the specificity is 0.667; 28 of 42 patients with lung diseases and 6 of 9 patients with lung diseases can be accurately identified, and the ROC curve is shown in FIG. 4.

To distinguish patients with benign lung disease from those with lung cancer, the model LC (model LC) was constructed

The Model LC has 5 kinds of agglutinin, which are SNA, ACA, ConA, LCA and Jacalin, wherein the AUC value of other agglutinin except the Model LC is less than 0.7, so it has no diagnostic significance. The AUC value of the Model LC is 0.990, the cut-off value is 0.097 (the detection value is more than or equal to 0.097 for LC patients, and the detection value is less than 0.097 for benign patients in lung) the sensitivity is 0.975, and the specificity is 0.900; the ROC curves of 39 out of 40 patients with lung disease and 10 out of 11 COPD patients can be accurately identified as shown in fig. 5.

In order to distinguish three different types of lung cancer (ADC, SCC and SCLC), models ADC (model ADC), SCC (model SCC) and SCLC (model SCLC) are sequentially constructed for rapidly distinguishing different types of lung cancer patients.

The Model ADC comprises 4 lectins, namely PHA-E + L, STL, WFA and Jacalin, the AUC value of the Model ADC is 0.798, the cut-off value is 0.697 (the detection value is more than or equal to 0.697, the detection value is an ADC patient, and on the contrary, the detection value is less than 0.697, other subjects are detected), the sensitivity is 0.750, and the specificity is 0.733; the ROC curves of 11 of 15 patients with lung adenocarcinoma and 26 of 36 other patients tested were accurately identified as shown in fig. 6.

The Model SCC comprises 6 lectins, namely PHA-E + L, PWM, WGA, AAL and BS-I, wherein the AUC value of the Model SCC is 0.782, the cut-off value is 0.797 (the detection value is less than or equal to 0.797, the detection value is an SCC patient, and on the contrary, the detection value is more than 0.797, the detection value is other subjects), the sensitivity is 0.706, and the specificity is 0.733; 12 of 17 patients with squamous cell lung carcinoma 17 and 24 of 34 other patients tested can be accurately identified, and the ROC curve is shown in FIG. 7.

2 kinds of constituting lectins of ModelSCLC are WGA and HHL, the AUC value of ModelSCLC is 0.738, cut-off value is 0.784 (the detection value is less than or equal to 0.784, SCLC patients are, on the contrary, other subjects are detected with the detection value of more than 0.784), the sensitivity is 0.767, and the specificity is 0.625; the ROC curves of 6 out of 8 patients with small cell carcinoma and 26 out of 43 other patients tested were accurately identified as shown in fig. 8.

Claims (6)

1. Use of specific lectin combination to construct a test tool for identifying benign lung disease/lung cancer based on the glycoprotein carbohydrate chains in saliva, characterized in that: the specific lectin combination is selected from five lectins SNA, ACA, ConA, LCA and Jacalin; the specific lectin combination is significantly differentially expressed in benign lung disease samples compared to lung cancer samples.

2. Use according to claim 1, characterized in that: the test tool is a lectin chip, a kit or a lectin detection intelligent terminal.

3. An intelligent terminal for identifying benign lung disease/lung cancer based on glycoprotein salivary sugar chains, comprising a processor and a program memory, wherein: the program stored in the program memory when loaded by the processor performs the steps of:

obtaining lectin test results of saliva samples, wherein the lectin test results represent expression levels of glycoprotein sugar chains corresponding to lectins SNA, ACA, ConA, LCA and Jacalin;

calculating a detection value of the following model LC (model LC) based on the lectin test result;

outputting and displaying the calculated detection value, and prompting an identification conclusion; if the detection value is more than or equal to 0.097, the saliva sample belongs to the main body of the Lung Cancer (LC) patient, otherwise, if the detection value is less than 0.097, the saliva sample belongs to the Lung Cancer (LC) patient.

4. A human-computer interaction device comprises a display screen, and is characterized in that: the display screen displays the following interfaces in sequence during the operation of the human-computer interaction equipment:

an input interface for lectin test results of the saliva sample; the lectin test results represent the expression levels of glycoprotein sugar chains corresponding to the lectins SNA, ACA, ConA, LCA, and Jacalin;

an output interface of lung cancer identification information; the lung cancer identification information comprises a reference value 0.097 of a model LC (model LC) and a detection value and/or identification conclusion; the model lc (model lc) is:

if the detection value is more than or equal to 0.097, the saliva sample belongs to the main body of the Lung Cancer (LC) patient, otherwise, if the detection value is less than 0.097, the saliva sample belongs to the Lung Cancer (LC) patient.

5. A system for identifying benign lung disease/lung cancer based on glycoprotein chains in saliva, comprising:

A. a device for obtaining an expression level of a specific glycoprotein sugar chain structure of a saliva sample, the specific glycoprotein sugar chain structure corresponding to the specific lectin combination set forth in claim 1;

B. the intelligent terminal for discriminating benign lung disease/lung cancer based on sialyl glycoprotein sugar chain according to claim 3.

6. The system for identifying benign disease/lung cancer of the lung based on sialyl glycoprotein sugar chain according to claim 5, characterized in that: the device for acquiring the expression level of the specific glycoprotein sugar chain structure of the saliva sample comprises a lectin chip, an incubation box and a biochip scanning system, wherein the specific lectin combination is arranged on the lectin chip.

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