Lectin chip, EV glycosylation modification detection kit and application thereof

Abstract

The invention provides a lectin chip, an EV glycosylation modification detection kit and application thereof, the lectin chip comprises lectin probes, and the lectin probes are selected from more than two of LCA, ABA, PSA, TL, NPL, PTL I, SNA, IAA, HMA, HHL, MNA G and CSA. The lectin chip disclosed by the invention can be used for performing glycosylation analysis on different EV subgroups, has the advantages of high flux, high sensitivity, high analysis and detection speed and low sample demand quantity, can be used for indicating gastric cancer, judging gastric cancer prognosis and evaluating gastric cancer immunotherapy curative effect in a high-sensitivity and high-specificity manner, and has excellent prediction performance.

Description

Technical Field

[0001] The present invention belongs to the field of biological detection, and in particular relates to a lectin chip, a kit for detecting EV glycosylation modification, and applications thereof. Background Art

[0002] Timely diagnosis of gastric cancer (GC) helps early intervention and is the key to improving the prognosis of gastric cancer patients. Since most gastric cancers are adenocarcinomas, there are no obvious symptoms in the early stage, or only non-specific symptoms such as upper abdominal discomfort and belching appear. They are often ignored because of the similar symptoms to chronic gastric diseases such as gastritis and gastric ulcers, resulting in a low early diagnosis rate of gastric cancer. Once it progresses to the late stage, it is crucial to monitor the survival prognosis of different gastric cancer patients. As a highly heterogeneous malignant tumor, gastric cancer has significant differences in the survival prognosis of different patients, which poses a challenge to the implementation of clinical treatment decisions. The choice of treatment decision for gastric cancer patients is the key to improving efficacy and prognosis, especially for patients with advanced gastric cancer. In recent years, targeted therapy for immune checkpoint inhibitors (such as PD-1 / PD-L1) has also made some progress. In summary, the development of a program that can monitor the occurrence, development and treatment effect of gastric cancer can provide important clinical guidance value for gastric cancer patients at different stages (precancerous state, early gastric cancer, and advanced gastric cancer).

[0003] In recent years, liquid biopsy, as a key means of monitoring the occurrence and development of diseases, is rapidly being widely used and promoted in clinical practice due to its advantages such as non-invasiveness, high safety and the ability to achieve dynamic monitoring. EVs are protected by a phospholipid bilayer, can stably exist in human body fluids, and contain various bioactive molecules. They have been proven to be a reliable liquid biopsy marker. Compared with the monitoring of RNA and proteins carried by EVs, glycosylation as an epigenetic modification can reveal the dynamic changes of disease progression in more detail. Glycosylated products usually have the advantage of being more stable than RNA and proteins and are not easily degraded during the detection process. Therefore, monitoring the glycosylation level of EVs has extremely high prospects and application potential. Glycosylation has been shown to be widely characterized on EVs, and various sugar conjugates are involved in the process of EV recognition and uptake by receptor cells. There are differences in the surface glycosylation modification characteristics of EVs from different cells or different disease sources. However, there is currently no research on the monitoring of EV glycosylation levels in the diagnosis and treatment of gastric cancer. Summary of the invention

[0004] In view of this, the present invention aims to propose a lectin chip, a kit for detecting EV glycosylation modification and its application, to complete the detection of EV glycosylation level based on the specific binding of lectin probe to the sugar chain on the surface of EV, and to realize gastric cancer diagnosis, judgement of gastric cancer prognosis and evaluation of gastric cancer immunotherapy efficacy by identifying the arrangement and combination of EV glycosylation markers related to gastric cancer, so as to improve the stability and accuracy of EV glycosylation level monitoring.

[0005] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] In a first aspect, the present invention provides a lectin chip, comprising lectin probes, wherein the lectin probes are selected from two or more of LCA, ABA, PSA, TL, NPL, PTL I, SNA, IAA, HMA, HHL, MNA G, and CSA.

[0007] Furthermore, the lectin probe is one of the following three combinations:

[0008] Combination 1: LCA, ABA, PSA, TL, NPL and PTL I;

[0009] Combination 2: PSA, SNA, IAA, HMA and HHL;

[0010] Combination 3: MNA G, IAA, NPL and CSA.

[0011] In a second aspect, the present invention provides a kit for detecting EV glycosylation modification, comprising the lectin chip as described above.

[0012] Furthermore, it also includes EV detection probes, fluorescently labeled secondary antibodies, biotin-labeled specific EV detection antibodies, and streptavidin coupled to fluorescent probes.

[0013] Furthermore, the EV detection probe is selected from one or any combination of anti-PD-L1 antibody and anti-EpCAM antibody. Preferably, the anti-PD-L1 antibody and anti-EpCAM antibody are antibodies from different species; further preferably, the different species are mouse or rabbit.

[0014] Furthermore, the secondary antibody is anti-mouse IgG and / or anti-rabbit IgG.

[0015] Furthermore, the specific EV detection antibody is one or any combination of anti-CD63 antibody, anti-CD81 antibody, and anti-CD9 antibody conjugated with biotin; preferably, the specific EV detection antibody is selected from one or any combination of Anti-CD63-Biotin, Anti-CD81-Biotin, and Anti-CD9-Biotin.

[0016] Furthermore, the streptavidin coupled to the fluorescent probe is Cy3-Streptavidin.

[0017] In a third aspect, the present invention provides a method for detecting EV glycosylation modification, using the lectin chip as described above or any of the kits as described above, comprising the following steps:

[0018] S1, adding the test sample containing EVs to the lectin chip for incubation;

[0019] S2, adding EV detection probe to the lectin chip for incubation;

[0020] S3, adding fluorescently labeled secondary antibodies to the lectin chip for incubation;

[0021] S4, adding biotin-labeled specific EV detection antibody to the lectin chip for incubation;

[0022] S5, adding streptavidin coupled with fluorescent probe to the lectin chip for incubation;

[0023] S6. Scan the chip with a scanner to obtain the fluorescence value.

[0024] Furthermore, the test sample includes but is not limited to whole blood, serum, plasma, ascites, cerebrospinal fluid, bone marrow puncture fluid, bronchoalveolar lavage fluid, urine, semen, vaginal secretions, mucus, saliva, sputum, or clarified tissue fluid obtained from biological tissue samples, or cell culture supernatant. Preferably, the sample to be tested is plasma, and the amount of plasma used is ≤10 μL.

[0025] Furthermore, the method for detecting EV glycosylation modification comprises the following steps:

[0026] S1. Add the test sample containing EVs to the lectin chip for incubation. The lectin probe on the lectin chip recognizes the sugar chains on the surface of EVs, thereby capturing the complete EVs.

[0027] S2, adding anti-PD-L1 antibodies and anti-EpCAM antibodies from different species to the lectin chip for incubation to label the captured EVs;

[0028] S3, adding two secondary antibodies coupled with different fluorescent probes to the lectin chip for incubation, and detecting the surface glycosylation modification of the PD-L1(+)EV subpopulation (PD-L1(+)EV) and EpCAM(+)EV subpopulation (EpCAM(+)EV);

[0029] S4. After the chip is washed, biotin-labeled specific EV detection antibodies (Anti-CD63-Biotin, Anti-CD81-Biotin, Anti-CD9-Biotin) are added to the lectin chip for incubation;

[0030] S5, adding streptavidin coupled with fluorescent probe to the lectin chip for incubation to label the total EVs;

[0031] S6. Scan the chip with a scanner to obtain the fluorescence value, and obtain the PD-L1(+) EV subpopulation, EpCAM(+) EV subpopulation, and total EV surface lectin binding map based on the fluorescence signal analysis, so as to perform differential analysis of EV surface glycosylation modification.

[0032] In a fourth aspect, the present invention provides the use of the lectin chip as described above or any of the kits as described above in the preparation of a gastric cancer diagnostic product, wherein the lectin probe comprises one or any combination of LCA, ABA, PSA, TL, NPL, and PTL I.

[0033] Furthermore, the lectin probes are LCA, ABA, PSA, TL, NPL and PTL I.

[0034] In a fifth aspect, the present invention provides the use of the lectin chip as described above or any of the kits as described above in the preparation of a gastric cancer survival prognosis monitoring product, wherein the lectin probe comprises one or any combination of PSA, SNA, IAA, HMA, and HHL.

[0035] Furthermore, the lectin probes are PSA, SNA, IAA, HMA and HHL.

[0036] In a sixth aspect, the present invention provides the use of the lectin chip as described above or any of the kits as described above in the preparation of a product for evaluating the efficacy of gastric cancer immunotherapy, wherein the lectin probe comprises one or any combination of MNA G, IAA, NPL, and CSA.

[0037] Furthermore, the lectin probes are MNA G, IAA, NPL and CSA.

[0038] Furthermore, the immunotherapy method is a PD1 inhibitor.

[0039] Compared with the prior art, the lectin chip, the kit for detecting EV glycosylation modification and the application thereof of the present invention have the following advantages:

[0040] (1) The lectin chip described in the present invention can perform glycosylation analysis on different EV subpopulations, and has the advantages of high throughput, high sensitivity, fast analysis and detection speed, and small sample requirement, overcoming the defects of existing detection tools with large sample volume and inability to perform rapid and real-time analysis specifically on the EV surface;

[0041] (2) The lectin chip described in the present invention can simultaneously detect and analyze the glycosylation modifications of multiple EV subpopulations in gastric cancer samples;

[0042] (3) The lectin chip described in the present invention can detect EV glycosylation modification in gastric cancer samples and screen the obtained EV glycosylation markers, which can indicate gastric cancer with high sensitivity and high specificity, determine the prognosis of gastric cancer and evaluate the efficacy of gastric cancer immunotherapy, and has excellent diagnostic performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 This is a schematic diagram of the clustering heat map analysis results of glycosylation modifications of different EV subpopulations in Example 1 of the present invention;

[0045] Figure 2 This is a schematic diagram of the volcano plot analysis results of the heterogeneous EV glycosylation patterns of gastric cancer patients and healthy people in Example 2 of the present invention;

[0046] Figure 3 This is a schematic diagram of the histogram analysis results of the gastric cancer diagnostic markers according to Example 2 of the present invention;

[0047] Figure 4 This is a schematic diagram of the violin plot analysis results of the gastric cancer combined diagnostic marker Lasso score according to Example 2 of the present invention;

[0048] Figure 5 This is a schematic diagram of the ROC curve analysis results of the gastric cancer diagnostic markers according to Example 2 of the present invention;

[0049] Figure 6 This is a schematic diagram of the ROC curve analysis results of multi-marker combined diagnosis of gastric cancer obtained by using a machine learning algorithm in Example 2 of the present invention;

[0050] Figure 7 This is a schematic diagram of the forest plot analysis results of gastric cancer prognosis prediction markers according to Example 3 of the present invention;

[0051] Figure 8 This is a schematic diagram of the Kaplan-Meier survival curve analysis results of the gastric cancer prognosis markers according to Example 3 of the present invention;

[0052] Fig. 9 Schematic diagram of the ROC curve analysis results of gastric cancer prognosis markers in Example 3 of the present invention;

[0053] Fig.10 This is a schematic diagram of the DCA curve analysis results of the gastric cancer prognosis combined prediction markers in Example 3 of the present invention;

[0054] Fig.11 This is a schematic diagram of the ROC curve analysis results of the multi-marker joint prediction of gastric cancer prognosis obtained by using a machine learning algorithm in Example 3 of the present invention;

[0055] Fig.12 This is a schematic diagram of the clustering heat map analysis results of glycosylation modifications of different EV subpopulations in gastric cancer immunotherapy patient samples in Example 4 of the present invention;

[0056] Fig.13 This is a schematic diagram of a bar chart analysis of joint prediction markers for gastric cancer immunotherapy efficacy according to Example 4 of the present invention;

[0057] Fig.14 Schematic diagram of ROC curve analysis of markers for predicting the efficacy of gastric cancer immunotherapy according to Example 4 of the present invention;

[0058] Fig.15 This is a schematic diagram of ROC curve analysis of combined prediction of multiple markers for predicting the efficacy of gastric cancer immunotherapy obtained by using a machine learning algorithm in Example 4 of the present invention;

[0059] Fig.16 The figure is a schematic diagram of the principle of the present invention for diagnosing gastric cancer, judging the prognosis of gastric cancer and evaluating the efficacy of gastric cancer immunotherapy by using a lectin chip. DETAILED DESCRIPTION

[0060] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0061] like Fig.16 As shown, the present invention provides a method for diagnosing gastric cancer, judging the prognosis of gastric cancer, and evaluating the efficacy of gastric cancer immunotherapy by performing glycosylation analysis on different EV subpopulations through a lectin chip and using the analysis results to train a machine algorithm. The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0062] Example 1: Detection of EV surface glycosylation modification

[0063] The plasma samples to be tested were grouped according to the source of plasma: Advanced gastric cancer (AG) patient group, Early gastric cancer (EG) patient group, Gastric cancer immunotherapy benefited patient group, Gastric cancer immunotherapy non-benefited patient group, Benign gastric disease (BD) patient group and Non-disease control (NC) group.

[0064] The detection method of this embodiment comprises the following steps:

[0065] (1) Sample preparation: Take 10 μL of each plasma sample and then dilute it to 100 μL with PBS.

[0066] (2) Chip reaction: The lectin chip used in this example includes lectin probes, including AAL, ABA, ACL, AMA, ASA, Black bean crude, BPL, CALSEPA, ConA, CSA, DBA, DSL, ECL, EEL, GHA, GNL, GSL I-B4, GSL II, GSL-IA4, HHL, HMA, HPA, IAA, IRA, Jacalin, LAL, LCA, LEL, LTL, MAA, MAL I, MAL II, MNA-G, MNA-M, MPL, NPL, PHA-E, PHA-L, PNA, PSA, PTL I, PWM, RCA I, SBA, SNA, SNA-I, SSA, STL, TL, UDA, UEA I, VFA, VVA mannose, VVL, WFA and WGA. 100 μL of diluted plasma sample was added to each well of the chip and incubated at room temperature for 60 min.

[0067] (3) PD-L1(+)EV and EpCAM(+)EV subpopulation labeling: After the chip was washed with PBS, 100 μL of a mixed detection reagent of anti-PD-L1 antibody (from mouse) and anti-EpCAM antibody (from rabbit) was added to each well of the chip and incubated at room temperature for 60 min.

[0068] (4) After the chip was washed with PBS, 100 μL of a mixed detection reagent of goat anti-rabbit IgG coupled with fluorescent probe Alexa fluor 488 and goat anti-mouse IgG coupled with fluorescent probe Alexa fluor 647 was added and incubated at room temperature for 60 min.

[0069] (5) Total EV labeling: After the above step (4), the chip was washed with PBS, and then biotin-labeled EV detection antibodies (Anti-CD63-Biotin, Anti-CD81-Biotin, Anti-CD9-Biotin) were added and incubated at room temperature for 60 min.

[0070] (6) The chip was washed with PBS, and then 100 μL of Cy3-Streptavidin was added to each chip well. The cells were incubated at room temperature in the dark for 30 min to obtain a detection chip.

[0071] (7) Chip signal reading: The detection chip is dried and a laser scanner is used. The laser scanner model used in this embodiment is Axon GenePix. Channel scanning signals with excitation frequencies of 488nm, 532nm and 635nm are used. The fluorescence value of the chip is read using GenePix software.

[0072] (8) Off-machine data quality control and processing: After removing outliers, calculate the mean of the three replicate points, and then deduct the reading value of the PBS-negative point in the chip well to obtain the lectin reading value.

[0073] (9) Figure 1 As shown in the figure, cluster heat map analysis shows the expression differences of surface glycosylation modification characteristics of different EV subgroups in each group. Subsequently, based on the grouping information, gastric cancer diagnostic markers, gastric cancer prognostic markers, and gastric cancer immunotherapy efficacy prediction markers can be screened and analyzed.

[0074] Example 2: Screening and analysis of gastric cancer diagnostic markers

[0075] According to the lectin chip test results of Example 1, the gastric cancer patient group and the healthy control group were screened and analyzed for gastric cancer diagnostic markers. The specific results are as follows:

[0076] (1) Volcano plot analysis results (such as Figure 2 As shown in Figure 3, compared with the healthy control group, in gastric cancer patients, the lectins with EV affinity that were significantly upregulated included NPL, TL, ABA, PTL I, EEL, HMA, IRA, SNA, and MAL II. The lectins with EV affinity that were significantly downregulated included VVA mannose, LCA, PSA, SBA, and VFA.

[0077] (2) Based on the screening of Lasso regression algorithm, 8 glycosylation biomarkers were identified (such as Figure 3As shown). According to the expression levels of lectins PSA, TL, NPL, PTL I, ABA bound to the EpCAM(+) EV subgroup; lectin LCA bound to the PD-L1(+) EV subgroup and lectin LCA and ABA bound to the total EV, it can be used to distinguish and identify gastric cancer patients and healthy controls. Based on the Lasso coefficient assigned to each glycosylation, we calculated the overall score (Integrated score) of the 8 glycosylation markers, and the results are shown as follows Figure 4 As shown in the figure, the overall score of gastric cancer patients was significantly lower than that of the healthy control group.

[0078] (3) ROC curve analysis results of gastric cancer diagnostic markers Figure 5 As shown, the AUC area of ​​gastric cancer diagnostic markers Total EV-LCA is 0.901, and the AUC area of ​​Total EV-ABA is 0.793. The AUC area of ​​EpCAM(+)EV-PSA is 0.920, the AUC area of ​​EpCAM(+)EV-TL is 0.790, the AUC area of ​​EpCAM(+)EV-NPL is 0.716, the AUC area of ​​EpCAM(+)EV-PTL I is 0.807, and the AUC area of ​​EpCAM(+)EV-ABA is 0.813. The AUC area of ​​PD-L1(+)EV-LCA is 0.891. According to Figure 4 The ROC curve analysis of the calculated overall score showed an AUC area of ​​0.986. The above results indicate that the gastric cancer diagnostic marker based on EV glycosylation modification characteristics has a good diagnostic value in the diagnosis of gastric cancer.

[0079] (4) Based on Figure 3 The expression levels of the 8 gastric cancer diagnostic markers involved were fitted with marker data based on different machine learning algorithms, and the ROC curve analysis of the gastric cancer diagnostic performance of the 8 glycosylation markers after fitting was performed. Figure 6As shown in the figure, under different machine learning algorithms (k-Nearest Neighbor (KNN), Logistic, Random Forest (RF), Relevance Vector Machine linear (RVM-linear), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost)), the diagnostic AUC areas based on these 8 glycosylation markers are all greater than 0.984, of which the AUC areas under RF and XGBoost are 1.00. The above results show that the use of machine learning can effectively improve the diagnostic effect of gastric cancer combined diagnostic markers. At the same time, it is once again proved that the glycosylation level of EV has a very high diagnostic value in the diagnosis of gastric cancer.

[0080] Example 3: Screening and analysis of gastric cancer prognostic markers

[0081] Based on the lectin chip test results of Example 1, the group of patients with advanced gastric cancer was screened and analyzed for gastric cancer prognostic markers. The specific results are as follows:

[0082] (1) Forest plot analysis results of gastric cancer prognostic markers, such as Figure 7 As shown. EpCAM(+)EV subpopulation-bound lectins PSA and LCA and total EV-bound lectin PSA were associated with decreased overall survival in gastric cancer patients. Total EV-bound lectins SNA and HHL, EpCAM(+)EV subpopulation-bound lectins IAA and HMA and PD-L1(+)EV subpopulation-bound lectin HHL were associated with increased overall survival in gastric cancer patients.

[0083] (2) Based on the screening of the Lasso regression algorithm, six glycosylation biomarkers were identified, namely, lectins PSA, IAA, and HMA that bind to the EpCAM(+) EV subpopulation; lectin HHL that binds to the PD-L1(+) EV subpopulation; and lectins PSA and SNA that bind to total EVs. The results of the Kaplan-Meier survival curve for the joint analysis of multiple markers after integration based on the Lasso coefficient are shown in Figure 2. Figure 8 The results showed that the higher the integrated score, the worse the prognosis of gastric cancer; the lower the score, the better the prognosis of gastric cancer.

[0084] (3) ROC curve analysis results of gastric cancer prognostic markers Fig. 9As shown. The AUC area of ​​gastric cancer prognosis prediction markers Total EV-PSA is 0.674, and the AUC area of ​​Total EV-SNA is 0.719. The AUC area of ​​EpCAM(+)EV-PSA is 0.677, the AUC area of ​​EpCAM(+)EV-IAA is 0.612, the AUC area of ​​EpCAM(+)EV-HMA is 0.698, and the AUC area of ​​PD-L1(+)EV-HHL is 0.725. The ROC curve analysis results of the comprehensive score based on the integration of the Lasso coefficient showed that its AUC area was 0.899. The above results show that gastric cancer prognostic markers based on the EV surface glycosylation modification characteristics have good predictive value in monitoring the survival prognosis status of gastric cancer.

[0085] (4) DCA curve analysis was performed based on the comprehensive score of gastric cancer prognostic markers integrated based on the Lasso coefficient. The results are as follows Fig.10 Compared with predictive indicators such as age and tumor metastasis status, EV glycosylation level is the best for predicting the one-year survival rate of gastric cancer patients.

[0086] (5) Using machine learning to Fig. 9 The various glycosylation markers included were integrated to jointly predict the survival status of gastric cancer patients. Fig.11 As shown in the figure, under the fitting of different machine learning algorithms, the AUC area of ​​glycosylation markers for monitoring the prognosis of gastric cancer patients was greater than 0.838, among which the AUC area under the XGBoost algorithm was 1.00. The above results show that the use of machine learning can effectively improve the prediction effect of gastric cancer prognostic markers. At the same time, it is proved that glycosylation markers have a high predictive value in gastric cancer prognosis.

[0087] Example 4: Screening and analysis of markers predicting the efficacy of gastric cancer immunotherapy

[0088] According to the lectin chip test results of Example 1, the gastric cancer immunotherapy benefit patient group and the gastric cancer immunotherapy non-benefit control group were screened and analyzed for gastric cancer immunotherapy efficacy prediction markers. The specific results are as follows:

[0089] (1) Cluster heat map analysis results are as follows Fig.12 As shown in the figure, compared with patients who did not benefit from gastric cancer immunotherapy, the upregulated EV-affinity lectins in patients who benefited from gastric cancer immunotherapy included MNA G and IAA, while the downregulated EV-affinity lectins included NPL and CSA.

[0090] (2) Based on the expression levels of lectins NPL and MNA G bound to the EpCAM(+) EV subgroup and lectins CSA and IAA bound to the PD-L1(+) EV subgroup, the scores of each sample after integration were calculated based on the coefficient of Lasso regression. The scores were normalized to zero, and then the distribution of the integrated scores in each sample was displayed in the form of a bar graph ( Fig.13 ). Compared with patients who did not benefit from gastric cancer immunotherapy, patients who benefited from it had higher scores. This result indicates that the higher the overall score of EV glycosylation, the better the effect of PD1 inhibitor immunotherapy on gastric cancer patients.

[0091] (3) ROC curve analysis results of predictive markers for gastric cancer immunotherapy efficacy Fig.14 As shown. The AUC area of ​​EpCAM(+)EV-NPL, a marker for predicting the efficacy of gastric cancer immunotherapy, was 0.711, the AUC area of ​​EpCAM(+)EV-MNA G was 0.662, the AUC area of ​​PD-L1(+)EV-CSA was 0.746, and the AUC area of ​​PD-L1(+)EV-IAA was 0.716. The results of ROC curve analysis of the integrated overall score showed that its AUC area was 0.771. The above results show that the marker for predicting the efficacy of gastric cancer immunotherapy based on the glycosylation modification characteristics of the EV surface has a good predictive value in predicting the efficacy of gastric cancer immunotherapy.

[0092] (4) Based on the expression levels of markers predicting the efficacy of gastric cancer immunotherapy, a joint analysis of markers based on a machine learning algorithm was performed. Fig.15 As shown. Under different machine learning algorithms, Fig.12 After fitting the four glycosylation markers involved, the overall prediction AUC area was greater than 0.866, of which the AUC area under the RF algorithm was 1.00. The above results show that the use of machine learning can effectively improve the prediction effect of gastric cancer immunotherapy efficacy markers. At the same time, the combined diagnostic markers for predicting the efficacy of gastric cancer immunotherapy have a high diagnostic value in predicting the efficacy of gastric cancer immunotherapy and can be used for medication guidance.

[0093] The embodiments described above are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work shall all fall within the scope of protection of the present invention.

Claims

1. A lectin chip for gastric cancer detection, characterized in that: It includes lectin probes, and the lectin probes are selected from two or more of LCA, ABA, PSA, TL, NPL, PTL I, SNA, IAA, HMA, HHL, MNA G, and CSA.

2. The lectin chip according to claim 1, characterized in that The lectin probe is selected from one of the following three combinations: Combination 1: LCA, ABA, PSA, TL, NPL and PTL I; Combination 2: PSA, SNA, IAA, HMA and HHL; Combination 3: MNA G, IAA, NPL and CSA.

3. A kit for detecting EV glycosylation modification, characterized in that: Comprising the lectin chip as described in claim 1 or 2.

4. The kit according to claim 3, characterized in that: It also includes EV detection probes, fluorescently labeled secondary antibodies, biotin-labeled specific EV detection antibodies, and streptavidin coupled to fluorescent probes.

5. The kit according to claim 4, characterized in that: The EV detection probe is selected from at least one of anti-PD-L1 antibodies and anti-EpCAM antibodies.

6. The kit according to claim 4, characterized in that: The secondary antibody is anti-mouse IgG and / or anti-rabbit IgG.

7. The kit according to claim 4, characterized in that: The specific EV detection antibody is one or any combination of anti-CD63 antibody, anti-CD81 antibody, and anti-CD9 antibody conjugated with biotin.

8. The kit according to claim 4, characterized in that: The streptavidin coupled to the fluorescent probe is Cy3-Streptavidin.

9. A method for detecting EV glycosylation modification, using the lectin chip according to claim 1 or 2 or the kit according to any one of claims 3 to 8, characterized in that: The following steps are involved: S1, adding the test sample containing EVs to the lectin chip for incubation; S2, adding EV detection probe to the lectin chip for incubation; S3, adding fluorescently labeled secondary antibodies to the lectin chip for incubation; S4, adding biotin-labeled specific EV detection antibody to the lectin chip for incubation; S5, adding streptavidin coupled with fluorescent probe to the lectin chip for incubation; S6. Scan the chip with a scanner to obtain the fluorescence value.

10. Use of the lectin chip according to claim 1 or 2 or the kit according to any one of claims 3 to 8, characterized in that: The application is selected from one of products for preparing gastric cancer diagnosis, products for gastric cancer prognosis monitoring, and products for evaluating the efficacy of gastric cancer immunotherapy.

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