Application of extracellular vesicle membrane protein in preparation of early gastric cancer diagnostic kit

Abstract

The invention discloses application of extracellular vesicle membrane protein in preparation of an early gastric cancer diagnostic kit. A series of novel EV membrane protein markers, namely transforming growth factor beta 1, annexin A2, tendon protein N, phospholipase-like structural domain protein 8, transferrin, fibroblast activation protein alpha and cadherin 5, are jointly screened by using a proteomics technology and an EVArray platform. Clinical verification proves that the application potential of the gene in early diagnosis and postoperative monitoring of GC gastric cancer. Wherein the transforming growth factor beta 1 and the tendon protein N have the most significant value in GC early diagnosis.

Description

Technical Field

[0001] The present invention belongs to the field of medical biological detection, and particularly relates to the application of extracellular vesicle membrane protein in the preparation of an early gastric cancer diagnosis kit. Background Art

[0002] Gastric cancer (GC) is one of the most common malignant tumors worldwide, with a high morbidity and mortality rate. Clinical data show that the five-year survival rate for patients with early-stage GC can exceed 90%, while that for patients in the advanced stage plummets to below 30%. This significant difference highlights the critical role of early diagnosis and treatment. Current screening methods, such as fecal occult blood tests and serum markers (CEA, CA199, etc.), suffer from insufficient sensitivity and limited specificity. Although endoscopy and biopsy are considered the gold standard for diagnosis, their invasive nature and potential for missed diagnoses limit their effectiveness in early screening. Therefore, the search for novel, highly sensitive and specific circulating biomarkers is of great value in improving the early diagnosis and postoperative monitoring of gastric cancer.

[0003] Extracellular vesicles (EVs) are nanoscale lipid bilayer membrane vesicles released by cells. They contain a variety of bioactive molecules derived from the parent cell, including proteins, nucleic acids (DNA, RNA), and lipids. They can stably exist in body fluids such as blood, urine, and saliva. Among the components carried by EVs, functional nucleic acids and active proteins are two types of biomarkers that have attracted much attention, especially EV membrane proteins. Studies have shown that these membrane proteins, as key effector molecules, play an important role in the development and progression of cancer, and therefore have the potential to become new biomarkers for early cancer diagnosis.

[0004] Traditional strategies for discovering membrane protein markers in EVs primarily involve genomics and proteomics. Although gene expression studies can provide information, they do not always correlate with the abundance and variation of the encoded proteins, which are often affected by proteolytic cleavage or post-translational modifications. Proteomic approaches focus on characterizing protein expression and alterations, providing key insights into protein dynamics, including function, post-translational modifications, interactions with other biomolecules, and responses to environmental factors. This allows for direct, in-depth, and quantitative analysis of the expression levels of various cancer-related and cancer-specific proteins. With rapid advances in mass spectrometry (MS) detection technology and data processing methods, high-throughput and high-precision MS-based proteomics technologies have developed rapidly. Membrane protein markers play an important role in early diagnosis of tumors and therapeutic efficacy assessment, and the application of proteomics to the search for early cancer biomarkers is becoming increasingly widespread. Commonly used proteomic quantitative techniques can be divided into two categories, namely, untargeted and targeted quantitative proteomics, depending on whether the target protein is quantified. Non-targeted quantitative proteomics is an indiscriminate quantitative analysis of all proteins in a sample, aiming to discover and detect more proteins in the sample, thereby generating a list of candidate biomarkers to build a crucial bridge for subsequent stage analysis. It is suitable for the early research stage of biomarker discovery. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and provide an application of extracellular vesicle membrane protein in the preparation of an early gastric cancer diagnosis kit.

[0006] The object of the present invention is achieved through the following technical solution: use of an extracellular vesicle membrane protein in the preparation of an early gastric cancer diagnostic kit, wherein the extracellular vesicle membrane protein is at least one of transforming growth factor beta1 (TGF-β1), annexin A2 (ANXA2), tenascin N (TNN), phospholipase domain-like protein 8 (PNPLA8), transferrin (TF), fibroblast activation protein alpha (FAP) and cadherin 5 (CDH5); preferably at least one of transforming growth factor beta1 and tenascin N.

[0007] Early gastric cancer refers to a tumor that only invades the gastric mucosa or submucosa without breaking through the muscular layer of the gastric wall.

[0008] The kit contains reagents for determining the content of extracellular vesicle membrane proteins.

[0009] The reagent for measuring the content of extracellular vesicle membrane proteins comprises a TGF-β1 antibody-oligonucleotide complex, a TNN antibody-oligonucleotide complex, and CD9 / CD63 / CD81 functionalized magnetic beads.

[0010] The reagent for determining the content of extracellular vesicle membrane protein further comprises RNAse.

[0011] The RNase is preferably RNase A / T1, more preferably 5000 U / mL RNase A / T1.

[0012] The TGF-β1 antibody-oligonucleotide complex is a complex obtained by sequentially connecting TGF-β1 antibody, linker, linker-connected DNA, RNA that can be specifically cleaved by RNase, and template DNA for initiating signal amplification.

[0013] The TNN antibody-oligonucleotide complex is a complex obtained by sequentially connecting the TNN antibody, a linker, a linker-connected DNA, an RNA that can be specifically cleaved by an RNase, and a template DNA for initiating signal amplification.

[0014] The sequence of the Linker for connecting DNA is as follows: 5'- AAGTATT / ACCAGAAA -3'; wherein the slash represents the position where dRep specifically recognizes, cuts and connects single-stranded DNA.

[0015] The sequence of the RNA that can be specifically cleaved by RNase is as follows: 5'-GCUGUG-3'.

[0016] The template DNA for initiating signal amplification can be arbitrarily designed, preferably without a linker DNA and easy to amplify; the preferred sequence is one of the nucleic acids shown below: 5'-TGTTGTAAGGGCCCGTGACTATGTCGAAGCGACCCGGCGATATAATCATTTCCACGCCCGTC-3'; 5'-CATAGGAGAAACTGAGATGCCAACTGTGATGAATGGGCTTATGGTTTGGTGCATTGAAAATGGAACCTCGCCA-3'.

[0017] The CD9 / CD63 / CD81 functionalized magnetic beads are preferably prepared by the following method: 1) Incubate Sulfo-NHS-LC-Biotin with CD9 antibody, CD63 antibody, and CD81 antibody to obtain biotinylated antibodies; 2) The biotinylated antibody was coupled to streptavidin magnetic beads to obtain CD9 / CD63 / CD81 functionalized magnetic beads.

[0018] The biotinylated antibodies described in step 1) can be mixed first and then biotinylated, or the antibodies can be biotinylated separately and then mixed.

[0019] The amount of Sulfo-NHS-LC-Biotin used in step 1) is preferably calculated based on 2.4-2.7 μg antibody to 0.7 nmol; more preferably, 2.6-2.7 μg antibody to 0.7 nmol.

[0020] The incubation operation in step 1) is preferably as follows: first incubate at 20-30°C for 20-40 minutes, then transfer to 2-8°C for incubation for 12-20 hours; more preferably as follows: first incubate at 24-26°C for 25-35 minutes, then transfer to 4°C for incubation for 12-16 hours.

[0021] The coupling operation described in step 2) is preferably as follows: dilute the biotin antibody with PBS, add the streptavidin magnetic beads washed with PBS and incubate; after the incubation, wash with PBS and then block with bovine serum albumin; after blocking, wash with PBS and then resuspend with PBS.

[0022] The amount of magnetic beads used in step 2) is preferably calculated based on 2.4-2.7 μg of antibody to 1 mg of magnetic beads.

[0023] The present invention has the following advantages and effects compared to the prior art: This study used proteomics technology and the EVArray platform to screen a series of novel EV membrane protein markers. Clinical validation confirmed their potential for early diagnosis and postoperative monitoring of GC. In particular, transforming growth factor-β1 and tenascin N showed the most significant value in early GC diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Figure 1: Flowchart of proteomic analysis of early GC diagnostic markers (A) and schematic diagram of preliminary validation of seven EV membrane protein candidate markers in GC-EVArray detection (B).

[0025] Figure 2It is a proteomic analysis diagram of early GC diagnostic markers; among them, the two Venn diagrams in A show the intersection of the up-regulated proteins identified by proteomic analysis and the UniProt public membrane protein database (upper figure) and the intersection of the up-regulated membrane proteins and the Vesiclepedia EV protein database (lower figure); B is a volcano plot of 48 differentially expressed EV membrane proteins; C is a heat map showing the 48 differentially expressed EV membrane proteins in the proteomic analysis; D is a PCA score diagram of EV membrane proteins, showing a clear separation between the healthy control group and stage I GC patients.

[0026] Figure 3 is the KEGG and GO analysis of EV membrane proteins from healthy individuals and stage I GC patients; A is the KEGG pathway analysis of differentially expressed EV membrane proteins, listing the top 10 enriched pathways; B is the GO classification of differentially expressed EV membrane proteins, listing the top 10 enriched entries in biological process (BP), cellular component (CC) and molecular function (MF).

[0027] Figure 4 Box plot of the expression levels of seven candidate markers (TGF-β1, TNN, PNPLA8, CDH5, TF, FAP, and ANXA2); ** indicates P < 0.05, ** indicates P < 0.01, *** indicates P < 0.001, and **** indicates P < 0.0001.

[0028] Figure 5 Figure 2 is the discovery and preliminary validation results of early GC diagnostic markers using GC-EVArray; A is a representative fluorescence image of a healthy individual and a stage I GC patient in the GC-EVArray assay; B is a heat map of seven EV membrane proteins in the GC-EVArray assay; C to I are the relative expression levels of TNN (C), TGF-β1 (D), ANXA2 (E), PNPLA8 (F), FAP (G), CDH5 (H), and TF (I) in the GC-EVArray assay, respectively; * indicates P < 0.05, ** indicates P < 0.01, and ns indicates no statistically significant difference.

[0029] Figure 6 shows the ROC curves of various markers on EVs used to distinguish healthy individuals from stage I GC patients; among them, in GC-EVArray, A is TNN (AUC=0.86, 95% CI=69.35%-100%), B is TGF-β1 (AUC=0.82, 95% CI=61.33%-100%), C is ANXA2 (AUC=0.81, 95% CI=60.56%-100%), D is PNPLA8 (AUC=0.78, 95% CI=57.61%-98.39%), E is FAP (AUC=0.73, 95% CI=50.53%-95.47%), F is CDH5 (AUC=0.70, 95% CI=46.38%-93.62%), and G is TF (AUC=0.74, 95% CI=51.83%-69.17%).

[0030] Figure 7 Figure 1 is a graph showing the diagnostic performance evaluation of early GC diagnostic markers; A is a heat map showing the expression of TGF-β1 and TNN in plasma samples, including healthy controls (n=20), benign disease patients (n=20), stage I GC (n=30), stage II GC (n=30), and stage III-IV GC (n=30); B–E are scatter plots showing the concentrations of TGF-β1 (B, D) and TNN (C, E) in different groups, based on the droplet detection method (*P<0.05, **P<0.01, ****P<0.0001, ns indicates no statistically significant difference); F–M are receiver operating characteristic (ROC) curve analyses evaluating the diagnostic performance of TGF-β1 and TNN in distinguishing healthy individuals and patients with benign diseases from patients with different stages of GC, including stage I GC (F, G), stage II GC (H, I), stage III-IV GC (J, K), and all GC patients (L, M).

[0031] Figure 8 Figure 3 is the ROC curve diagram of traditional markers used to distinguish healthy individuals or benign patients from patients with different stages and all GC patients; among them, A is the ROC curve used to distinguish healthy individuals from stage I GC patients, B is the ROC curve used to distinguish benign patients from stage I GC patients, C is the ROC curve used to distinguish healthy individuals from stage II GC patients, D is the ROC curve used to distinguish benign patients from stage II GC patients, E is the ROC curve used to distinguish healthy individuals from stage III-IV GC patients, F is the ROC curve used to distinguish benign patients from stage III-IV GC patients, G is the ROC curve used to distinguish healthy individuals from all GC patients, and H is the ROC curve used to distinguish benign patients from all GC patients.

[0032] Figure 9 shows the results of single-molecule analysis of EVs membrane proteins in postoperative monitoring; A is the paired connection diagram of TGF-β1 in GC patients before and after surgery, and B is the paired connection diagram of TNN in GC patients before and after surgery; n = 20, ****p<0.0001, ns indicates that the difference is not statistically significant.

[0033] Figure 10 Figure 2 shows photos of tumor growth in tumor-bearing mice at different time periods (A) and a graph showing the tumor volume growth curve (B).

[0034] Figure 11 Figure 3 is the application result of single-molecule analysis of EV membrane proteins in tumor burden assessment; A is a schematic diagram of mouse blood collection; B and E are the concentrations of EGFR (B), MUC1 (C), TGF-β1 (D), and TNN (E) detected in three mice at each time point, respectively. DETAILED DESCRIPTION

[0035] The present invention will be described in further detail below with reference to the embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0036] Example 1 1. Materials and Methods 1.1 Materials 1.1.1 Research subjects The use of clinical plasma samples in this study was approved by the Ethics Committee of Nanfang Hospital, Southern Medical University. Plasma samples from healthy individuals and patients with benign gastric disease were collected from the Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University (Guangzhou, China), and plasma samples from patients with GC were collected from the Department of General Surgery, Nanfang Hospital. The diagnosis of patients with benign gastric disease and GC was confirmed by pathological examination of tissue biopsies.

[0037] 1.1.2 Main reagents and consumables Table 1 Main reagents and consumables

[0038] Table 2 Summary of instrument names and manufacturers

[0039] Table 3 Nucleic acid sequences required for the experiment

[0040] 1.2 Methods 1.2.1 Collection and storage of plasma samples Venous blood samples were collected using EDTA tubes and centrifuged twice at 2,500 g for 15 minutes at room temperature to remove excess blood cells. Plasma was aliquoted and stored at −80°C for subsequent analysis.

[0041] 1.2.2 Extraction of plasma EVs In this study, pooled plasma from healthy individuals was diluted 1:2 by volume with 0.01 M phosphate-buffered saline (PBS), pH 7.4, and centrifuged at 3,000 g for 20 minutes to remove cellular debris. The supernatant was then centrifuged at 16,000 g for 30 minutes to remove microvesicles. The resulting supernatant was further subjected to ultracentrifugation (UC) at 135,000 g for 70 minutes, repeated twice, to isolate EVs. The resulting EVs were resuspended in 100 μL of PBS, and all centrifugation steps were performed at 4°C. The EVs were stored at −80°C for subsequent analysis.

[0042] 1.2.3 Protein Sample Preparation Process for Mass Spectrometry Analysis First, thaw the sample on ice, add the sample solution to an ultrafiltration tube, and make up to 200 µL of 8 M urea solution for protein extraction. After protein extraction, add dithiothreitol (DTT) solution to a final concentration of 10 mM and incubate at 37°C for 30 minutes. Then, add iodoacetamide (IAA) solution to a final concentration of 20 mM and incubate at room temperature in the dark for 30 minutes. Then, add 200 µL of 50 mM ammonium bicarbonate (NH4HCO3) and centrifuge at 12,000 g for 10 minutes. Then, add 200 µL of 50 mM NH4HCO3 and 1 µg of endoproteinase LysC to each sample and incubate at 37°C with shaking for 2 hours. Then, add 1 µg of trypsin and incubate at 37°C with shaking overnight. The reaction was terminated by adding 10 µL of 10% v / v trifluoroacetic acid (TFA). The peptides were then desalted by adding 100 µL of methanol to the SoLAµ HRP plate and centrifuging at 600 g for 1 minute. 100 µL of 80% v / v acetonitrile (ACN) / 0.1% v / v TFA was added and centrifuged at 1,000 g for 1 minute. 200 µL of 0.1% v / v TFA was added and centrifuged at 1,000 g for 1 minute. The sample was then added to the SoLAµ HRP plate and centrifuged at 1,000 g for 2 minutes. The sample loading was repeated once. 200 µL of 0.1% v / v TFA was added and centrifuged at 1,000 g for 2 minutes. 100 µL of 80% ACN / 0.1% TFA was added and centrifuged at 1,000 g for 3 minutes. The eluate was collected and dried in a centrifugal concentrator at 40°C, dissolved in 0.1% formic acid (FA) to 0.5 µg / µL, and 2 µg was loaded on the mass spectrometer for quantification.

[0043] 1.2.4 Mass spectrometry data processing DIA Data Analysis: Data were imported into DIA-NN v1.8.0 for targeted extraction using the predicted human proteomics database. All other parameters were set to default to maintain a false discovery rate (FDR) of <1% at both the peptide and protein levels. Statistical Analysis: Protein intensities calculated by DIA-NN were output as the mean of the top three peptides. These protein intensity data were imported into Perseus and Metaboanalyst for statistical analysis. Differentially expressed proteins were identified based on a 2-fold change and a p-value <0.05. Principal component analysis (PCA), cluster analysis, and correlation analysis were performed in R 4.0.5. GO and KEGG enrichment analyses were performed using Cluster Profile v3.18. Upregulated and downregulated proteins were imported into the String database for protein-protein interaction annotation, using a confidence level of 0.7 for interactions. The membrane protein database UniProt and the EV database Vesiclepedia were obtained from https: / / www.uniprot.org / and http: / / www.microvesicles.org / , respectively.

[0044] 1.2.5 GC-EVArray Detection Process Each antibody microarray well was first blocked with 100 μL of 3% w / v BSA solution at room temperature for 30 minutes, after which the solution was removed. Next, 100 μL of diluted sample was added to the well and incubated for 60 minutes. The well was then washed three times with 200 μL of PBS. Next, 100 μL of biotinylated detection antibody (diluted 1:1000 to 1:1500 in PBS) was added and incubated for 30 minutes. The well was then washed three times with 200 μL of PBS. Then, 100 μL of Cy3-streptavidin (diluted 1:1500 in PBS) was added and incubated at room temperature for 30 minutes in the dark. The supernatant was removed and the well was washed three more times with 200 μL of PBS. After air drying, the signal was scanned at 532 nm using a laser scanner. Fluorescence intensity was quantified using GenePix software.

[0045] 1.2.6 Single-molecule Detection of EV Membrane Proteins Based on Droplet Microfluidics 2 μL of Ts1 or Ts2 (100 μM), 10 μL of 2× ssDNA buffer (100 mM HEPES, 300 mM NaCl, 2 mM MgCl₂, 2 mM MnCl₂, pH 8.0), 4.5 μL of ultrapure water, and 3.5 μL of Linker (1 mg / mL) were mixed and incubated at 37°C for 1 hour to produce Linker-Ts1 and Linker-Ts2. 5 μL of Linker-Ts1 and 3 μL of TGF-β1 antibody were mixed and incubated at 4°C for 1 hour to produce TGF-β1 antibody-Ts1 complexes. 5 μL of Linker-Ts2 and 3 μL of TNN antibody were mixed and incubated at 4°C for 1 hour to produce TNN antibody-Ts2 complexes. EGFR antibody-Ts2 complexes and MUC1 antibody-Ts1 complexes were obtained using the same method.

[0046] Sulfo-NHS-LC-Biotin was dissolved in PBS to a concentration of 10 mM and further diluted 100-fold with PBS to a final concentration of 0.1 mM. Subsequently, 7 μL of Sulfo-NHS-LC-Biotin was added to 1.65 μL of 0.53 mg / mL CD9 antibody, 1.65 μL of 0.53 mg / mL CD63 antibody, and 1.65 μL of 0.53 mg / mL CD81 antibody. The mixture was incubated at room temperature (25°C) for 30 minutes and then transferred to 4°C for overnight incubation (16 hours) to obtain biotinylated antibodies. 100 μL of 10 mg / mL streptavidin-coated magnetic beads was aspirated and washed twice with 100 μL PBS. 100 μL PBS was added to the biotinylated antibody solution, and 100 μL of antibody solution was incubated with the magnetic beads at room temperature for 50 minutes. Subsequently, the magnetic beads were washed three times with 100 μL PBS and blocked with 100 μL 1% bovine serum albumin (BSA) for 30 minutes. The beads were washed three times again with 100 μL PBS and finally resuspended in 100 μL PBS to obtain CD9 / CD63 / CD81 functionalized magnetic beads.

[0047] To 50 μL of plasma, 2 μL of 10 mg / mL CD9 / CD63 / CD81-functionalized magnetic beads was added. The mixture was incubated on a rotary mixer at room temperature for 1 hour to capture and enrich EVs. The EV-captured beads were then recovered by magnetic attraction on a magnetic rack. The sample was then washed twice with 50 μL of PBS to remove residual plasma. Next, 2.5 μL of a 60 pM TGF-β1 antibody-Ts1 complex and 2.5 μL of a 60 pM TNN antibody-Ts2 complex were added, and the volume was made up to 50 μL with blocking buffer (5% BSA, 0.05% dextran sulfate, and 0.2 mg / mL salmon sperm DNA in 1× PBS) and incubated at room temperature for 1 hour. After incubation, the cells were washed twice with wash buffer 1 (5% BSA, 0.05% dextran sulfate in PBS) and then twice with wash buffer 2 (0.1% salmon sperm in PBS), each wash volume (50 μL). 0.1 μL RNase A / T1 was then added, and the volume was made up to 50 μL with PBS. After incubation at room temperature for 20 minutes, the supernatant was collected for ddPCR analysis. The amplification system was as follows: 10 μL 2× ddPCR universal probe mix, 1.8 μL forward primer, 1.8 μL reverse primer, 0.6 μL 10× probe, 3.8 μL nuclease-free water, and 2 μL template reaction solution (Ts1 using forward primer 1, reverse primer 1, and probe 1-CY5; Ts2 using forward primer 2, reverse primer 2, and probe 2-FAM). Amplification conditions were as follows: initial denaturation at 94°C for 5 minutes; 45 cycles of denaturation at 94°C for 30 seconds, annealing at 58°C for 2 minutes, and extension; and finally, slow cooling to 4°C. The amplified droplets were injected into a droplet analysis chip, and the experimental results were read and analyzed using the ExoStar Droplet Reader.

[0048] 1.2.7 Preparation of tumor-bearing mice Human gastric cancer cells MKN28 were used, and the injection volume was 5×10 6 The cells were injected into the axilla. Three 5-week-old nude mice were subjected to the same procedure. Every seven days after model establishment, 200 μL of blood was collected from the submandibular area of ​​the mice. Plasma samples were processed and stored using the same procedures as described above, and subsequently analyzed by ddPCR.

[0049] 2. EVs membrane protein marker screening process 2.1 Proteomics technology To explore early markers of gastric cancer, we performed proteomic analysis of plasma-derived EVs in an early-stage GC cohort consisting of 10 healthy individuals and 10 stage I GC patients ( Figure 1A in Figure 1). Compared with healthy controls, we identified 325 differentially expressed proteins in stage I GC patients, of which 178 were upregulated and 147 were downregulated. By cross-aligning the EV protein dataset with the public membrane protein database Uniprot, we screened out 48 differentially expressed membrane proteins, of which 23 were upregulated. Further cross-alignment of these 23 membrane proteins with the public EV protein database Vesiclepedia revealed that 22 of them were in the EV protein database (see Figure 2 A in ). Figure 2 Figure B is a volcano plot of 48 differentially expressed EV membrane proteins; Figure 2 The cluster heat map shown in Figure C shows the expression of 48 differentially expressed membrane proteins in each sample, indicating that these screened proteins can effectively distinguish the healthy group from the early GC group; in addition, PCA further revealed the differences between the two groups of samples, highlighting the proteomic characteristics of early GC samples and healthy controls (see Figure 2 D in ), thus providing a theoretical basis for the subsequent screening of new markers.

[0050] Subsequently, we used bioinformatics methods to perform functional analysis of the differentially expressed EV membrane proteins. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that these differentially expressed EV membrane proteins were mainly involved in biological processes related to cancer, such as cell adhesion molecules and neutrophil extracellular traps (see Figure 3 In addition, Gene Ontology (GO) analysis showed that the differentially expressed EV membrane proteins in the stage I GC group, compared with the healthy control group, showed specific signals related to focal adhesion, cell-matrix adhesion, and cell adhesion regulation (see Figure 3 These findings may reflect the functional roles and molecular heterogeneity of differentially expressed EV membrane proteins in the occurrence and development of GC.

[0051] Among the 22 upregulated EV membrane proteins, we screened out 7 differential membrane proteins as candidate biomarkers based on their fold change ranking and annotation of the Human Protein Atlas (HPA) database for further early GC analysis. These 7 proteins include transforming growth factor beta 1 (TGF-β1), annexin A2 (ANXA2), tenascin N (TNN), phospholipase domain-containing protein 8 (PNPLA8), transferrin (TF), fibroblast activation protein alpha (FAP), and cadherin 5 (CDH5). Figure 4 As shown in the results, there were significant statistical differences in the expression levels of these seven EV membrane proteins between healthy donors and GC patients.

[0052] 2.2 EVArray Platform Subsequently, we used the EVArray chip to preliminarily validate these seven EV membrane protein candidate markers in another early GC validation cohort (10 healthy individuals and 10 stage I GC patients). Figure 1 As shown in Figure B. The final expression readings for each membrane protein were obtained by calculating the average signal intensity of three replicate spots on the GC-EVArray and subtracting the signal from the PBS spot in the same well.

[0053] The layout of the GC-EVArray chip is as follows Figure 5 As shown in A. Figure 5 B in Figure 1 is a heat map showing the expression intensity of the seven markers in the validation cohort. The scatter plot further shows the expression distribution of the seven markers in the GC-EVArray validation cohort, among which TNN, TGF-β1, ANXA2 and PNPLA8 have statistically significant differences (see Figure 5 In addition, we used ROC curve analysis to evaluate the AUC values ​​of the seven markers in distinguishing healthy individuals from stage I GC patients (see Figure 6The results showed that the AUC values ​​for TNN (0.86, 95% CI = 69.35%-100%) and TGF-β1 (0.82, 95% CI = 61.33%-100%) were higher than those of the other five biomarkers. Currently, no studies have evaluated the utility of EV-expressed TNN and TGF-β1 membrane proteins for the early diagnosis of GC. Therefore, we selected TNN and TGF-β1 as novel biomarkers for early GC diagnosis for further validation.

[0054] To evaluate the performance of the screened TGF-β1 and TNN in clinical diagnosis, we applied droplet microfluidics-based EV membrane protein single-molecule detection technology to a GC cohort (n = 130), including 20 healthy individuals (Helalthy), 20 patients with benign gastric disease (Benigh), and 90 patients with malignant tumors (30 stage I, 30 stage II, and 30 stage III-IV). Figure 7 A in Figure 3 shows the expression heatmap of the two markers in each sample. Figure 7 The BC in the figure shows the concentrations of TGF-β1 and TNN in patients with different GC stages and compares them with the healthy and benign groups. It can be seen that although there is no significant difference in expression levels between different GC stages, the concentrations of markers in patients with stage I and II are significantly higher than those in healthy individuals and patients with benign gastric diseases. In addition, Figure 7 The DE in the figure further showed the concentration distribution of TGF-β1 and TNN in the healthy group, benign group and all GC patients. The results showed that the expression level of markers in GC patients was significantly higher than that in the healthy and benign groups. Subsequently, we performed ROC curve analysis on TGF-β1 and TNN to evaluate their diagnostic performance in distinguishing patients with different GC stages from healthy individuals or patients with benign diseases (see Figure 7 and compared with traditional markers ( Figure 8In differentiating healthy individuals from stage I GC patients, the AUC values ​​of TGF-β1 and TNN were 0.8883 (95% CI = 0.7937-0.9829) and 0.7550 (95% CI = 0.6117-0.8983), respectively. Combined detection of these two markers increased the AUC value to 0.8900 (95% CI = 0.7958-0.9842). In differentiating patients with benign gastric disease from patients with stage I GC, the AUC values ​​of TGF-β1 and TNN were 0.9000 (95% CI = 0.8162–0.9838) and 0.7550 (95% CI = 0.6225-0.8875), respectively. Combined detection of these two markers achieved the highest AUC value, reaching 0.9050 (95% CI = 0.8236-0.9864). However, when differentiating healthy individuals or benign patients from patients with different stages or all GC patients, TGF-β1 alone and the combined detection of TGF-β1 and TNN had similar AUC values. This result may indicate that TGF-β1 has better potential than TNN in the early diagnosis of GC. In addition, in all the above analysis groups, the diagnostic performance of TGF-β1 and TNN was significantly better than that of traditional markers (CEA and CA-199), further demonstrating the advantages of these two EV membrane proteins in the early diagnosis of GC.

[0055] To further evaluate the clinical application value of the two screened markers, we used EV membrane protein single-molecule detection technology to dynamically monitor two EV membrane proteins (TGF-β1 and TNN) in plasma samples from GC patients. A total of 20 patients were included in the study, and blood samples were collected 24 hours before surgery and 1 week after surgery, and the changes in protein expression levels before and after surgery were compared. The results showed that the expression level of TGF-β1 was significantly reduced after surgery, with statistical differences. Although TNN showed a downward trend in some patient samples, there was no statistical difference overall, which may be related to the failure of TNN markers to respond to surgical treatment in a timely manner (see Figure 9 ).

[0056] To evaluate the potential of this method in monitoring tumor progression, we validated it in a tumor-bearing mouse model. Figure 10 A shows the typical tumor growth of GC tumor-bearing mice, and the tumor volume of mice was measured every 3-4 days (see Figure 10 B in ).

[0057] To investigate the expression changes of four EV membrane proteins in plasma, we collected blood samples from the submandibular vein of mice every 7 days and used this method to measure the concentrations of markers at different time points in three mice (see Figure 11The results showed that the concentrations of the four markers increased with the extension of tumor-bearing time. In addition, on day 7, EGFR and MUC1 were still undetectable, while TGF-β1 showed an increasing trend in all three experimental mice, and TNN was only detected in one mouse (see Figure 11 This result further validates the value of EV membrane proteins, particularly TGF-β1, in the early diagnosis of GC. In summary, our method has significant potential for postoperative monitoring and tumor progression assessment.

[0058] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. Use of an extracellular vesicle membrane protein in the preparation of a diagnostic kit for early gastric cancer, characterized in that: The extracellular vesicle membrane protein is at least one of transforming growth factor β1, annexin A2, tenascin N, phospholipase domain-like protein 8, transferrin, fibroblast activation protein α and cadherin 5.

2. Use of the extracellular vesicle membrane protein according to claim 1 in preparing a diagnostic kit for early gastric cancer, characterized in that: The extracellular vesicle membrane protein is at least one of transforming growth factor β1 and tenascin N.

3. Use of the extracellular vesicle membrane protein according to claim 1 or 2 in preparing a diagnostic kit for early gastric cancer, characterized in that: The kit contains reagents for determining the content of extracellular vesicle membrane proteins.

4. Use of the extracellular vesicle membrane protein according to claim 3 in preparing a diagnostic kit for early gastric cancer, characterized in that: The reagent for measuring the content of extracellular vesicle membrane proteins comprises a TGF-β1 antibody-oligonucleotide complex, a TNN antibody-oligonucleotide complex, and CD9 / CD63 / CD81 functionalized magnetic beads.

5. Use of the extracellular vesicle membrane protein according to claim 4 in preparing a diagnostic kit for early gastric cancer, characterized in that: The reagent for determining the content of extracellular vesicle membrane protein further comprises RNAse.

6. Use of the extracellular vesicle membrane protein according to claim 4 or 5 in preparing a diagnostic kit for early gastric cancer, characterized in that: The TGF-β1 antibody-oligonucleotide complex is a complex obtained by sequentially connecting a TGF-β1 antibody, a linker, a DNA connected to the linker, an RNA that can be specifically cleaved by an RNase, and a template DNA for initiating signal amplification; The TNN antibody-oligonucleotide complex is a complex obtained by sequentially connecting the TNN antibody, a linker, a linker-connected DNA, an RNA that can be specifically cleaved by an RNase, and a template DNA for initiating signal amplification; The sequence of the linker connecting DNA is as follows: 5'- AAGTATT / ACCAGAAA -3'; The sequence of the RNA that can be specifically cleaved by RNase is as follows: 5'-GCUGUG-3'.

7. Use of the extracellular vesicle membrane protein according to claim 6 in preparing a diagnostic kit for early gastric cancer, characterized in that: The template DNA used to initiate signal amplification is the template DNA-1 with a nucleotide sequence as shown in SEQ ID NO.1 or the template DNA-2 with a nucleotide sequence as shown in SEQ ID NO.

2.

8. Use of the extracellular vesicle membrane protein according to claim 4 or 5 in preparing a diagnostic kit for early gastric cancer, characterized in that: The CD9 / CD63 / CD81 functionalized magnetic beads were prepared by the following method: 1) Incubate Sulfo-NHS-LC-Biotin with CD9 antibody, CD63 antibody, and CD81 antibody to obtain biotinylated antibodies; 2) The biotinylated antibody was coupled to streptavidin magnetic beads to obtain CD9 / CD63 / CD81 functionalized magnetic beads.

9. Use of the extracellular vesicle membrane protein according to claim 8 in preparing a diagnostic kit for early gastric cancer, characterized in that: The dosage of Sulfo-NHS-LC-Biotin described in step 1) is calculated as 2.4-2.7 μg antibody to 0.7 nmol; The amount of magnetic beads described in step 2) is calculated based on 2.4-2.7 μg antibody to 1 mg magnetic beads.

10. Use of the extracellular vesicle membrane protein according to claim 8 in preparing a diagnostic kit for early gastric cancer, characterized in that: The incubation operation described in step 1) is as follows: first incubate at 20-30°C for 20-40 minutes, then transfer to 2-8°C and incubate for 12-20 hours; The coupling operation described in step 2) is as follows: dilute the biotin antibody with PBS, add the streptavidin magnetic beads washed with PBS and incubate; after the incubation, wash with PBS and then block with bovine serum albumin; after blocking, wash with PBS and resuspend in PBS.

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