An early screening system and kit for esophageal cancer based on exosome protein markers

Abstract

The present invention discloses an early screening system and kit for esophageal cancer based on exosome protein markers, and specifically relates to the field of medical diagnosis technology. The system provided by the present invention includes an information acquisition module, and an optional calculation module and / or diagnosis module. The system combines multiple protein markers such as LAMP2, NCSTN, CTSB, TM9SF1, TFRC, MMP14, TGM1, KRT4 and VCL for the first time for early screening of esophageal cancer, and has high diagnostic sensitivity and specificity.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis technology, and in particular to an early screening system and kit for esophageal cancer based on exosome protein markers. Background Art

[0002] Global cancer epidemiological statistics show that in 2020, there were approximately 604,000 new cases of esophageal cancer and approximately 544,000 new deaths from esophageal cancer worldwide, accounting for 3.1% and 5.5% of all new cases and deaths of malignant tumors, respectively.

[0003] Because esophageal cancer poses a significant threat to human health, promoting and implementing esophageal cancer screening, early diagnosis, and early treatment are crucial for effectively controlling the high mortality rate of esophageal cancer. Screening, early diagnosis, and early treatment are the most effective means of reducing esophageal cancer mortality and are key components of secondary prevention. Endoscopy and biopsy are currently the gold standard for diagnosing early esophageal cancer. Endoscopy allows for intuitive observation of esophageal mucosal changes, assessment of tumor status, imaging or recording of lesions, and assessment of lesion nature, location, margins, and extent through staining and magnification, enabling a single-step screening and early diagnosis. Endoscopic examination time is one of the most subjective operator factors in diagnostic endoscopy. Ideally, longer endoscopic examinations increase the detection rate of high-risk lesions during the examination. However, in clinical practice, time is limited, and prolonged endoscopic procedures can reduce patient tolerance and significantly impact the resources of endoscopy centers. Furthermore, the invasive nature and high cost of endoscopy also limit its effectiveness. Therefore, finding biomarkers for early diagnosis of esophageal cancer and optimizing the screening program for patients with early esophageal cancer are the key to improving the effectiveness of esophageal cancer screening or preventive treatment.

[0004] Currently, tumor markers for the early diagnosis of esophageal cancer are still immature. The combined use of tumor markers such as carcinoembryonic antigen (CEA), carbohydrate antigen CA199, cytokeratin fragment 19 (CYFRA21-1), squamous cell carcinoma antigen (SCC), and tissue polypeptide-specific antigen (TPS) can improve the accuracy of diagnosis, prognosis, and follow-up observation for patients with advanced or advanced esophageal cancer. However, these protein tumor markers have low sensitivity and specificity in the diagnosis of early esophageal cancer, resulting in suboptimal results for early diagnosis of esophageal cancer using protein tumor markers.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] The present invention aims to provide an early screening system and kit for esophageal cancer based on exosome protein markers. The system and diagnostic kit provided by the present invention have good diagnostic sensitivity and specificity for early screening of esophageal cancer.

[0007] This invention is implemented as follows:

[0008] In a first aspect, the present invention provides an early screening system for esophageal cancer, characterized in that it comprises:

[0009] an information acquisition module, and optionally a calculation module and / or a diagnosis module;

[0010] The information acquisition module is used to perform the step of acquiring the concentration information of the target protein of the subject; the target protein is selected from at least three of the following proteomes: LAMP2, NCSTN, CTSB, TM9SF1, TFRC, MMP14, TGM1, KRT4 and VCL;

[0011] The calculation module is used to execute the step of substituting the concentration information into a calculation model to calculate the risk threshold;

[0012] The diagnostic module is used to execute the step of judging the health condition of the subject according to the risk threshold.

[0013] The system of this invention uses at least three proteins from LAMP2, NCSTN, CTSB, TM9SF1, TFRC, MMP14, TGM1, KRT4 and VCL as biomarkers for early screening of esophageal cancer, which greatly improves the specificity and sensitivity of early diagnosis of esophageal cancer and provides a key technical means to improve the effectiveness of esophageal cancer screening or preventive treatment.

[0014] Optionally, in some embodiments of the present invention, the mathematical formula of the calculation model is expressed as:

[0015] ECSD = 1.13 × C TGM1 +1.279×C KRT4 +0.928×C VCL +1.044×C LAMP2 +1.561×C NCSTN +0.92×C CTSB +0.872×C TM9SF1 +1.14×C TFRC +1.126×C MMP14 ;

[0016] Among them, ECSD represents the risk threshold of esophageal cancer, C TGM1 、C KRT4 、C VCL 、C LAMP2 、C NCSTN 、C CTSB 、C TM9SF1 、C TFRC and C MMP14, represent the concentration values ​​of TGM1 (Protein-glutamine gamma-glutamyltransferase K), KRT4 (Keratin type II cytoskeletal 4), VCL (Vinculin), LAMP2 (Lysosome-associatedmembrane glycoprotein 2), NCSTN (Nicastrin), CTSB (Cathepsin B), TM9SF1 (Transmembrane 9 superfamily member 1), TFRC (Transferrin receptor protein 1) and MMP14 (Matrix metalloproteinase-14), respectively.

[0017] It should be noted that the fixed coefficients (e.g., 1.13, 1.279, etc.) in the mathematical formula of the above model can fluctuate within a certain range, such as ±3%. Therefore, in other embodiments, any calculation model within this range is within the protection scope of this invention.

[0018] Alternatively, in some embodiments of the present invention, the subject is diagnosed with esophageal epithelial squamous dysplasia.

[0019] Optionally, in some embodiments of the present invention, the diagnostic module makes a judgment based on the risk threshold obtained by the calculation model, and the judgment method is as follows:

[0020] If ECSD < 620, it indicates that the subject's assessment of esophageal squamous dysplasia is benign.

[0021] If 620≤ECSD<660, the subject was instructed to undergo follow-up endoscopic examination or retest within 1 month;

[0022] If ECSD ≥ 660, it indicates that the subject's esophageal squamous dysplasia assessment result is malignant and highly suspected to be esophageal cancer.

[0023] Optionally, in some embodiments of the present invention, the concentration information is derived from a plasma sample of the subject.

[0024] Alternatively, in some embodiments of the present invention, the concentration information comes from small extracellular vesicles in the subject's plasma.

[0025] Small extracellular vesicles (SECs) are secreted by cells, with diameters ranging from 40 to 150 nm. They circulate in body fluids such as blood and urine. These vesicles carry signaling molecules derived from the parent cell. Tumor cell-secreted vesicles can transport important signaling molecules within the tumor microenvironment, promoting tumor progression and metastasis. Therefore, detecting SEC-derived signaling molecules for early cancer screening, diagnosis, and prognosis monitoring is currently a key focus of scientific research and industrial transformation.

[0026] The present invention creatively identifies the above protein markers from small extracellular vesicles for early screening of esophageal cancer with high specificity and sensitivity, providing an effective diagnostic means for timely treatment of esophageal cancer.

[0027] Optionally, in some embodiments of the present invention, the unit of the concentration information is IU / mL (protein concentration in sEVs harvested per mL of plasma).

[0028] Optionally, in some embodiments of the present invention, the concentration information is obtained by chemiluminescence detection.

[0029] It should be noted that the detection method of the above-mentioned target protein can be achieved through detection platforms currently widely used in medical institutions, such as flow cytometry and mass spectrometry, and then its concentration information can be measured, which is easy to achieve for ordinary technicians in this field.

[0030] Optionally, in some embodiments of the present invention, the system further comprises a result display module, and the result display module is used to display the diagnosis result obtained by the diagnosis module.

[0031] Optionally, in some embodiments of the present invention, the result display module displays the diagnosis result by screen display, voice broadcast or printing.

[0032] In a second aspect, the present invention provides a diagnostic kit for early screening of esophageal cancer, which includes a detection reagent for detecting a target protein, wherein the target protein is selected from at least three of the following protein groups: LAMP2, NCSTN, CTSB, TM9SF1, TFRC, MMP14, TGM1, KRT4 and VCL.

[0033] Optionally, in some embodiments of the present invention, the diagnostic kit comprises an antibody that can specifically bind to the target protein.

[0034] In one aspect, the present invention provides the use of a detection reagent in the preparation of a diagnostic kit for early screening of esophageal cancer, the detection reagent being capable of detecting a target protein selected from at least three of the following proteomes: LAMP2, NCSTN, CTSB, TM9SF1, TFRC, MMP14, TGM1, KRT4, and VCL.

[0035] Optionally, in some embodiments of the present invention, the detection reagent includes an antibody that can specifically bind to the target protein.

[0036] It should be noted that, based on this invention, those skilled in the art can readily obtain antibodies that specifically bind to the aforementioned target protein. These antibodies could be IgG / IgG, single-domain antibodies, or receptors or ligands that specifically bind to the target protein. This is easily achievable by those skilled in the art, and all antibody types fall within the scope of protection of this invention. Of course, determining the concentration of the target protein using other reagents (such as reagents used in mass spectrometry or flow cytometry) also falls within the scope of protection of this invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This study used chemiluminescence immunoassay to detect nine plasma sEV-derived protein targets in 70 patients with stage IB esophageal squamous cell carcinoma and 30 age-matched healthy controls. TGM1, KRT4, and VCL showed high expression in healthy controls, while the other six protein targets showed high expression in patients with early-stage esophageal squamous cell carcinoma. **** represents p-value < 0.0001.

[0039] Figure 2 The results of Pearson correlation analysis between PRM (after homogenization) and ECL (after homogenization) mass spectrometry data of 100 plasma sEVs are presented. The correlation R-values ​​for TGM1, NCSTN, and TM9SF1 targets in the two datasets were 0.79, 0.82, and 0.76 respectively.

[0040] Figure 3Data from ECL analysis of plasma sEVs (sEVs) targeting transmembrane proteins MMP14, TM9SF1, and LAMP2 in patients with esophageal squamous cell carcinoma, gastric cancer, hepatocellular carcinoma, and colorectal cancer, as well as healthy controls, were analyzed. MMP14 and LAMP2 were specifically and highly expressed in plasma sEVs from patients with esophageal squamous cell carcinoma, while TM9SF1 was highly enriched in all tumor samples except the healthy control group, making it a common sEV target protein across multiple cancer types.

[0041] Figure 4 ROC curves were generated for the ESCD (Esophageal Cancer Score Diagnosis) values ​​of the samples; on the left, the ROC curve was generated using the ESCD values ​​of 134 samples, with an AUC of 0.995, indicating that the sensitivity of this model for the early diagnosis of esophageal squamous cell carcinoma clinical staging is 100% and the specificity is 97.22%; on the right, the ROC curve was generated using the ECSD values ​​of 30 samples of high-grade esophageal epithelial squamous dysplasia, with an AUC of 0.990, indicating that the sensitivity of this model for the auxiliary diagnosis of benign and malignant high-grade esophageal hyperplasia is 100% and the specificity is 94.70%. DETAILED DESCRIPTION

[0042] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer are used. Where the manufacturer of the reagents or instruments is not specified, all are conventional products that can be purchased commercially.

[0043] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0044] Example 1

[0045] Protein combinatorial discovery pipeline.

[0046] 1. Obtain 50 samples of esophageal cancer tissue and corresponding adjacent tissue samples diagnosed as stage IB postoperatively. Isolate tissue-derived sEVs, extract total sEV protein, and perform enzymatic hydrolysis. Quantify the final peptide concentration in each sample to 0.2 μg / μL (total amount not less than 10 μg).

[0047] 2. Load the samples and analyze them using the Orbitrap LC-MS platform. Quantitative information for 100 sEV protein samples was obtained using Thermo Scientific Proteome Discoverer software.

[0048] 3. Using the R language package DEqMS, we analyzed the differentially expressed proteins in sEVs derived from 50 tumor tissues and 50 adjacent paracancerous tissues, identifying 138 proteins highly expressed in sEVs derived from tumor tissues and 150 proteins highly expressed in sEVs derived from adjacent paracancerous tissues.

[0049] 4. The 288 protein expression data from 100 samples were randomly divided into a training set of 70 and a validation set of 30, based on the screening criteria of at least 10 adjacent normal controls for the training set and at least 10 adjacent normal controls for the validation set. The randomization was repeated 50 times, and in each round, a random forest machine learning model was used to screen for esophageal cancer-specific proteins, resulting in 50 sets of esophageal cancer tissue sEV-specific protein expression profiles.

[0050] 5. The protein expression profiles of the above 50 rounds were intersected to obtain 29 protein combinations, including 16 proteins highly expressed in sEV of tumor tissues and 13 proteins highly expressed in sEV of adjacent tissues;

[0051] 6. Using the PRM detection platform, a new round of testing was conducted on the plasma sEV proteins of esophageal cancer, lung cancer, gastric cancer, liver cancer, colorectal cancer, and healthy volunteers whose age matched that of the tumor patients. The expression profiles of 29 proteins in 140 plasma sEVs were obtained. According to the screening criteria that the training set includes at least 10 cases of esophageal cancer plasma sEV data and the validation set includes at least 10 cases of healthy plasma sEVs, the 140 samples were randomly divided into a training set of 100 cases and a validation set of 40 cases. The random grouping was carried out for 50 rounds, and in each round, the random forest machine learning model was used to screen the esophageal cancer plasma sEV-specific proteins, and 50 groups of esophageal cancer plasma sEV-specific protein expression profiles were obtained.

[0052] The protein expression profiles of the above 50 rounds were intersected, and finally 9 protein combinations were obtained, including 6 proteins with high expression in esophageal cancer plasma sEV (LAMP2, NCSTN, CTSB, TM9SF1, TFRC and MMP14) and 3 proteins with low expression in esophageal cancer plasma sEV (TGM1, KRT4 and VCL).

[0053] Example 2

[0054] The detection of 9 protein targets derived from plasma sEVs showed statistical differences and had clinical value in the diagnosis of esophageal cancer.

[0055] Using PRM-targeted protein profiling of sEV protein samples (70 cases in stage IB EC group and 30 cases in healthy control group), the 9 protein target values ​​obtained by random forest machine learning model were detected by chemiluminescence instrument (see Figure 1 ).

[0056] Among them, the plasma sEV-derived TGM1 in the EC group was 51.78±22.64 IU / mL, and that in the healthy control group was 337.35±164.37 IU / mL;

[0057] KRT4 was 44.36 ± 14.96 IU / mL in the EC group and 246.91 ± 33.10 IU / mL in the healthy control group;

[0058] VCL was 28.71 ± 9.60 IU / mL in the EC group and 81.09 ± 13.36 IU / mL in the healthy control group;

[0059] LAMP2 was 60.43±14.23 IU / mL in the EC group and 21.58±5.40 IU / mL in the healthy control group;

[0060] The NCSTN level was 170.43±47.33 IU / mL in the EC group and 31.57±11.86 IU / mL in the healthy control group.

[0061] The level of CTSB in the EC group was 185.83±88.14 IU / mL, while that in the healthy control group was 41.94±22.42 IU / mL.

[0062] TM9SF1 was 116.30 ± 60.35 IU / mL in the EC group and 18.02 ± 12.07 IU / mL in the healthy control group;

[0063] The TFRC level was 217.42±72.36 IU / mL in the EC group and 53.62±23.92 IU / mL in the healthy control group.

[0064] The MMP14 level in the EC group was 332.22±140.64 IU / mL, while that in the healthy control group was 30.09±18.17 IU / mL (****p<0.0001).

[0065] Among them, TGM1, KRT4 and VCL were highly expressed in the healthy group, and the other six protein targets were highly expressed in the EC group. There were very significant statistical differences between the two groups, which preliminarily showed the effective diagnostic value of these nine protein targets for esophageal cancer.

[0066] Example 3

[0067] In PRM-targeted proteomics, the data on protein targets are highly correlated with the data on protein targets detected by chemiluminescence immunoassay.

[0068] The data (normalized) from the PRM method of plasma sEVs from 70 stage IB EC patients and 30 healthy controls were used as the Y-axis, and the data (normalized) from the chemiluminescence (ECL) method of these 100 plasma sEV samples were used as the X-axis. The Pearson correlation coefficient of the two sets of normalized data was calculated to analyze the correlation between the data obtained by the two detection methodologies for the same sample. The correlation R value of the TGM1 target in the two sets of data was 0.79, the correlation R value of the NCSTN target in the two sets of data was 0.82, and the correlation R value of the TM9SF1 target in the two sets of data was 0.76 (see Figure 2 ). From this analysis, it can be concluded that both the targeted mass spectrometry PRM method and the clinical conventional chemiluminescence method can effectively detect esophageal cancer-specific targets, and the data correlation between the different methodologies is high.

[0069] Example 4

[0070] Membrane protein targets have high esophageal cancer specificity.

[0071] sEVs secreted by tumor cells have biological specificity of the cells of origin, especially the membrane proteins of tissue-specific origin on the sEV membrane. Among the nine protein targets with diagnostic value for esophageal cancer in this invention, three are transmembrane proteins of sEVs, namely MMP14, TM9SF1, and LAMP2. 30 healthy subjects, 10 non-small cell lung cancer, 10 gastric cancer, 10 hepatocellular carcinoma, and 10 colorectal cancer samples in the plasma test set were tested by chemiluminescence to obtain data. After statistical confirmation (see Figure 3 ), the expression levels of the membrane protein target LAMP2 in the four tumor samples were no different from those in the healthy group. The average values ​​in the samples of each group were 21.58 IU / mL (Health), 28.79 IU / mL (NCSLC), 23.28 IU / mL (GC), 26.46 IU / mL (HCC), and 27.27 IU / mL (ColC).

[0072] The expression levels of MMP14 in the three tumors other than gastric cancer were not different from those in the healthy group. The average values ​​in the samples of each group were 34.09 IU / mL (Health), 42.59 IU / mL (NCSLC), 103.02 IU / mL (GC), 43.01 IU / mL (HCC), and 45.82 IU / mL (ColC). Combined with the data results in Example 2, it can be determined that LAMP2 is highly enriched only in esophageal cancer and belongs to the specific sEV target protein of esophageal cancer; MMP14 is highly enriched in both esophageal and gastric cancer and belongs to the sEV target protein common to both esophageal and gastric cancer. In addition, the average values ​​of TM9SF1 in samples from each group were: 18.03 IU / mL (Health), 130.20 IU / mL (NCSLC), 131.46 IU / mL (GC), 147.42 IU / mL (HCC), and 126.56 IU / mL (ColC). Therefore, TM9SF1 was highly enriched in all tumor samples except the healthy group and is a sEV target protein common to multiple cancer types.

[0073] Example 5

[0074] Chemiluminescence scoring model and clinical diagnostic efficacy of esophageal cancer.

[0075] The esophageal cancer diagnosis model containing 9 proteins obtained through random forest machine learning is:

[0076] ECSD = 1.130 × TGM1 (IU / mL) + 1.279 × KRT4 (IU / mL) + 0.928 × VCL (IU / mL) + 1.044 × LAMP2 (IU / mL) + 1.561 × NCSTN (IU / mL) + 0.920 × CTSB (IU / mL) + 0.872 × TM9SF1 (IU / mL) + 1.140 × TFRC (IU / mL) + 1.126 × MMP14 (IU / mL). TGM1 (IU / mL) represents the concentration of TGM1 protein;

[0077] If ECSD is less than 620, the evaluation result of esophageal epithelial squamous dysplasia is benign; if it is 620≤ECSD<660, the ECSD model should be followed up by endoscopic examination or retested within 1 month; if ECSD is greater than or equal to 660, the evaluation result of esophageal epithelial squamous dysplasia is malignant, and esophageal cancer is highly suspected.

[0078] 180 subjects with endoscopic esophageal epithelial squamous dysplasia were enrolled. The ECSD model was used to differentiate between benign and malignant esophageal epithelial squamous dysplasia in these 180 subjects. The clinical pathological diagnosis results after 12 months of follow-up were compared with the model results to evaluate the sensitivity and specificity of the auxiliary diagnosis model for differentiating benign and malignant esophageal epithelial squamous dysplasia (see Table 1 below). Excluding 40 cases lost to follow-up and 6 cases without a clear pathological diagnosis, the ECSD values ​​of 134 samples were used to generate the ROC curve ( Figure 4 , left), with an AUC of 0.995, indicating that the sensitivity of this model for the auxiliary diagnosis of benign and malignant esophageal squamous dysplasia is 100% and the specificity is 97.22%. The ROC curve was generated using only the ECSD values ​​of 30 samples of high-grade esophageal squamous dysplasia ( Figure 4 , right), with an AUC of 0.990, indicating a 100% sensitivity and 94.70% specificity for the auxiliary diagnosis of benign and malignant high-grade esophageal hyperplasia using this model. Existing technologies using exosomal CD54 have low specificity for diagnosing early-stage EC, with AUC values ​​of 0.513 and 0.534 for stages I and II, respectively, making them unsuitable for use as liquid biopsy markers for early EC screening. This suggests that the markers provided by the present invention have higher sensitivity and specificity for esophageal cancer screening compared to existing technologies.

[0079] Table 1

[0080] Esophageal squamous dysplasia (endoscopic screening) ECSD diagnosis results Pathological diagnosis results of 12-month follow-up 80 cases of mild esophageal squamous dysplasia 78 benign cases; 2 malignant cases 79 cases were benign; 1 case had no pathological diagnosis 24 cases of moderate esophageal squamous dysplasia 19 cases were benign; 5 cases were malignant 19 cases were benign; 4 cases were esophageal cancer; 1 case had no pathological diagnosis Thirty cases of high-grade esophageal squamous dysplasia 11 cases were benign; 19 cases were malignant 11 cases were benign; 18 cases were esophageal cancer; 1 case had no pathological diagnosis

[0081] Example 6

[0082] This example provides an exemplary process for detecting nine esophageal cancer-specific proteins in plasma sEVs using a chemiluminescence platform.

[0083] 1. Purify plasma sEVs and extract sEV-derived total protein;

[0084] 1.1 Centrifuge 3 mL of whole blood separated in an anticoagulant blood collection tube at 2000 × g for 10 minutes at room temperature and aspirate 1 mL of upper plasma;

[0085] 1.2 Turn on the ExoStation (Shengran Technology, Model G1000), an automatic sEV separator. After the instrument self-checks, click Plasma > Sample Volume 1 mL > High-Purity sEV Component on the screen.

[0086] 1.3 Add 1 mL of plasma to the sEV purification reagent 500 UltraColumn (brand: Shengran Technology) pre-placed on ExoStation, close the operation window and click the start button on the screen interface. After hearing the purification completion prompt, open the operation window and take out the EP tube that has completed sEV separation.

[0087] 1.4 Add 0.5 mL of RIPA lysis and extraction buffer (ThermoFisher, Cat. No. 89900) to the EP tube and incubate at room temperature for 5 minutes to complete the extraction of sEV-derived total protein.

[0088] 2. Prepare magnetic beads coated with antibodies corresponding to 9 proteins;

[0089] 2.1 Wash the magnetic beads and dilute the washed magnetic beads (ThermoFisher, Cat. No. 21353) to 0.2 mg / mL;

[0090] 2.2 Dilute the primary antibodies corresponding to the nine proteins to 1:100 (Abcam, anti-TGM1 Catalog No.: ab183351; anti-LAMP2 Catalog No.: ab13524; anti-MMP14 Catalog No.: ab51074; Sigma-Aldrich, anti-KRT4 Catalog No.: WH0003851M1; anti-VCL Catalog No.: HPA002131; anti-TFRC Catalog No.: HPA028598; ThermoFisher, anti-TM9SF1 Catalog No.: PA5-84406; anti-NCSTN Catalog No.: PA5-20304; anti-CTSB Catalog No.: PA5-47975), and incubate the diluted primary antibodies with the washed magnetic beads at 37°C for 1 hour to complete the coating;

[0091] 2.3 Add BSA to the coated antibody-magnetic bead mixture for blocking, react at 37°C for 1 hour, wash off excess BSA, and finally place the completed blocking antibody-magnetic bead solution at 4°C until use.

[0092] 3. Prepare ALP-labeled primary antibody;

[0093] According to the instructions of the ALP Conjugation Kit - Lightning-Link® (Abcam, Catalog No. ab102850), each of the nine primary antibodies in step 2 was ALP-labeled and set aside.

[0094] 4. Incubate the magnetic beads, plasma sEV protein, and ALP-labeled primary antibody;

[0095] 4.1 Prepare 9 EP tubes, add 50 μL of plasma sEV protein extract, 50 μL of each primary antibody magnetic bead mixture, and 5 μL of each ALP-conjugated primary antibody to each tube and incubate at 37°C for 10 minutes;

[0096] 4.2 Wash and remove excess ALP labeled primary antibody and plasma sEV protein, leaving only the double sandwich magnetic beads plasma sEV protein and ALP after incubation

[0097] The complex was washed twice and then prepared for the luminescence reaction.

[0098] 5. Chemiluminescence reaction and numerical reading;

[0099] 5.1 Add the ALP substrate AMPPD (MedChemExpress, Catalog No. 122341-56-4) to the complex and react at room temperature in the dark for 1 minute.

[0100] The solution after the reaction was placed in a fully automatic chemiluminescence immunoassay analyzer (brand: Cosmay, model: Venus100) for numerical reading.

[0101] In summary, the present invention combines proteomics of sEVs derived from tumor and adjacent tissues with proteomics of sEVs derived from plasma for the first time. By combining a large number of clinical samples with machine learning algorithms, a set of protein targets has been discovered. Using multiple protein detection platforms, a scoring system combining these detection results has been developed. Data from numerous clinical trials confirm that these scoring systems can be used for early screening of esophageal cancer. Compared to existing technologies, this technology can achieve precise diagnosis and treatment of high-risk or confirmed esophageal cancer patients using widely used detection platforms such as chemiluminescence and flow cytometry.

[0102] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. An early screening system for esophageal cancer, characterized in that: It includes: An information acquisition module, and optionally a calculation module and / or a diagnosis module; The information acquisition module is used to execute the step of acquiring the concentration information of the target protein of the subject; the target proteins include the following proteins: LAMP2, NCSTN, CTSB, TM9SF1, TFRC, MMP14, TGM1, KRT4 and VCL; the concentration information comes from small extracellular vesicles in the plasma of the subject; The calculation module is used to execute the step of substituting the concentration information into the calculation model to calculate the risk threshold; The mathematical formula of the calculation model is expressed as: ECSD=1.13×C TGM1 +1.279×C KRT4 +0.928×C VCL +1.044×C LAMP2 +1.561×C NCSTN +0.92×C CTSB +0.872×C TM9SF1 +1.14×C TFRC +1.126×C MMP14 , Among them, ECSD represents the risk threshold of esophageal cancer, C TGM1 , C KRT4 , C VCL , C LAMP2 , C NCSTN , C CTSB , C TM9SF1 , C TFRC and C MMP14 , represent the concentration values ​​of TGM1, KRT4, VCL, LAMP2, NCSTN, CTSB, TM9SF1, TFRC, and MMP14, respectively; The diagnostic module is used to execute the step of judging the health condition of the subject according to the risk threshold.

2. The system according to claim 1, characterized in that The subject is diagnosed with esophageal epithelial squamous dysplasia.

3. The system according to claim 2, characterized in that The diagnosis module makes a judgment based on the risk threshold obtained by the calculation model, and the judgment method is as follows: If ECSD is less than 620, it indicates that the evaluation result of the subject's esophageal epithelial squamous dysplasia is benign; If 620≤ECSD<660, the subject was instructed to undergo follow-up endoscopic testing or retest within 1 month; If ECSD is ≥ 660, the evaluation result of the subject's esophageal epithelial squamous dysplasia is malignant and highly suspected of esophageal cancer; The unit of the concentration information is IU / mL.

4. A diagnostic kit for early screening of esophageal cancer, characterized in that: The method comprises a detection reagent for detecting target proteins, wherein the target proteins include the following proteins: LAMP2, NCSTN, CTSB, TM9SF1, TFRC, MMP14, TGM1, KRT4 and VCL.

5. The diagnostic kit according to claim 4, characterized in that The diagnostic kit includes an antibody that can specifically bind to the target protein.

6. Use of the detection reagent in the preparation of a diagnostic kit for early screening of esophageal cancer, characterized in that: The detection reagent can detect target proteins, including the following proteins: LAMP2, NCSTN, CTSB, TM9SF1, TFRC, MMP14, TGM1, KRT4 and VCL.

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