Extracellular vesicle biomarkers for diagnosis of non-small cell lung cancer and their applications

Abstract

The present invention provides protein biomarkers for the diagnosis of non-small cell lung cancer based on proteins MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4 and APOD in extracellular vesicles of plasma obtained from patients in vitro. These biomarkers have the advantages of high sensitivity and high specificity, and have important scientific research and clinical application value. Through the biomarkers provided by the present invention, non-small cell lung cancer can be pre-diagnosed without biopsy of lung nodule tissue or imaging examination, the patient suffers less pain, and the detection can be performed by protein immunoassay technology or proteomics technology, and the detection efficiency is high; thus, an auxiliary diagnostic kit can be convenient for clinical application, providing laboratory support for the screening and diagnosis and treatment of non-small cell lung cancer. The integration of data information obtained by omics technology and clinical information will help to better understand the pathological process of non-small cell lung cancer, find new intervention targets, and thus improve the clinical management of patients.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and in particular relates to an extracellular vesicle biomarker for diagnosing non-small cell lung cancer and an application thereof. Background Art

[0002] Lung cancer is the leading cause of cancer-related deaths worldwide, causing approximately 1.82 million deaths in 2022. Non-small cell lung cancer (NSCLC) accounts for approximately 80%-90% of all lung cancer cases, with the most common subtypes being lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Despite advances in diagnostic technology, targeted therapy, and immunotherapy in recent years, the 5-year overall survival rate of NSCLC remains unsatisfactory. Lung cancer is mostly diagnosed in the advanced stage, which greatly affects treatment and prognosis. Early detection is crucial for high-risk individuals as it can greatly improve clinical outcomes. Physical examination screening can effectively increase the early detection rate of cancer and reduce the incidence of progression to the middle and late stages in high-risk populations.

[0003] Tumor biomarkers refer to substances synthesized and released by tumor cells and produced by the body's response to tumors during the development of tumors. These substances exist in the body fluid circulation and show dynamic changes as the disease progresses. They can indicate the occurrence and development of tumors and can be used for tumor diagnosis, prognosis judgment and treatment monitoring. The markers currently widely used in the clinical diagnosis of non-small cell lung cancer are mainly tumor-associated antigens in serum / plasma, including carcinoembryonic antigen, cytokeratin fragment 19, squamous cell carcinoma antigen, etc. Although these markers have been widely used in clinical applications as auxiliary diagnosis, early diagnosis of tumors still poses great challenges and has received great attention. The main reason is that in the early stages of tumors, the abundance of tumor markers is very low, and there are technical difficulties in accurately detecting and quantifying these markers. Even with the advancement of technology, these detection projects still face health economics dilemmas. Secondly, there are currently no tumor markers that are specific to cancer, and they can only play an auxiliary diagnostic role to a certain extent. They are really limited in judging primary and metastatic lesions and monitoring treatment effects. Therefore, the clinical application of tumor markers is still limited to auxiliary diagnosis, efficacy and prognosis evaluation of tumors. However, for cancers that have to be examined invasively, the development of plasma / serum tumor markers is of great value. It can not only alleviate the pain of patients, but also facilitate disease screening and benefit more people.

[0004] The clinical diagnosis of non-small cell lung cancer is mainly based on CT-based imaging and pathology. The development of hematological tests to detect circulating tumor materials, the so-called "liquid biopsy", is currently an active research area. Liquid biopsies include a series of non-invasive biomarkers, such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating autoantibodies (circulating autoantibodies) and extracellular vesicles (EVs). Such minimally invasive biomarkers allow continuous monitoring of tumor dynamics, including early emergence of treatment resistance, residual disease after treatment, and disease recurrence. More and more studies are focusing on the potential of various biomarkers for the diagnosis and prognosis of tumors. Compared with other circulating biomarkers, extracellular vesicles have performed better due to their abundance, stability and diverse expression profiles.

[0005] Extracellular vesicles are submicron or micron-sized substances released by cells and play a key role in intercellular communication. Tumor cell-derived extracellular vesicles (TDEVs) carry bioactive molecules that can affect the tumor microenvironment and immune system, which may lead to adverse tumor progression. Studies on the molecular expression profile of extracellular vesicles have shown that their contents are very similar to those of parental cells. Therefore, TDEVs have received considerable attention as a "liquid biopsy" for non-invasive tumor diagnosis and prognostic assessment. Body fluids contain a heterogeneous mixture of extracellular vesicles from different cell sources, and TDEVs are only a small part of circulating extracellular vesicles. In order to apply TDEVs as a reliable substitute for tumor cells or circulating tumor cells in clinical research, it is crucial to separate TDEVs from the large number of vesicles produced by non-malignant cells. This step ensures that the extracellular vesicles obtained are derived from tumor cells rather than other types of cells in the body. However, given the heterogeneity of extracellular vesicles in body fluids, it is very important to develop robust separation and identification methods to ensure specific and accurate identification of TDEVs. Continued research and technological advancement in this field will help clinical translation of TDEVs as valuable biomarkers in cancer disease diagnosis and health management.

[0006] In the process of realizing the present invention, the inventors found that there are at least the following problems in the prior art: body fluids contain a heterogeneous mixture of extracellular vesicles from different cell sources, and TDEVs are only a small part of circulating extracellular vesicles. However, due to the interference of extracellular vesicles from non-tumor cells, the use of total extracellular vesicles for diagnosis and prognosis assessment has always been challenging. On the other hand, analyzing the molecular expression profile of extracellular vesicles in the plasma of cancer patients can discover potential biomarkers. However, this method cannot clearly determine whether these molecules are derived from tumors, which is crucial when looking for biomarkers for treatment monitoring. Because when the research goal is a biomarker for treatment monitoring, anti-cancer treatment not only affects tumor cells, but may also affect non-malignant cells. Summary of the invention

[0007] Based on the above problems, the present invention provides, on the one hand, seven serum / plasma extracellular vesicle protein markers for the diagnosis of non-small cell lung cancer, wherein the markers are MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4 and APOD.

[0008] Another aspect of the present invention also provides a detection kit for seven protein markers of serum / plasma extracellular vesicles. The detection kit for seven protein markers of serum / plasma extracellular vesicles for diagnosis of non-small cell lung cancer includes a confusion matrix diagnostic model composed of seven proteins, and may also include enzymes and reagents commonly used in proteomics, such as trypsin, buffer, etc.; it may also contain standard substances and / or control substances. The markers are MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4 and APOD.

[0009] Preferably, the quantitative method of the above protein can be a commonly used method in the art, such as one or more of enzyme-linked immunosorbent assay (ELISA), colorimetry, fluorescence or LC-MS / MS.

[0010] More preferably, LC-MS / MS is selected to quantify the protein markers.

[0011] On the other hand, the present invention also provides the use of the above kit, which can be used to screen high-risk populations for NSCLC by detecting the level of the above protein in plasma / serum extracellular vesicles, and to determine the prognosis of patients by detecting the level of the above protein in plasma / serum extracellular vesicles. Or it can be used to detect the level of the above protein in plasma / serum to determine whether surgery, drug treatment, etc. for NSCLC is effective or when to stop treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1: The characterization results of the extracellular vesicles extracted during the implementation of the present invention include observation of the morphology of the extracellular vesicles by electron transmission microscopy ( Figure 1 A) Nanoparticle size tracking analysis to detect the size distribution and concentration of extracellular vesicles ( Figure 1 B), Western blot analysis of the expression of extracellular vesicle positive markers (CD63, ALIX, TSG101 and Syntenin) in three independent samples ( Figure 1 C).

[0013] Figure 2 :Proteomic analysis of extracellular vesicle biomarkers for screening NSCLC patients during the implementation of the present invention. Analysis of the number of extracellular vesicle proteomics identifications in the non-small cell lung cancer group and the healthy control group ( Figure 2 A), PCA results of extracellular vesicle proteomics in non-small cell lung cancer group and healthy control group ( Figure 2 B), Random Forest algorithm evaluates the importance of candidate markers ( Figure 2 C).

[0014] Figure 3 : Proteomic analysis of the extracellular vesicle biomarker validation phase for screening NSCLC patients during the implementation of the present invention. Violin plot of the expression of 21 differentially expressed proteins ( Figure 3 A). ROC curve analysis of the diagnostic efficacy of seven markers and their combination ( Figure 3 B). Confusion matrix algorithm to evaluate the diagnostic performance of seven biomarker combinations ( Figure 3 C). DETAILED DESCRIPTION

[0015] Example 1 Isolation of extracellular vesicles from plasma samples

[0016] The patients and healthy participants involved in the present invention were recruited by Shiyan People's Hospital. According to the relevant medical ethics review methods of the Ministry of Health, all steps and processes of this experiment were reviewed and approved by the Ethics Committee of Shiyan People's Hospital and informed consent was signed with the patients. A total of 112 patients with non-small cell lung cancer and 190 healthy controls were recruited in this study. All patients included in this study were newly diagnosed and no patients received any treatment. Whole blood was collected into EDTA vacuum tubes and centrifuged at 3000 g for 10 min. Plasma collected from the supernatant was stored at -80 ˚C until use. 0.5-2 ml of plasma samples to be tested were collected.

[0017] The present invention is based on the difference in the expression of plasma extracellular vesicle whole protein, and selects plasma extracellular vesicle protein markers and their combinations through machine learning. The screening process includes the following aspects:

[0018] 1. Isolation of Extracellular Vesicles from Plasma Samples

[0019] 1.1 Mix plasma and PBS in a ratio of 1:9, centrifuge at 300 g, 4 °C for 10 min, and retain the supernatant.

[0020] 1.2 Centrifuge the supernatant at 1000 g, 4 °C for 15 min and retain the supernatant.

[0021] 1.3 Centrifuge the supernatant at 3000 g, 4 °C for 20 min and retain the supernatant.

[0022] 1.4 Centrifuge the supernatant at 10,000 g, 4 °C for 30 min and retain the supernatant.

[0023] 1.5 Filter the supernatant using a 0.22 μm disposable filter, centrifuge at 100,000 g, 4 °C for 120 min, discard the supernatant, and resuspend in 1 ml PBS.

[0024] 1.6 Add 20 μl Protein A / G Magnetic Beads (MCE, HY-K0202) to the above resuspension and rotate at 4 ℃ for 4 h.

[0025] 1.7 Magnetic separation of magnetic beads, retain the supernatant, wash the magnetic beads 3 to 5 times with 1 ml wash buffer (PBS, 0.01% Tween-20), mix the wash buffer and supernatant to reduce the loss of extracellular vesicles, and discard the magnetic beads. This step can effectively remove the immunoglobulins that precipitate during the centrifugation process.

[0026] 1.8 Add 2 μg anti-Albumin antibody (Sino Biological, 68001-R101) to the above mixture and rotate at 4 ℃ for 12 h.

[0027] 1.9 Add 20 μl Protein A / G magnetic beads (MCE, HY-K0202) to the above solution and rotate at 4℃ for 4 h.

[0028] 1.10 Magnetic separation of magnetic beads, retain the supernatant, wash the magnetic beads 3-5 times with 1 ml wash buffer (PBS, 0.01% Tween-20), mix the wash buffer and supernatant to reduce the loss of extracellular vesicles, discard the magnetic beads, and add PBS to the volume to 30 ml. This step can effectively remove the albumin that precipitates during the centrifugation process.

[0029] 1.11 Centrifuge the mixture at 100,000 g, 4°C for 120 min, discard the supernatant, and resuspend in 100 μl PBS.

[0030] The above steps are the standard process for the extraction of extracellular vesicles from plasma. Transmission electron microscopy, Western blot and Nanoparticle tracking analysis were used to characterize extracellular vesicles. The specific results are shown in Figure 1 Transmission electron microscopy images show the typical cup-shaped morphology of extracellular vesicles, with a size range of 60-200 nm ( Figure 1 A). Nanoparticle tracking analysis determined that the average diameter of extracellular vesicles was 163.2 ± 8.6 nm, and the main diameter was 159.8 nm ( Figure 1 B). Western blot results showed that the extracellular vesicle positive markers CD63, TSG101, ALIX and Syntenin were positively expressed ( Figure 1 C). These results demonstrate successful isolation of extracellular vesicles from plasma samples.

[0031] Example 2 Discovery of Extracellular Vesicle Biomarkers

[0032] The study of plasma extracellular vesicle proteomics is divided into two stages. The first stage is the biomarker discovery stage, the purpose of which is to identify differential proteins from the whole proteome of extracellular vesicles for subsequent verification; the second stage is the biomarker verification, screening and diagnostic model construction stage, the purpose of which is to monitor the differential proteins determined in the first stage and screen out a protein biomarker combination with diagnostic performance for non-small cell lung cancer through machine learning.

[0033] The first stage is the discovery of extracellular vesicle biomarkers. The specific steps are as follows:

[0034] 2. Immunocapture of Extracellular Vesicles in Plasma Samples

[0035] 2.1 5 μg of biotinylated anti-CD155 antibody (Sino Biological, 10109-RP02) was added to each of the 5 mixed extracellular vesicle samples in the non-small cell lung cancer group. 5 μg of biotinylated anti-CD63 antibody (Santa Cruz Biotechnology, sc-5275) was added to each of the 7 mixed extracellular vesicle samples in the healthy control group. The samples were rotated and shaken at 4 °C for 12 h. The biotinylation of anti-CD155 antibody and anti-CD63 antibody was performed according to the instructions of the biotinylation kit (Bomeida Life Science, G-MM-IGT).

[0036] 2.2 Add 40 μl streptavidin magnetic beads (MedChemExpress, HY-K0208) to the above solution and rotate and shake at 4 ℃ for 4 h.

[0037] 2.3 Magnetic separation of magnetic beads, discard the supernatant, wash the magnetic beads 3-5 times with 1 ml wash buffer (PBS, 0.01% Tween-20), retain the magnetic beads, resuspend them with 30 μl 1× SDS loading buffer, boil them in a boiling water bath for 10 min, perform SDS-PAGE electrophoresis, and cut the entire gel strip.

[0038] 3. Detection of Extracellular Vesicle Proteins

[0039] Proteomics was used to analyze the protein expression in the extracellular vesicles obtained in step 2.3.

[0040] 3.1 The extracellular vesicle proteins enriched in step 2.3 are enzymatically hydrolyzed. The enzymatic hydrolysis process includes:

[0041] (1) Cut the selected gel strip with a surgical blade, place it in an EP tube, record the strip number and corresponding position, and cut the strip into 2 mm 3 size;

[0042] (2) Add 200 μl of ultrapure water (the volume of the liquid should be sufficient to cover the particles) and wash twice, 10 min each time, and then drain the liquid;

[0043] (3) Add 200 μl of acetonitrile (ACN) / 50 mM NH4HCO3 = 2:3 solution, decolorize at 37 °C for 20 min, and absorb the liquid;

[0044] (4) Repeat step 3 until the blue color fades;

[0045] (5) Add 500 μl of ACN to dehydrate until the particles turn completely white, then suck dry and vacuum dry for 5 min;

[0046] (6) Add 50 μl of 10 mM dithiothreitol (DTT) (prepared with 10 μl 1M DTT and 990 μl 25 mM NH4HCO3) and incubate in a 56°C water bath for 45 min; (directly prepared to 1.54 mg / ml)

[0047] (7) After cooling to room temperature, aspirate the liquid, quickly add 50 μl of 55 mM iodoacetamide (IAM) (prepared with 22 μl 1M IAM and 378 μl 25 mM NH4HCO3), and place in the dark for 45 min; (directly prepared to 10.2 mg / ml)

[0048] (8) Wash with 25 mM NH4HCO3, 50% ACN solution and ACN in sequence, dehydrate with ACN until the particles turn completely white, absorb the liquid and vacuum dry for 5 min;

[0049] (9) Dilute the 0.1 μg / μl enzyme stock solution 20 times with 25 mM NH4HCO3. The mass ratio of enzyme to substrate is estimated to be 1:40, and can be in the range of 1:20-1:100). The amount of enzyme solution added to each EP tube is based on completely wetting the colloid particles. Allow the enzyme solution to fully contact the colloid particles. Place it at 4°C or on ice for 5 min. When the solution is completely absorbed by the colloid blocks, absorb the excess enzyme solution, add 30-45 μl of 25 mM NH4HCO3 (based on an excess of 30 μl to submerge the colloid particles), place it in a 37°C water bath, and digest overnight.

[0050] (10) Add 5% formic acid (FA) to terminate the reaction, pH < 4 (final FA concentration is 0.2%-0.5%), shake and mix, and centrifuge;

[0051] (11) Take out the solution and transfer it to a new EP tube, mark it, add 80 μl of 60% ACN (ACN:ddH2O) to the silver-stained sample and ultrasonically extract it twice in the tube with the colloid particles, each time for 10 min. The three solutions are combined, the solution is vacuum dried, and 20 μl of 0.1% FA aqueous solution is added to dissolve the peptide;

[0052] 3.2 Liquid chromatography-mass spectrometry (LC-MS / MS) detection of the whole proteome

[0053] The LC-MS / MS instrument is a Q Exactive mass spectrometer (Thermo Fisher Scientific) equipped with an EASY-nLC 1000 HPLC system. The analytical liquid phase and mass spectrometry conditions are as follows: a chromatographic column is filled with ReproSil-Pur C18 AQ filler from Dr. Maisch GmbH, Germany: specification 75 μm id×20 cm, 3.0 μm; the mobile phase is: A: water containing 0.1% formic acid, B: acetonitrile containing 0.1% formic acid; the flow rate is 300 nl / min; gradient elution: 0 min: 96% A+4% B, 8 min: 5-8% B, 50 min: 8-22% B, 12 min: 22-32% B, 1 min: 32-95% B, 7 min: 95% B. Ion source: electrospray ion source; positive ion scanning; electrospray voltage: 2.1 kV; tandem mass spectrometry collision energy 32%; dynamic exclusion time 50 s; separation mass window 2.0 Da; primary mass spectrometry mass range 400–1210 m / z; primary mass spectrometry resolution 120,000; secondary mass spectrometry resolution 30,000.

[0054] The mass spectrometry data were searched to obtain quantitative information of the whole proteome. Protein Discoverer (version 2.2, Thermo Fisher Scientific) software was used to complete the search, and the search parameters were set as follows: the database selected was the human library downloaded from Uniprot in November 2022, trypsin full digestion was selected, a maximum of 2 missed cleavage sites were allowed, the length of the digested peptide amino acids was set to 6-144, cysteine ​​carbamidomethylation modification, peptide N-terminus and lysine TMT modification were set to fixed modification, methionine oxidation was set to variable modification, the parent ion and fragment ion mass errors were set to 10 ppm and 0.02Da, respectively, and the false positive rate FDR of proteins and peptides was set to 1%.

[0055] 4. Determination of extracellular vesicle protein detection results.

[0056] 4.1 Interpretation of proteomics results in the biomarker discovery phase.

[0057] The differentially expressed proteins were determined from the proteomic data. The t-test statistical test was used to calculate the difference between the two groups. P The differential protein screening conditions are: fold change>2 and P <0.05 for up-regulated proteins, fold change<0.5 and P <0.05 indicates down-regulated protein.

[0058] According to the above data retrieval method, 2021 proteins were identified in the 5 EVs samples of the NSCLC group, while 2234 proteins were identified in the 7 EVs samples of the HC group ( Figure 3 A). There were 281 significantly different proteins between the NSCLC-EVs group and the HC-EVs group, of which 126 were upregulated and 155 were downregulated. PCA results showed that these differentially expressed proteins effectively distinguished NSCLC patients from healthy individuals ( Figure 3 B). Use the machine learning model of the random forest algorithm to perform deep learning on the expression profiles of differentially expressed proteins and obtain the importance ranking of these molecules ( Figure 3 C).

[0059] Example 3 Screening and verification of extracellular vesicle biomarkers

[0060] The specific steps are as follows:

[0061] The test set consisted of 49 plasma samples, and extracellular vesicles were extracted separately. The extracellular vesicle enrichment method was the same as steps 1.1-1.11 and the protein enzymatic process was the same as step 3.2. Parallel reaction monitoring (PRM) targeted quantitative analysis was performed on the peptide samples obtained by enzymatic hydrolysis. The LC-MS / MS instrument used in this stage was an Orbitrap Eclipse Tribrid mass spectrometer (Thermo Fisher Scientific) equipped with an EASY-nLC 1200 HPLC system.

[0062] Before formal testing, a PRM detection method needs to be established. Peptide quantification was performed using the Thermo Scientific™ Pierce™ Quantitative Colorimetric Peptide Assay. After quantification by the peptide kit, 1 µg of each sample was taken and mixed in equal amounts, and 10×iRT standard peptides (Biognosys) were added for data-dependent mass spectrometry analysis to construct a spectral library from which a parent ion monitoring list that can be used for subsequent targeted quantitative analysis was obtained.

[0063] The mass spectrometry data used for spectral library construction were analyzed using SpectroDive v10.4 (Biognosys) software. The following parameters were used to screen the precursor ions corresponding to the differentially expressed proteins identified in the whole proteome to establish the PRM detection list: precursor ion mass-to-charge ratio range 350-1500; precursor ion charge number, 2-3; missed cleavage site, 0; peptide length, 8-25; peptide mass-to-charge ratio range, 300-1800; maximum product ion charge, 2; ion type, b, y ions; top 6 fragment ions were selected; after retention time correction using iRT, the retention time window was set to ± 5 min.

[0064] After the parent ion detection list is determined, the PRM data of the sample is formally collected. The mass spectrometry parameters of the PRM experiment are set as follows: tandem mass spectrometry collision energy 30%; primary spectrum resolution 60, 000, maximum ion injection time 20 ms, AGC 4e5; secondary mass spectrometry resolution 30,000, separation mass window 1.0 Da; maximum ion injection time 80 ms, AGC 2e5. The obtained PRM raw files are processed using SpectroDive v10.4. In the software, parent ions with q-value < 0.01 are selected, and the daughter ion signal peaks with significant interference signals around the peak tip are excluded. More than three ion pairs are selected for quantification for each parent ion.

[0065] In the comparison between the two groups of PRM targeted quantitative analysis, the t-test statistical test method was used to calculate P value. P Proteins with p<0.05 were considered to be significantly different.

[0066] 5. Construction and evaluation of diagnostic models based on machine learning methods

[0067] 49 proteins were successfully quantified in the PRM targeted quantitative analysis, such as Figure 3 As shown in A, 21 of them were statistically different between the two groups. We used a step-by-step machine learning approach to screen out the marker combination with the best diagnostic performance from the 21 differentially expressed proteins. The machine learning method was used to screen out a combination of seven biomarkers (MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4, and APOD), the AUC value of which in the ROC model was 1.0, and the sensitivity and specificity were both 100%. Figure 3 As shown in Figure C, the sensitivity in the confusion matrix algorithm was 92.3% and the specificity was 88.9%, while the combined sensitivity of all 21 differentially expressed proteins was 76.9% and the specificity was 77.8%. Figure 3As shown in Figure C, compared with all 21 differentially expressed proteins, PCA analysis showed that the model better distinguished NSCLC patients from healthy individuals. Table 1 shows the expression levels of these 7 proteins in 49 samples of the training set.

[0068] Table 1.

[0069] ID MVP GYS1 SERPINA3 HECTD3 SERPING1 CP APOD 1 97044.86 89873.6 967482.6 71194.98 664522.8 207301.9 402953.6 2 12072.82 3038.951 622822.8 6763.094 834715.6 122919.9 349673 3 131842.8 42822.54 731453.5 60443.35 2740564 155818.4 364693.4 4 40287.16 28009.1 575620 19280.07 555585.7 99128.63 431311.4 5 50640.9 13724.58 625509 35924.36 475807.3 102810.4 833002.1 6 26582.07 4600.355 868846.5 9311.111 1684172 124538 456960.9 7 61712.53 49036.23 701377.1 37104.11 568580.6 125704.6 880828.3 8 81201.32 20904.26 835458.7 14127.59 1289370 138465.5 319489 9 22094.16 2824.534 703759.3 11910.92 1048007 81571.15 426718.5 10 33198.13 23213.43 820122.8 22374.69 643482.5 323592.9 463066.6 11 48563.28 20721.36 790518.3 43616.42 328699.3 163677.9 436271 12 97613.68 26598.55 1129485 70192.14 622202.1 126428.5 278106.4 13 112138.6 29423.75 1350764 51452.69 4780259 129989.4 385614.1 14 41205.83 18758.38 641752.3 13625.21 423664.8 288988.6 238080.1 15 21391.84 14472.94 1079481 16628.82 1279843 141599.8 342132.2 16 57619.06 11964.9 785863.5 34308.81 541641.6 155603.1 654832 17 31104.5 13900.66 781384.8 29126.48 1661006 186694.4 1214207 18 109346.7 25816.73 937132.3 57096.68 1592096 176491.3 364770.3 19 159411.6 63698.79 1544014 107370 1143364 273333.4 458864.9 20 169260.1 48943.41 1058196 33393.88 1299727 155576.9 464914.1 21 255349.6 67462.68 1088774 33873.99 528621.8 190729.2 396162.9 22 132945.4 33651.65 975870.8 83292.88 1495392 238098.3 363742 23 14505.66 4152.832 724581.2 4558.924 847311.9 124663.5 305243.5 24 339842.8 52330.84 897807.9 89045.41 1017486 212455.6 329187.1 25 22809.34 16506.61 503926.9 31993.23 545990.4 180823.1 371345.6 26 26767.43 5741.894 680580.7 18715.86 1022689 96041.94 390049.8 27 49372.29 11272 1052242 31703.46 335877.5 91612.31 323007.4 28 94169.3 40700.96 701470.9 18180.7 1992708 188923.7 407385.8 29 19833.07 8048.927 654131.9 13543.25 572161.3 184473 1105674 30 166831.3 59914.22 553386.4 90108.49 1128250 140286.4 399698.6 31 45339.13 19445.1 776299 45039.15 588670.5 133936.4 652416.8 32 54384.54 23189.19 829704.8 23012.56 1442900 258243.5 419108.4 33 238798.7 39719.94 952468.7 68779.04 1243607 276846.7 624400.2 34 240412.7 81168.44 924149.9 60505.89 874175.5 132075.4 400643.1 35 7469.325 1250.092 510553.8 3015.128 1231350 74580.99 310659 36 15868.57 2861.92 692680.9 4843.142 1381177 142610.1 334862.9 37 161774.9 29469.75 1376593 103645.9 2760038 201522.9 551081.2 38 76778.95 18652 622630.1 17322.65 735082.3 157246 350138.4 39 68967.01 17268.32 796595.8 67744.79 658202.7 162352.5 460053.9 40 24990.92 15474.11 683503.5 19439.62 442368.4 142718 409703.6 41 34809.91 15472.79 740546.2 15021.87 1143263 135661.9 310453.9 42 26937.76 6001.334 846610.9 16203.83 387562.7 128536.4 289766.9 43 31984.63 12289.6 643951.4 26177.74 492229.4 147014.4 397866.4 44 116695.6 63946.7 1522421 53537.77 2320681 246348.2 345985.9 45 95609.65 26107.4 1046494 67986.9 2199465 168578.1 518467.4 46 39729.91 12442.02 639547.1 21929.22 500889.7 119311.6 413146.9 47 310580.5 78429.65 1521343 131977.4 1365887 370490.5 662612.2 48 73108.96 15156.15 926576.9 28749.36 931383.2 193718.2 807764.5 49 27816.93 20662.32 558584.1 14878.22 271184.6 73422.67 215235.1

[0070] Example 4 Validation of protein markers

[0071] (1) A validation set of 22 plasma samples was used. 1.5 ml of plasma was collected from each sample. Extracellular vesicles were extracted using the method in steps 1.1-1.11 and then subjected to SDS-PAGE electrophoresis.

[0072] (2) Cut the selected gel strip with a surgical blade, place it in an EP tube, record the strip number and corresponding position, and cut the strip into 2 mm 3 size;

[0073] (3) Add 200 μl of ultrapure water (the volume of the liquid should be sufficient to cover the particles) and wash twice, 10 min each time, and then drain the liquid;

[0074] (4) Add 200 μl of acetonitrile (ACN) / 50 mM NH4HCO3 = 2:3 solution, decolorize at 37 °C for 20 min, and aspirate the liquid;

[0075] (5) Repeat step 3 until the blue color fades;

[0076] (6) Add 500 μl of ACN to dehydrate until the particles turn completely white, then suck dry and vacuum dry for 5 min;

[0077] (7) Add 50 μl of 10 mM dithiothreitol (DTT) (prepared with 10 μl 1M DTT and 990 μl 25 mM NH4HCO3) and incubate in a 56°C water bath for 45 min; (directly prepared to 1.54 mg / ml);

[0078] (8) After cooling to room temperature, aspirate the liquid, quickly add 50 μl of 55 mM iodoacetamide (IAM) (prepared with 22 μl 1M IAM and 378 μl 25 mM NH4HCO3), and place in the dark for 45 min; (directly prepared to 10.2 mg / ml);

[0079] (9) Wash with 25 mM NH4HCO3, 50% ACN solution and ACN in sequence. Dehydrate with ACN until the particles turn completely white. Absorb the liquid and vacuum dry for 5 min.

[0080] (10) Dilute the 0.1 μg / μl enzyme stock solution 20 times with 25 mM NH4HCO3. The mass ratio of enzyme to substrate is estimated to be 1:40 (it can be in the range of 1:20-1:100). The amount of enzyme solution added to each EP tube is based on completely wetting the colloid particles. Allow the enzyme solution to fully contact the colloid particles. Place it at 4°C or on ice for 5 min. When the solution is completely absorbed by the colloid blocks, remove the excess enzyme solution, add 30-45 μl of 25 mM NH4HCO3 (30 μl is required to immerse the colloid particles), place it in a 37°C water bath, and digest overnight.

[0081] (11) Add 5% formic acid (FA) to terminate the reaction, pH < 4 (final FA concentration is 0.2%-0.5%), shake and mix, and centrifuge;

[0082] (12) Take out the solution and transfer it to a new EP tube. Add 80 μl of 60% ACN (ACN:ddH2O) to the sample and ultrasonically extract it twice in the tube with the colloid particles, each time for 10 min. Combine the three solutions, vacuum dry the solution, and add 20 μl of 0.1% FA aqueous solution to dissolve the peptide.

[0083] The plasma EVs peptide samples obtained above were subjected to parallel reaction monitoring (PRM) targeted quantitative mass spectrometry analysis. The specific operation is as follows:

[0084] (13) 1 µg of each sample was mixed in equal amounts and then added with 10× iRT standard peptides (Biognosys). The PRM-MS / MS analysis instrument was an Orbitrap Eclipse Tribrid mass spectrometer (Thermo Fisher Scientific) equipped with an EASY-nLC 1200 HPLC system. The mass spectrometry data were collected in DDA mode for the construction of the spectral library. Seven protein biomarkers were screened from the spectral library to form the detection list. The spectral library data analysis was completed using SpectroDive v10.4 (Biognosys) software. The following parameters were screened for 7 protein biomarkers to establish a PRM detection list: precursor ion mass-to-charge ratio range 350-1500; precursor ion charge number, 2-3; missed cleavage site, 0; peptide length, 8-25; peptide mass-to-charge ratio range, 300-1800; maximum product ion charge, 2; ion type, b, y ions; top 6 fragment ions were selected; after retention time correction using iRT, the retention time window was set to ± 5 min.

[0085] (14) After the detection list was constructed, PRM data acquisition was performed to obtain targeted quantitative mass spectrometry data. The mass spectrometry parameters of the PRM experiment were set as follows: tandem mass spectrometry collision energy 30%; primary spectrum resolution 60,000, maximum ion injection time 20 ms, AGC 4e5; secondary mass spectrometry resolution 30,000, separation mass window 1.0 Da; maximum ion injection time 80 ms, AGC 2e5;

[0086] (15) The obtained PRM raw files were processed using SpectroDive v10.4. Peptides with q-value < 0.01 were selected, and the daughter ion signal peaks with significant interference signals around the peak tip were excluded. More than three ion pairs were selected for quantification for each parent ion. Table 1 shows the relative expression levels of seven protein markers detected in 22 samples of the validation set using PRM technology.

[0087] Table 2

[0088] ID MVP GYS1 SERPINA3 HECTD3 SERPING1 CP APOD 1 416564.4 83644 1682873 169256.4 1160564 332144.8 982741.2 2 121435.1 37207.95 1312908 85044.93 5683702 446132.1 644353.2 3 78358.02 21071.82 772800 35258.45 1423664 187306.4 409381.1 4 120251.4 22663.57 1072442 57027.07 1890899 209799.9 475412.2 5 99154.69 109087.2 891191.9 75550.8 1136416 157524 552672.8 6 44933.05 21193.16 794348.6 12834.75 1227294 188718.8 333322.5 7 39207.41 10771.95 625541.5 26398.61 1625474 183701.8 300118.6 8 64610.19 88396.02 778846.3 43870.8 509651.2 114424.4 229309.7 9 9925.003 2450.946 741768.5 11259.91 332654.2 94292.64 242806.4 10 47380.75 14125.74 653885.5 21504.44 1055752 121777.9 266339.7 11 217186.9 74174.8 784620.3 144287.8 1761315 162271.6 467470.5 12 38437.8 21316.75 1320418 16215.58 1276007 249500.7 494438.4 13 309746 74237.46 999249.9 21800.12 2032968 168109.9 585271.9 14 11338.27 42518.22 783143.3 14440.5 386078.2 97359.9 370370.1 15 33372.62 13712.08 612344.2 27680.24 376847.3 84146.34 541963.6 16 11110.14 1687.004 688471 2223.879 690775.1 77576.35 256264.6 17 42149.11 16324.81 891156.4 20968.55 546514 118286.1 613518.5 18 16139.95 3350.303 674750.2 5793.125 774986.7 100366.6 334226.4 19 23117.52 2746.175 1115607 6834.526 1322086 186475.9 567663.1 20 82573.69 31719.59 765475.3 43677.45 543422.2 138526.9 590562.7 21 66254.54 24312.46 938549.7 71927.68 835200.7 148182.5 1000534 22 40963.92 52211.09 922169.8 24846.59 542300.5 133999.2 415837.3

[0089] The mass spectrometry signals or relative expression levels of the seven protein markers of extracellular vesicles in the samples to be tested were imported into the confusion matrix model based on the random forest algorithm that we established. The sensitivity was 92.3%, the specificity was 88.9%, and the AUC value in the ROC model was 1.0. The sensitivity and specificity both reached 100%, indicating that this marker combination has a strong potential to distinguish NSCLC patients from healthy individuals.

[0090] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A protein biomarker combination for diagnosis of non-small cell lung cancer, characterized in that: Contains the following proteins: MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4 and APOD.

2. The marker combination according to claim 1, wherein the sample is derived from plasma or serum extracellular vesicles.

3. A kit for diagnosing non-small cell lung cancer, comprising a reagent for detecting the expression level of the marker combination according to claim 1, wherein the expression level of the marker combination is determined using one or more of enzyme-linked immunosorbent assay (ELISA), colorimetry, fluorescence or LC-MS / MS.

4. Use of the kit according to claim 3 in the preparation of products for diagnosing non-small cell lung cancer.

5. The use according to claim 4, wherein the sample is derived from plasma or serum extracellular vesicles.

6. The use according to claim 4, comprising the following steps: (1) extracting extracellular vesicles from plasma or serum of the subject to be tested; (2) Determine the contents of MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4 and APOD; (3) The results are determined based on the content of the above-mentioned marker protein combination.

7. The use according to claim 4, comprising determining whether surgery or drug treatment for NSCLC is effective or when to stop treatment by detecting the level of the above protein combination in extracellular vesicles of plasma or serum.

8. Use of the marker combination according to claim 1 in screening therapeutic drugs for non-small cell lung cancer.

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