Plasma protein markers for distinguishing lung adenocarcinoma from benign pulmonary nodules and their application

Abstract

The present invention provides a plasma protein marker for distinguishing lung adenocarcinoma and benign lung nodules, wherein the plasma protein marker comprises one or more of PRDX2, PON1 or APOC3; the novel plasma proteins DPRX2, PON1, APOC3 are combined with nine variables including CT indicators of nodule spiculation sign, nodule vascular notch sign, nodule lobulation sign and clinical traditional tumor markers CEA, CA125 and CYFRA21-1 to construct a differential diagnosis model for LUAD and BPN, which has good differential diagnostic efficacy for patients with lung adenocarcinoma and benign lung nodules, and is of great significance for the management and identification of patients with lung nodules.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology, and in particular relates to a plasma protein marker for distinguishing lung adenocarcinoma and benign lung nodules and an application thereof. Background Art

[0002] Lung cancer can be divided into two major categories: non-small cell lung carcinoma (NSCLC) and small cell lung carcinoma (SCLC). NSCLC mainly includes three types: lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large cell lung cancer. Among them, LUAD is the most common histological subtype of NSCLC, accounting for about 40% of lung cancer. A large number of studies have shown that lung adenocarcinoma often occurs in women and non-smokers. After radical surgery with complete resection, the five-year tumor-free survival rate of lung cancer patients with adenocarcinoma in situ and microinvasive adenocarcinoma is close to 100%. Therefore, the improvement of early diagnosis strategies for lung adenocarcinoma is the key to reducing the mortality rate of lung cancer. In recent years, low-dose computed tomography (LDCT) has been widely used in lung cancer screening for high-risk populations, significantly reducing the mortality rate of lung cancer. But at the same time, a large number of subjects were found to have lung nodules, and the clinical management of such patients has become a major challenge. The vast majority of patients with lung nodules are unable to obtain important information from routine examinations, that is, they are unable to effectively distinguish between benign and malignant tumors. Among patients diagnosed with LC by LDCT, the false positive rate is as high as 21.8%-26.6%. Therefore, overdiagnosis causes unnecessary harm to patients both physically and financially. Blood tumor-related marker detection is the most easily accepted diagnostic method by patients, but the tumor markers currently used in clinical lung cancer detection (CEA, CA125, and CYFRA21-1, etc.) can effectively distinguish between lung cancer patients and healthy people, but they are not effective in distinguishing between benign and malignant tumors in patients with lung nodules. Therefore, further research, development, and verification of non-invasive biological markers or their combinations that can be effectively used for the differential diagnosis of benign and malignant lung nodules are still needed.

[0003] The plasma proteome is one of the most complex proteomes in the human body, containing secretory proteins from multiple organs and tissues, which reflects the physiological function of individuals to a certain extent. In addition, compared with other tissue sampling methods such as bone marrow puncture, blood is easy to obtain under standard operations, and this sampling method with little trauma and easy acceptance by patients has greatly promoted the widespread application of biomarkers in clinical practice. Therefore, this study aims to conduct a quantitative proteomic study on plasma samples from patients with lung adenocarcinoma and benign pulmonary nodules (BPN) based on the label-free quantitative technology of mass spectrometry, and then screen for differential proteins between patients with lung adenocarcinoma and benign pulmonary nodules. Through further verification, plasma protein markers or their combinations with good differential diagnostic value will be used as potential tools for the management and differential diagnosis of patients with pulmonary nodules in clinical practice. Summary of the invention

[0004] In view of the deficiencies of the prior art, the object of the present invention is to provide a plasma protein marker for distinguishing lung adenocarcinoma from benign lung nodules and its application.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A plasma protein marker for distinguishing lung adenocarcinoma from benign lung nodules, wherein the plasma protein marker comprises one or more of PRDX2, PON1 or APOC3.

[0007] A detection kit for distinguishing lung adenocarcinoma from benign lung nodules, the kit comprising the plasma protein marker.

[0008] The application of plasma protein markers for distinguishing lung adenocarcinoma from benign lung nodules.

[0009] The present invention also provides a method for screening plasma protein markers for distinguishing lung adenocarcinoma from benign lung nodules, which specifically comprises the following steps:

[0010] (1) Blood samples were collected from patients with lung adenocarcinoma and lung nodules, and plasma samples were obtained after separation and processing;

[0011] (2) Label-free quantitative proteomics detection;

[0012] (3) Screening of plasma protein markers for differentiation of lung adenocarcinoma and benign lung nodules;

[0013] The non-labeled proteomics detection includes: protein extraction, trypsin hydrolysis, liquid chromatography-mass spectrometry analysis, database search, and calculation of protein FC value and P value;

[0014] After the step (3) of screening and identifying the plasma protein markers of lung adenocarcinoma and benign lung nodules, the step also includes: ELISA verification of the differential plasma proteins of lung adenocarcinoma and benign lung nodules; the ELISA verification of the differential plasma proteins includes: determining the optimal plasma dilution multiple of the differential proteins; performing ELISA testing on the plasma samples according to the instructions of the kit; and performing statistical analysis on the obtained data.

[0015] The present invention also provides a method for constructing a diagnostic model for distinguishing lung adenocarcinoma from benign lung nodules, which uses a Logistic regression analysis input method to combine the three plasma proteins (PRDX2, PON1 and APOC3) for distinguishing lung adenocarcinoma from benign lung nodules, three CT indicators (spicule sign, vascular notch and lobulation sign) and three clinical markers (CEA, CA125 and CYFRA21-1) to construct a lung adenocarcinoma diagnostic model PRE (P = LUAD, model) = 1 / (1 + EXP (-(0.010 × PRDX2 + 0.052 × APOC3 + 0.002 × PON1 + 1.112 × spicule sign + 1.161 × vascular notch sign + 0.535 × lobulation sign - 0.084 × CEA - 0.004 × CA125 - 0.404 × CYFRA21-1 - 7.697))).

[0016] Furthermore, the method for constructing the diagnostic model specifically comprises the following steps:

[0017] (1) Grouping: Patients with complete CT information and clinical traditional tumor markers CEA, CA125, and CYFRA21-1 were selected as samples and divided into a lung adenocarcinoma group and a benign lung nodule group;

[0018] (2) The three plasma proteins PRDX2, PON1 and APOC3 for distinguishing lung adenocarcinoma from benign lung nodules; 12 CT indices including number of nodules, diameter, margin, cavitation sign, spicule sign, vascular notch, lobulation sign, spinous process sign, pleural indentation sign, mediastinal lymphadenopathy, emphysema and calcification; and three clinical tumor markers CEA, CA125 and CYFRA21-1 were used for univariate analysis of the samples described in step (1) for differential diagnosis; 13 indices with statistically significant differences (P<0.01) between patients with lung adenocarcinoma and patients with benign lung nodules were obtained: the three plasma proteins PRDX2, PON1 and APOC3 for distinguishing lung adenocarcinoma from benign lung nodules, seven CT indices (spicule sign, vascular notch, lobulation sign, spinous process sign, pleural indentation sign, mediastinal lymphadenopathy and calcification); and three clinical markers (CEA, CA125 and CYFRA21-1). and CYFRA21-1);

[0019] (3) The differential diagnostic efficacy of the above 13 indicators was evaluated by ROC analysis, and 9 significant indicators were screened out with the criteria of AUC>0.5 and P<0.05, including the three plasma proteins PRDX2, PON1 and APOC3 for distinguishing lung adenocarcinoma from benign lung nodules, three CT indicators spiculation sign, vascular notch and lobulation sign, and three clinical markers CEA, CA125 and CYFRA21-1.

[0020] (4) Based on the 9 meaningful indicators screened out, the diagnostic models were constructed using the Logistic regression analysis input method.

[0021] Beneficial Effects

[0022] The present invention screens and identifies novel plasma protein markers (PRDX2, PON1 and APOC3) that can be used for differential diagnosis of LUAD (lung adenocarcinoma) and BPN (benign pulmonary nodules) by non-label quantitative proteomics technology and enzyme-linked immunosorbent assay. The novel plasma proteins (PRDX2, PON1 and APOC3) with differential diagnosis potential are combined with nine variables including CT indexes of nodule spiculation sign, nodule vascular notch sign, nodule lobulation sign and clinical traditional tumor markers (CEA, CA125 and CYFRA21-1) to construct a differential diagnosis model for LUAD and BPN. The model has good differential diagnosis efficacy for patients with lung adenocarcinoma and benign pulmonary nodules, and provides help for better clinical management of patients with pulmonary nodules. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a diagram of the screening and preliminary analysis of differential protein markers in Example 1;

[0024] Among them, (A) Volcano plot: Up represents up-regulated proteins, Down represents down-regulated proteins; (B) Heat map: relative abundance of 12 differentially expressed proteins in LUAD group and BPN group; (C) PCA analysis of 12 differentially expressed proteins in LUAD group and BPN group; (D) contribution ratio of 12 principal components obtained by PCA analysis; Note: LUAD: lung adenocarcinoma; BPN: benign lung nodule; PCA: principal component analysis; FC: difference fold.

[0025] Figure 2 It is the ELISA test result of 9 differentially expressed proteins in the validation group 1 of Example 1;

[0026] Among them, (AI) Expression of 9 differentially expressed proteins in patients with lung adenocarcinoma (n=39) and patients with benign pulmonary nodules (n=39); (JL) Evaluation of the diagnostic efficacy of 3 candidate proteins in patients with lung adenocarcinoma and benign pulmonary nodules; Note: LUAD: lung adenocarcinoma; BPN: benign pulmonary nodule.

[0027] Figure 3 It is the ELISA test result of the three candidate proteins in the second verification group of Example 1;

[0028] Among them, (AC) the expression of the three candidate proteins in patients with benign pulmonary nodules (n=107), lung adenocarcinoma (n=107), lung squamous cell carcinoma (n=58), small cell lung cancer (n=48) and normal controls (n=44); (DF) the diagnostic efficacy evaluation of the three candidate proteins in patients with lung adenocarcinoma and benign pulmonary nodules; Note: LUAD: lung adenocarcinoma; BPN: benign pulmonary nodule; LUSC: lung squamous cell carcinoma; SCLC: small cell lung cancer.

[0029] Figure 4 The diagnostic efficacy of the three candidate protein combinations, CT index combination, clinical marker combination and differential diagnosis model in Example 2 in patients with lung adenocarcinoma (n=97) and benign lung nodules (n=71) was evaluated;

[0030] Among them, (A) ROC analysis of different combinations in patients with lung adenocarcinoma and benign lung nodules; (B) The predictive efficacy of various combinations for benign and malignant diseases compared with gold standard pathological evaluation; Note: 3proteins: PRDX2 / PON1 / APOC3, candidate protein combination; 3CT features: spicule sign / vascular notch / lobulation sign, CT indicator combination; 3markers: CEA / CA125 / CYFRA21-1, clinical marker combination.

[0031] Figure 5 is the diagnostic efficacy of the differential diagnosis model in Example 2 in patients with lung adenocarcinoma with different clinical characteristics;

[0032] Among them, (A) the sensitivity of the differential diagnosis model for distinguishing patients with lung adenocarcinoma and benign pulmonary nodules with different clinical characteristics; (B) the AUC of the differential diagnosis model for distinguishing patients with lung adenocarcinoma and benign pulmonary nodules with different clinical characteristics; Note: The model prediction value corresponding to the maximum Youden index is defined as the cutoff value, and the comparison between different subgroups of lung adenocarcinoma patients and benign pulmonary nodules patients uses the same cutoff value, with a specificity of 90.14%; the dotted line in the figure represents the sensitivity and AUC of the differential diagnosis model in distinguishing all patients with lung adenocarcinoma and benign pulmonary nodules; DMP: positive for distant metastasis; DMN: negative for distant metastasis; LMP: positive for lymph node metastasis; LMN: negative for lymph node metastasis; Advanced: late stage; Early: early stage. DETAILED DESCRIPTION

[0033] The technical solution of the present invention is described in detail below in conjunction with specific embodiments and drawings. Unless otherwise specified, the raw materials used in the present invention can be obtained from the market, and the methods used are conventional methods unless otherwise specified.

[0034] Example 1

[0035] Screening and validation of plasma protein markers

[0036] 1. Clinical data collection of research subjects

[0037] By comparing the subject ID, name, gender and age, the clinical information of all patients with benign and malignant pulmonary nodules was reviewed and collected in the hospital patient management system, including smoking history, nature of pulmonary nodules, pathological type, TNM stage (malignant only), and lymph node metastasis and distant metastasis (malignant only). Experienced imaging experts judged and recorded the CT features of patients with pulmonary nodules, including the number of nodules, diameter (longest diameter), edge (regular, irregular), cavitation sign (yes, no), burr sign (yes, no), vascular notch (yes, no), lobulation sign (yes, no), spinous process sign (yes, no), pleural indentation sign (yes, no), mediastinal lymphadenopathy (yes, no), emphysema (yes, no) and calcification (yes, no). The results of clinical traditional tumor markers were collected from patients with pulmonary nodules, mainly including 3 lung cancer-related indicators (CEA, CA125, CYFRA21-1).

[0038] In this study, 462 subjects were included, divided into screening group, validation group 1 and validation group 2. The screening group included 10 patients with lung adenocarcinoma and 10 patients with benign lung nodules; validation group 1 included 39 patients with lung adenocarcinoma and 39 patients with benign lung nodules; validation group 2 included 107 patients with lung adenocarcinoma, 107 patients with benign lung nodules, 44 normal controls, 58 patients with lung squamous cell carcinoma and 48 patients with small cell lung cancer. The basic information, pathological stage and clinical traditional tumor marker levels of all subjects were collected and sorted through the internal system of the hospital with the consent of the patients and the approval of the institutional review board and the hospital ethics committee. The basic information of the specific subjects is shown in Table 1.

[0039] Table 1 Basic characteristics of the research subjects

[0040]

[0041]

[0042] Note: LUAD: lung adenocarcinoma; BPN: benign pulmonary nodule; NC: normal control; LUSC: lung squamous cell carcinoma; SCLC: small cell lung cancer; Mean ± SD

[0043] 2. Collection and processing of plasma samples

[0044] All subjects were fasting and 5 ml of peripheral venous blood was collected using a vacuum blood collection tube containing EDTA-K2 anticoagulant. The samples were sent to the laboratory within 2 hours after blood collection and centrifuged at 3000 rpm / min for 5 minutes at room temperature to separate plasma and blood cells. The upper plasma was aspirated with a disposable pipette and aliquoted into 1.5 ml Eppendorf tubes according to the specification of 500 μl / tube, marked with the disease type, number and date and stored in an ultra-low temperature refrigerator at minus 80°C. The sample storage location and the corresponding patient basic information were recorded in the specimen registration book. When in use, the required samples were placed in a 4°C refrigerator to slowly thaw, and were divided into 96-well plates according to the required amount and layout of the experiment to avoid repeated freezing and thawing of the original tube.

[0045] 3. Screening of plasma differential proteins based on label-free quantitative plasma proteomics technology

[0046] The label-free quantitative plasma proteomics analysis was commissioned to Hangzhou Jingjie Biotechnology Co., Ltd., which used a series of cutting-edge technologies, such as label-free quantitative technology and mass spectrometry-based quantitative proteomics technology, to conduct quantitative proteomics research on plasma samples from 10 patients with lung adenocarcinoma and 10 patients with benign lung nodules.

[0047] 3.1 Experimental specific steps

[0048] 1) Protein extraction: First, take the plasma sample out of the -80℃ refrigerator, thaw it in a 4℃ refrigerator, and centrifuge it (4℃, 12000g, 10min) to remove cell debris. Then, transfer the supernatant to a new centrifuge tube and use the kit produced by Thermo Company according to Pierce TM Top 12 Abundant Protein Depletion Spin Columns Kit Instructions Remove high-abundance proteins. Finally, the protein concentration was determined using a BCA kit and the quality of the extracted protein was analyzed by SDS-PAGE electrophoresis.

[0049] 2) Trypsin hydrolysis: According to the concentration determination results in the above steps, take equal amounts of protein, take the sample with the largest volume as the standard, adjust the volume of other samples with 8M urea to keep it consistent, add dithiothreitol (DTT) with a final concentration of 5mM and incubate at 56°C for 30 minutes, cool to room temperature, add iodoacetamide (IAM) with a final concentration of 11mM and incubate in the dark for 15 minutes. Transfer the alkylated sample to an ultrafiltration tube for centrifugation (room temperature, 12000g, 10min), and replace the sample with ammonium bicarbonate three times to completely replace the urea in the sample. Add trypsin solution (protease: protein = 1:50, m / m) and hydrolyze at 37°C overnight. Centrifuge (room temperature, 12000g, 10min) to recover the peptides, add enzymatic buffer and centrifuge again (room temperature, 12000g, 10min) to recover the peptides, and finally combine the peptide solutions obtained twice.

[0050] 3) HPLC-MS analysis: The peptide solution was dissolved in HPLC mobile phase A and separated using the EASY-nLC 1000 ultra-high performance liquid phase system. The liquid phase concentration was set as follows: 0-90min, 6%-25% B; 90-112min, 25%-35% B; 112-116min, 35%-80% B; 116-120min, 80% B, and the flow rate was maintained at 500nL / min. Mobile phase A was an aqueous solution containing 0.1% formic acid and 2% acetonitrile; mobile phase B was an aqueous solution containing 0.1% formic acid and 90% acetonitrile. After separation by the ultra-high performance liquid phase system, the peptides were injected into the NSI ion source for ionization and then entered the Q ExactivePlus mass spectrometer for analysis. The ion source voltage was set to 2.2kV, and the peptide parent ions and their secondary fragments were detected and analyzed using a high-resolution Orbitrap. The primary mass spectrometry scanning range was set to 350-1800 m / z, and the scanning resolution was set to 70000; the secondary mass spectrometry scanning range was fixed at 100 m / z, and the resolution was set to 17500. The data acquisition mode used a data-dependent scanning program, that is, after the primary scan, the top 10 peptide precursor ions with the highest signal intensity were selected to enter the HCD collision cell in turn, and 28% fragmentation energy was used for fragmentation, and the secondary mass spectrometry analysis was also performed in turn. In order to improve the effective utilization of the mass spectrometry, the automatic gain control was set to 5E4, the signal threshold was set to 40000 ions / s, the maximum injection time was set to 100 ms, and the dynamic exclusion time of the tandem mass spectrometry scan was set to 30 seconds to avoid repeated scanning of the precursor ion.

[0051] 4) Database search: The secondary mass spectrometry data was imported into Maxquant software (V1.5.2.8) for retrieval. The retrieval parameters were set as follows: the database was Homo_sapiens_9606_SP_20191115 (20380 sequences), a reverse library was added to calculate the false positive false discovery rate (FDR) caused by random matching, and a common contamination library was added to the database to eliminate the influence of contaminating proteins in the identification results; the enzyme cutting method was set to Trypsin / P; the number of missed cutting sites was set to 2; the minimum length of the peptide was set to 7 amino acid residues; the maximum number of peptide modifications was set to 5; the mass error tolerance of the primary parent ion of the First search and Main search was set to 20ppm and 5ppm, respectively, and the mass error tolerance of the secondary fragment ion was 0.02Da. Cysteine ​​alkylation was set as a fixed modification, and the variable modification was defined as oxidation of methionine, acetylation of the protein N-terminus, and deamidation. The FDR for protein identification and PSM identification was set to 1%.

[0052] 5) Calculate the FC value and P value of the protein between the two groups: The present invention detects the corresponding signal abundance of the protein in each plasma sample by mass spectrometry, obtains the non-labeled quantitative intensity (Lable-Free Quantitative intensity, LFQ intensity) of the protein in each sample by a non-labeled quantitative calculation method, and calculates the relative quantitative value between each sample according to the LFQ intensity of the protein between different samples. The first step is to calculate the differential expression of the protein between the two groups, i.e., the FC value (first calculate the average value of the relative quantitative value of each sample in each group, and then calculate the ratio of the average values ​​between the two groups---lung adenocarcinoma group / benign pulmonary nodule group, and the ratio is used as the final differential expression of the two groups). The second step is to calculate the significance of the differential expression of the protein between the two groups, i.e., the P value (first take the relative quantitative value of the protein in each sample log2, so that the data conforms to the normal distribution, and then use the method of the two-sample two-tailed T test to calculate the P value).

[0053] 6) Differential protein screening: When P < 0.05, FC> 1.2 was used as the threshold for significant upregulation, and FC < 1 / 1.2 was used as the threshold for significant downregulation, and differential protein markers were screened.

[0054] 7) Result analysis: After the above-mentioned protein extraction, trypsin hydrolysis, mass spectrometry analysis, database search and other steps, the plasma proteome was detected to obtain the relative quantitative value of the quantifiable protein in each sample. By comparing the average relative quantitative value of the protein between the two comparison groups, the fold change (FC) of the protein between the two groups was obtained; and the P value was calculated using the t-test method. When P<0.05, FC>1.2 was used as the threshold for significant upregulation, and FC<1 / 1.2 was used as the threshold for significant downregulation. Finally, 12 differentially expressed proteins were screened out, including 8 upregulated proteins (SERPINA7, LYZ, FCGR3B, HGFAC, CLEC3B, ICAM1, ENPP2, LUM) and 4 downregulated proteins (PRDX2, CA2, PON1, APOC3) ( Figure 1 A). The omics expression results of 12 differentially expressed proteins were significantly different between the lung adenocarcinoma group and the benign lung nodule group ( Figure 1 B), and the results of principal component analysis (PCA) showed that these 12 differentially expressed proteins could better distinguish the patients between the two comparison groups ( Figure 1 CD).

[0055] 4. Use ELISA to verify plasma differential protein indicators

[0056] 4.1 Reagents required for the experiment

[0057] The ELISA kits used in the present invention for detecting differential protein expression in plasma are all from Wuhan Cloud-Clone Technology Co., Ltd.

[0058] 4.2 Preliminary experiments to explore the optimal plasma dilution multiples for 12 differentially expressed proteins

[0059] Plasma samples from 4 patients with lung adenocarcinoma and 4 patients with benign lung nodules were randomly selected. According to the dilution factor recommended in the kit instructions, 2 concentration gradients were set above and below the factor. After the experiment, the optimal plasma dilution factor was selected based on the sample OD value and the standard OD value to ensure that the color development time of the sample and the standard was consistent and the sample OD value was between the highest and lowest OD values ​​of the standard.

[0060] 4.3 Specific experimental steps (taking APOC3 protein indicator as an example)

[0061] 1) Before use, slowly equilibrate all reagents and specimens to room temperature (18-25°C). If the kit cannot be completely used within a period of time, only take out the enzyme label strips and reagents required for this test, and store the remaining enzyme label strips and reagents under specified conditions.

[0062] 2) Prepare standard solution (take APOC3 protein as an example, refer to the instructions for other indicators): add 1mL of standard diluent to each bottle of standard, cover and let stand at room temperature for about 10 minutes, and shake gently (avoid foaming). This solution is the storage solution with a concentration of 6000pg / mL. Prepare 7 Eppendorf tubes for diluting the standard, add 500μL of standard diluent to each Eppendorf tube, and dilute in turn to 6000pg / mL, 3000pg / mL, 1500pg / mL, 750pg / mL, 375pg / mL, 187.5pg / mL, 93.75pg / mL, and the standard diluent (0pg / mL) is directly used as the blank well. To ensure the validity of the experimental results, a new standard solution must be used for each experiment.

[0063] 3) Sample addition: Set up standard wells, standard blank wells, sample wells to be tested (different types of samples are randomly arranged on the plate), and sample blank wells. Set up 7 standard wells, and add 100μL of the above standards of different concentrations in sequence. Add 100μL of standard diluent to the standard blank wells, and add 100μL of the sample to be tested to the remaining wells (dilute the plasma sample in advance according to the optimal dilution factor explored in the preliminary experiment), cover the ELISA plate with a film, and incubate at 37℃ for 1 hour.

[0064] 4) Preparation of detection solution A working solution and detection solution B working solution: Before use, shake detection solution A (biotinylated antibody) and detection solution B (enzyme-labeled avidin) by hand for several times or centrifuge briefly to allow the liquid adhering to the tube wall or bottle cap to settle to the bottom of the tube. Dilute them with detection diluent A or B at a ratio of 1:100 (e.g.: 10μL detection solution A / 990μL detection diluent A) before use, mix thoroughly, and prepare according to the pre-calculated total amount required for each experiment (100μL / well) before dilution. When actually preparing, prepare 0.1-0.2mL more.

[0065] 5) After the incubation time is over, discard the liquid in the wells and spin dry without washing.

[0066] 6) Add detection solution A working solution (prepared before use), 100 μL / well, cover the ELISA plate, and incubate at 37°C for 1 hour.

[0067] 7) Discard the liquid in the wells and wash the plate three times with the washing solution provided in the kit (dilute it to 1X with pure water before use), 350 μL / well, 1.5 minutes / time. After the last wash, discard the remaining liquid in the wells and pat it dry on absorbent paper.

[0068] 8) Add detection solution B working solution (prepared before use), 100 μL / well, cover the ELISA plate, and incubate at 37°C for 30 minutes.

[0069] 9) Discard the liquid in the wells and repeat the plate washing 5 times using the same method as step 7).

[0070] 10) Add TMB substrate solution, 90 μL / well, cover the plate, and incubate at 37°C. (The reaction time is controlled within 30 minutes. When the first four wells of the standard wells have a clear color gradient and the gradient of the last four wells is not obvious, it can be terminated.)

[0071] 11) Add stop solution, 50 μL / well. The color of the liquid in the well changes from blue to yellow.

[0072] 12) After ensuring that there are no bubbles in the well and no water droplets at the bottom of the well, immediately measure the absorbance (OD value) at a wavelength of 450 nm using an ELISA reader and save the data.

[0073] 13) Prepare a standard curve: Use the concentration of the standard as the ordinate and the OD value as the abscissa to draw a standard curve. The best equation should be determined by the R2 value calculated by the regression equation. The closer R2 is to 1, the better.

[0074] 14) Substitute the sample OD value into the equation to calculate the sample concentration, and then multiply it by the dilution factor to get the actual concentration of the indicator in the sample.

[0075] 4.4 Results Analysis

[0076] 1) ELISA preliminary experiments explored the optimal plasma dilution multiples for 12 differentially expressed proteins.

[0077] A small number of samples (plasma from 4 patients with lung adenocarcinoma and 4 patients with benign lung nodules) were pre-experimentally analyzed using a double antibody sandwich ELISA experiment to explore the optimal plasma dilution multiple for each protein index in this experiment. The optimal plasma dilution multiples for the 12 differentially expressed proteins are shown in Table 2. Among them, the three up-regulated proteins FCGR3B, HGFAC, and LUM failed to pass the ELISA method validation, so the subsequent validation of these three indicators was abandoned, and the other 9 differentially expressed proteins were subsequently validated.

[0078] Table 2 Optimal plasma dilution multiples for 12 differentially expressed proteins in ELISA preliminary experiments

[0079]

[0080] Note: -: No optimal plasma dilution multiple

[0081] 2) The ELISA experiment preliminarily verified the differential diagnostic efficacy of 9 plasma differential proteins.

[0082] Based on the optimal plasma dilution factor obtained in the preliminary experiment, the expression of 9 plasma differential proteins was detected in the validation group 1 containing 39 plasma samples of patients with lung adenocarcinoma and 39 plasma samples of patients with benign lung nodules. Figure 2 As shown in the results, among the five up-regulated proteins, only SERPINA7 and ICAM1 showed significant differences in the plasma between patients with lung adenocarcinoma and benign lung nodules in ELISA (P<0.001), but they were all expressed at a higher level in patients with benign lung nodules, which was contrary to the results of plasma proteomics. The validation results of the four down-regulated proteins showed that, except for CA2, the expression levels of PRDX2, PON1 and APOC3 in the plasma of patients with lung adenocarcinoma were significantly lower than those in patients with benign lung nodules (P<0.01). The receiver operating characteristic (ROC) curve analysis showed that PRDX2, PON1 and APOC3 had good differential diagnostic efficacy in patients with lung adenocarcinoma and benign lung nodules, with the areas under the ROC curve (AUC) of 0.707, 0.725 and 0.713, respectively (P<0.01).

[0083] 3) The differential diagnostic efficacy of the three candidate proteins was further verified by ELISA experiments.

[0084] Based on the preliminary validation results in validation group 1, protein indicators (PRDX2, PON1, and APOC3) that were significantly different between lung adenocarcinoma and benign lung nodules and consistent with the proteomics results were selected as candidate proteins. Next, the expression of the three candidate proteins was detected in validation group 2, which contained plasma from 107 patients with lung adenocarcinoma, 107 patients with benign lung nodules, 44 normal controls, 58 patients with lung squamous cell carcinoma, and 48 patients with small cell lung cancer. The results are shown in Figure 2. Figure 3 As shown in the figure, the expression levels of the three candidate proteins in the plasma of lung adenocarcinoma were significantly lower than those in benign lung nodules, and the diagnostic efficacy evaluation showed that their AUC (95% CI) were 0.603 (0.527-0.678), 0.772 (0.710-0.835) and 0.706 (0.638-0.775), respectively. At the same time, we also detected the expression levels of the three candidate proteins in other lung cancer types and normal controls. Although the expression level of PRDX2 in LUAD patients was significantly lower than that in BPN patients (P<0.01), it was significantly higher in LUSC and SCLC patients than in BPN patients (P<0.01). The plasma expression levels of PON1 and APOC3 in the three subtypes of lung cancer were significantly lower than those in BPN patients (P<0.001). In addition, among the three candidate proteins, only APOC3 had a significant difference in the expression level in the plasma of LUAD patients and normal controls (P<0.05).

[0085] Example 2

[0086] Construction of a diagnostic model for differentiating lung adenocarcinoma from benign pulmonary nodules

[0087] (1) Screening of meaningful indicators (candidate proteins, CT indicators, clinical markers) for constructing differential diagnosis models.

[0088] The patients with lung adenocarcinoma and benign lung nodules in validation group 1 and validation group 2 were combined, and 97 patients with lung adenocarcinoma and 71 patients with benign lung nodules with complete CT information and clinical traditional tumor markers (CEA, CA125, and CYFRA21-1) were selected. The relevant information is summarized in Table 3.

[0089] Table 3 CT information and clinical marker information of patients with lung adenocarcinoma and benign lung nodules for constructing differential diagnosis models

[0090]

[0091]

[0092] Note: CEA: carcinoembryonic antigen; CA125: cancer antigen 125; CYFRA21-1: cytokeratin 19 fragment; Mean ± SD

[0093] Univariate analysis of three candidate proteins, 12 CT indices and three clinical tumor markers for differential diagnosis of lung adenocarcinoma and benign pulmonary nodules was performed in the above sample set. The results showed that the differences in the three candidate proteins (PRDX2, PON1 and APOC3), seven CT indices (spicule sign, vascular notch, lobulation sign, spinous process sign, pleural indentation sign, mediastinal lymphadenopathy and calcification) and three clinical markers (CEA, CA125 and CYFRA21-1) between patients with lung adenocarcinoma and patients with benign pulmonary nodules were statistically significant (P<0.01) (Table 4).

[0094] The differential diagnostic efficacy of these 13 indicators was further evaluated by ROC analysis, and 9 significant indicators were screened out with the standard of AUC>0.5 and P<0.05, including 3 candidate proteins (PRDX2, PON1 and APOC3), 3 CT indicators (spicule sign, vascular notch and lobulation sign) and 3 clinical markers (CEA, CA125 and CYFRA21-1). The AUC of the 9 indicators ranged from 0.595 to 0.762, among which the AUC (95% CI) of PON1 was the largest, which was 0.762 (0.689-0.836) (Table 4).

[0095] Table 4 Univariate analysis and ROC analysis results of 3 candidate proteins, 12 CT indices and 3 clinical markers for differential diagnosis of lung adenocarcinoma and benign pulmonary nodules

[0096]

[0097]

[0098] Note: CEA: carcinoembryonic antigen; CA125: cancer antigen 125; CYFRA21-1: cytokeratin 19 fragment

[0099] (2) A differential diagnosis model for lung adenocarcinoma and benign lung nodules was constructed by combining candidate proteins, CT indicators and clinical markers.

[0100] Based on the 9 meaningful indices screened out, the Logistic regression analysis input method was used to construct candidate protein combinations (PRDX2, PON1 and APOC3), CT index combinations (spicule sign, vascular notch and lobulation sign), clinical marker combinations (CEA, CA125 and CYFRA21-1) and differential diagnosis models (including 9 meaningful indices). The ROC analysis results of various indicators are shown in the figure. Figure 4 As shown. The differential diagnosis model is to use Logistic regression to combine 9 variables including 3 candidate proteins (PRDX2, PON1 and APOC3), 3 CT indicators (spicule sign, vascular notch and lobulation sign) and 3 clinical markers (CEA, CA125 and CYFRA21-1) to construct a lung adenocarcinoma diagnosis model PRE (P = LUAD, model) = 1 / (1 + EXP (-(0.010 × PRDX2 + 0.052 × APOC3 + 0.002 × PON1 + 1.112 × spicule sign + 1.161 × vascular notch sign + 0.535 × lobulation sign - 0.084 × CEA - 0.004 × CA125 - 0.404 × CYFRA21-1 - 7.697))).

[0101] Depend on Figure 4 It can be seen that compared with the single type indicator combination (candidate protein combination: AUC = 0.805, CT indicator combination: AUC = 0.757, clinical marker combination: AUC = 0.748), the construction of a differential diagnosis model by combining different types of indicators can significantly improve its diagnostic efficiency (differential diagnosis model: AUC = 0.904) ( Figure 4 A). In addition, Figure 4 The bar graph of B shows intuitively that the differential diagnosis model (sensitivity = 81.44%, specificity = 90.14%) significantly improved the sensitivity of the CT index combination (sensitivity = 59.79%, specificity = 83.10%) and the clinical marker combination (sensitivity = 49.48%, specificity = 94.37%), and significantly improved the specificity of the candidate protein combination (sensitivity = 81.44%, specificity = 70.42%).

[0102] (3) Evaluation of the diagnostic efficacy of the diagnostic model in lung adenocarcinoma with different clinical characteristics.

[0103] The 97 patients with lung adenocarcinoma used to construct the differential diagnosis model were divided into early-stage and late-stage lung adenocarcinoma patients, lung adenocarcinoma patients with and without lymph node metastasis, and lung adenocarcinoma patients with and without distant metastasis according to clinical stage, lymph node metastasis, and distant metastasis. The 71 patients with benign lung nodules were compared with patients with different categories of lung adenocarcinoma to evaluate the diagnostic efficacy of the differential diagnosis model in lung adenocarcinoma with different clinical characteristics. The results are shown in Table 5 and Table 6. Figure 5 As shown in the data, the sensitivity, specificity, AUC (95% CI) and accuracy of the differential diagnosis model in patients with advanced lung adenocarcinoma were 96.55%, 90.14%, 0.956 (0.917-0.996) and 92.00%, respectively; its diagnostic efficacy was also considerable in patients with early lung adenocarcinoma, with a sensitivity, specificity, AUC (95% CI) and accuracy of 65.63%, 90.14%, 0.868 (0.799-0.937) and 82.52%, respectively; in addition, the model showed the best diagnostic efficacy in patients with lung adenocarcinoma with distant metastasis, with a sensitivity, specificity, AUC (95% CI) and accuracy of 100%, 90.14%, 0.976 (0.951-1) and 92.47%, respectively.

[0104] Table 5 Evaluation of the diagnostic efficacy of the differential diagnosis model in patients with lung adenocarcinoma with different clinical characteristics

[0105]

[0106] Note: Sen: sensitivity; Spe: specificity; AUC: area under the ROC curve; PPV: positive predictive value; NPV: negative predictive value; LR: likelihood ratio

[0107] The statistical analysis method of the present invention is to use SPSS 26.0, GraphPad Prism 8.0 and R 4.0 software to perform statistical analysis on experimental data and visualize the results. The difference between each index or model in different groups is analyzed using the method of non-parametric test (Mann-Whitney U test). The AUC, sensitivity, and specificity (the corresponding index concentration or model prediction probability when the cutoff value is the maximum Youden index) of each index or model in the differential diagnosis of LUAD and BPN are obtained using ROC curve. All statistical analyses use two-sided tests, and when P<0.05, the difference is considered to be statistically significant.

[0108] The above embodiments are only preferred specific implementation methods of the present invention, and the protection scope of the present invention is not limited thereto. Any simple changes or equivalent replacements of the technical solutions that can be obviously obtained by any technician familiar with the technical field within the technical scope disclosed in the present invention all fall within the protection scope of the present invention.

Claims

1. Use of a reagent for detecting plasma protein markers in the preparation of a detection kit for distinguishing lung adenocarcinoma from benign lung nodules, It is characterized in that The plasma protein marker is a combination of PRDX2, PON1 and APOC3.

2. The use according to claim 1, It is characterized in that The screening method for the plasma protein markers specifically comprises the following steps: (1) Blood samples were collected from patients with lung adenocarcinoma and benign lung nodules, and plasma samples were obtained after separation and processing; (2) Label-free quantitative proteomics detection; (3) Screening of plasma protein markers for differentiation of lung adenocarcinoma and benign lung nodules; The non-labeled quantitative proteomics detection includes: protein extraction, trypsin hydrolysis, liquid chromatography-mass spectrometry analysis, database search, and calculation of protein FC value and P value; After the step (3) of screening and identifying the plasma protein markers of lung adenocarcinoma and benign lung nodules, the step also includes: ELISA verification of the differential plasma proteins of lung adenocarcinoma and benign lung nodules; the ELISA verification of the differential plasma proteins includes: determining the optimal plasma dilution multiple of the differential proteins; performing ELISA testing on the plasma samples according to the instructions of the kit; and performing statistical analysis on the obtained data.

3. A method for constructing a diagnostic model for distinguishing lung adenocarcinoma from benign lung nodules, It is characterized in that The Logistic regression analysis input method was used to combine three plasma proteins PRDX2, PON1 and APOC3 for distinguishing lung adenocarcinoma and benign pulmonary nodules, three CT indicators spiculation sign, vascular notch sign and lobulation sign, and three clinical markers CEA, CA125 and CYFRA21-1 to construct a diagnostic model for lung adenocarcinoma and benign pulmonary nodules. PRE(P=LUAD, model)=1 / (1+EXP(-(0.010×PRDX2+0.052×APOC3+0.002×PON1+1.112×spiculation sign+1.161×vascular notch sign+0.535×lobulation sign-0.084×CEA-0.004×CA125-0.404×CYFRA21-1-7.697))).

4. The method for constructing a diagnostic model according to claim 3, It is characterized in that The specific steps include: (1) Grouping: Patients with complete CT information and clinical traditional tumor markers CEA, CA125, and CYFRA21-1 were selected as samples and divided into a lung adenocarcinoma group and a benign lung nodule group; (2) The three plasma proteins PRDX2, PON1 and APOC3 for distinguishing lung adenocarcinoma from benign lung nodules, 12 CT indicators including the number of nodules, the longest diameter, regular or irregular margins, cavitation sign, spicule sign, vascular notch sign, lobulation sign, spinous process sign, pleural indentation sign, mediastinal lymphadenopathy, emphysema and calcification, and three clinical tumor markers CEA, CA125 and CYFRA21-1 were used for univariate analysis of the samples described in step (1) of differential diagnosis; 13 indicators with statistically significant differences between patients with lung adenocarcinoma and patients with benign lung nodules were obtained: the three plasma proteins PRDX2, PON1 and APOC3 for distinguishing lung adenocarcinoma from benign lung nodules; the seven CT indicators including spicule sign, vascular notch sign, lobulation sign, spinous process sign, pleural indentation sign, mediastinal lymphadenopathy and calcification, and the three clinical markers CEA, CA125 and CYFRA21-1; (3) The differential diagnostic efficacy of the above 13 indicators was evaluated by ROC analysis, and 9 significant indicators were screened out with the criteria of AUC>0.5 and P<0.05, including the three plasma proteins PRDX2, PON1 and APOC3 for distinguishing lung adenocarcinoma from benign lung nodules, the three CT indicators spiculation sign, vascular notch sign and lobulation sign, and the three clinical markers CEA, CA125 and CYFRA21-1; (4) Based on the 9 meaningful indicators screened out, a diagnostic model was constructed using the Logistic regression analysis input method.

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