A marker group for lung cancer screening and diagnosis and use thereof

Abstract

The application discloses a marker group for lung cancer screening and diagnosis and application thereof, and belongs to the field of molecular biology technology.The marker group comprises at least four polypeptides shown in sequences of SEQ ID NO.1-40.The marker group constructed by the application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of lung cancer.The auxiliary diagnosis result is better than that of the early lung cancer screening method LDCT recommended by the current American National Comprehensive Cancer Network guideline (sensitivity is 94.4%, and specificity is 72.6%), the early screening result is better than that of the detection of seven tumor autoantibodies (p53, GAGE7, etc.) selected for Chinese population (sensitivity and specificity are 61% and 90% respectively), and the detection based on a 5-microRNA kit (sensitivity and specificity are 83.0% and 90.7% respectively).

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of molecular biology technology, and particularly relates to a marker group for lung cancer screening and diagnosis and use thereof. BACKGROUND

[0002] Early clinical symptoms of lung cancer are mostly occult, and it is difficult to diagnose, so the best treatment opportunity is easily missed. Therefore, improving the level of early screening and detection of lung cancer is crucial for the next step of standardized treatment and improving prognosis. Methods for diagnosing early lung cancer include imaging examination, lung cancer marker detection, liquid biopsy, and gene detection, and accurate diagnosis helps to improve the effective rate of treatment.

[0003] Many commonly used procedures for diagnosing lung cancer include fiberoptic bronchoscopy, endobronchial ultrasound, image-guided transthoracic needle aspiration, mediastinoscopy, pleural fluid analysis (pleuroscopy analysis), thoracoscopy, and surgical methods. However, these procedures are prone to complications and may require more samples. And since it is an invasive surgery, it is not suitable for early screening of high-risk groups. Traditional imaging examination is a common method for preliminary diagnosis of lung cancer, and computer tomography (CT) can observe the volume, location, edge features, and internal structure of the tumor, and is particularly suitable for diagnosing small lung lesions and early bronchial lesions, but for lung nodules less than 1 cm in diameter, the scanning accuracy of ordinary CT still needs to be improved. Although the application of low-dose spiral CT (LDCT) has improved the diagnosis rate of early lung cancer to some extent, however, this technology is still limited by low specificity, over-diagnosis, radiation exposure, and difficulty in qualitative diagnosis of lung nodules.

[0004] In recent years, finding biomarkers in human fluids is an attractive approach. Currently, the tumor markers commonly used in clinical practice to assess lung cancer include carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin fragment 19 (CYFRA21-1), progastrin-releasing peptide (ProGRP), and squamous cell carcinoma antigen (SCC). CEA belongs to a class of tumor markers that are present in higher levels in the blood of some cancer patients, especially lung cancer patients. Although CEA testing can be used to assist in the diagnosis of cancer, evaluate treatment effectiveness, and monitor disease recurrence, it is not used as an independent diagnostic basis for cancer due to its poor specificity. CYFRA21-1 is a protein biomarker commonly used to diagnose and monitor lung cancer, especially non-small cell lung cancer. CYFRA21-1 is usually detected with blood samples, which is a non-invasive method that is easy to operate but has poor accuracy. It needs to be combined with other diagnostic results and clinical symptoms for comprehensive evaluation. The overall sensitivity for diagnosing lung squamous cell carcinoma is 72%, and the specificity is 94%. The sensitivity of NSE combined with ProGRP for diagnosing small cell lung cancer is 88.1%, and the specificity is 98.0%. Tumor markers face many challenges in the early diagnosis of lung cancer, including the diversity and heterogeneity of tumor pathology, and the limitations of detection technology. These factors limit the sensitivity and specificity of tumor markers in lung cancer diagnosis, which cannot reach the ideal level. Therefore, it is still necessary to combine other examination methods and consider multiple factors to evaluate and judge.

[0005] Circulating tumor cells (CTCs) are derived from malignant tumors in the body and enter the blood circulation. The detection of CTCs in patients with non-small cell lung cancer before surgery using cell capture systems and filtration techniques showed that patients with positive CTCs had a shorter disease-free survival, indicating that the detection of CTCs is an important means of evaluating the prognosis of patients after surgery. CT has certain limitations in the single diagnosis of lung diseases, and the combination of CT and CTCs can make up for its shortcomings and reduce the risk of invasive examination for patients. However, this method also has many clinical limitations. Because the content of CTCs in peripheral blood is very low, further exploration is needed to learn how to effectively reduce background cells, effectively enrich CTCs, and improve the accuracy of CTC detection. Currently, the transmission law of CTCs is not clear, and the appropriate detection time cannot be determined. Studies have found that methylated ctDNA in blood shows potential as an early diagnostic tool for lung cancer, with a total sensitivity of about 46.9% and a total specificity of about 92.9%. This indicates that methylated ctDNA has high specificity in identifying potential lung cancer cases, but there is room for further improvement in sensitivity. Tumor biomarkers have always faced problems such as poor accuracy and low sensitivity in early tumor diagnosis. SUMMARY

[0006] In view of the above deficiencies in the prior art, the present application provides a marker group for lung cancer screening and diagnosis and uses thereof, which has excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of lung cancer, and is expected to be applied to the diagnosis and treatment of lung cancer.

[0007] To achieve the above object, the technical scheme adopted by the present application to solve its technical problems is:

[0008] A marker group for lung cancer screening and diagnosis comprises at least four of the polypeptides shown in sequences SEQ ID NO. 1-40, and the specific sequences are shown in Table 1.

[0009] Table 1 Polypeptide sequences

[0010]

[0011] Among them, the third amino acid G in polypeptide 7 has Phospho modification; the fourth amino acid G in polypeptide 8 has Phospho modification; the second amino acid K in polypeptide 15 has Acetyl modification; the first amino acid Q in polypeptide 23 has Gln->pyro-Glu modification, and the sixth amino acid N has Dehydrated modification; the fourth amino acid G in polypeptide 32 has Dehydrated modification.

[0012] Further, the marker group comprises polypeptides shown in sequences 1 and 2, and the following polypeptide combinations:

[0013] The polypeptide combination is one of sequences 3 and 4; sequences 5 and 6; sequences 15 and 16; sequences 30 and 40; sequences 3, 4 and 5; sequences 5, 6 and 7; sequences 15, 16 and 20; sequences 9, 30 and 40.

[0014] Further, the marker group comprises polypeptides shown in sequences 7 and 8, and the following polypeptide combinations:

[0015] The polypeptide combination is one of sequences 3 and 4; sequences 9 and 10; sequences 3, 4 and 9; sequences 9, 10 and 11.

[0016] Further, the marker group comprises polypeptides shown in sequences 1, 10 and 30, and polypeptides shown in sequences 40; 20; 20 and 28; or 28 and 40.

[0017] Further, the marker group comprises polypeptides as shown in SEQ ID NO. 5 and SEQ ID NO. 18, and the following polypeptide combinations:

[0018] The polypeptide combination is SEQ ID NO. 2 and SEQ ID NO. 19; or SEQ ID NO. 2, SEQ ID NO. 19 and SEQ ID NO. 20.

[0019] Further, the marker group comprises polypeptides as shown in SEQ ID NO. 4 and SEQ ID NO. 8, and the following polypeptide combinations:

[0020] The polypeptide combination is SEQ ID NO. 19 and SEQ ID NO. 20; SEQ ID NO. 2 and SEQ ID NO. 6; or polypeptides as shown in SEQ ID NO. 19, SEQ ID NO. 20 and SEQ ID NO. 22.

[0021] Further, the marker group comprises polypeptides as shown in SEQ ID NO. 7, SEQ ID NO. 10, SEQ ID NO. 17, SEQ ID NO. 18 and / or SEQ ID NO. 20.

[0022] Further, the marker group comprises polypeptides as shown in SEQ ID NO. 5, SEQ ID NO. 15, SEQ ID NO. 25, and SEQ ID NO. 40, or SEQ ID NO. 28 and SEQ ID NO. 40.

[0023] Further, the marker group comprises polypeptides as shown in SEQ ID NO. 1~40.

[0024] The use of the above marker group in the preparation of a preparation for lung cancer screening and diagnosis.

[0025] The use of the above marker group in medical basic research for non-diagnostic / therapeutic purposes.

[0026] Further, the medical basic research is Western Blot, immunohistochemistry or flow cytometry, etc.

[0027] Advantages of the present application:

[0028] The marker combination constructed by the present application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of lung cancer. The auxiliary diagnosis result is better than the current American National Comprehensive Cancer Network guideline recommended early lung cancer screening method LDCT (sensitivity of 94.4%, specificity of 72.6%), and the early screening result is better than the detection of the current seven tumor autoantibodies (p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, GBU4-5) selected for Chinese population (sensitivity and specificity are 61% and 90%, respectively), and the detection of the kit based on 5 microRNAs (sensitivity and specificity are 83.0% and 90.7%, respectively). BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 ROC curve diagram of marker combination 1;

[0030] Figure 2ROC curve plot for marker combination 2;

[0031] Figure 3 ROC curve plot for marker combination 3;

[0032] Figure 4 ROC curve plot for marker combination 4;

[0033] Figure 5 ROC curve plot for marker combination 5;

[0034] Figure 6 ROC curve plot for marker combination 6;

[0035] Figure 7 ROC curve plot for marker combination 7;

[0036] Figure 8 ROC curve plot for marker combination 8;

[0037] Figure 9 ROC curve plot for marker combination 9;

[0038] Figure 10 ROC curve plot for marker combination 10;

[0039] Figure 11 ROC curve plot for marker combination 11;

[0040] Figure 12 ROC curve plot for marker combination 12;

[0041] Figure 13 ROC curve plot for marker combination 13;

[0042] Figure 14 ROC curve plot for marker combination 14;

[0043] Figure 15 ROC curve plot for marker combination 15;

[0044] Figure 16 ROC curve plot for marker combination 16;

[0045] Figure 17 ROC curve plot for marker combination 17;

[0046] Figure 18 ROC curve plot for marker combination 18;

[0047] Figure 19 ROC curve plot for marker combination 19;

[0048] Figure 20 ROC curve plot for marker combination 20;

[0049] Figure 21 ROC curve plot for marker combination 21;

[0050] Figure 22 ROC curve plot for marker combination 22;

[0051] Figure 23 ROC curve plot for marker combination 23;

[0052] Figure 24 ROC curve plot for marker combination 24;

[0053] Figure 25 ROC curve plot for marker combination 25;

[0054] Figure 26 ROC curve plot for marker combination 26. DETAILED DESCRIPTION

[0055] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0056] The patient samples used in the present application are from Zhongshan Hospital, Affiliated Hospital of Fudan University, and have passed the ethical review.

[0057] The experimental methods used in the present application are as follows:

[0058] I. Serum sample collection

[0059] 1) Sample type: serum.

[0060] 2) Collection requirements: fasting collection, 5 mL of venous blood is drawn using a coagulation tube, and after standing for 30 min, 3000 rpm centrifugation for 15 min, about 1 mL of serum is taken out and placed in a cryopreservation tube.

[0061] 3) Sample storage:

[0062] Used on the same day, the storage condition is 2-8℃;

[0063] If it cannot be used on the same day, it is stored at -20℃, and can be stored for 30 days;

[0064] If stored for a long time (more than one month), it needs to be stored at -80℃.

[0065] Repeated freezing and thawing should not exceed 3 times.

[0066] II. Extraction of the analyte from serum

[0067] 1) After the calibration of the mass spectrometer instrument, open the solid-phase sample preparation system SPS1000 / SPS4000, and place the consumables, matched reagent kit and sample to be tested;

[0068] Select the program method "solid-phase sample preparation";

[0069] Run the program:

[0070] a. Open the hole;

[0071] b. Take not less than 10 μL of serum sample and activate the reagent, mix them in a ratio of 1:1, and place them in the G row reserved hole for later use;

[0072] c. Wash the customized suction head in cleaning reagent 1 and cleaning reagent 2 in order. When washing, take not less than 10 μL of liquid each time, and repeat the suction and beating not less than 3 times;

[0073] d. Use the cleaned customized suction head to process the serum mixture in the G row hole. When processing, take not less than 10 μL of solution each time, and repeat the suction and beating not less than 3 times;

[0074] e. Use cleaning reagent 3 to clean the customized suction head after adsorbing the serum mixture. When cleaning, take not less than 10 μL of liquid each time, and repeat the suction and beating not less than 3 times;

[0075] f. Transfer not less than 10 μL of buffer reagent to the H row reserved hole, and place the customized suction head after using cleaning reagent 3 in the liquid to take not less than 10 μL of liquid, and repeat the suction and beating not less than 3 times;

[0076] g. Transfer not less than 10 μL of sample matrix solution to the H row reserved hole to complete the sample processing;

[0077] h. Take 2.0 μL of the solution in the H hole and spot it on the hydrophobic coating biochip (Wuxi Pimcore Technology Co., Ltd.);

[0078] i. Vacuum dry for 240 s.

[0079] The main components of each reagent are shown in Table 2.

[0080] Table 2 Reagent composition

[0081]

[0082] III. Mass spectrometry data acquisition and uploading

[0083] Place the vacuum-dried hydrophobic coating biochip (Wuxi Pimcore Technology Co., Ltd.) into the mass spectrometer;

[0084] Data collection was performed using the set SP1 voltage (target high voltage), SP2 voltage (pulse high voltage), focusing voltage (lens high voltage), detector voltage (MCP voltage), pulse delay time, acquisition card range, target spot diameter, laser frequency, calibration method, and laser intensity.

[0085] IV. Quality control

[0086] 1) After data collection, the data was uploaded to the mass spectrometry data analysis software.

[0087] 2) The software reads the sample information and signal spectrum, and determines whether the sample and sample pretreatment are qualified according to the quality control model. Quality control failure can include multiple possibilities, including non-lung lesion samples, signal spectrum intensity not meeting standards, etc.

[0088] 3) If the quality control is not qualified, according to the quality control results, the corresponding parameters are corrected, and the serum sample extraction process is re-performed.

[0089] 4) If the quality control is qualified, the next process is entered.

[0090] V. Establishment of positive judgment value and result analysis

[0091] The study of the positive judgment value uses lung cancer samples with clear diagnostic information and normal samples, covering benign non-lung cancer samples such as pneumonia, tuberculosis, chronic bronchitis, and fibroma. The core algorithm is based on known lung cancer sample spectrum for supervised learning, through a series of processes such as smoothing denoising and baseline removal, screening characteristic peaks, building a classification model, and calculating the similarity between the hormone signal spectrum and the known hormone signal spectrum stored in the software (Cannataro M, Guzzi P H, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data [J]. 2005.). Finally, the maximum Youden index method is used to determine the similarity score positive judgment value of the kit. When the similarity score < positive judgment value, the sample test result is negative; when the similarity score ≥ positive judgment value, the sample test result is positive. Taking the maximum similarity score as the positive judgment value, the sensitivity = true positive number / (true positive number + false negative number) x 100%, and the specificity = true negative number / (true negative number + false positive number) x 100% when assisting in the diagnosis of lung cancer or early screening of lung cancer.

[0092] Example 1: Screening and identification of markers

[0093] Through time-of-flight mass spectrometry testing on 400 normal samples (208 males (52.0%), 192 females (48.0%), age distribution range: 25-70 years old, average age: 46.5 ± 11.9 years old. Specific age distribution: 56 cases of 25-34 years old, 108 cases of 35-44 years old, 117 cases of 45-54 years old, 85 cases of 55-64 years old, and 34 cases of 65-70 years old), 400 lung cancer samples (273 males (68.3%), 127 females (31.7%), age distribution range: 38-79 years old, average age: 62.8 ± 9.4 years old. Specific age distribution: 42 cases of 38-47 years old, 118 cases of 48-57 years old, 163 cases of 58-67 years old, and 77 cases of 68-79 years old), through primary mass spectrometry testing, comprehensive consideration of the relative abundance differences of characteristic peak data in normal people and lung cancer patients, statistical differences of data (p<0.05, t test), influence factor ranking of feature screening by machine learning algorithm (random forest), and matching degree of data in the database, 40 polypeptide substances in blood were found to have lung cancer diagnostic ability. The mass-to-charge ratio (m / z) of these polypeptides, relative abundance and influence factor are shown in Table 1. The relative abundance is normalized based on the normal sample, that is, ln (average signal intensity of cancer patient sample / average signal intensity of normal sample). The screening of characteristic peaks in machine learning is based on the feature importance evaluation of ensemble learning. By constructing multiple decision trees, the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction is quantified. The feature importance score, that is, the influence factor, is obtained by calculating the average reduction of impurity brought by the feature at all tree node splits (Biau, G., Scornet, E. A random forest guided tour. TEST 25, 197-227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0094] The specific primary mass spectrometry parameters are as follows:

[0095] Ionization method: matrix-assisted laser desorption ionization (MALDI), with α-cyano-4-hydroxycinnamic acid (CHCA) as the matrix.

[0096] Mass range: 100-4000 Da.

[0097] Resolution: 20000 (full mass range).

[0098] Laser energy: 30-40%.

[0099] Acquisition mode: positive ion mode.

[0100] Calibration: external mass calibration was performed using peptide standards (Bruker Peptide Calibration Standard).

[0101] Subsequently, the sequences of the 40 substances in the clinical serum were confirmed by secondary mass spectrometry (secondary mass spectrometry (MS / MS or TOF / TOF) is the recommended method for polypeptide identification by the China Food and Drug Administration and the U.S. Food and Drug Administration (FDA) guidelines). The secondary mass spectrometry data analysis process is as follows:

[0102] Data analysis method: Database search was performed using Mascot software (version 2.8), and the database was the UniProt human proteome database (released in 2023). Search parameters: enzyme setting "no enzyme digestion", parent ion mass error allowed ±0.5 Da, fragment ion mass error allowed ±0.3 Da, fixed modification cysteine urea methylation, variable modification methionine oxidation.

[0103] Sequence confirmation standard: The confirmation of polypeptide sequence is based on the matching of fragment ion spectrum (b- and y-ions) with theoretical spectrum, and Mascot score higher than 30 (p<0.05) is considered significant.

[0104] False positive exclusion: Specificity is verified by reverse database search, and the false positive rate is controlled below 1%.

[0105] Secondary mass spectrometry parameters:

[0106] Collision-induced dissociation (CID).

[0107] Collision energy: 30 eV.

[0108] Fragment ion mass range: 100-3500 Da.

[0109] Data acquisition: At least 1000 laser scans per sample were collected to improve the signal-to-noise ratio.

[0110] The sequences and specificities of the 40 markers were confirmed by secondary mass spectrometry, and the false positive problem was excluded. The specific sequences are shown in Table 1.

[0111] Example 2 Verification of marker combination

[0112] According to the 40 polypeptide markers identified and confirmed in Example 1 (see Table 1 for sequences and mass-to-charge ratios), different marker combinations shown in Table 3 were formed, and the sensitivity and specificity of the marker combinations in the auxiliary diagnosis and early screening of lung cancer were verified. The analysis process was as follows: for all verification queue samples, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain mass spectrometric peak intensity data for all markers in Table 1. The entire detection process was carried out in a blind manner, i.e., the experimental operator was unaware of the grouping information of the samples. The raw mass spectrometric data were processed by baseline correction, smoothing and normalization (using the internal standard peak intensity as the reference), and then the peak area or intensity value of each marker was extracted. Statistical analysis was performed using R software (version 4.0.2). The preprocessed marker intensity data were input into the logistic regression (Logistic Regression) model. For auxiliary diagnosis verification, ten-fold cross-validation was performed using all samples in the queue (Sun T, Liu J, Yuan H, Li X, Yan H. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm. Front Oncol. 2024 Aug 9;14:1403392. doi: 10.3389 / fonc.2024.1403392. PMID: 39184040; PMCID: PMC11341396.). For early screening verification, due to the uneven sample size, the SMOTE (Synthetic Minority Over-sampling Technique) technique was used to process the data before model training and testing (van den Goorbergh R, van Smeden M, Timmerman D, Van Calster B. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J Am Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093. PMID: 35686364; PMCID: PMC9382395.).The data analysis procedure referred to general guidelines for clinical prediction model construction (Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993 Apr;39(4):561-77. Erratum in: Clin Chem 1993 Aug;39(8):1589. PMID: 8472349.). The specific calculation results of performance indicators are shown in Table 3, and the corresponding ROC curves are shown in Figures 1-26 .

[0113] 1) 500 lung cancer patients (341 males (68.2%), 159 females (31.8%), age range 40-78 years, average age 63.2±9.1 years. The specific distribution is: 58 cases of 40-49 years old, 126 cases of 50-59 years old, 187 cases of 60-69 years old, and 129 cases of 70-78 years old) and 500 healthy people (256 males (51.2%), 244 females (48.8%), age range 28-72 years, average age 47.6±11.3 years. The specific distribution is: 78 cases of 28-37 years old, 126 cases of 38-47 years old, 138 cases of 48-57 years old, 108 cases of 58-67 years old, and 50 cases of 68-72 years old) were used to verify the auxiliary diagnosis of lung cancer by each marker combination.

[0114] 2) 400 lung cancer patients (273 males (68.3%), 127 females (31.7%), age range 42-76 years, average age 61.8±9.5 years. The specific distribution is: 53 cases of 42-51 years old, 118 cases of 52-61 years old, 163 cases of 62-71 years old, and 66 cases of 72-76 years old) and 3000 healthy people (1532 males (51.1%), 1468 females (48.9%), age range 30-74 years, average age 48.9±11.7 years. The specific distribution is: 412 cases of 30-39 years old, 587 cases of 40-49 years old, 698 cases of 50-59 years old, 823 cases of 60-69 years old, and 480 cases of 70-74 years old) were used to verify the early screening of lung cancer by each marker combination.

[0115] Table 3 Sensitivity and specificity of marker combination for auxiliary diagnosis and early screening

[0116]

[0117] Table continued

[0118]

[0119] Table 1

[0120]

[0121] According to the detection results of Table 3 and Figures 1-26 The auxiliary diagnosis results of the marker combination selected in the present application are better than the current American National Comprehensive Cancer Network guideline recommended early lung cancer screening method LDCT (sensitivity is 94.4%, specificity is 72.6%), the early screening results are better than the detection of the seven tumor autoantibodies (p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, GBU4-5) currently selected for Chinese population (sensitivity and specificity are 61% and 90%, respectively), and the detection of the kit based on 5 microRNAs (sensitivity and specificity are 83.0% and 90.7%, respectively).

[0122] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to examples, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A biomarker set for lung cancer screening and diagnosis, characterized in that, The biomarker group is selected from one of the following combinations of peptides: A) Sequence 1, Sequence 2, Sequence 3 and Sequence 4; B) Sequences 1, 2, 5, and 6; C) Sequence 1, Sequence 2, Sequence 15 and Sequence 16; D) Sequences 1, 2, 30, and 40; E) Sequence 1, Sequence 2, Sequence 3, Sequence 4 and Sequence 5; F) Sequence 1, Sequence 2, Sequence 5, Sequence 6 and Sequence 7; G) Sequence 1, Sequence 2, Sequence 15, Sequence 16 and Sequence 20; H) Sequence 1, Sequence 2, Sequence 9, Sequence 30 and Sequence 40; I) Sequences 2, 19, 5, and 18; J) Sequence 2, Sequence 19, Sequence 20, Sequence 5 and Sequence 18; K) Sequence 2, Sequence 6, Sequence 4 and Sequence 8; L) Sequences 1 to 40; The amino acid sequences of sequences 1 to 40 are shown in SEQ ID NO. 1 to 40; In sequence 7, the third amino acid G has a Phospho modification; in sequence 8, the fourth amino acid G has a Phospho modification; in sequence 15, the second amino acid K has an Acetyl modification; in sequence 23, the first amino acid Q has a Gln->pyro-Glu modification, and the sixth amino acid N has a Dehydrated modification; and in sequence 32, the fourth amino acid G has a Dehydrated modification.

2. Use of the reagent for detecting the biomarker group of claim 1 in the preparation of preparations for lung cancer screening and diagnosis.

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