A marker group for screening and diagnosing brain glioma and use thereof

Abstract

The application discloses a marker group for brain glioma screening and diagnosis and application thereof, and belongs to the field of molecular biological technology. The marker group comprises at least four polypeptides shown in sequences SEQ ID NO. 1-36. The marker group constructed by the application has more excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of brain glioma. The auxiliary diagnosis result of the marker group selected by the application is superior to that of an intraoperative ultrasound-based image group model (sensitivity and specificity are 81.82% and 77.78% respectively) and cerebrospinal fluid combined with blood routine detection (sensitivity and specificity are 81.0% and 94.1% respectively). The early screening result is superior to that of conventional MRI (sensitivity and specificity are 91% and 86% respectively).

Description

TECHNICAL FIELD

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

[0002] Brain glioma is a malignant tumor originating from neuroglia cells, and is one of the most common primary tumors in the central nervous system (CNS) of adults, with significant heterogeneity and high invasiveness. According to the grading standard of the World Health Organization (WHO), glioma can be divided into four grades, and gliomas of different grades have significant differences in molecular characteristics, pathological manifestations and clinical prognosis.

[0003] Traditionally, histopathology is regarded as the "gold standard" for tumor typing and diagnosis, and provides an important basis for tumor typing and grading. However, traditional pathological diagnosis relies on the judgment of experienced pathologists, and has a certain subjectivity. In the face of complex neuroglioma samples, the understanding and judgment of pathologists always exist differences. Complementary to histopathology, imageomics starts from a macroscopic perspective and can non-invasively evaluate the grade, molecular typing and prognosis of glioma. The current main imaging methods include CT, MRI and PET-CT, etc. However, similarly, manual image recognition may exist missed recognition or misrecognition, which poses a potential threat to the health and safety of patients. The diagnostic specificity of brain glioma patients with atypical or early imaging manifestations is poor. In recent years, with the development of molecular biology technology, the typing of glioma has gradually changed from traditional histological grading to typing based on molecular characteristics. The WHO introduced key molecular markers, such as isocitrate dehydrogenase (IDH), 1p / 19q co-deletion and telomerase reverse transcriptase (TERT) promoter, in the latest classification of CNS tumors, to assist in the precise classification of glioma at the molecular level. However, the detection of mutations often relies on the molecular pathological diagnosis results made based on surgical specimens, which has a certain lag.

[0004] Liquid biopsy can overcome the influence of tumor heterogeneity, provide more comprehensive tumor molecular information, and provide strong support for guiding clinical treatment and evaluating prognosis. Most brain gliomas are not easy to find in the early stage, and are often diagnosed after symptoms appear. Traditional diagnostic techniques, including cranial imaging and lesion biopsy, have certain limitations. Liquid biopsy is convenient, fast, non-invasive, and can be sampled multiple times, which is more in line with the current trend of precise diagnosis and treatment of tumors. Therefore, researching early liquid biopsy strategies suitable for brain glioma, screening brain glioma-specific liquid biopsy tumor markers, especially markers for early diagnosis and recurrence monitoring, is an important means to achieve precise diagnosis and treatment. Circulating tumor cells (CTC), circulating tumor DNA (ct-DNA), and proteins are considered markers for early diagnosis and real-time monitoring of brain glioma. CTC refers to tumor cells that have fallen off from solid tumors into the peripheral circulation system, which retains specific molecular characteristics from the primary tumor and can be used for clinical diagnosis of the primary tumor. However, there are few studies on CTC for brain glioma, and the sample size is small. Moreover, different methods are used to isolate CTC in each study, making the research results not comparable. In addition, the content of CTC in the body fluid of brain glioma patients is much lower than that of normal cells, and only one or a few tumor cells can be found in 10 mL of blood, with low purity, making it difficult to reflect the true situation of the whole brain glioma. The detection of ct-DNA in cerebrospinal fluid is also very demanding on the environment before and during analysis, making it difficult to ensure the stability of the detection, leading to many difficulties in clinical application. Glioma cerebrospinal fluid exosome liquid biopsy is mainly used to detect changes in some specific molecules, including EGFRVIII, IDH1 mutant protein, or miRNA. Studies have shown that in a multicenter study of 71 GBM patients, the specificity of detecting EGFRVIII in cerebrospinal fluid exosomes was as high as 98%, but the sensitivity was less than 61%. IDH1 mutant transcripts can also be detected in the cerebrospinal fluid exosomes of glioma patients, with a sensitivity of 62.5% and a specificity of 100%, but not in serum exosomes. SUMMARY

[0005] In view of the above shortcomings in the prior art, the present application provides a marker group for brain glioma screening and diagnosis and its use. In the auxiliary diagnosis and early screening of brain glioma, it has excellent sensitivity and specificity, and is expected to be applied to the diagnosis and treatment of brain glioma.

[0006] To achieve the above-mentioned purpose, the technical scheme adopted by the present application to solve its technical problems is:

[0007] A marker group for brain glioma screening and diagnosis, comprising at least four of the polypeptides shown in sequences SEQ ID NO. 1~36, and the specific sequences are shown in Table 1.

[0008] Table 1 Polypeptide sequences

[0009]

[0010] Among them, the second amino acid K in polypeptide 9 has Acetyl modification; the third amino acid G in polypeptide 11 has Phospho modification; the fourth amino acid G in polypeptide 12 has Dehydrated modification; the fourth amino acid G in polypeptide 21 has Phospho modification; the first amino acid Q in polypeptide 26 has Gln->pyro-Glu modification, and the sixth amino acid N has Dehydrated modification.

[0011] Further, the marker group comprises polypeptides shown in sequences 1 and 2, and the following polypeptide combinations:

[0012] The polypeptide combination is one of sequences 3 and 4; sequences 5 and 6; sequences 11 and 12; sequences 30 and 35; sequences 3, 4 and 5; sequences 5, 6 and 7; sequences 11, 12 and 20; sequences 9, 30 and 35.

[0013] Further, the marker group comprises polypeptides shown in sequences 7 and 8, and the following polypeptide combinations:

[0014] The polypeptide combination is one of sequences 3 and 4; sequences 9 and 10; sequences 3, 4 and 9; sequences 9, 10 and 11.

[0015] Further, the marker group comprises polypeptides shown in sequences 1, 10 and 30, and polypeptides shown in sequences 35; 20; 35 and 9 or 20 and 22.

[0016] Further, the marker group comprises polypeptides shown in sequences 5 and 15, and the following polypeptide combinations:

[0017] The polypeptide combination is one of sequences 25 and 35; sequences 2 and 16; sequences 9, 25 and 35; sequences 2, 16 and 20.

[0018] Further, the marker group comprises polypeptides shown in sequences 4 and 8, and the following polypeptide combinations:

[0019] The polypeptide combination is the polypeptide shown in sequence 24 and sequence 25, or sequence 19, sequence 20 and sequence 22.

[0020] Further, the marker combination comprises the polypeptide shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 22.

[0021] Further, the marker combination comprises the polypeptide shown in sequence 2, sequence 3, sequence 9 and sequence 12.

[0022] Further, the marker combination comprises the polypeptide shown in sequence 2, sequence 3, sequence 9 and sequence 12.

[0023] The use of the above marker combination in the preparation of a preparation for screening and diagnosis of brain glioma.

[0024] The use of the above marker combination or in medical basic research for non-diagnostic / therapeutic purposes.

[0025] Further, the medical basic research is Western Blot, immunohistochemistry or flow cytometry, etc.

[0026] Advantages of the present application:

[0027] The marker combination constructed by the present application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of brain glioma, and is expected to be applied to the diagnosis and treatment of brain glioma. The auxiliary diagnosis result of the marker combination selected by the present application is better than the image-based model based on intraoperative ultrasound (the sensitivity and specificity are 81.82% and 77.78%, respectively) and the combined detection of cerebrospinal fluid and routine blood test (the sensitivity and specificity are 81.0% and 94.1%, respectively). The early screening result is better than the conventional MRI (the sensitivity and specificity are 91% and 86%, respectively). BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The ROC curve diagram of the marker combination 1;

[0029] Figure 2 The ROC curve diagram of the marker combination 2;

[0030] Figure 3 The ROC curve diagram of the marker combination 3;

[0031] Figure 4 The ROC curve diagram of the marker combination 4;

[0032] Figure 5 The ROC curve diagram of the marker combination 5;

[0033] Figure 6 The ROC curve diagram of the marker combination 6;

[0034] Figure 7 ROC curve plot for marker combination 7;

[0035] Figure 8 ROC curve plot for marker combination 8;

[0036] Figure 9 ROC curve plot for marker combination 9;

[0037] Figure 10 ROC curve plot for marker combination 10;

[0038] Figure 11 ROC curve plot for marker combination 11;

[0039] Figure 12 ROC curve plot for marker combination 12;

[0040] Figure 13 ROC curve plot for marker combination 13;

[0041] Figure 14 ROC curve plot for marker combination 14;

[0042] Figure 15 ROC curve plot for marker combination 15;

[0043] Figure 16 ROC curve plot for marker combination 16;

[0044] Figure 17 ROC curve plot for marker combination 17;

[0045] Figure 18 ROC curve plot for marker combination 18;

[0046] Figure 19 ROC curve plot for marker combination 19;

[0047] Figure 20 ROC curve plot for marker combination 20;

[0048] Figure 21 ROC curve plot for marker combination 21;

[0049] Figure 22 ROC curve plot for marker combination 22;

[0050] Figure 23 ROC curve plot for marker combination 23;

[0051] Figure 24 ROC curve plot for marker combination 24;

[0052] Figure 25 ROC curve plot of the marker combination 25;

[0053] Figure 26 ROC curve plot of the marker combination 26. DETAILED DESCRIPTION

[0054] 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.

[0055] The patient samples used in the present application are from Zhongshan Hospital Affiliated to Fudan University, and have passed the ethical review.

[0056] The experimental methods used in the present application are as follows:

[0057] I. Serum sample collection

[0058] 1) Sample type: serum.

[0059] 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.

[0060] 3) Sample storage:

[0061] Used on the same day, the storage condition is 2-8℃;

[0062] If it cannot be used on the same day, it is stored at -20℃, and can be stored for 30 days;

[0063] If stored for a long time (more than one month), it needs to be stored at -80℃.

[0064] The repeated freezing and thawing should not exceed 3 times.

[0065] II. Extraction of the analyte in serum

[0066] 1) After calibrating the mass spectrometer instrument, open the solid-phase extraction automatic sample analysis system instrument SPS1000 / SPS4000, put in consumables, matched reagent kit and sample to be tested;

[0067] Select the program method "solid-phase extraction sample addition";

[0068] Run the program:

[0069] a, open the hole;

[0070] b. Aspirate no less than 10 μL of serum sample and mix with activation reagent at a ratio of 1:1 and place in G row reserved wells for later use;

[0071] c. Wash the custom tip in cleaning reagent 1 and cleaning reagent 2 in order. Each time aspirate no less than 10 μL of liquid and repeat the aspiration and dispensing no less than 3 times;

[0072] d. Use the washed custom tip to process the serum mixture in G row wells. Each time aspirate no less than 10 μL of solution and repeat the aspiration and dispensing no less than 3 times;

[0073] e. Wash the custom tip after aspirating the serum mixture with cleaning reagent 3. Each time aspirate no less than 10 μL of liquid and repeat the aspiration and dispensing no less than 3 times;

[0074] f. Transfer no less than 10 μL of buffer reagent to H row reserved wells. Place the custom tip after using cleaning reagent 3 into the liquid and aspirate no less than 10 μL of liquid. Repeat the aspiration and dispensing no less than 3 times;

[0075] g. Transfer no less than 10 μL of sample matrix solution to H row reserved wells to complete sample processing;

[0076] h. Aspirate 2.0 μL of solution in H wells to the hydrophobic coated biochip (Pemtron Technology Co., Ltd.);

[0077] i. Vacuum dry for 240 s.

[0078] The main components of each reagent are shown in Table 2.

[0079] Table 2 Reagent composition

[0080]

[0081] III. Mass spectrometry data acquisition and uploading

[0082] Place the vacuum dried hydrophobic coated biochip (Pemtron Technology Co., Ltd.) into the mass spectrometer;

[0083] Use 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 point diameter, laser frequency, calibration method, and laser intensity to perform data acquisition.

[0084] IV. Quality control

[0085] 1) After data acquisition, upload the data to the mass spectrometry data analysis software;

[0086] 2) Software reads sample information and signal atlas, and judges whether the sample and sample pretreatment are qualified according to the quality control model. The unqualified quality control may include multiple possibilities, including non-disease sample, signal atlas intensity not meeting the standard, etc.

[0087] 3) If the quality control is unqualified, the corresponding parameters are corrected according to the quality control results, and the extraction process of the analyte in serum is performed again;

[0088] 4) If the quality control is qualified, the next process is entered.

[0089] Five, establishment of positive judgment value and result analysis

[0090] The research of positive judgment value uses brain glioma samples with clear diagnostic information and normal samples, covering non-tumor patients such as encephalitis, demyelinating disease, cerebral infarction, etc. The core algorithm is supervised learning based on known brain glioma sample atlas, through a series of processes such as smoothing denoising baseline, screening characteristic peaks, constructing classification model, and calculating the similarity of hormone signal atlas and known hormone signal atlas 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 of the kit as the positive judgment value. When the similarity score < positive judgment value, the detection result of the sample is negative; when the similarity score ≥ positive judgment value, the detection result of the sample is positive. Taking the maximum similarity score as the positive judgment value, when assisting in the diagnosis of brain glioma or performing brain glioma early screening, 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%.

[0091] Example 1 Screening and identification of markers

[0092] The application carries out time-of-flight mass spectrometry on 420 normal human samples (218 males (51.9%), 202 females (48.1%), age distribution range: 18-70 years old, average age: 44.3±13.2 years old. The specific age distribution is: 42 cases of 18-25 years old, 87 cases of 26-35 years old, 108 cases of 36-45 years old, 98 cases of 46-55 years old, 68 cases of 56-65 years old, and 17 cases of 66-70 years old), 320 brain glioma samples (182 males (56.9%), 138 females (43.1%), age distribution range: 16-68 years old, average age: 47.8±14.6 years old. The specific age distribution is: 38 cases of 16-25 years old, 63 cases of 26-35 years old, 72 cases of 36-45 years old, 87 cases of 46-55 years old, and 60 cases of 56-68 years old), through primary mass spectrometry test, the relative abundance difference of the characteristic peak data in normal people and brain glioma patients is considered, the statistical difference (p<0.05, t test) of the data, the influence factor sorting of the machine learning algorithm (random forest) and the matching degree of the data in the database, and 36 polypeptide substances in the blood are found to have brain glioma 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).

[0093] The specific primary mass spectrometry parameters are as follows:

[0094] Ionization method: matrix-assisted laser desorption ionization (MALDI), and the matrix is α-cyano-4-hydroxycinnamic acid (CHCA).

[0095] Mass range: 100-4000 Da.

[0096] Resolution: 20000 (full mass range).

[0097] Laser energy: 30-40%.

[0098] Acquisition mode: positive ion mode.

[0099] Calibration: External mass calibration was performed using peptide standards (Bruker Peptide Calibration Standard).

[0100] Subsequently, the sequences of the 36 substances in the clinical serum were confirmed by secondary mass spectrometry (secondary mass spectrometry (MS / MS or TOF / TOF) is recommended by the China Food and Drug Administration and the U.S. Food and Drug Administration (FDA) guidelines for polypeptide identification). The secondary mass spectrometry data analysis process is as follows:

[0101] Data analysis method: Database search was performed using Mascot software (version 2.8), and the database was the UniProt human proteome database (published 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.

[0102] 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.

[0103] False positive exclusion: Specificity is verified by reverse database search, and the false positive rate is controlled below 1%.

[0104] Secondary mass spectrometry parameters:

[0105] Collision-induced dissociation (CID).

[0106] Collision energy: 30 eV.

[0107] Fragment ion mass range: 100-3500 Da.

[0108] Data acquisition: At least 1000 laser scans were collected for each sample to improve the signal-to-noise ratio.

[0109] The sequences and specificities of the 36 markers were confirmed by secondary mass spectrometry, and the false positive problem was excluded, and the specific sequences are shown in Table 1.

[0110] Example 2 Verification of marker combination

[0111] According to the 36 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 brain glioma were verified. The analysis process was as follows: for all the samples in the verification cohort, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain the mass spectrometric peak intensity data of all the 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 the samples in the cohort (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.). Sensitivity, Specificity and Area under the Receiver Operating Characteristic Curve (AUC) were calculated for each marker combination. The detailed calculation results of performance indicators are shown in Table 3, and the corresponding ROC curves are shown in Figure 3. Figures 1-26 .

[0112] 1) 300 cases of brain glioma patients (male 170 cases (56.7%), female 130 cases (43.3%), age range 18-65 years old, average age 46.3±13.8 years old. The specific distribution is: 32 cases of 18-25 years old, 43 cases of 26-35 years old, 87 cases of 36-45 years old, 92 cases of 46-55 years old, 46 cases of 56-65 years old) and 300 cases of healthy people samples (male 153 cases (51.0%), female 147 cases (49.0%), age range 20-68 years old, average age 43.7±12.6 years old. The specific distribution is: 48 cases of 20-29 years old, 73 cases of 30-39 years old, 86 cases of 40-49 years old, 68 cases of 50-59 years old, 25 cases of 60-68 years old) were used to verify the auxiliary diagnosis of brain glioma for each marker combination.

[0113] 2) 350 cases of brain glioma patients (male 198 cases (56.6%), female 152 cases (43.4%), age range 20-67 years old, average age 45.2±14.1 years old. The specific distribution is: 38 cases of 20-29 years old, 63 cases of 30-39 years old, 97 cases of 40-49 years old, 108 cases of 50-59 years old, 44 cases of 60-67 years old) and 3000 cases of healthy people samples (male 1527 cases (50.9%), female 1473 cases (49.1%), age range 22-70 years old, average age 44.8±13.5 years old. The specific distribution is: 423 cases of 22-31 years old, 587 cases of 32-41 years old, 698 cases of 42-51 years old, 723 cases of 52-61 years old, 569 cases of 62-70 years old) were used to verify the early screening of brain glioma for each marker combination.

[0114] Table 3 Sensitivity and specificity of marker combination for auxiliary diagnosis and early screening

[0115]

[0116] Table 1 continued

[0117]

[0118] Table 1 continued

[0119]

[0120] According to the detection results of Table 3 and Figures 1-26 The auxiliary diagnosis result of the marker combination selected by the present application is better than the image-based ultrasound model during operation (the sensitivity and specificity are 81.82% and 77.78%, respectively) and the combined detection of cerebrospinal fluid and blood routine (the sensitivity and specificity are 81.0% and 94.1%, respectively). The early screening result is better than the conventional MRI (the sensitivity and specificity are 91% and 86%, respectively).

[0121] 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 marker group for brain glioma screening, diagnosis, characterized in that, The marker group is selected from one of the following polypeptide combinations: A) Sequence 1, Sequence 2, Sequence 3 and Sequence 4; B) Sequence 1, Sequence 2, Sequence 5 and Sequence 6; C) Sequence 1, Sequence 2, Sequence 11 and Sequence 12; D) Sequence 1, Sequence 2, Sequence 30 and Sequence 35; 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 11, Sequence 12 and Sequence 20; H) Sequence 1, Sequence 2, Sequence 9, Sequence 30 and Sequence 35; I) Sequence 2, Sequence 16, Sequence 5 and Sequence 15; J) Sequence 2, Sequence 16, Sequence 20, Sequence 5 and Sequence 15; K) Sequence 2, Sequence 3, Sequence 9 and Sequence 12; L) Sequence 1~Sequence 36; The amino acid sequences of the Sequence 1~Sequence 36 are shown in SEQ ID NO. 1~36; Wherein, the second amino acid K in Sequence 9 has Acetyl modification; the third amino acid G in Sequence 11 has Phospho modification; the fourth amino acid G in Sequence 12 has Dehydrated modification; the fourth amino acid G in Sequence 21 has Phospho modification; the first amino acid Q in Sequence 26 has Gln->pyro-Glu modification, and the sixth amino acid N has Dehydrated modification.

2. Use of the reagent for detecting the marker group of claim 1 in the preparation of preparations for screening and diagnosis of brain glioma.

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