Marker group for screening and diagnosing brain glioma and application of marker group

Abstract

The invention discloses a marker group for screening and diagnosing brain glioma and application of the marker group, and belongs to the technical field of molecular biology. The marker group comprises at least four of polypeptides as shown in sequences SEQ ID NO. 1 to 36. The marker combination constructed by the invention has more excellent sensitivity and specificity when being used for auxiliary diagnosis and early screening of brain glioma. The auxiliary diagnosis result of the selected marker combination is superior to that of a radiomics model based on intraoperative ultrasound (the sensitivity and the specificity are 81.82% and 77.78% respectively) and that of cerebrospinal fluid combined blood routine detection (the sensitivity and the 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] This invention belongs to the field of molecular biology technology, specifically relating to a biomarker group for screening and diagnosis of glioma and its uses. Background Technology

[0002] Gliomas are malignant tumors originating from glial cells and are among the most common primary tumors of the adult central nervous system (CNS), exhibiting significant heterogeneity and high invasiveness. According to the World Health Organization (WHO) classification system, gliomas are divided into four grades, with significant differences in molecular characteristics, pathological features, and clinical prognosis between different grades.

[0003] Traditionally, pathomics has been considered the "gold standard" for tumor classification and diagnosis, providing crucial information for tumor subtyping and grading. However, traditional pathological diagnosis relies on the judgment of experienced pathologists and is inherently subjective. Even with complex glioma samples, pathologists' understanding and judgment often differ. Complementing pathomics, radiomics offers a macroscopic perspective, enabling non-invasive assessment of glioma grading, molecular subtyping, and prognosis. Current major imaging techniques include CT, MRI, and PET-CT. However, manual image identification can lead to missed or incorrect identifications, posing a potential threat to patient health and safety. Diagnostic specificity is also poor for patients with atypical imaging findings or early-stage gliomas. In recent years, with the development of molecular biology techniques, glioma classification has gradually shifted from traditional histological grading to molecular characteristic-based classification. The WHO has introduced key molecular markers in its latest classification of CNS tumors, such as isocitrate dehydrogenase (IDH), co-deletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p / 19q), and the telomerase reverse transcriptase (TERT) promoter, to aid in the precise molecular-level classification of gliomas. However, mutation detection often relies on molecular pathological diagnoses based on surgical specimens, which has a certain time lag.

[0004] Liquid biopsy can overcome the impact of tumor heterogeneity, providing more comprehensive molecular information about tumors and offering strong support for guiding clinical treatment and assessing prognosis. Most gliomas are difficult to detect in their early stages, often only being diagnosed after symptoms appear. Traditional diagnostic techniques, including cranial imaging and lesion biopsy, have limitations. Liquid biopsy is convenient, rapid, non-invasive, and allows for repeated sampling, aligning better with the current trend of precision oncology. Therefore, researching suitable early liquid biopsy strategies for gliomas and screening for glioma-specific liquid biopsy tumor markers, especially those for early diagnosis and recurrence monitoring, is crucial for achieving precision medicine. Circulating tumor cells (CTCs), circulating tumor DNA (ct-DNA), and proteins are considered biomarkers for the early diagnosis and real-time monitoring of gliomas. CTCs are tumor cells that detach from solid tumors and enter the peripheral circulation system. They retain specific molecular characteristics from the primary tumor and can be used for the clinical diagnosis of the primary tumor. However, current research on CTCs in gliomas is limited, with small sample sizes, and different methods are used to isolate CTCs in each study, making the results incomparable. Furthermore, the concentration of CTCs in the body fluids of glioma patients is far lower than that of normal cells; only one or a few tumor cells can be found in 10 mL of blood, and the purity is low, making it difficult to reflect the true situation of the entire glioma. The detection of ct-DNA in cerebrospinal fluid also has very stringent environmental requirements before and during analysis, making it difficult to guarantee the stability of the detection and leading to many difficulties in clinical application. Liquid biopsy of cerebrospinal fluid exosomes in gliomas is mainly used clinically to detect changes in certain molecules, including EGFRVⅢ, IDH1 mutant proteins, or miRNAs. Studies have shown that in a multicenter study of 71 GBM patients, the specificity for detecting EGFRVⅢ in cerebrospinal fluid exosomes was as high as 98%, but the sensitivity was low, less than 61%. Mutant transcripts of IDH1 can also be detected in cerebrospinal fluid exosomes of glioma patients with a sensitivity of 62.5% and a specificity of 100%, but they were not detected in serum exosomes. Summary of the Invention

[0005] In view of the above-mentioned shortcomings in the prior art, the present invention provides a set of biomarkers for screening and diagnosis of glioma and their uses. When used for auxiliary diagnosis and early screening of glioma, it has excellent sensitivity and specificity and is expected to be applied to the diagnosis and treatment of glioma.

[0006] To achieve the above objectives, the technical solution adopted by the present invention to solve its technical problem is as follows: A biomarker group for screening and diagnosis of glioma includes at least four of the polypeptides shown in SEQ ID NO. 1-36, the specific sequences of which are shown in Table 1.

[0007] Table 1. Peptide Sequences

[0008] Among them, the second amino acid K in peptide 9 is modified with Acetyl; the third amino acid G in peptide 11 is modified with Phospho; the fourth amino acid G in peptide 12 is modified with Dehydrated; the fourth amino acid G in peptide 21 is modified with Phospho; the first amino acid Q in peptide 26 is modified with Gln->pyro-Glu, and the sixth amino acid N is modified with Dehydrated.

[0009] Furthermore, the biomarker set includes the peptides shown in sequences 1 and 2, as well as the following combinations of peptides: The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 11 and sequence 12; sequence 30 and sequence 35; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 11, sequence 12 and sequence 20; sequence 9, sequence 30 and sequence 35.

[0010] Furthermore, the biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides: The polypeptide combination is one of the following: sequence 3 and sequence 4; sequence 9 and sequence 10; sequence 3, sequence 4 and sequence 9; or sequence 9, sequence 10 and sequence 11.

[0011] Furthermore, the biomarker group includes peptides shown in sequences 1, 10, and 30, as well as peptides shown in sequences 35, 20, 35, and 9, or sequences 20 and 22.

[0012] Furthermore, the biomarker set includes the peptides shown in sequences 5 and 15, as well as the following combinations of peptides: The polypeptide combination is one of the following: sequence 25 and sequence 35; sequence 2 and sequence 16; sequence 9, sequence 25 and sequence 35; sequence 2, sequence 16 and sequence 20.

[0013] Furthermore, the biomarker set includes the peptides described in sequences 4 and 8, as well as combinations of the following peptides: The polypeptide combination is the polypeptide shown in sequences 24 and 25, or sequences 19, 20 and 22.

[0014] Furthermore, the biomarker set includes the polypeptides shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 22.

[0015] Furthermore, the biomarker set includes the peptides shown in sequences 2, 3, 9, and 12.

[0016] Furthermore, the biomarker group includes polypeptides as shown in SEQ ID NO. 1~36.

[0017] The use of the above biomarkers in the preparation of formulations for the screening and diagnosis of gliomas.

[0018] The above biomarkers may be used in basic medical research for non-diagnostic / therapeutic purposes.

[0019] Further, basic medical research includes Western blotting, immunohistochemistry, or flow cytometry.

[0020] The beneficial effects of this invention are: The biomarker combination constructed in this invention exhibits superior sensitivity and specificity in the auxiliary diagnosis and early screening of gliomas, and holds promise for its application in the diagnosis and treatment of gliomas. The auxiliary diagnostic results of the biomarker combination selected in this invention are superior to those of a radiomics model based on intraoperative ultrasound (sensitivity and specificity of 81.82% and 77.78%, respectively) and cerebrospinal fluid combined with routine blood tests (sensitivity and specificity of 81.0% and 94.1%, respectively). The early screening results are superior to conventional MRI (sensitivity and specificity of 91% and 86%, respectively). Attached Figure Description

[0021] Figure 1 ROC curve for marker combination 1; Figure 2 ROC curve for marker combination 2; Figure 3 ROC curve for marker combination 3; Figure 4 ROC curve for marker combination 4; Figure 5 ROC curve for marker combination 5; Figure 6 ROC curve for marker combination 6; Figure 7 ROC curve for marker combination 7; Figure 8 ROC curve for marker combination 8; Figure 9 ROC curve for marker combination 9; Figure 10ROC curve for marker combination 10; Figure 11 ROC curve for marker combination 11; Figure 12 ROC curve for marker combination 12; Figure 13 ROC curve for marker combination 13; Figure 14 ROC curve for marker combination 14; Figure 15 ROC curve for marker combination 15; Figure 16 ROC curve for marker combination 16; Figure 17 ROC curve for marker combination 17; Figure 18 ROC curve for marker combination 18; Figure 19 ROC curve for marker combination 19; Figure 20 ROC curve for marker combination 20; Figure 21 ROC curve for marker combination 21; Figure 22 ROC curve for marker combination 22; Figure 23 ROC curve for marker combination 23; Figure 24 ROC curve for marker combination 24; Figure 25 ROC curve for marker combination 25; Figure 26 ROC curve for marker combination 26. Detailed Implementation

[0022] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0023] The patient samples used in this invention are all from Zhongshan Hospital affiliated with Fudan University and have passed ethical review.

[0024] The experimental methods used in this invention are as follows: I. Serum Sample Collection 1) Sample type: serum.

[0025] 2) Collection requirements: Fasting is required. Use a coagulation tube to draw 5 mL of venous blood, let it stand for 30 min, centrifuge at 3000 rpm for 15 min, and take out about 1 mL of serum and put it into a cryopreservation tube.

[0026] 3) Sample storage: Use on the same day; store at 2-8℃. If not used on the same day, store at -20℃ for up to 30 days; If stored for an extended period (more than one month), it should be kept at -80°C.

[0027] The freeze-thaw cycle should not exceed 3 times.

[0028] II. Extraction of analytes from serum 1) After calibrating the mass spectrometer, turn on the Solid Bio Fully Automated Sample Analysis System SPS1000 / SPS4000, and put in the consumables, matching reagent kits and the sample to be tested; Select the procedure method "Concentrated loading"; Run the program: a. Opening a hole; b. Take at least 10µL of serum sample and activation reagent, mix them in a 1:1 ratio, and place them in the G-row pre-reserved well for later use; c. Clean the custom pipette tip in cleaning reagent 1 and cleaning reagent 2 in sequence. Each time, aspirate at least 10µL of liquid and repeat the aspiration and dispensing process at least 3 times. d. Process the serum mixture in the G-row wells using the cleaned custom pipette tips. Aspirate at least 10 µL of solution each time, repeating the process at least three times. e. Clean the custom pipette tip after adsorbing the serum mixture using cleaning reagent 3. During cleaning, aspirate at least 10µL of liquid each time, repeating the aspiration and dispensing process at least 3 times. f. Transfer no less than 10µL of buffer reagent into the H-row pre-reserved hole, and place the customized pipette tip after using cleaning reagent 3 into the liquid to draw no less than 10µL of liquid. Repeat the suction and aspiration at least 3 times. g. Transfer at least 10µL of sample matrix solution into the H-row pre-reserved well to complete sample processing; h. Spot 2.0 µL of the solution from well H onto the hydrophobic-coated biochip (Wuxi Pimo Technology Co., Ltd.). i. Vacuum drying for 240 seconds.

[0029] The main components of each reagent are shown in Table 2.

[0030] Table 2 Reagent Composition

[0031] III. Mass Spectrometry Data Acquisition and Upload The hydrophobic coated biochip (Wuxi Pimo Technology Co., Ltd.) was vacuum dried and placed into a mass spectrometer; Data acquisition is performed using the pre-defined 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 diameter, laser frequency, calibration method, and laser intensity.

[0032] IV. Quality Control 1) After data collection, the data will be uploaded to the "Mass Spectrometry Data Analysis Software"; 2) The software reads the sample information and signal spectrum, and judges whether the sample and sample pretreatment are qualified according to the quality control model; quality control failure may include a variety of possibilities, including the sample is not a diseased sample, the signal spectrum intensity is not up to standard, etc.

[0033] 3) If the quality control fails, adjust the corresponding parameters according to the quality control results and repeat the serum analyte extraction process; 4) If the quality control is qualified, proceed to the next process.

[0034] V. Establishment of Positive Criterion Value and Result Analysis The study of positive cutoff values ​​used glioma samples with clear diagnostic information and normal human samples, covering patients with non-tumor lesions such as encephalitis, demyelinating diseases, and cerebral infarction. The core algorithm is based on supervised learning of known glioma sample atlases. Through a series of processes such as smoothing, noise reduction, and baseline removal, characteristic peaks are screened, a classification model is constructed, and the similarity between hormone signal atlases and known hormone signal atlases stored in the software is calculated (Cannataro M, Guzzi PH, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data[J]. 2005.). Finally, the similarity score positive cutoff value of the kit is determined by the Youden index maximization method. When the similarity score < positive cutoff value, the sample test result is negative; when the similarity score ≥ positive cutoff value, the sample test result is positive. When using the maximum similarity score as the positive cutoff value to assist in the diagnosis of glioma or to perform early screening for glioma, the sensitivity is calculated as: Sensitivity = (Number of true positives / (Number of true positives + Number of false negatives)) × 100%, and Specificity is calculated as: (Number of true negatives / (Number of true negatives + Number of false positives)) × 100%.

[0035] Example 1: Screening and Identification of Biomarkers This invention analyzed 420 normal human samples (218 males (51.9%), 202 females (48.1%), aged 18 to 70 years, with a mean age of 44.3 ± 13.2 years; specifically, the age distribution was as follows: 18-25 years (42 cases), 26-35 years (87 cases), 36-45 years (108 cases), 46-55 years (98 cases), 56-65 years (68 cases), and 66-70 years (17 cases)) and 320 glioma samples (182 males (56.9%), 138 females (43.1%), aged 16 to 68 years, with a mean age of 44.3 ± 13.2 years). The mean age of the patients was 7.8 ± 14.6 years. The specific age distribution was as follows: 38 cases aged 16-25, 63 cases aged 26-35, 72 cases aged 36-45, 87 cases aged 46-55, and 60 cases aged 56-68. Time-of-flight mass spectrometry (TOF-MS) was performed. Through first-level mass spectrometry testing, the relative abundance differences of characteristic peak data in normal individuals and glioma patients were comprehensively considered, along with statistical differences (p < 0.05, t-test). A machine learning algorithm (random forest) was used to rank the influence factors for feature selection and to assess the matching degree of data in the database. This identified 36 blood peptides with diagnostic capabilities for glioma. The mass-to-charge ratio (m / z), relative abundance, and influence factors of these peptides are shown in Table 1. The relative abundance was normalized to ln(mean signal intensity of cancer patient samples / mean signal intensity of normal samples) based on the normal human sample. The feature peak selection in machine learning was based on feature importance assessment using ensemble learning. Multiple decision trees were constructed to quantify the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction. The feature importance score, or influence factor, is obtained by calculating the mean reduction in impurity caused by the feature when splitting across all tree nodes (Biau, G., Scornet, E. Arandom forest guided tour. TEST 25, 197–227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0036] The specific parameters for first-order mass spectrometry are as follows: Ionization method: Matrix-assisted laser desorption / ionization (MALDI), with α-cyano-4-hydroxycinnamic acid (CHCA) as the matrix.

[0037] Quality range: 100-4000 Da.

[0038] Resolution: 20000 (full quality range).

[0039] Laser energy: 30-40%.

[0040] Acquisition mode: Positive ion mode.

[0041] Calibration: External quality calibration was performed using the Bruker Peptide Calibration Standard.

[0042] Subsequently, the sequences of these 36 substances in the clinical serum were confirmed using secondary mass spectrometry (MS / MS or TOF / TOF is a peptide identification method recommended by the guidelines of the China Food and Drug Administration and the U.S. Food and Drug Administration). The secondary mass spectrometry data analysis process is as follows: Data analysis methods: Mascot software (version 2.8) was used for database searching, with the UniProt Human Proteome Database (released in 2023) as the target database. Search parameters: Enzyme was set to "no digestion", parent ion mass error was allowed ±0.5 Da, fragment ion mass error was allowed ±0.3 Da, fixed modification was cysteine ​​urea methylation, and variable modification was methionine oxidation.

[0043] Sequence confirmation criteria: The confirmation of peptide sequences is based on the matching of fragment ion spectra (b- and y- ions) with theoretical spectra. A Mascot score higher than 30 (p<0.05) is considered significant.

[0044] False positive exclusion: Specificity was verified through reverse database search, and the false positive rate was controlled to below 1%.

[0045] Secondary mass spectrometry parameters: Collision-induced dissociation (CID).

[0046] Collision energy: 30 eV.

[0047] Fragment ion mass range: 100-3500 Da.

[0048] Data acquisition: Each sample is scanned at least 1000 times with lasers to improve the signal-to-noise ratio.

[0049] The sequences and specificity of these 36 biomarkers were confirmed by secondary mass spectrometry, ruling out false positives. The specific sequences are shown in Table 1.

[0050] Example 2: Validation of Marker Combinations Based on the 36 polypeptide biomarkers identified and confirmed in Example 1 (sequences and mass-to-charge ratios are shown in Table 1), different biomarker combinations were formed as shown in Table 3, and the sensitivity and specificity of the biomarker combinations in the auxiliary diagnosis and early screening of glioma were verified. The analysis process is as follows: For all validation cohort samples, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain the mass spectrometry peak intensity data of all biomarkers in Table 1. The entire detection process was blinded, meaning that the experimental operators were unaware of the sample grouping information. The raw mass spectrometry data were processed by baseline correction, smoothing, and normalization (based on the internal standard peak intensity), and then the peak area or intensity value of each biomarker was extracted. Statistical analysis was performed using R software (version 4.0.2). The preprocessed biomarker intensity data were input into a logistic regression model. For auxiliary diagnostic validation, ten-fold cross-validation was performed using all samples from this 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 validation, given the imbalanced sample size, oversampling (SMOTE) 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 riskprediction models: illustration and simulation using logistic regression. JAm Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093.PMID: 35686364; PMCID: PMC9382395.).The data analysis process referenced general guidelines for constructing clinical predictive models (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 (AUC) curve were calculated for each biomarker combination. The specific calculation results for the performance indicators are shown in Table 3, and the corresponding ROC curves are shown in [Table data missing]. Figures 1-26 .

[0051] 1) The use of various biomarker combinations in the auxiliary diagnosis of glioma was validated using 300 patients with glioma (170 males (56.7%), 130 females (43.3%), aged 18 to 65 years, with a mean age of 46.3 ± 13.8 years. Specifically, the distribution was as follows: 32 patients aged 18-25, 43 patients aged 26-35, 87 patients aged 36-45, 92 patients aged 46-55, and 46 patients aged 56-65) and 300 healthy individuals (153 males (51.0%), 147 females (49.0%), aged 20 to 68 years, with a mean age of 43.7 ± 12.6 years. Specifically, the distribution was as follows: 48 patients aged 20-29, 73 patients aged 30-39, 86 patients aged 40-49, 68 patients aged 50-59, and 25 patients aged 60-68).

[0052] 2) Through a sample of 350 patients with gliomas (198 males (56.6%) and 152 females (43.4%), aged 20 to 67 years, with a mean age of 45.2 ± 14.1 years. Specifically, the distribution was as follows: 38 patients aged 20-29, 63 patients aged 30-39, 97 patients aged 40-49, 108 patients aged 50-59, and 44 patients aged 60-67) and 3000 healthy individuals (…), the study compared the results with a sample of 3000 healthy individuals. Of the participants, 1527 were male (50.9%) and 1473 were female (49.1%), with an age range of 22 to 70 years and a mean age of 44.8 ± 13.5 years. The specific distribution was as follows: 423 cases aged 22-31, 587 cases aged 32-41, 698 cases aged 42-51, 723 cases aged 52-61, and 569 cases aged 62-70. The effectiveness of various biomarker combinations in early screening for gliomas was validated.

[0053] Table 3 Sensitivity and specificity of biomarker combinations for assisted diagnosis and early screening

[0054] (Continued from the table above)

[0055] (Continued from the table above)

[0056] According to Table 3 and Figures 1-26 The test results show that the auxiliary diagnostic results of the biomarker combination selected in this invention are superior to those of the intraoperative ultrasound-based radiomics model (sensitivity and specificity of 81.82% and 77.78%, respectively) and the combined cerebrospinal fluid and blood routine test (sensitivity and specificity of 81.0% and 94.1%, respectively). Early screening results are superior to conventional MRI (sensitivity and specificity of 91% and 86%, respectively).

[0057] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A biomarker set for screening and diagnosis of gliomas, characterized in that, The biomarker group includes at least four of the polypeptides represented by sequences SEQ ID NO.1-36.

2. The marker set according to claim 1, characterized in that, The biomarker set includes the peptides shown in Sequence 1 and Sequence 2, as well as the following combinations of peptides: The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 11 and sequence 12; sequence 30 and sequence 35; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 11, sequence 12 and sequence 20; sequence 9, sequence 30 and sequence 35.

3. The marker set according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides: The polypeptide combination is one of the following: sequence 3 and sequence 4; sequence 9 and sequence 10; sequence 3, sequence 4 and sequence 9; sequence 9, sequence 10 and sequence 11.

4. The marker set according to claim 1, characterized in that, The biomarker set includes polypeptides represented by sequences 1, 10, and 30, as well as polypeptides represented by sequences 35, 20, 35, and 9, or sequences 20 and 22.

5. The set of markers according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 5 and 15, as well as the following combinations of peptides: The polypeptide combination is one of the following: sequence 25 and sequence 35; sequence 2 and sequence 16; sequence 9, sequence 25 and sequence 35; sequence 2, sequence 16 and sequence 20.

6. The set of markers according to claim 1, characterized in that, The biomarker group includes the peptides described in sequences 4 and 8, and the following peptide combinations: the peptide combinations are the peptides described in sequences 24 and 25, or sequences 19, 20 and 22.

7. The set of markers according to claim 1, characterized in that, The biomarker set includes the polypeptides shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 22.

8. The set of markers according to claim 1, characterized in that, The biomarker set includes the polypeptides shown in sequences 2, 3, 9, and 12.

9. The set of markers according to claim 1, characterized in that, The biomarker group includes polypeptides as shown in SEQ ID NO. 1~36.

10. Use of the biomarker group according to any one of claims 1 to 9 in the preparation of a formulation for screening and diagnosis of glioma.

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