Marker group for screening and diagnosing nasopharynx cancer and application of marker group

Abstract

The invention discloses a marker group for screening and diagnosing nasopharyngeal carcinoma 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 nasopharynx cancer. The auxiliary diagnosis results of the selected marker combination are superior to those of common MSCT (the sensitivity and the specificity are 84.90% and 72.22% respectively). Early screening results are superior to those of nasopharynx cancer diagnosis markers, namely EBV related markers, which are most widely and maturely applied clinically at present.

Description

Technical Field

[0001] This invention belongs to the field of molecular biotechnology, specifically relating to a biomarker group for nasopharyngeal carcinoma screening and diagnosis and its applications. Background Technology

[0002] Nasopharyngeal carcinoma (NPC) is one of the endemic malignant tumors prevalent in East and Southeast Asia. Currently, the diagnosis and follow-up of routine NPC mainly rely on pathological tissue analysis, serum tumor markers, and medical imaging examinations. Pathological examination, considered the "gold standard" for diagnosing NPC, is technically challenging and, as an invasive procedure, causes significant discomfort to patients, making it difficult to perform in early-stage screening. Nasopharyngeal imaging is crucial for the diagnosis and clinical staging of NPC. CT scans help assess the extent of tumor involvement in surrounding bone structures, such as the skull base and orbit, but CT is less sensitive than MRI in diagnosing early-stage NPC. Both nasal endoscopy and CT scans can miss early-stage, occult NPC.

[0003] Non-keratinizing nasopharyngeal carcinoma (NPC) is the main type of NPC in my country. Epstein-Barr virus (EBV) infection is the most significant causative factor for NPC, with almost all NPC patients exhibiting EBV infection. Therefore, EBV-related biomarkers are a major component of NPC biomarkers and are currently the most widely used and mature diagnostic and prognostic biomarkers in clinical practice. Recent studies have found that quantitative detection of multiple indicators in the serum of NPC patients, including EBV nuclear antigen 1 (EBNA1) IgA antibody, EBV viral capsid antigen (VCA) IgA antibody, EBV antigen titer, and EBV DNA, followed by comprehensive analysis of these results, demonstrates high specificity. This has significant predictive value for assessing the risk of NPC in high-risk populations in clinical practice. While EBV testing has high sensitivity and specificity, its diagnostic accuracy is not 100%. That is, all nasopharyngeal carcinoma patients test positive for EBV antibodies, but a positive EBV result does not necessarily indicate nasopharyngeal carcinoma; it only indicates a past EBV infection. Generally, combined antibody testing yields higher accuracy. Currently, there is a lack of a unified standard for EBV antibody testing, so consensus on testing methods is still needed. In early-stage (stage I + II) nasopharyngeal carcinoma, the sensitivity and specificity of EBNA1 / IgA are 77.8% and 96.9%, respectively. EBV-DNA has high sensitivity in advanced nasopharyngeal carcinoma, but its drawback is a higher false-negative rate in stage I nasopharyngeal carcinoma; therefore, it is more recommended for the auxiliary diagnosis of advanced patients.

[0004] Liquid biopsy, as an emerging diagnostic technology, can overcome the influence of tumor heterogeneity and provide more comprehensive molecular information about tumors, offering strong support for guiding clinical treatment and assessing prognosis. Studies have shown that peripheral blood EBV-DNA is a reliable indicator for early detection of nasopharyngeal carcinoma (NPC) and holds promise for population screening in areas with a high incidence of NPC. Due to the superficiality of the nasopharynx, other researchers have attempted to directly sample exfoliated cells from the nasopharynx. For example, Octavia Ramayaanti et al. collected nasopharyngeal swabs, peripheral blood, and pathological tissue specimens from subjects and detected EBV-DNA copy number, whole-genome methylation status, and RNA expression profiles in the three samples. The results showed that exfoliated nasal cells more accurately reflect the state of the primary cancer and represent a potentially minimally invasive sampling method for early screening of NPC. Gourzones et al. found a significant increase in miR-BART17 in plasma specimens from NPC patients, and this significant increase was associated with an increase in tumor mass in a patient, with a sensitivity of 77% and a specificity of 90%. This suggests that the concentration of miR-BART17 in plasma may be related to the progression of NPC. Compared to EBV DNA, plasma EBV miRNAs may more directly reflect tumor activity. This may be because EBV-miR-BARTs, as viral oncogenes, play a crucial role in host cell survival, immune evasion, cell proliferation, apoptosis, and tumor metabolism, promoting the development of NPC. However, EBV miRNAs are currently only in the experimental stage and have not undergone large-scale validation. Furthermore, issues such as factors affecting detection quality, a lack of large-scale prospective studies, and the need for methodological standardization urgently require resolution. Summary of the Invention

[0005] In view of the above-mentioned shortcomings in the prior art, the present invention provides a biomarker set for nasopharyngeal carcinoma screening and diagnosis and its uses. It has excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of nasopharyngeal carcinoma, and is expected to be applied to the diagnosis and treatment of nasopharyngeal carcinoma.

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

[0007] Table 1. Peptide Sequences

[0008] Among them, the fourth amino acid G in peptide 5 is modified with Phospho; the third amino acid G in peptide 10 is modified with Phospho; the second amino acid K in peptide 20 is modified with Acetyl; the fourth amino acid G in peptide 28 is modified with Dehydrated; and the first amino acid Q in peptide 32 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 polypeptides shown in sequences 4, 8, 19, 20 and / or 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 polypeptides shown in sequences 2, 4, 9, and 11.

[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 nasopharyngeal carcinoma screening and diagnosis.

[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 nasopharyngeal carcinoma, and holds promise for its application in the diagnosis and treatment of nasopharyngeal carcinoma. The auxiliary diagnostic results of the biomarker combination selected in this invention are all superior to those of conventional MSCT (sensitivity and specificity are 84.90% and 72.22%, respectively). The early screening results are superior to the most widely used and mature nasopharyngeal carcinoma diagnostic biomarkers currently available in clinical practice: EBV-related biomarkers. In early-stage (stage I + II) nasopharyngeal carcinoma, the sensitivity and specificity of EBNA1 / IgA are 77.8% and 96.9%, respectively. Serum EBV-DNA positivity (EBV-DNA ≥ 500 copies / mL) has a sensitivity and specificity of 89.1% (95% CI: 87.0%~90.9%) and 85.0% (95% CI: 83.0%~86.9%) for the diagnosis of nasopharyngeal carcinoma, 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 10 ROC curve for marker combination 10; Figure 11 ROC curve for marker combination 11; Figure 12 ROC curve for marker combination 12; Figure 13ROC 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 nasopharyngeal carcinoma samples with clear diagnostic information and normal human samples, covering patients with benign lesions such as nasopharyngeal inflammation, adenoid hyperplasia, tuberculosis, or lymphoma. The core algorithm is based on supervised learning of known nasopharyngeal carcinoma 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 nasopharyngeal carcinoma or to perform early screening for nasopharyngeal carcinoma, the sensitivity is calculated as: Sensitivity = (Number of true positives / (Number of true positives + Number of false negatives)) × 100%, and Specificity = (Number of true negatives / (Number of true negatives + Number of false positives)) × 100%.

[0035] Example 1: Screening and Identification of Biomarkers This invention analyzed 350 normal individuals (178 males (50.9%) and 172 females (49.1%), aged 25 to 68 years, with a mean age of 45.8 ± 11.6 years; specifically, the age distribution was: 58 cases aged 25-34 years, 87 cases aged 35-44 years, 108 cases aged 45-54 years, and 97 cases aged 55-68 years) and 350 nasopharyngeal carcinoma samples (246 males (70.3%) and 104 females (29.7%), aged 30 to 65 years, with a mean age of 50.3 ± 11.6 years). The participants were aged 0.8 years. The specific age distribution was as follows: 63 cases aged 30-39, 128 cases aged 40-49, 126 cases aged 50-59, and 33 cases aged 60-65. 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 nasopharyngeal carcinoma 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 the matching degree of data in the database was also considered. This resulted in the discovery of 36 blood peptides with diagnostic capabilities for nasopharyngeal carcinoma. 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 based on the normal human sample, i.e., ln(mean signal intensity of cancer patient samples / mean signal intensity of normal human samples). In machine learning, the selection of characteristic peaks was based on feature importance assessment using ensemble learning. By constructing multiple decision trees, the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction was quantified. The feature importance score, or influence factor, is obtained by calculating the average reduction in impurity caused by the feature when it splits across all tree nodes (Biau, G., Scornet, E. A random forest guided tour. TEST 25, 197–227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7). Specific first-order mass spectrometry parameters are as follows: Ionization method: Matrix-assisted laser desorption / ionization (MALDI), with α-cyano-4-hydroxycinnamic acid (CHCA) as the matrix.

[0036] Quality range: 100-4000 Da.

[0037] Resolution: 20000 (full quality range).

[0038] Laser energy: 30-40%.

[0039] Acquisition mode: Positive ion mode.

[0040] Calibration: External quality calibration was performed using the Bruker Peptide Calibration Standard.

[0041] 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.

[0042] 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.

[0043] False positive exclusion: Specificity was verified through reverse database search, and the false positive rate was controlled to below 1%.

[0044] Secondary mass spectrometry parameters: Collision-induced dissociation (CID).

[0045] Collision energy: 30 eV.

[0046] Fragment ion mass range: 100-3500 Da.

[0047] Data acquisition: Each sample is scanned at least 1000 times with lasers to improve the signal-to-noise ratio.

[0048] 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.

[0049] Example 2: Validation of Marker Combinations Based on the 35 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 nasopharyngeal carcinoma 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 In the figure, AUC is the area under the curve, sens is the sensitivity value, and spec is the specificity value.

[0050] 1) The use of various biomarker combinations in the auxiliary diagnosis of nasopharyngeal carcinoma was validated in 350 patients (246 males (70.3%), 104 females (29.7%), aged 32 to 64 years, with a mean age of 49.8 ± 9.5 years. Specifically, the distribution was: 58 patients aged 32-39, 128 patients aged 40-49, 126 patients aged 50-59, and 38 patients aged 60-64) and 350 healthy individuals (178 males (50.9%), 172 females (49.1%), aged 25 to 65 years, with a mean age of 44.3 ± 11.2 years. Specifically, the distribution was: 63 patients aged 25-34, 98 patients aged 35-44, 108 patients aged 45-54, and 81 patients aged 55-65).

[0051] 2) The early screening efficacy of various biomarker combinations for nasopharyngeal carcinoma was validated using a sample of 400 patients with nasopharyngeal carcinoma (282 males (70.5%), 118 females (29.5%), aged 30 to 63 years, with a mean age of 48.6 ± 9.7 years. Specifically, the distribution was as follows: 63 patients aged 30-38, 128 patients aged 39-47, 143 patients aged 48-56, and 66 patients aged 57-63) and 4000 healthy individuals (2036 males (50.9%), 1964 females (49.1%), aged 22 to 68 years, with a mean age of 43.8 ± 12.4 years. Specifically, the distribution was as follows: 523 patients aged 22-31, 812 patients aged 32-41, 1087 patients aged 42-51, 987 patients aged 52-61, and 591 patients aged 62-68).

[0052] Table 3 Sensitivity and specificity of biomarker combinations for assisted diagnosis and early screening

[0053] (Continued from the table above)

[0054] (Continued from the table above)

[0055] 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 ordinary MSCT (sensitivity and specificity are 84.90% and 72.22%, respectively). The early screening results are superior to the most widely used and mature nasopharyngeal carcinoma diagnostic biomarkers currently available in clinical practice: EBV-related biomarkers. In early-stage (stage I + II) nasopharyngeal carcinoma, the sensitivity and specificity of EBNA1 / IgA are 77.8% and 96.9%, respectively. The sensitivity and specificity of serum EBV-DNA positivity (EBV-DNA ≥ 500 copies / mL) for the diagnosis of nasopharyngeal carcinoma are 89.1% (95% CI: 87.0%~90.9%) and 85.0% (95% CI: 83.0%~86.9%), respectively.

[0056] 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 nasopharyngeal carcinoma screening and diagnosis, characterized in that, The biomarker group includes at least four of the polypeptides represented by sequences SEQ ID NO.1 to 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 set includes the polypeptides shown in sequence 4, sequence 8, sequence 19, sequence 20 and / or sequence 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, 4, 9, and 11.

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 nasopharyngeal carcinoma screening and diagnosis.

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