Use of a combination of sugar chain markers in the preparation of a product for predicting the prognosis of nasopharyngeal carcinoma patients

Abstract

The application discloses application of a sugar chain marker combination in preparation of a product for predicting prognosis of a nasopharyngeal carcinoma patient. The sugar chain marker combination comprises one or more of the following N-glycan chains: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb and NA4F2b. A prognosis prediction model is constructed based on the sugar chain marker combination, the relative content of the sugar chain markers in a serum sample is detected, a prediction value is calculated by substituting into a prediction equation, and the prognosis state of the nasopharyngeal carcinoma patient can be judged by comparing with a threshold value. The sugar chain marker combination and the prediction model provided by the application can realize precise and non-invasive evaluation of the prognosis of the nasopharyngeal carcinoma patient after treatment, help clinicians to timely adjust a treatment scheme, improve the survival rate of the patient, and have important clinical application and transformation values.

Description

Technical Field

[0001] This invention belongs to the field of molecular biomedicine and in vitro diagnostic technology, specifically relating to the application of combinations of glycan biomarkers in the preparation of products for predicting the prognosis of nasopharyngeal carcinoma patients. Background Technology

[0002] Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the mucosal epithelium of the nasopharynx. It exhibits significant geographical clustering and is associated with the Epstein-Barr virus (EBV), posing a serious threat to human life and health. The prognosis of NPC is closely related to the stage of diagnosis. The 5-year survival rate for early-stage NPC patients can reach over 90%, while for mid-to-late-stage patients it is only 40%–50%. However, due to the anatomically hidden location of the nasopharynx and the lack of specific early symptoms, it is easily overlooked. Clinical data shows that approximately 60%–70% of patients are diagnosed at stage III-IV, missing the optimal treatment window (Chinese Anti-Cancer Association. Chinese Guidelines for Integrated Diagnosis and Treatment of Cancer (CACA)—Nasopharyngeal Carcinoma (2025 Edition) [M]. Tianjin: Tianjin Science and Technology Press, 2025). Therefore, developing biomarkers that can accurately assess the prognosis of NPC patients is of great significance for guiding adjustments to clinical treatment plans and improving patient survival rates.

[0003] Currently, the main biomarkers used in clinical prognostic assessment of nasopharyngeal carcinoma include EBV-related biomarkers, microRNAs, immune biomarkers, and small cell extracellular vesicle protein biomarkers (Chen YP, Chan ATC, Le QT, et al. Nasopharyngeal carcinoma. Lancet. 2019 Jul 6;394(10192):64-80.). Among them, EBV-related biomarkers (such as plasma EBV DNA, EBNA1-IgA, EA-IgA, etc.) are the most widely used, but they have problems with insufficient sensitivity or specificity. For example, although EA-IgA has high specificity, its sensitivity is only 55% (Liu W, Chen G, Gong X, et al. The diagnostic value of EBV-DNA and EBV-related antibodies detection for nasopharyngeal carcinoma: a meta-analysis. Cancer Cell Int. 2021 Mar 10;21(1):164.). While novel biomarkers such as microRNAs and immune biomarkers have shown some prognostic potential, they still require large-sample validation and lack unified judgment criteria, making it difficult to achieve accurate assessment of patient prognosis.

[0004] Glycoproteins are a class of proteins composed of oligosaccharide chains covalently linked to specific amino acid residues in peptide chains. They are widely distributed in nature, found in vertebrates, invertebrates, plants, single-celled organisms, and viruses. Based on their form and location characteristics, glycoproteins can be divided into three main categories: soluble glycoproteins, membrane-bound glycoproteins, and structural glycoproteins. Secretory proteins and proteins on the outer surface of cell membranes are mostly in the form of glycoproteins, primarily distributed in intracellular fluid, various body fluids, and mucus secreted by glands in cavities. Except for albumin, the vast majority of human plasma proteins belong to the glycoprotein category. Glycoproteins have rich biological functions, with core functions including participation in cell recognition, signal transduction, substance transport, and immune regulation. The sialic acid residues at the ends of their oligosaccharide chains determine the metabolic fate of proteins. Membrane-bound glycoproteins serve as cell surface markers involved in cell adhesion, growth, development, and differentiation regulation. Soluble glycoproteins, such as enzymes, antibodies, and peptide hormones, participate in physiological processes such as metabolism and immune defense. In addition, the glycan chains of glycoproteins can maintain the native conformation and structural stability of peptide chains, endowing glycoproteins with physicochemical properties such as resistance to heat inactivation and resistance to protease hydrolysis, thus playing an indispensable role in the normal physiological activities of the body.

[0005] In recent years, with the continuous deepening of glycomics research, the association between glycoproteins and tumors has been widely revealed. Abnormal protein glycosylation has been confirmed as an important feature in the occurrence and development of tumors, among which abnormal N-glycan modification is particularly significant (Lin Y, Lubman DM. The role of N-glycosylation in cancer. Acta Pharm Sin B.2024 Mar;14(3):1098-1110.). N-glycosylation is the most common glycoprotein modification, referring to the linkage of oligosaccharide chains to the amino groups of asparagine (Asn) residues in peptide chains via β-1-glycosidic bonds. Its core structure contains three mannose residues and two N-acetylglucosamine residues. After processing and modification by the endoplasmic reticulum and Golgi apparatus, it forms three types of N-glycan chains: complex, high-mannose, and hybrid (Xu X, Peng Q, Jiang X, et al. Altered glycosylation in cancer:molecular functions and therapeutic potential. Cancer Commun (Lond). 2024 Nov;44(11):1316-1336.). Currently, abnormal changes in the structure and number of protein N-glycan chains have been found in various malignant tumors such as lung cancer, gastric cancer, colorectal cancer, and liver cancer. These abnormal changes are closely related to pathological processes such as tumor cell proliferation, apoptosis, invasion, metastasis, and immune escape.

[0006] Chinese patent application CN105277718A discloses a product and method for screening, early diagnosis, prognostic assessment, risk assessment, disease monitoring, and / or efficacy evaluation of various malignant tumors by detecting the degree of galactosylation (Gal ratio) at the ends of complex N-glycan chains with dual antennae on the surface of immunoglobulin G (IgG). The patent indicates that the abundance ratio (Gal ratio) of degalactose (Gal0) to galactose glycan chains (Gal1 and Gal2) at the ends of complex N-glycan chains with dual antennae on the surface of IgG shows significant differences between patients with various malignant tumors, including liver cancer, gastric cancer, lung cancer, ovarian cancer, colorectal cancer, and nasopharyngeal carcinoma, and healthy controls, and can be used for universal screening of malignant tumors. However, the technical solution still has the following shortcomings: (1) Its detection target is the N-glycan on the surface of a specific glycoprotein (IgG), rather than the N-glycan spectrum of all glycoproteins in the serum, which may miss the more disease-specific glycan change information carried on other glycoproteins; (2) The Gal ratio index it provides is mainly applicable to the screening and early diagnosis of various malignant tumors. For the prognosis prediction of nasopharyngeal carcinoma, especially for the accurate assessment of the survival outcome of patients after treatment, there is a lack of a dedicated prediction model and validation; (3) This literature does not disclose any specific glycan marker combination for the prognosis of nasopharyngeal carcinoma and its quantitative prediction model, which cannot meet the clinical needs for individualized prognostic assessment of nasopharyngeal carcinoma patients.

[0007] In summary, there is an urgent need in this field to develop a combination of glycan biomarkers and their predictive models that are highly sensitive and specific for predicting the prognosis of nasopharyngeal carcinoma patients, so as to achieve accurate assessment of the prognostic status of nasopharyngeal carcinoma patients after treatment and provide a scientific basis for clinicians to adjust treatment plans in a timely manner. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides the application of a combination of glycan biomarkers in the preparation of products for predicting the prognosis of nasopharyngeal carcinoma patients. Through a specific combination of glycan biomarkers and the NPC-GlycoPro quantitative prediction model, it achieves accurate, personalized, and non-invasive assessment of the prognosis of nasopharyngeal carcinoma patients, which has significant clinical translational value.

[0009] This invention is achieved through the following technical solution:

[0010] The application of a glycan biomarker combination in the preparation of products for predicting the prognosis of patients with nasopharyngeal carcinoma, the glycan biomarker combination comprising one or more of the following N-glycans: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb, NA4F2b; wherein NG1A2F-1 and NG1A2F-2 are isomers.

[0011] Preferably, the combination of glycan biomarkers used to predict prognosis is: NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3, NA3Fb, NA4Fb, and NA4F2b.

[0012] Preferably, the prediction is achieved using the NPC-GlycoPro model, which uses the relative abundance of the glycan biomarker combination as an input variable and calculates the predicted value according to the following equation:

[0013] Predicted value = 1.355 × relative content of NG1A2F-1 - 0.147 × relative content of NG1A2F-2 + 0.112 × relative content of NA2F - 0.874 × relative content of NA2FB - 0.113 × relative content of NA3 - 0.026 × relative content of NA3Fb + 1.198 × relative content of NA4Fb - 0.048 × relative content of NA4F2b;

[0014] A predicted value ≥ 1.70 indicates a poor prognosis, while a predicted value < 1.70 indicates a better prognosis.

[0015] Preferably, the product is a reagent or kit for detecting the relative content of the glycan marker combination in a sample.

[0016] Preferably, the sample is serum.

[0017] Preferably, the prognosis is assessed using total survival time as the evaluation metric.

[0018] A kit for predicting the prognosis of nasopharyngeal carcinoma patients includes a substance for detecting the relative content of a combination of glycan biomarkers in a sample; the combination of glycan biomarkers includes one or more of the following N-glycans: NGA2F, NGA2FB, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb, NA4F2b; wherein NG1A2F-1 and NG1A2F-2 are isomers.

[0019] Preferably, the combination of glycan markers is: NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3, NA3Fb, NA4Fb, NA4F2b.

[0020] A predictive system for predicting the prognosis of nasopharyngeal carcinoma patients includes:

[0021] The data acquisition module is used to acquire the relative content data of the combination of glycan biomarkers in the sample of the nasopharyngeal carcinoma patient to be tested; the combination of glycan biomarkers is: NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3, NA3Fb, NA4Fb, NA4F2b;

[0022] The model calculation module is used to calculate the predicted value based on the relative content data according to the following equation:

[0023] Predicted value = 1.355 × relative content of NG1A2F-1 - 0.147 × relative content of NG1A2F-2 + 0.112 × relative content of NA2F - 0.874 × relative content of NA2FB - 0.113 × relative content of NA3 - 0.026 × relative content of NA3Fb + 1.198 × relative content of NA4Fb - 0.048 × relative content of NA4F2b;

[0024] The result determination module is used to compare the predicted value with the threshold 1.70. If the predicted value is ≥1.70, a poor prognosis result is output; if the predicted value is <1.70, a good prognosis result is output.

[0025] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the functions of one or more modules in the aforementioned prediction system.

[0026] The beneficial effects of this invention are as follows:

[0027] (1) The present invention first screened and obtained a specific biomarker combination composed of 8 N-glycans, namely NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3, NA3Fb, NA4Fb and NA4F2b. This combination can specifically reflect the prognostic status of nasopharyngeal carcinoma patients and fill the gap in the existing technology of lacking specific glycan prognostic biomarkers for nasopharyngeal carcinoma.

[0028] (2) Based on the selected combination of glycan markers, the present invention establishes the NPC-GlycoPro prognostic prediction model. The predicted value is calculated by linear equation and compared with the threshold, which can objectively and quantitatively assess the prognostic risk of nasopharyngeal carcinoma patients and avoid the bias of subjective judgment.

[0029] (3) The present invention can divide patients into a better prognosis group and a worse prognosis group according to the relative content of glycan markers in the patient's serum, which helps clinicians to develop individualized follow-up and treatment plans for patients with different risk levels and improve the efficiency of medical resource utilization.

[0030] (4) The present invention uses serum as the test sample, which has the advantages of being non-invasive and convenient to sample; the test process is standardized and easy to promote and use in clinical laboratory departments, and is suitable for large-scale clinical application.

[0031] (5) The combination of glycan biomarkers provided by this invention can be prepared into reagents or kits, and can be used in conjunction with computer equipment to achieve automated prediction, making it easy to promote and use in clinical laboratory departments. At the same time, the discovery of this biomarker combination also provides new targets and ideas for glycomics research in nasopharyngeal carcinoma, and has important scientific research value and industrialization prospects. Attached Figure Description

[0032] Figure 1 To train a serum glycan profile of a representative group of nasopharyngeal carcinoma patients;

[0033] Figure 2 ROC curves for training the NPC-GlycoPro model to predict the 3-year prognosis of nasopharyngeal carcinoma patients;

[0034] Figure 3 Survival curves based on the NPC-GlycoPro model prognostic scoring system in the training set;

[0035] Figure 4 ROC curves for predicting 3-year prognosis of nasopharyngeal carcinoma patients using the NPC-GlycoPro model in an independent validation set;

[0036] Figure 5 Survival curves for the prognostic scoring system based on the NPC-GlycoPro model in an independent validation set. Detailed Implementation

[0037] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0038] Unless otherwise specified, the technical means used in the following embodiments are all conventional means well known to those skilled in the art, and the experimental methods without specific conditions are all conventional methods in the art.

[0039] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0040] Example 1

[0041] 1. Test Sample

[0042] This study collected serum samples from 266 patients with nasopharyngeal carcinoma (NPC) at Sun Yat-sen University Cancer Center. All samples were pathologically confirmed as NPC, with tumors stage IVB. Patients underwent standard treatment and were followed up for prognosis. Sample collection was approved by the ethics committee, and all participants signed informed consent forms.

[0043] Stratified random sampling with treatment regimen as the stratification factor was used to randomly divide all samples into a training set and an independent validation set in a 7:3 ratio. The stratification variables included three treatment regimen subtypes: FP, RT, and GP. The clinical characteristics distribution of the two groups of samples is shown in Table 1. The training set of 186 cases was used for model construction and feature selection, while the independent validation set of 80 cases was used for independent evaluation of model performance. The randomization process ensured a balanced distribution of clinical characteristics between the training and validation sets, avoiding selection bias.

[0044] Table 1. Distribution of treatment regimens for patients in the training set and independent validation set.

[0045]

[0046] 2. Instruments and reagents

[0047] Capillary electrophoresis analyzer (ABI 3500 sequencer), fully automated biochemical analyzer, centrifuge.

[0048] Reagent A: NH4HCO3 with a concentration of 2~10 mM is added to a 5% SDS solution;

[0049] Reagent B1: Glycoside exonuclease solution with a concentration of 2~5 U / μL;

[0050] Reagent B2: Glycoside endonuclease solution with a concentration of 2~5 U / μL;

[0051] Reagent C: ddH2O;

[0052] Reagent D: A mixture of 2-20 mM fluorescent labeling solution (trisodium 8-aminopyrene-1,3,6-trisulfonic acid) and 1 M DMSO solution.

[0053] 3. Glycan mapping detection

[0054] (1) Release of N-oligosaccharide chains

[0055] Take 3-10 μL of serum sample, add 5-10 μL of reagent A, heat at 90-100℃ for 10-20 min to denature, cool to 4℃, add 5-10 μL of reagent B1 and reagent B2 premixed in a 1:1 ratio, react at 35-40℃ for 2-3 h, and add 50 μL of reagent C to terminate the reaction.

[0056] (2) Fluorescent labeling of N-oligosaccharide chains

[0057] Take 5-10 μL of the above reaction solution and dry it at 70-80℃ for 35-40 min. Then add 3 μL of reagent D and place it at 80-95℃ for 1-2 h. Finally, add 50 μL of reagent C to dilute it.

[0058] (3) Detection of N-oligosaccharide chains

[0059] 5–10 μL of labeled oligosaccharide samples were placed in ABI 96-well plates and analyzed by capillary electrophoresis using an ABI 3500 sequencer to obtain serum glycan profiles. A total of 12 N-glycans were isolated. Using a representative serum sample from a nasopharyngeal carcinoma patient in the training set as an example, the glycan profile is shown below. Figure 1 The following are examples: NGA2F (P1, non-galactosyl α-1, 6-core fucosylated biantennary N-glycan), NGA2FB (P2, non-galactosyl α-1, 6-core fucosylated biantennary N-glycan), NG1A2F-1 (P3, monobranched galactosyl α-1, 6-core fucosylated biantennary N-glycan), NG1A2F-2 (P4, monobranched galactosyl α-1, 6-core fucosylated biantennary N-glycan), NA2 (P5, galactosylated biantennary N-glycan), NA2F (P6, galactosylated α-1, 6-core fucosylated biantennary N-glycan), NA2 FB (P7, galactosyl α-1,6 core fucosylated biantennary N-glycan), NA3 (P8, galactosylated triantennary N-glycan), NA3Fb (P9, galactosylated α-1,3 branched fucosylated triantennary N-glycan), NA4 (P10, galactosylated tetraantennary N-glycan), NA4Fb (P11, galactosylated α-1,3 branched fucosylated tetraantennary N-glycan), NA4F2b (P12, galactosylated di-α-1,3 branched fucosylated tetraantennary N-glycan), among which NG1A2F-1 (P3) and NG1A2F-2 (P4) are isomers.

[0060] (4) Data processing

[0061] The detection results of 12 oligosaccharide chains were quantified. The peak height of each peak was divided by the sum of the heights of all peaks to obtain the relative content (percentage) of each glycan chain, thus obtaining glycan chain data for each nasopharyngeal carcinoma patient.

[0062] 4. Prognostic indicators

[0063] Overall survival (OS) was used as the prognostic indicator. OS was defined as the time from the start of treatment after enrollment to death from any cause.

[0064] 5. Screening characteristic glycans based on the training set

[0065] Cox regression univariate analysis was used to analyze the correlation between the relative content of 12 N-glycans and overall survival (OS) in 186 nasopharyngeal carcinoma patients in the training set, and characteristic glycans with significant differences (P < 0.05) were screened out. The results are shown in Table 2.

[0066] Table 2. Results of Cox regression univariate analysis of 12 sugar chains (training set, n=186)

[0067]

[0068] Table 2 Note: * indicates a significant difference.

[0069] Based on the results in Table 2, eight characteristic sugar chains with statistical significance (P < 0.05) were selected: NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3, NA3Fb, NA4Fb, and NA4F2b.

[0070] 6. Construction of the prediction model on the training set

[0071] Using the relative content of the above eight characteristic glycans as variables, multivariate Cox regression analysis was employed to obtain the regression coefficients for each glycan. An NPC-GlycoPro risk assessment model was constructed, and the predicted values ​​were calculated using the following formula:

[0072] Predicted value = 1.355 × relative content of NG1A2F-1 - 0.147 × relative content of NG1A2F-2 + 0.112 × relative content of NA2F - 0.874 × relative content of NA2FB - 0.113 × relative content of NA3 - 0.026 × relative content of NA3Fb + 1.198 × relative content of NA4Fb - 0.048 × relative content of NA4F2b.

[0073] Based on the aforementioned NPC-GlycoPro risk assessment model, the prognostic risk prediction value for each nasopharyngeal carcinoma patient was calculated. Using the patient's 3-year overall survival outcome as a binary dependent variable (survival assigned a value of 0, all-cause mortality assigned a value of 1), receiver operating characteristic (ROC) curves were plotted using the predicted values ​​and clinical endpoint data to quantitatively evaluate the model's predictive efficacy for the 3-year survival prognosis of nasopharyngeal carcinoma patients. Figure 2 As shown, the AUC of the NPC-GlycoPro risk assessment model in the training set is 0.859, indicating good discriminative power.

[0074] 7. Determination of the optimal critical threshold (training set)

[0075] The optimal cutoff value was determined using the log-rank test maximization method: Based on the overall survival (OS) and survival status data of the subject cohort, the survminer software package was used to perform univariate cutoff value analysis on the prognostic risk prediction values ​​of the NPC-GlycoPro model. After traversing all candidate cutoff points, the optimal cutoff value for the prognostic risk prediction values ​​of the NPC-GlycoPro model was determined to be 1.70, using the largest log-rank test statistic (smallest P-value) of the survival difference between the two groups as the screening criterion. That is, a prediction value ≥ 1.70 indicates a poor prognosis; a prediction value < 1.70 indicates a good prognosis.

[0076] 8. Evaluation of training set model performance

[0077] Based on the aforementioned threshold of 1.70, 186 nasopharyngeal carcinoma patients in the training set were divided into a high-score group (predictive value ≥ 1.70) and a low-score group (predictive value < 1.70). Survival curves for the two groups were plotted using the Kaplan-Meier method, and the results are as follows: Figure 3 As shown.

[0078] The results showed that the three-year mortality rate was 48.4% (30 / 62) in the high-risk group and 12.9% (16 / 124) in the low-risk group, with a highly significant difference in survival curves between the two groups (log-rank test, P < 0.001). Using the high-risk group (risk score ≥ 1.70) as a reference, univariate Cox regression analysis indicated that the all-cause mortality risk in the low-risk group was only 23.4% of that in the high-risk group, representing a relative decrease of 76.6%, and the difference in survival risk between the groups was statistically significant (HR = 0.234, 95% CI: 0.127–0.431, P < 0.001).

[0079] 9. Performance verification of the model on the independent validation set

[0080] Based on independent validation set data, and again using the 3-year overall survival outcome as a binary dependent variable, an ROC curve was plotted to evaluate the model's effectiveness. Figure 4 As shown, the AUC of the NPC-GlycoPro model in the validation set is 0.885, indicating that the model has excellent discriminative power for the 3-year survival prognosis of patients.

[0081] Based on a threshold of 1.70 determined from the training set, 80 nasopharyngeal carcinoma patients in the independent validation set were divided into a high-score group (predictive value ≥ 1.70) and a low-score group (predictive value < 1.70). Survival curves for the two groups were plotted using the Kaplan-Meier method, and the results are as follows: Figure 5 As shown.

[0082] The results showed that the three-year mortality rate was 41.4% (12 / 29) in the high-score group and 9.8% (5 / 51) in the low-score group, with a highly significant difference in survival curves between the two groups (log-rank test, P < 0.001). Using the high-risk group (risk score ≥ 1.70) as a reference, univariate Cox regression analysis indicated that the all-cause mortality risk in the low-risk group was only 19.2% of that in the high-risk group, representing a relative decrease of 80.8%, and the difference in survival risk between the groups was statistically significant (HR = 0.192, 95% CI: 0.067–0.549, P = 0.002).

[0083] The experimental results above demonstrate that the NPC-GlycoPro model constructed in this invention can effectively distinguish between nasopharyngeal carcinoma patients with better and worse prognoses in both the training and independent validation sets, exhibiting good stability and generalization ability. Clinicians can use the prediction results to strengthen follow-up and intervention for high-risk patients and avoid overtreatment for low-risk patients, thereby achieving personalized precision medicine and improving the overall survival rate of nasopharyngeal carcinoma patients.

[0084] The embodiments described above are only some, not all, of the embodiments of the present invention. The detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments. The scope of protection of the present invention is determined by the scope claimed in the claims. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. The application of a combination of glycan biomarkers in the preparation of products for predicting the prognosis of nasopharyngeal carcinoma patients, characterized in that, The glycan marker combination is: NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3, NA3Fb, NA4Fb, NA4F2b; wherein NG1A2F-1 and NG1A2F-2 are isomers.

2. The application according to claim 1, characterized in that, The prediction is achieved using the NPC-GlycoPro model, which takes the relative abundance of the glycan biomarker combination as input variable and calculates the predicted value according to the following equation: Predicted value = 1.355 × relative content of NG1A2F-1 - 0.147 × relative content of NG1A2F-2 + 0.112 × relative content of NA2F - 0.874 × relative content of NA2FB - 0.113 × relative content of NA3 - 0.026 × relative content of NA3Fb + 1.198 × relative content of NA4Fb - 0.048 × relative content of NA4F2b; A predicted value ≥ 1.70 indicates a poor prognosis, while a predicted value < 1.70 indicates a better prognosis.

3. The application according to claim 1, characterized in that, The product is a reagent or kit for detecting the relative content of the glycan biomarker combination in a sample.

4. The application according to claim 3, characterized in that, The sample was serum.

5. The application according to claim 1, characterized in that, The prognosis is assessed using total survival time as the indicator.

6. A kit for predicting the prognosis of nasopharyngeal carcinoma patients, characterized in that, Includes substances for detecting the relative content of a combination of glycan biomarkers in a sample; the combination of glycan biomarkers is: NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3, NA3Fb, NA4Fb, NA4F2b; wherein NG1A2F-1 and NG1A2F-2 are isomers.

7. A predictive system for predicting the prognosis of nasopharyngeal carcinoma patients, characterized in that, include: The data acquisition module is used to acquire the relative content data of the combination of glycan biomarkers in the sample of the nasopharyngeal carcinoma patient to be tested; the combination of glycan biomarkers is: NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3, NA3Fb, NA4Fb, NA4F2b; The model calculation module is used to calculate the predicted value based on the relative content data according to the following equation: Predicted value = 1.355 × relative content of NG1A2F-1 - 0.147 × relative content of NG1A2F-2 + 0.112 × relative content of NA2F - 0.874 × relative content of NA2FB - 0.113 × relative content of NA3 - 0.026 × relative content of NA3Fb + 1.198 × relative content of NA4Fb - 0.048 × relative content of NA4F2b; The result determination module is used to compare the predicted value with the threshold 1.

70. If the predicted value is ≥1.70, a poor prognosis result is output; if the predicted value is <1.70, a good prognosis result is output.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the functions of one or more modules in the prediction system as described in claim 7.

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