Method for predicting survival prognosis of diffuse large B-cell lymphoma patient through N-sugar chain marker, prediction scoring system and application

Abstract

The invention provides a method for predicting survival prognosis of a diffuse large B-cell lymphoma patient by using an N-carbohydrate chain marker, a prediction scoring system and application. The method comprises the following steps: collecting a blood sample of the diffuse large B-cell lymphoma patient; the method comprises the following steps: detecting an N-sugar chain map in a blood sample to obtain an N-sugar chain map and N-sugar chain data; the method comprises the following steps: constructing a classifier by taking detection data of each single N-sugar chain marker as an independent variable and predicting survival prognosis as a dependent variable, training a model to obtain a classification model, and predicting survival prognosis of a to-be-detected sample according to the classification model. According to the invention, the correlation between the N-sugar chain marker in the blood and the survival prognosis during diagnosis of the DLBCL patient is found for the first time, and the N-sugar chain marker can be used as an index for predicting the survival prognosis of the DLBCL patient for the first time; the N-carbohydrate chain marker prognosis prediction scoring system provided by the invention is used for predicting the survival prognosis of DLBCL patients, and a new technical means is provided for predicting the survival prognosis of the DLBCL patients.

Description

Technical Field

[0001] The present invention relates to a method for predicting the survival prognosis of diffuse large B-cell lymphoma patients using an N-sugar chain marker, a prediction scoring system and uses thereof, and belongs to the technical field of molecular biomedicine. Background Art

[0002] Diffuse large B-cell lymphoma (DLBCL) is a malignant tumor of the hematopoietic system, with approximately 150,000 new cases worldwide each year, accounting for about 30% of all non-Hodgkin lymphomas (NHL). Patients usually present with progressive lymphadenopathy and / or extranodal lesions, and most patients are in the advanced stage when they seek medical treatment. DLBCL is highly heterogeneous, and the 5-year survival rate of patients is only 63.9%. Early detection of the patient's survival prognosis and timely adjustment of the treatment plan will greatly improve the patient's quality of life and survival time. However, there is currently no marker that can efficiently and independently predict the survival prognosis of DLBCL patients. Therefore, finding an effective way to predict the survival prognosis of DLBCL patients is an urgent clinical problem that needs to be solved.

[0003] The sugar chains (Glycan) on N-glycoproteins in the human body's blood are involved in all aspects of the biological processes of cells and organisms, and are the material basis for the body to perform normal functions and pathological processes. Changes in the structure and quantity of sugar chains on N-glycoproteins have been found to be closely related to the occurrence and development of DLBCL. Through a series of research discoveries, the present invention application focuses on the application value of the N-glycochain marker predictive survival prognosis scoring system in DLBCL patients. When DLBCL patients are diagnosed, the N-glycochain markers in the blood samples can predict the patient's survival prognosis, providing a new N-glycochain marker and predictive scoring system for predicting survival prognosis for clinical DLBCL patients. Summary of the invention

[0004] In view of the limitations of the prior art, the purpose of the present invention is to provide an N-glycan marker and a scoring system thereof for predicting the survival prognosis of DLBCL patients after diagnosis, thereby providing a new biomarker and a scoring system thereof for predicting the survival prognosis of DLBCL patients through a new technical means.

[0005] To achieve the above object, the present invention provides a method for predicting the survival prognosis of diffuse large B-cell lymphoma patients using N-sugar chain markers, a prediction scoring system and uses.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A method for predicting the survival prognosis of diffuse large B-cell lymphoma patients using N-glycan markers, comprising the following steps:

[0008] Step 1: Collect samples

[0009] Collect blood samples from patients with diffuse large B-cell lymphoma;

[0010] Step 2: Detection of sample sugar chains and data collection

[0011] The N-glycan profile in the blood sample is detected by using a capillary electrophoresis (CE) method to obtain the N-glycan profile and N-glycan data;

[0012] Step 3: Prognostic Assessment

[0013] The detection data of each individual N-glycan marker is used as the independent variable, and the predicted survival prognosis is used as the dependent variable. A classifier is constructed, and the classification model is obtained after model training. The survival prognosis of the sample to be tested is then predicted based on the classification model.

[0014] Preferably, the blood sample type in the step is venous blood or peripheral blood, serum or plasma.

[0015] Preferably, the N-glycan markers in step 3 include NGA2F (galactosyl α-1,6 core fucosylated biantennary N-glycan), NGA2FB (galactosyl α-1,6 core fucosylated bisected N-glycan), NG1A2F (single branched galactosyl α-1,6 core fucosylated biantennary N-glycan), NA2 (galactosyl biantennary N-glycan), NA2F (galactosyl α-1 ,6 core fucosylated two-antennary N-glycan chain), NA2FB (galactosyl-free α-1,6 core fucosylated bisected N-glycan chain), NA3 (galactosylated three-antennary N-glycan chain), NA3Fb (galactosylated α-1,3 branched fucosylated three-antennary N-glycan chain) and NA4 (galactosylated four-antennary N-glycan chain), NA4Fb (galactosylated α-1,3 branched fucosylated four-antennary N-glycan chain).

[0016] Preferably, the classifier in step three is a classifier constructed by Cox (Cox Regression) regression algorithm.

[0017] Preferably, the survival prognosis in step three refers to the overall survival of the patient after diagnosis.

[0018] A composition is used in a reagent for predicting the survival prognosis of patients with diffuse large B-cell lymphoma. The composition consists of NGA2F, NGA2FB, NG1A2F, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, and NA4Fb among N-sugar chain markers. The composition predicts the survival prognosis of patients with diffuse large B-cell lymphoma through the calculation formula DLBCL-GPSS=0.153×NGA2F+1.356×NGA2FB+0.313×NG1A2F-0.068×NA2-0.013×NA2F-0.008×NA2FB-0.104×NA3+0.146×NA3Fb-0.110×NA4+0.628×NA4Fb of a classification model.

[0019] Preferably, the classification model DLBCL-GPSS uses the risk of survival prognosis as an output value.

[0020] Preferably, the classification model DLBCL-GPSS prediction score is a high or low risk value for survival prognosis.

[0021] Preferably, when the DLBCL-GPSS prediction score is ≥0.80, the patient has a worse survival prognosis, and when the DLBCL-GPSS prediction score is <0.80, the patient has a better survival prognosis.

[0022] A scoring system for predicting survival in patients with diffuse large B-cell lymphoma includes:

[0023] N-glycan detection module, used to detect N-glycans in blood samples obtained when patients with diffuse large B-cell lymphoma are diagnosed;

[0024] The analysis module is used to take various N-glycan values ​​as independent variables and survival prognosis as dependent variables, build a classifier, obtain a classification model after model training, and then predict the survival prognosis of the sample to be tested based on the classification model.

[0025] The present invention has the following beneficial effects:

[0026] (1) The present invention is the first to discover that N-glycan markers in the blood of DLBCL patients at the time of diagnosis are correlated with survival prognosis, and is the first to propose that N-glycan markers can be used as indicators for predicting survival prognosis of DLBCL patients.

[0027] (2) The N-glycan marker prognosis prediction scoring system provided by the present invention is used to predict the survival prognosis of DLBCL patients, providing a new technical means for predicting the survival prognosis of DLBCL patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1This is the study design and experimental flow chart of the N-glycan marker predictive prognostic scoring system for predicting the survival prognosis of DLBCL patients.

[0029] Figure 2 These are the survival curves of the patients in the training set with high (red) and low (blue) scores in the predictive prognostic scoring system.

[0030] Figure 3 This is the survival curve of the validation set patients with high (red) and low (blue) scores in the predictive prognostic scoring system. DETAILED DESCRIPTION

[0031] The present invention will be further described in detail below in conjunction with examples and accompanying drawings. It should be noted that the following examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the application.

[0032] The implementation content of this application is further described in detail below through specific examples.

[0033] Example

[0034] The present invention provides for the first time a predictive scoring system for the relationship between N-glycan markers and the prediction of the survival prognosis of diffuse large B-cell lymphoma patients based on the N-glycans in the blood samples of patients diagnosed with diffuse large B-cell lymphoma. The system can predict the survival prognosis risk of diffuse large B-cell lymphoma patients.

[0035] The experimental method steps of the present invention are as shown in the attached Figure 1 shown.

[0036] Step 1: Collect samples

[0037] The present invention involves an experiment in which 150 blood samples of DLBCL patients were collected when they were diagnosed. The overall survival (OS) in the present invention refers to the time from the diagnosis of the disease to death due to any reason. The 150 DLBCL patients were randomly divided into a training set and a validation set, wherein the training set consisted of 121 cases and the validation set consisted of 29 cases.

[0038] Step 2: Detection of sample sugar chains and data collection

[0039] Detection of N-glycans in blood samples:

[0040] The blood samples were subjected to protein denaturation, glycosidase treatment, fluorescence labeling, N-glycan map detection and data collection to obtain the relative amounts of 10 N-glycans in each sample. The 10 N-glycans were NGA2F (non-galactosyl α-1,6 core fucosylated biantennary N-glycan), NGA2FB (non-galactosyl α-1,6 core fucosylated bisected N-glycan), NG1A2F (single branched galactosyl α-1,6 core fucosylated biantennary N-glycan), NA2 (galactosyl α-1,6 core fucosylated biantennary N-glycan). The N-glycans were galactosylated, NA2F (galactosylated α-1,6 core fucosylated bi-antennary N-glycan), NA2FB (asexual α-1,6 core fucosylated bisected N-glycan), NA3 (galactosylated tri-antennary N-glycan), NA3Fb (galactosylated α-1,3 branched fucosylated tri-antennary N-glycan), NA4 (galactosylated tetra-antennary N-glycan), and NA4Fb (galactosylated α-1,3 branched fucosylated tetra-antennary N-glycan).

[0041] Screening of N-glycans in establishing a survival prognosis prediction scoring model:

[0042] The 10 N-glycan data of 150 patients and the OS data of survival prognosis were analyzed by Cox regression univariate analysis of the hazard ratio (HR) and statistical results p value of the effect of N-glycan on OS. N-glycans with p value less than 0.1 were selected as markers included in the predictive scoring model, as shown in Table 1.

[0043] Table 1 HR values ​​and statistical p values ​​for predicting survival prognosis of 10 N-glycans

[0044]

[0045]

[0046] Division of data sets in training and validation sets:

[0047] The 150 samples were randomly divided into a training set and a validation set in a ratio of 8:2, with 121 samples in the training set and 29 samples in the validation set.

[0048] Step 3: Prognostic Assessment

[0049] Construction of survival prognosis prediction scoring model:

[0050] In the training set, the 10 screened N-glycans were used to calculate the predicted survival prognosis score using the Cox multivariate regression equation to construct a predictive prognosis score model. The N-glycans used in the calculation of the prognostic risk score and the corresponding coefficients are shown in Table 2.

[0051] Table 2 β coefficients for predicting survival prognosis in Cox regression multivariate analysis of 10 N-glycans

[0052]

[0053] The calculation formula of the prediction model is as follows:

[0054] DLBCL-GPSS = 0.153 × NGA2F + 1.356 × NGA2FB + 0.313 × NG1A2F - 0.068 × NA2 - 0.013 × NA2F - 0.008 × NA2FB - 0.104 × NA3 + 0.146 × NA3Fb - 0.110 × NA4 + 0.628 × NA4Fb. The threshold of the prediction model score was 0.80. When the prediction score was ≥ 0.80, the high-risk group had a worse survival prognosis, while the low-risk group with a prediction score < 0.80 had a better survival prognosis. The survival analysis showed a significant statistical difference (p = 0.0003), as shown in the attached figure. Figure 2 shown.

[0055] Validation of the predictive scoring model:

[0056] Ten N-glycans (NGA2F, NGA2FB, NG1A2F, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, and NA4Fb) from 29 samples in the independent validation set were input into the constructed model to verify the model performance. The validation set was divided into high-risk and low-risk groups based on a prediction score of 0.80. The high-risk group with a prediction score ≥ 0.80 had a worse survival prognosis, while the low-risk group with a prediction score < 0.80 had a better survival prognosis. The survival analysis showed a significant statistical difference (p = 0.034), as shown in the attached figure. Figure 3 As shown. It can be seen that the N-glycan markers obtained in this experiment can well predict the survival prognosis of DLBCL patients.

[0057] Attached Figure 1 This is a flowchart of the study design of the N-glycan marker prognostic prediction scoring system for predicting the survival prognosis of DLBCL patients.

[0058] Attached Figure 2 It is a predictive survival prognosis model established based on N-glycan markers NGA2F, NGA2FB, NG1A2F, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, and NA4Fb in the training set patients, that is,

[0059] DLBCL-GPSS=0.153×NGA2F+1.356×NGA2FB+0.313×NG1A2F-0.068×NA2-0.013×NA2F-0.008×NA2FB-0.104×NA3+0.146×NA3Fb-0.110×NA4+0.628×NA4Fb. The threshold (cutoff value) of the prediction score of this model is 0.80. When the prediction score is ≥0.80, the high-risk group has a worse survival prognosis, while the prediction score is <0.80, the low-risk group has a better survival prognosis. The survival analysis has significant statistical differences.

[0060] Attached Figure 3 The DLBCL-GPSS prediction model for predicting survival in the validation set patients was divided into high-risk and low-risk groups with a prediction value of 0.80. The high-risk group had a worse survival prognosis, while the low-risk group with a prediction score <0.80 had a better survival prognosis. The survival analysis showed significant statistical differences.

[0061] The specific embodiments described above in conjunction with the accompanying drawings further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention, but is not a limitation to the scope of protection of the present invention. Technical personnel in the relevant field should understand that any modifications, equivalent substitutions, improvements, etc. that can be made within the spirit and principles of the present invention without creative work should be included in the scope of protection of the present invention.

Claims

1. A method for predicting the survival prognosis of diffuse large B-cell lymphoma patients using N-glycan markers, characterized in that: The steps include: Step 1: Collect samples Collect blood samples from patients with diffuse large B-cell lymphoma; Step 2: Detection of sample sugar chains and data collection Detecting the N-glycan map in the blood sample to obtain the N-glycan map and obtain N-glycan data; Step 3: Prognostic Assessment The detection data of each individual N-glycan marker is used as the independent variable, and the predicted survival prognosis is used as the dependent variable. A classifier is constructed, and the classification model is obtained after model training. The survival prognosis of the sample to be tested is then predicted based on the classification model.

2. The method for predicting the survival prognosis of diffuse large B-cell lymphoma patients using N-glycan markers according to claim 1, characterized in that: In the step, a blood sample type is venous blood or peripheral blood, serum or plasma.

3. The method for predicting the survival prognosis of diffuse large B-cell lymphoma patients using N-glycan markers according to claim 1, characterized in that: The N-glycan markers in step three include NGA2F, NGA2FB, NG1A2F, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4 and NA4Fb.

4. The method for predicting the survival prognosis of diffuse large B-cell lymphoma patients using N-glycan markers according to claim 1, characterized in that: The classifier in step three is a classifier constructed by the Cox regression algorithm.

5. The method for predicting the survival prognosis of diffuse large B-cell lymphoma patients using N-glycan markers according to claim 1, characterized in that: The survival prognosis in step three refers to the overall survival of the patient after diagnosis.

6. Use of a composition in a reagent for predicting the survival prognosis of patients with diffuse large B-cell lymphoma, characterized in that: The composition is composed of NGA2F, NGA2FB, NG1A2F, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, and NA4Fb in the N-sugar chain markers, and the composition is calculated by the classification model formula DLBCL-GPSS=0.153×NGA2F+1.356×NGA2FB+0.313×NG1A2F-0.068×NA2-0.013×NA2F-0.008×NA2FB-0.104×NA3+0.146×NA3Fb-0.110×NA4+0.628×NA4Fb is used to predict the survival prognosis of patients with diffuse large B-cell lymphoma.

7. Use of the composition according to claim 6 in a reagent for predicting the survival prognosis of patients with diffuse large B-cell lymphoma, characterized in that: The classification model DLBCL-GPSS uses the risk level of survival prognosis as an output value.

8. Use of the composition according to claim 6 in a reagent for predicting the survival prognosis of patients with diffuse large B-cell lymphoma, characterized in that: The classification model DLBCL-GPSS prediction score is a high-risk and low-risk value for survival prognosis.

9. Use of the composition according to claim 8 in a reagent for predicting the survival prognosis of patients with diffuse large B-cell lymphoma, characterized in that: When the DLBCL-GPSS prediction score was ≥0.80, the patient had a worse survival prognosis, and when the DLBCL-GPSS prediction score was <0.80, the patient had a better survival prognosis.

10. A scoring system for predicting the survival prognosis of patients with diffuse large B-cell lymphoma, characterized in that: include: N-glycan detection module, used to detect N-glycans in blood samples obtained when patients with diffuse large B-cell lymphoma are diagnosed; The analysis module is used to take various N-glycan values ​​as independent variables and survival prognosis as dependent variables, build a classifier, obtain a classification model after model training, and then predict the survival prognosis of the sample to be tested based on the classification model.

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