N-sugar chain marker combination for predicting curative effect state of IgA and IgG type MM as well as prediction scoring system and application of N-sugar chain marker combination

Abstract

The invention discloses an N-sugar chain marker combination for predicting the curative effect state of IgA and IgG type MM and a prediction scoring system and application of the N-sugar chain marker combination. The N-sugar chain marker combination is composed of the following 11 N-sugar chains: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4 and NA4Fb. The method has the advantages that (1) the relevance between the N-carbohydrate chain and the curative effect in the blood of the IgA and IgG type MM patients in different curative effect states is found for the first time, and the N-carbohydrate chain can be used as an index for predicting the curative effect states of the IgA and IgG type MM patients for the first time; and (2) the N-carbohydrate chain curative effect prediction scoring system provided by the invention is used for predicting the curative effect states of IgA and IgG type MM patients, and a new technical means is provided for predicting the curative effects of the IgA and IgG type MM patients.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of molecular biology medicine, and particularly relates to an N-glycan marker combination for predicting the curative effect state of IgG and IgA type MM patients and a prediction scoring system and use thereof. BACKGROUND

[0002] Multiple myeloma (MM) belongs to plasma cell tumors and is the second most common malignant tumor in the blood system, accounting for about 10% of blood system tumors. It is common in the elderly and is currently incurable. Among them, IgG type MM patients account for about 45%, and IgA type MM patients account for about 20%. After conventional treatment, most MM patients will eventually die of disease recurrence, indicating that there are residual lesions in the body of the patients that cannot be detected by conventional serological methods, leading to disease recurrence. Early detection of the curative effect state of patients after treatment, treatment response evaluation and prognosis assessment, and timely adjustment of treatment regimens will greatly improve the quality of life and survival time of patients. However, there is currently a lack of markers that can efficiently and independently predict the curative effect state of IgG and IgA type MM patients. Therefore, finding markers that can effectively predict the curative effect of IgG or IgA type MM patients is a problem that needs to be solved in clinical practice.

[0003] Research has found that IgG type MM patients produce abnormal glycoprotein IgG, and the N-glycan on IgG has changed significantly, and IgA type MM patients produce abnormal glycoprotein IgA, and the N-glycan on IgA has also changed significantly. The change in the structure and quantity of N-glycan on glycoprotein is closely related to the curative effect state of patients. The present application has found that the N-glycan in the blood samples of IgG and IgA type MM patients in different disease states can predict the curative effect of patients, providing a new N-glycan marker and prediction scoring system for predicting the curative effect state. Therefore, the present application focuses on the application value of the N-glycan prediction scoring system in IgG and IgA type MM patients. SUMMARY

[0004] The technical problem solved by the present application is that, in view of the limitations of the prior art, the present application provides an N-glycan marker combination for predicting the curative effect state of IgA and IgG type MM and a prediction scoring system and use thereof, which can be used to predict the curative effect state of IgA and IgG type MM patients, thereby providing a biomarker combination and scoring system for predicting the curative effect of IgA and IgG type MM patients, and providing a solution for predicting the curative effect state of IgA and IgG type MM patients after treatment.

[0005] Technical solution: A combination of N-glycan markers for predicting the efficacy status of IgA and IgG type multiple myeloma (MM) is composed of the following 11 N-glycans: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb.

[0006] A scoring system for predicting the efficacy status of IgA and IgG type MM, comprising: a data input module for receiving the relative content detection values of the 11 N-glycans in claim 1 in the sample; an analysis and calculation module for storing classification models IgA-MM-GEES and IgG-MM-GEES, which are constructed by a logistic regression algorithm, and the calculation formula is: IgA-MM-GEES=exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75.33) / [1+exp(1.607×NGA2+1.263×NGA2F-1.072×NG1A2F-1+0.591×NG1A2F-2+0.743×NA2+0.787×NA2F+1.530×NA2FB+0.819×NA3+1.192×NA3Fb+0.419×NA4-1.202×NA4Fb-75.33)];

[0007] IgG-MM-GEES=exp(-0.026×NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2F-2+0.006×NA2-0.403×NA2FB+1.697×NA3-4.837×NA4+2.644×NA4Fb-16.889) / [1+exp(-0.026×NGA2+0.688×NGA2F-4.323×NGA2FB+1.293×NG1A2F-1+0.617×NG1A2F-2+0.006×NA2-0.403×NA2FB+1.697×NA3-4.837×NA4+2.644×NA4Fb-16.889)];result output module: output IgA-MM-GEES or IgG-MM-GEES score value.

[0008] The threshold value of IgA-MM-GEES in the above analysis and calculation module is set to 0.505, and the threshold value of IgG-MM-GEES is set to 0.642.

[0009] The application of a reagent for detecting the N-glycan marker combination in the preparation of a kit for predicting the efficacy state of IgA and IgG type MM.

[0010] The detection is achieved by capillary electrophoresis.

[0011] A kit for predicting the efficacy state of IgA and IgG type MM, comprising: (a) a reagent for detecting the 11 N-glycans; (b) a computing device storing the scoring system.

[0012] The detection reagent includes a fluorescently labeled reagent for capillary electrophoresis analysis and a glycosidase.

[0013] A method for constructing the scoring system, comprising: (a) obtaining blood samples of IgA and IgG type MM patients in different efficacy states, and detecting the relative contents of 11 N-glycans; (b) taking the N-glycan data as the independent variable and the efficacy state as the dependent variable, and training a classification model IgA-MM-GEES, IgG-MM-GEES and its coefficients by a logistic regression algorithm.

[0014] The efficacy state includes: complete remission: the clinical evaluation meets the complete remission standard; and incomplete remission: including initial diagnosis and disease progression state.

[0015] The sample in step (a) includes venous blood, serum or plasma.

[0016] Beneficial effects: (1) The present application first discovers that N-glycans in blood of IgA and IgG type MM patients in different efficacy states are correlated with efficacy, and first proposes that N-glycans can be used as an index for predicting the efficacy state of IgA and IgG type MM patients. (2) The N-glycan efficacy prediction scoring system provided by the present application is used for predicting the efficacy state of IgA and IgG type MM patients, and provides a new technical means for predicting the efficacy of IgA and IgG type MM patients. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 FIG. 4 is a ROC curve diagram of the efficacy evaluation prediction scoring system distinguishing complete remission and incomplete remission patients in the training set and validation set for IgA type MM efficacy evaluation, and the AUC values are 0.969 and 0.889, respectively.

[0018] Figure 2 FIG. 5 is an N-glycan profile of an IgA type MM patient at initial diagnosis, complete remission and disease progression.

[0019] Figure 3is the comparison result of the prediction value of the N-glycan prediction scoring system of IgA MM patients in the training set, the prediction value of the model of newly diagnosed and disease progression patients is significantly higher than that of the complete remission patients.

[0020] Figure 4 is the comparison result of the prediction value of the N-glycan prediction scoring system of IgA MM patients in the training set, the prediction value of the model of newly diagnosed and disease progression patients is significantly higher than that of the complete remission patients.

[0021] Figure 5 is the ROC curve of the efficacy evaluation scoring system in the training set and the validation set for the efficacy evaluation of IgG MM, which distinguishes complete remission and incomplete remission patients, and the AUC values are 0.971 and 0.986, respectively.

[0022] Figure 6 is the N-glycan profile of IgG MM patients at the time of initial diagnosis, complete remission, and disease progression.

[0023] Figure 7 is the comparison result of the prediction value of the N-glycan prediction scoring system of IgG MM patients in the training set, the prediction value of the model of newly diagnosed and disease progression patients is significantly higher than that of the complete remission patients.

[0024] Figure 8 is the comparison result of the prediction value of the N-glycan prediction scoring system of IgG MM patients in the training set, the prediction value of the model of newly diagnosed and disease progression patients is significantly higher than that of the complete remission patients. DETAILED DESCRIPTION

[0025] The application will be further described in detail below in conjunction with the embodiments and the accompanying drawings. It should be noted that the following embodiments are only used to illustrate the application and not to limit the scope of the application. The present application can also be implemented or applied by other different specific embodiments, and various modifications or changes can be made to the details in this specification without departing from the spirit of the present application.

[0026] The implementation content of the present application will be further described in detail below through specific embodiments.

[0027] A method for predicting the efficacy status of IgA and IgG MM patients by an N-glycan scoring system, comprising the following steps:

[0028] Step one: Collect samples

[0029] Collect blood, serum, and plasma samples from IgA and IgG MM patients with different efficacy statuses.

[0030] Step two Detection of sample N-glycan and data collection

[0031] The technical platform of detecting N-glycan by capillary electrophoresis (CE method) is used to detect N-glycan in blood samples, obtain N-glycan spectrum, and obtain N-glycan data. The specific detection method is as follows: first, 2 μL of the sample of the subject is added to a test tube, then 5 μL of 5% SDS is added, and after mixing well, high temperature denaturation treatment is carried out at 95℃ for 5 min, and the sample is cooled to 4℃; 3 μL of 2.2 units / μL glycosamidase is added to the sample, mixed and centrifuged, and kept in an incubator at 37℃ for 3 h, and then cooled to 4℃; 50 μL of deionized water is added to the cooled sample to terminate the reaction for 1 min, and stored at low temperature of -20℃; 10 μL of the low-temperature stored sample is dried in a metal bath for 90 min, and then cooled to 4℃; 2 μL of a mixture of 100 mM trisulfonic acid trisodium salt fluorescent marker and 1 M organic reductant prepared at a volume ratio of 1:1 is added to the dried sample, centrifuged, and kept in an incubator at 37℃ for 16 h for fluorescent labeling, and then cooled to 4℃; 100 μL of deionized water is added to terminate the reaction for 1 min, mixed and centrifuged, and then stored at low temperature of -20℃; 2 μL of the sample after termination of the reaction is added to 2 μL of sialidase, mixed and centrifuged, kept in an incubator at 37℃ for 16 h, and then 40 μL of deionized water is added to terminate the reaction for 1 min, mixed and centrifuged, and then 10 μL of the sample is taken for oligosaccharide chain fragment separation and detection by a gene sequencer to obtain oligosaccharide chain detection values.

[0032] Step three Efficacy evaluation

[0033] The N-glycan detection data is used as the independent variable, and the disease state is used as the dependent variable to construct a classifier and determine the N-glycan marker model related to efficacy.

[0034] Preferably, the sample type is blood, serum or plasma in venous blood or peripheral blood.

[0035] Preferably, the N-glycan marker includes NGA2 (non-galactosylated biantennary N-glycan), NGA2F (non-galactosylated a-1,6 core fucosylated biantennary N-glycan), NGA2FB (non-galactosylated a-1,6 core fucosylated bisecting N-glycan), NG1A2F-1 (single branch galactosylated a-1,6 core fucosylated biantennary N-glycan), NG1A2F-2 (single branch galactosylated a-1,6 core fucosylated biantennary N-glycan), NA2 (galactosylated biantennary N-glycan), NA2F (galactosylated a-1,6 core fucosylated biantennary N-glycan), NA2FB (galactosylated a-1,6 core fucosylated bisecting N-glycan), NA3 (galactosylated triantennary N-glycan), NA3Fb (galactosylated a-1,3 branch fucosylated triantennary N-glycan), NA4 (galactosylated tetraantennary N-glycan), NA4Fb (galactosylated a-1,3 branch fucosylated tetraantennary N-glycan).

[0036] Preferably, the classifier is a classifier constructed by a logistic regression algorithm.

[0037] Preferably, the therapeutic effect state refers to the clinical evaluation result of the patient after receiving treatment, including complete remission and disease progression.

[0038] The application of a composition in predicting the therapeutic effect state of IgA and IgG type MM reagents, wherein the composition is composed of NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, and NA4Fb in the N-glycan, and the composition is calculated by the formula of the classification model IgA-MM-GEES = exp (1.607 x NGA2 + 1.263 x NGA2F - 1.072 x NG1A2F-1 + 0.591 x NG1A2F-2 + 0.743 x NA2 + 0.787 x NA2F + 1.530 x NA2FB + 0.819 x NA3 + 1.192 x NA3Fb + 0.419 x NA4 - 1.202 x NA4Fb - 75.33) / [1 + exp (1.607 x NGA2 + 1.263 x NGA2F - 1.072 x NG1A2F-1 + 0.591 x NG1A2F-2 + 0.743 x NA2 + 0.787 x NA2F + 1.530 x NA2FB + 0.819 x NA3 + 1.192 x NA3Fb + 0.419 x NA4 - 1.202 x NA4Fb - 75.33)].

[0039] IgG-MM-GEES = exp(-0.026 x NGA2 + 0.688 x NGA2F - 4.323 x NGA2FB + 1.293 x NG1A2F-1 + 0.617 x NG1A2F-2 + 0.006 x NA2 - 0.403 x NA2FB + 1.697 x NA3 - 4.837 x NA4 + 2.644 x NA4Fb - 16.889) / [1 + exp(-0.026 x NGA2 + 0.688 x NGA2F - 4.323 x NGA2FB + 1.293 x NG1A2F-1 + 0.617 x NG1A2F-2 + 0.006 x NA2 - 0.403 x NA2FB + 1.697 x NA3 - 4.837 x NA4 + 2.644 x NA4Fb - 16.889)]; the score of the IgA-MM-GEES model is used to predict the efficacy of IgA type MM patients, and the score of the IgG-MM-GEES model is used to predict the efficacy of IgG type MM patients.

[0040] Preferably, the classification model takes the predicted value of the efficacy state as the output value.

[0041] Preferably, the classification model IgA-MM-GEES predicts the score as the predicted value of the efficacy state, and when the IgA-MM-GEES predicted score is ≥0.505, it indicates that the disease has not reached the complete remission state after treatment, and when the IgA-MM-GEES predicted score is <0.505, it indicates that the disease has reached the complete remission state after treatment. The classification model IgG-MM-GEES predicts the score as the predicted value of the efficacy state, and when the IgG-MM-GEES predicted score is ≥0.642, it indicates that the disease has not reached the complete remission state after treatment, and when the IgG-MM-GEES predicted score is <0.642, it indicates that the disease has reached the complete remission state after treatment.

[0042] Preferably, the treatment regimen used for disease treatment includes the treatment regimen recommended by clinical routine for IgA and IgG type MM patients.

[0043] A system for predicting the efficacy score of IgA and IgG type MM patients after treatment, comprising:

[0044] An N-glycan detection module for detecting N-glycans in blood samples obtained from IgA or IgG type MM patients in different efficacy states.

[0045] An analysis module takes the various N-glycan values obtained in the detection module as the independent variable and the efficacy state as the dependent variable, constructs a classifier, obtains a classification model, and then predicts the efficacy state according to the predicted value output by the classification model.

[0046] Example 1

[0047] The present application provides a prediction scoring system of the relationship between N-glycan and the therapeutic effect state for the first time based on N-glycan in blood samples of IgA type MM patients in different therapeutic effect states, which can predict the therapeutic effect state of IgA type MM patients.

[0048] The present application relates to 85 cases of IgA type MM patients in different therapeutic effect states collected in the experiment, including 32 cases of initial diagnosis samples, 30 cases of complete remission samples, and 23 cases of disease progression samples. Patients with complete remission and disease progression have received clinical conventional treatment in the early stage. The therapeutic effect state in the present application refers to the clinical evaluation results after treatment, including complete remission and disease progression.

[0049] The detection of N-glycan in blood samples is as follows: 2 μL of the sample of the subject is first added to a test tube, then 5 μL of 5% SDS is added, and after mixing thoroughly, high temperature denaturation treatment is performed at 95℃ for 5 min, and the sample is obtained after cooling to 4℃; 3 μL of 2.2 units / μL glycosamidase is added to the sample, mixed and centrifuged, and kept in a incubator at 37℃ for 3 h, and then cooled to 4℃; 50 μL of deionized water is added to the cooled sample to terminate the reaction for 1 min, and stored at low temperature of -20℃; 10 μL of the low-temperature stored sample is dried in a metal bath for 90 min, and then cooled to 4℃; 2 μL of a mixture of 100 mM trisulfonic acid trisodium salt fluorescent label and 1 M organic reductant prepared at a volume ratio of 1:1 is added to the dried sample, centrifuged, and kept in a incubator at 37℃ for 16 h for fluorescent labeling, and then cooled to 4℃; 100 μL of deionized water is added to terminate the reaction for 1 min, mixed and centrifuged, and then stored at low temperature of -20℃; 2 μL of the sample after termination of the reaction is added to 2 μL of sialidase, mixed and centrifuged, kept in a incubator at 37℃ for 16 h, and then 40 μL of deionized water is added to terminate the reaction for 1 min, mixed and centrifuged, and then 10 μL of the sample is taken for oligosaccharide chain fragment separation detection by a gene sequencer to obtain the oligosaccharide chain detection value.

[0050] The blood sample is subjected to protein denaturation, glycosidase treatment, fluorescent labeling, N-glycan profile detection and data acquisition to obtain the relative amount of 12 kinds of N-glycan in each sample, and the 12 kinds of N-glycan are NGA2 (non-galactosylated biantennary N-glycan), NGA2F (non-galactosylated α-1, 6 core fucosylated biantennary N-glycan), NGA2FB (non-galactosylated α-1, 6 core fucosylated bisecting N-glycan), NG1A2F-1 (single branched galactosylated α-1, 6 core fucosylated biantennary N-glycan), NG1A2F-2 (single branched galactosylated α-1, 6 core fucosylated biantennary N-glycan), NA2 (galactosylated biantennary N-glycan), NA2F (galactosylated α-1, 6 core fucosylated biantennary N-glycan), NA2FB (galactosylated α-1, 6 core fucosylated bisecting N-glycan), NA3 (galactosylated trisialylated N-glycan), NA3Fb (galactosylated α-1, 3 branched fucosylated trisialylated N-glycan), NA4 (galactosylated tetrasialylated N-glycan), and NA4Fb (galactosylated α-1, 3 branched fucosylated tetrasialylated N-glycan).

[0051] Screening of N-glycan in the establishment of efficacy evaluation prediction score model

[0052] N-glycan data of IgA type MM patients in different efficacy states were compared and analyzed between groups

[0053] The 12 kinds of N-glycan data of 85 patients in different efficacy states were compared and analyzed, and the N-glycan with a statistical result p value less than 0.05 was selected as the marker for the prediction score model, as shown in Table 1.

[0054] Table 1 Comparison of 12 kinds of N-glycan data of IgA type MM patients in different disease states

[0055]

[0056] Data set division: The data set is randomly divided into training set and validation set, wherein there are 16 cases of initial diagnosis samples, 15 cases of complete remission samples and 12 cases of disease progression samples in the training set; there are 16 cases of initial diagnosis samples, 15 cases of complete remission samples and 11 cases of disease progression samples in the validation set.

[0057] The 11 N-glycan chains screened, the predictive efficacy score was calculated using a logistic regression equation in the training set, and a predictive model was constructed. The calculation formula of the predictive model is as follows: IgA-MM-GEES = exp(1.607 x NGA2+1.263 x NGA2F-1.072 x NG1A2F-1+0.591 x NG1A2F-2+0.743 x NA2+0.787 x NA2F+1.530 x NA2FB+0.819 x NA3+1.192 x NA3Fb+0.419 x NA4-1.202 x NA4Fb-75.33) / [1+exp(1.607 x NGA2+1.263 x NGA2F-1.072 x NG1A2F-1+0.591 x NG1A2F-2+0.743 x NA2+0.787 x NA2F+1.530 x NA2FB+0.819 x NA3+1.192 x NA3Fb+0.419 x NA4-1.202 x NA4Fb-75.33)]. The performance of the predictive model was verified using the validation set data.

[0058] The ROC curves of the training set and the validation set efficacy score predictive model IgA-MM-GEES distinguishing between complete remission and non-complete remission patients are shown in Figure 1 The AUC values are 0.969 and 0.889, respectively.

[0059] The threshold of the predictive score model is 0.505. When the IgA-MM-GEES predictive score is ≥0.505, it indicates that the disease has not reached complete remission after treatment. When the IgA-MM-GEES predictive score is <0.505, it indicates that the disease has reached complete remission after treatment. The total coincidence rate of the IgA-MM-GEES model with the clinical evaluation results in the training set reached 90.70%, as shown in Table 2. The total coincidence rate of the IgA-MM-GEES model with the clinical evaluation results in the validation set reached 83.33%, as shown in Table 3.

[0060] Table 2 Coincidence rate of efficacy prediction model with clinical evaluation results in training set

[0061]

[0062] Table 3 Coincidence rate of efficacy prediction model with clinical evaluation results in validation set

[0063]

[0064] When IgA MM patients are first diagnosed and then reach complete remission after treatment, N-glycan profile changes significantly; when the complete remission state changes to disease progression, N-glycan profile also changes significantly, as shown in Figure 2 .

[0065] The comparison results of IgA-MM-GEES prediction values of newly diagnosed, complete remission and disease progression patients in the training set are shown in Figure 3 The model prediction values of newly diagnosed and disease progression patients were significantly higher than those of complete remission patients (p<0.0001).

[0066] The comparison results of IgA-MM-GEES prediction values of newly diagnosed, complete remission and disease progression patients in the training set are shown in Figure 4 The model prediction values of newly diagnosed and disease progression patients were significantly higher than those of complete remission patients (p<0.05).

[0067] Figure 1 The ROC curve of the efficacy evaluation scoring system in the training set and the validation set to distinguish complete remission and non-complete remission patients is shown in Figure 1, and the AUC values are 0.969 and 0.889, respectively.

[0068] Figure 2 Figure 2 is the N-glycan profile of IgA MM patients at the time of initial diagnosis, complete remission and disease progression.

[0069] Figure 3 The comparison results of N-glycan scoring system prediction values of newly diagnosed, complete remission and disease progression IgA MM patients in the training set are shown in Figure 3, and the model prediction values of newly diagnosed and disease progression patients are significantly higher than those of complete remission patients.

[0070] Figure 4 The comparison results of N-glycan scoring system prediction values of newly diagnosed, complete remission and disease progression IgA MM patients in the training set are shown in Figure 3, and the model prediction values of newly diagnosed and disease progression patients are significantly higher than those of complete remission patients.

[0071] Example 2

[0072] The present application provides a prediction scoring system of the relationship between N-glycan and efficacy status based on N-glycan in blood samples of IgG MM patients in different efficacy states for the first time, which can predict the efficacy status of IgG MM patients.

[0073] The present application involves the collection of 106 blood samples of IgG MM patients in different efficacy states in experiments, including 40 newly diagnosed samples, 45 complete remission samples and 21 disease progression samples. The patients who achieved complete remission and disease progression received clinical routine treatment regimen in the early stage. The efficacy status in the present application refers to the clinical evaluation results after treatment, including complete remission and disease progression.

[0074] The detection of N-glycan in blood samples is specifically referred to Chinese invention CN202311858738.7.

[0075] The blood sample is subjected to protein denaturation, glycosidase treatment, fluorescent labeling, N-glycan profile detection, and data acquisition to obtain the relative amount of 12 types of N-glycans in each sample, including NGA2 (non-galactosylated biantennary N-glycan), NGA2F (non-galactosylated a-1, 6 core fucosylated biantennary N-glycan), NGA2FB (non-galactosylated a-1, 6 core fucosylated bisecting N-glycan), NG1A2F-1 (single branched galactosylated a-1, 6 core fucosylated biantennary N-glycan), NG1A2F-2 (single branched galactosylated a-1, 6 core fucosylated biantennary N-glycan), NA2 (galactosylated biantennary N-glycan), NA2F (galactosylated a-1, 6 core fucosylated biantennary N-glycan), NA2FB (galactosylated a-1, 6 core fucosylated bisecting N-glycan), NA3 (galactosylated trisialylated N-glycan), NA3Fb (galactosylated a-1, 3 branched fucosylated trisialylated N-glycan), NA4 (galactosylated tetrasialylated N-glycan), and NA4Fb (galactosylated a-1, 3 branched fucosylated tetrasialylated N-glycan).

[0076] Screening of N-glycans in the establishment of efficacy evaluation prediction score model.

[0077] N-glycan data of IgG MM patients in different efficacy states were compared between groups.

[0078] The 12 types of N-glycans of 106 patients in different efficacy states were compared and analyzed, and the N-glycans with a statistical result p value less than 0.05 were selected as the markers for the prediction score model, as shown in Table 4.

[0079] Table 4 Comparison results of 12 types of N-glycans of IgG MM patients in different disease states

[0080]

[0081] Dataset division: The dataset was randomly divided into a training set and a validation set, with 20 newly diagnosed samples, 23 complete remission samples, and 11 disease progression samples in the training set; and 20 newly diagnosed samples, 22 complete remission samples, and 10 disease progression samples in the validation set.

[0082] The 10 N-glycan chains screened, the predictive efficacy score was calculated using a logistic regression equation in the training set, and a predictive efficacy score model was constructed. The calculation formula of the predictive model is as follows: IgG-MM-GEES = exp(-0.026 x NGA2+0.688 x NG A2F-4.323 x NG A2FB+1.293 x NG1A2F-1+0.617 x NG1A2F-2+0.006 x NA2-0.403 x NA2FB+1.697 x NA3-4.837 x NA4+2.644 x NA4Fb-16.889) / [1+exp(-0.026 x NGA2+0.688 x NG A2F-4.323 x NG A2FB+1.293 x NG1A2F-1+0.617 x NG1A2F-2+0.006 x NA2-0.403 x NA2FB+1.697 x NA3-4.837 x NA4+2.644 x NA4Fb-16.889)]. The performance of the predictive model was verified using the validation set data.

[0083] The ROC curves of the efficacy score predictive model IgG-MM-GEES in the training set and the validation set for distinguishing between patients with complete remission and patients without complete remission are shown in FIG. 1, and the AUC values are 0.971 and 0.986, respectively. Figure 5

[0084] The threshold value of the predictive score model is 0.642. When the IgG-MM-GEES predictive score is greater than or equal to 0.642, it indicates that the disease has not reached complete remission after treatment. When the IgG-MM-GEES predictive score is less than 0.642, it indicates that the disease has reached complete remission after treatment. The total coincidence rate of the IgG-MM-GEES model with the clinical evaluation results in the training set reached 96.30%, as shown in Table 5. The total coincidence rate of the IgG-MM-GEES model with the clinical evaluation results in the validation set reached 94.23%, as shown in Table 6.

[0085] Table 5 Coincidence rate of efficacy prediction model with clinical evaluation results in training set

[0086]

[0087] Table 6 Coincidence rate of efficacy prediction model with clinical evaluation results in validation set

[0088]

[0089] When IgG type MM patients are first diagnosed and then reach complete remission after treatment, the N-glycan profile changes significantly; when the complete remission state changes to disease progression, the N-glycan profile also changes significantly, as shown in FIG. 2. Figure 6

[0090] ​​The comparison results of IgG-MM-GEES prediction values of newly diagnosed, complete remission and disease progression patients in the training set are shown in Figure 7 As shown in the table, the model prediction values of newly diagnosed and disease progression patients were significantly higher than those of complete remission patients (p<0.0001).

[0091] The comparison results of IgG-MM-GEES prediction values of newly diagnosed, complete remission and disease progression patients in the training set are shown in Figure 8 As shown in the table, the model prediction values of newly diagnosed and disease progression patients were significantly higher than those of complete remission patients (p<0.0001).

[0092] Figure 5 The ROC curve of the efficacy evaluation scoring system in the training set and the verification set to distinguish complete remission and non-complete remission patients is shown in the table, and the AUC values are 0.971 and 0.986, respectively.

[0093] Figure 6 The N-glycan profile of IgG MM patients at the time of newly diagnosed, complete remission and disease progression is shown in the table.

[0094] Figure 7 The comparison results of N-glycan scoring system prediction values of newly diagnosed, complete remission and disease progression IgG MM patients in the training set are shown in the table, and the model prediction values of newly diagnosed and disease progression patients are significantly higher than those of complete remission patients.

[0095] Figure 8 The comparison results of N-glycan scoring system prediction values of newly diagnosed, complete remission and disease progression IgG MM patients in the training set are shown in the table, and the model prediction values of newly diagnosed and disease progression patients are significantly higher than those of complete remission patients

[0096] The above specific embodiments described in conjunction with the drawings further detail the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application, but is not a limitation on the protection scope of the present application. Those skilled in the art should understand that any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application without creative labor should be included in the protection scope of the present application.

Claims

1. A combination of N-glycan markers for predicting the therapeutic efficacy status of IgA and IgG type MM, characterized by, consist of 11 N-glycan chains: NGA2, NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, NA4, NA4Fb.

2. A scoring system for predicting the therapeutic status of IgA and IgG MM, characterized by, It comprises: a data input module for receiving the detection values of the relative contents of the 11 N-glycan chains in the sample according to claim 1; an analysis and calculation module for storing the classification models IgA-MM-GEES and IgG-MM-GEES, which are constructed by a logistic regression algorithm, and the calculation formula is: IgA-MM-GEES = exp(1.607×NGA2 + 1.263×NGA2F - 1.072×NG1A2F-1 + 0.591×NG1A2F-2 + 0.743×NA2 + 0.787×NA2F + 1.530×NA2FB + 0.819×NA3 + 1.192×NA3Fb + 0.419×NA4 - 1.202×NA4Fb - 75.33) / [1 + exp(1.607×NGA2 + 1.263×NGA2F - 1.072×NG1A2F-1 + 0.591×NG1A2F-2 + 0.743×NA2 + 0.787×NA2F + 1.530×NA2FB + 0.819×NA3 + 1.192×NA3Fb + 0.419×NA4 - 1.202×NA4Fb - 75.33)]; IgG-MM-GEES = exp(-0.026×NGA2 + 0.688×NGA2F - 4.323×NGA2FB + 1.293×NG1A2F-1 + 0.617×NG1A2F-2 + 0.006×NA2 - 0.403×NA2FB + 1.697×NA3 - 4.837×NA4 + 2.644×NA4Fb - 16.889) / [1 + exp(-0.026×NGA2 + 0.688×NGA2F - 4.323×NGA2FB + 1.293×NG1A2F-1 + 0.617×NG1A2F-2 + 0.006×NA2 - 0.403×NA2FB + 1.697×NA3 - 4.837×NA4 + 2.644×NA4Fb - 16.889)]. The analysis and calculation module sets the threshold value of IgA-MM-GEES as 0.505 and the threshold value of IgG-MM-GEES as 0.

642.

3. The system of claim 2, wherein, 4. A reagent for detecting the N-glycan marker combination according to claim 1 in the preparation of a kit for predicting the therapeutic effect of IgA and IgG MM. The detection is achieved by capillary electrophoresis.

5. Use according to claim 4, characterized in that, It comprises: (a) a reagent for detecting the 11 N-glycan chains according to claim 1; and (b) a computing device storing the scoring system according to claim 2.

6. A kit for predicting the therapeutic efficacy status of IgA and IgG type MM, characterized by, The detection reagent includes a fluorescently labeled reagent for capillary electrophoresis analysis and a glycosidase.

7. The kit of claim 6, wherein It comprises:

8. A method of constructing the scoring system of claim 2, wherein, ​ (a) obtaining blood samples of IgG, IgA MM patients in different efficacy states, detecting the relative contents of 11 kinds of N-glycan chains; (b) taking N-glycan chain data as independent variables and efficacy state as dependent variables, and training classification models IgA-MM-GEES and IgG-MM-GEES and their coefficients through a logistic regression algorithm.

9. The method of claim 8, wherein, The efficacy state includes: complete remission: the clinical evaluation meets the complete remission standard; not complete remission: including initial diagnosis and disease progression state.

10. The method of claim 8, wherein, The sample in step (a) includes venous blood, serum or plasma.

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