Application of sugar chain marker combination in preparation of products for diagnosing and predicting pancreatic cancer prognosis

Abstract

The invention discloses an application of a sugar chain marker combination in preparation of a product for diagnosing and predicting pancreatic cancer prognosis. The marker combination comprises the following N-sugar chains: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3 and NA3Fb. The pancreatic cancer is detected by analyzing the correlation between the change of a sugar chain on seroglycoprotein and diseases and establishing a prediction model of a sugar chain marker combination, so that the pancreatic cancer and non-pancreatic cancer are diagnosed and distinguished in an auxiliary manner, and meanwhile, the prognosis state of pancreatic cancer patient treatment can be evaluated. The method for detecting the pancreatic cancer by adopting the carbohydrate chain marker combination has high sensitivity and high specificity on the pancreatic cancer, can help a pancreatic cancer patient to find and treat the pancreatic cancer patient early, predicts and evaluates the prognosis state of the pancreatic cancer patient, and improves the overall cure rate and survival rate of the patient.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and particularly relates to the application of a glycan biomarker combination in the preparation of a product for diagnosing and predicting the prognosis of pancreatic cancer. Background Art

[0002] Pancreatic cancer is a highly malignant cancer, known as the "king of cancers". In most countries in the world, including China, the five-year survival rate of pancreatic cancer is below 10%, far lower than that of other highly prevalent malignant tumors such as colorectal cancer, esophageal cancer, and liver cancer. One of the reasons for the extremely low survival rate of pancreatic cancer is that early-stage pancreatic cancer has no obvious clinical symptoms. Once pancreatic cancer progresses to the middle and late stages, the possibility of patients receiving radical surgical resection treatment is greatly reduced, and there is also a lack of high-performance screening and diagnostic markers for early and middle-stage pancreatic cancer in current clinical practice. Currently, the protein biomarker CA19-9 is the most common and widely used tumor biomarker for the diagnosis and prognosis monitoring of pancreatic cancer in clinical practice. However, the protein CA19-9 still has some limitations as a tumor biomarker, such as poor specificity, low expression level in the Lewis negative phenotype, and increased false positive rate when patients have benign diseases such as pancreatitis, cirrhosis, and acute cholangitis. Therefore, developing high-performance tumor biomarkers for screening pancreatic cancer, especially early-stage pancreatic cancer, is a huge unmet clinical need and an important direction that the scientific community and clinical oncology community are working towards.

[0003] Glycoproteins are a class of conjugated proteins formed through post-translational modification of proteins, namely glycosylation. Protein glycosylation is one of the most common post-translational modifications of proteins, which is a process in which sugars are transferred to proteins and specific amino acid residues on proteins under the action of glycosyltransferases to form glycosidic bonds. Most glycoproteins are secreted proteins and are widely present in cell membranes, extracellular matrices, plasma, and mucus. The N-glycans on proteins regulate the structure, stability, and activity of proteins through processing and modification. Therefore, the glycans in glycoproteins play an important role in maintaining the biological functions of the body, thus endowing glycoproteins with various biological functions. Therefore, understanding the changes in glycans helps to clarify the molecular mechanisms of abnormal biological behaviors such as inflammation, invasion, and metastasis of tumor cells to surrounding tissues.

[0004] Currently, abnormal changes in protein N-glycans have been found in various tumors. The abnormal glycan structures and quantities of N-glycoproteins are often closely related to diseases, but targeted research results still need to be developed. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides the application of a glycan biomarker combination in the preparation of a product for diagnosing and predicting the prognosis of pancreatic cancer. By detecting changes in N-glycans on serum glycoproteins, a glycan biomarker score is established for the diagnosis of pancreatic cancer, and at the same time, the prognosis of pancreatic cancer patients is evaluated. This method has the advantages of non-invasiveness, high sensitivity, and high specificity, which is conducive to the early diagnosis of pancreatic cancer and the timely evaluation of the prognosis status of pancreatic cancer patients during treatment.

[0006] The present invention is achieved through the following technical solutions:

[0007] The application of a glycan biomarker combination in the preparation of a product for diagnosing and predicting the prognosis of pancreatic cancer, wherein the glycan biomarker combination comprises a combination of one or more of the following N-glycan biomarkers: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb; wherein, NG1A2F-1 and NG1A2F-2 are isomers;

[0008] The prognosis of pancreatic cancer is predicted using the PDAC-GEES model, which takes the content of the glycan biomarker combination as an input variable, and the PDAC-GEES model calculates the predicted value using the following equation:

[0009] Predicted value = (0.452 × content of NGA2F) - (0.140 × content of NG1A2F-1) - (0.023 × content of NG1A2F-2) + (0.020 × content of NA2F) - (0.679 × content of NA2FB) + (0.128 × content of NA3Fb);

[0010] When the predicted value ≥ 2.50, it indicates a good prognosis, and when the predicted value < 2.50, it indicates a poor prognosis.

[0011] Preferably, the product comprises a reagent or kit for detecting the content of the glycan biomarker combination in a sample.

[0012] Preferably, the sample is blood, plasma, or serum of a subject.

[0013] A kit for diagnosing and predicting the prognosis of pancreatic cancer, comprising a substance for detecting the content of a glycan biomarker combination in a sample; the glycan biomarker combination comprises a combination of one or more of the following N-glycan biomarkers: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb; wherein, NG1A2F-1 and NG1A2F-2 are isomers.

[0014] A system for diagnosing pancreatic cancer, comprising a data analysis module and a result determination module;

[0015] The data analysis module is used to analyze the relative content of the glycoprotein marker combination in the subject sample, perform ROC curve analysis on the obtained relative content, and obtain the cutoff value of the glycoprotein marker combination; the glycoprotein marker combination includes a combination of one or more of the following N-glycan markers: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb; wherein, the NG1A2F-1 and the NG1A2F-2 are isomers;

[0016] The result determination module is used to compare the cutoff value obtained by the data processing module with a preset value. When the cutoff value ≥ the preset value, it is determined as positive, that is, the subject has pancreatic cancer;

[0017] The preset value is 0.702.

[0018] A computer device for predicting the prognosis of pancreatic cancer, comprising a memory and a processor, the memory stores a computer program, and the processor executes the following steps:

[0019] Step 1) Obtain the relative content data of the glycoprotein marker combination in the sample of the pancreatic cancer patient to be tested; the glycoprotein marker combination includes a combination of one or more of the following N-glycan markers: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb; wherein, the NG1A2F-1 and the NG1A2F-2 are isomers;

[0020] Step 2) Input the data into the PDAC-GEES model to calculate the predicted value; the PDAC-GEES model uses the content of the glycoprotein marker combination as the input variable, and the PDAC-GEES model calculates the predicted value using the following equation:

[0021] Predicted value = (0.452 × content of NGA2F) - (0.140 × content of NG1A2F-1) - (0.023 × content of NG1A2F-2) + (0.020 × content of NA2F) - (0.679 × content of NA2FB) + (0.128 × content of NA3Fb);

[0022] When the predicted value ≥ 2.50, it indicates a good prognosis, and when the predicted value < 2.50, it indicates a poor prognosis;

[0023] Step 3) Predict the prognosis status of the pancreatic cancer patient according to the predicted value.

[0024] The beneficial effects of the present invention are as follows:

[0025] By analyzing the correlation between the changes in sugar chains on serum glycoproteins and diseases, the present invention establishes a prediction model of a sugar chain biomarker combination to detect pancreatic cancer, thereby assisting in the diagnosis to distinguish pancreatic cancer from non-pancreatic cancer, and at the same time, the prognostic status of the treatment of pancreatic cancer patients can be evaluated. The method of detecting using the sugar chain biomarker combination of the present invention has high sensitivity and high specificity for pancreatic cancer, can help pancreatic cancer patients to be detected and treated early, and at the same time predict and evaluate the prognostic status of pancreatic cancer patients, improving the overall cure rate and survival rate of patients. This biomarker combination for detecting diseases has important application potential in screening, disease diagnosis, disease prognosis evaluation, and glycomics research, etc. Description of the Drawings

[0026] Figure 1 It is the serum sugar chain map of healthy subjects (A), non-pancreatic cancer subjects (B), and pancreatic cancer subjects (C) in Example 1;

[0027] Figure 2 It is the ROC curve of the sugar chain biomarker combination in Example 1 and Example 2;

[0028] Figure 3 It is the effect diagram of the prognosis evaluation of the sugar chain biomarker combination on pancreatic cancer patients in Example 3;

[0029] Figure 4 It is the effect diagram of the prognosis evaluation of the sugar chain biomarker combination on pancreatic cancer patients in Example 4. Detailed Embodiments

[0030] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0031] Unless otherwise specified, the technical means used in the following embodiments are all conventional means well-known to those skilled in the art. The experimental methods without specific conditions mentioned are all conventional methods in the art.

[0032] The materials, reagents, etc. used in the following embodiments can be obtained from commercial channels unless otherwise specified.

[0033] Example 1

[0034] In the early stage of the present invention, by collecting serum samples of pancreatic cancer patients, non-pancreatic cancer patients, and healthy people, the sugar chains are extracted, separated, and detected using the scheme of this example, the data of the detectable sugar chains are processed, and it is found through the t-test between groups that eight oligosaccharide chains have obvious differences between groups.

[0035] The eight oligosaccharide chains are specifically: NGA2F (agalactosyl α-1,6 core fucosylated biantennary N-glycan), NG1A2F-1 (mono-branched galactosyl α-1,6 core fucosylated biantennary N-glycan), NG1A2F-2 (mono-branched galactosyl α-1,6 core fucosylated biantennary N-glycan), NA2 (galactosylated biantennary N-glycan), NA2F (galactosylated α-1,6 core fucosylated biantennary N-glycan), NA2FB (galactosyl α-1,6 core fucosylated bisected biantennary N-glycan), NA3 (galactosylated triantennary N-glycan), NA3Fb (galactosylated α-1,3 branched fucosylated triantennary N-glycan), where NG1A2F-1 and NG1A2F-2 are isomers.

[0036] Based on the above glycan biomarker combination, a glycan pancreatic cancer prediction model is established to distinguish pancreatic cancer from non-pancreatic cancer. In this example, the training of this glycan pancreatic cancer prediction model is carried out as follows:

[0037] 1. Detection samples

[0038] In this example, a total of 157 pancreatic cancer serum samples, 163 non-pancreatic cancer serum samples, and 151 healthy subject serum samples were collected from the Jiangsu Provincial People's Hospital. All of the above pancreatic cancer samples were examined according to the recommendations in the clinical practice guidelines, and the test results met the clinical diagnostic criteria for pancreatic cancer. The following experiments have been reported to the ethics committee for filing and approval.

[0039] 2. Instrument and equipment

[0040] Capillary electrophoresis analyzer, automatic biochemical analyzer, PCR, centrifuge.

[0041] 3. Detection reagents

[0042] Reagent A: NH with a concentration of 2 - 10 mM 4 HCO 3 Add SDS solution with a mass concentration of 5%;

[0043] Reagent B1: Glycoside exohydrolase solution with a concentration of 2 - 5 U / μL;

[0044] Reagent B2: Glycoside endohydrolase solution with a concentration of 2 - 5 U / μL;

[0045] Reagent C: ddH 2 O;

[0046] Reagent D: A mixed solution composed of a fluorescent labeling solution (sodium 8-aminopyrene-1,3,6-trisulfonate) with a concentration of 2 - 20 mM and 1 M DMSO solution.

[0047] 4. Glycan map detection

[0048] (1) Release of N-oligosaccharide chains

[0049] Add 5 - 10 μL of reagent A to 3 - 10 μL of the sample, heat at 90 - 100 °C for 10 - 20 min for denaturation, cool to 4 °C, add 5 - 10 μL of reagent B1 and reagent B2 premixed in a 1:1 volume ratio, react at 35 - 40 °C for 2 - 3 h, and then add 50 μL of reagent C.

[0050] (2) Labeling of N-oligosaccharide chains

[0051] Take 5 - 10 μL of the sample solution from step (1), dry at 70 - 80 °C for 35 - 40 min, then add 3 μL of reagent D, place at 80 - 95 °C for 1 - 2 h, and finally add 50 μL of reagent C.

[0052] (3) Detection of N-oligosaccharide chains

[0053] Take 5 - 10 μL of the oligosaccharide chain sample prepared in step (2) and place it in a 96-well plate dedicated to ABI, and perform detection by the ABI3500 sequencer to obtain a glycan map.

[0054] The oligosaccharide chains include NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb, where NG1A2F-1 and NG1A2F-2 are isomers.

[0055] (4) Data processing

[0056] Perform quantitative calculation on the detection results of 8 oligosaccharide chains. Divide the peak height value of each peak by the sum of the heights of all peaks to calculate the relative content of each peak, and obtain a pancreatic cancer model.

[0057] (5) Data analysis of glycan biomarker combinations

[0058] As Figure 1 shown, compare and analyze the glycan data of healthy ( Figure 1 A in Figure 1 ), non-pancreatic cancer ( Figure 1 B in

[0059] As Figure 1As shown in Figure A, the glycan biomarker combination consists of 8 N-glycans, namely P1 (NGA2F), P2 (NG1A2F-1), P3 (NG1A2F-2), P4 (NA2), P5 (NA2F), P6 (NA2FB), P7 (NA3), and P8 (NA3Fb).

[0060] As can be seen from Figure 1 in comparison with healthy individuals ( Figure 1 Figure A), in the glycan profile of pancreatic cancer ( Figure 1 Figure C), the relative contents of P1 (NGA2F), P5 (NA2F), as well as P6 (NA2FB) and P7 (NA3) all decreased, while the relative contents of other glycans such as P2 (NG1A2F-1), P3 (NG1AF-2), P4 (NA2), and P8 (NA3Fb) all increased.

[0061] The combined diagnostic value of the above glycan biomarker combination was detected by analyzing the receiver operating characteristic curve (ROC) and calculating the area under the ROC curve (AUC). AUC is the area under the receiver operating curve and is an index representing diagnostic performance or precision. The closer the AUC is to 1, the better the diagnostic performance.

[0062] Based on the values calculated by the model (random seed is 3662) that distinguishes pancreatic cancer through the glycan biomarker combination, as Figure 2 shown, when the cutoff value of the prediction model established in this example is 0.702, the area AUC under its training ROC curve is 0.963, its sensitivity reaches 89.7%, and its specificity is 93.33%, indicating that the glycan biomarker combination in the subject samples can be used as a biomarker for the diagnosis of pancreatic cancer.

[0063] Example 2

[0064] Based on the glycan biomarker combination in Example 1 and the training results of the glycan pancreatic cancer prediction model, the prediction model was further verified as follows:

[0065] 1. Test samples

[0066] In this example, a total of 68 serum samples of pancreatic cancer, 70 serum samples of non-pancreatic cancer, and 65 serum samples of healthy subjects were collected from Nanjing Drum Tower Hospital. All of the above pancreatic cancer samples were examined according to the tests recommended in the clinical practice guidelines, and the test results met the clinical diagnostic criteria for pancreatic cancer. The following experiments have been filed with and approved by the ethics committee.

[0067] 2. Instrumentation

[0068] Same as Example 1.

[0069] 3. Detection reagents

[0070] Same as Example 1

[0071] 4. Glycan profiling detection

[0072] The detection steps are the same as those in Example 1.

[0073] The detection results are as Figure 2 shown. Eight glycan markers were placed in the glycan pancreatic cancer prediction model established in Example 1 for verification. When the cutoff value was 0.702, the area under the ROC curve (AUC) of the verification was 0.955, the sensitivity reached 88.7%, and the specificity was 90.1%, indicating that the combination of glycan markers in the subject samples could be used as markers for pancreatic cancer diagnosis.

[0074] Example 3

[0075] Based on the glycan marker combination in Example 1, a PDAC-GEES model was established to evaluate the prognostic status of pancreatic cancer patients after treatment, as follows:

[0076] 1. Detection samples

[0077] In this example, 76 serum samples of pancreatic cancer were collected from Jiangsu Provincial People's Hospital and divided into low-risk and high-risk groups according to the treatment results of pancreatic cancer patients. The above pancreatic cancer samples all underwent the examinations recommended in the clinical practice guidelines, and the detection results met the clinical diagnostic criteria for pancreatic cancer and were treated. The following experiments have been filed and approved by the ethics committee.

[0078] 2. Instrumentation

[0079] Same as Example 1.

[0080] 3. Detection reagents

[0081] Same as Example 1

[0082] 4. Glycan profiling detection

[0083] The detection steps are the same as those in Example 1.

[0084] 5. Prognostic status evaluation

[0085] The glycan peaks of the 8 glycan marker profiles were obtained, and the correlation screening with the prognostic efficacy was carried out. The screening results are shown in Table 1 below.

[0086] Table 1 Screening results

[0087]

[0088]

[0089] According to the data in Table 1, six peaks with statistical significance (P < 0.05) (NGA2F, NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3Fb) were selected, and a PDAC-GEES model was established by logistic regression. The predicted value was calculated using the following equation:

[0090] Predicted value = (0.452 × content of NGA2F) - (0.140 × content of NG1A2F-1) - (0.023 × content of NG1A2F-2) + (0.020 × content of NA2F) - (0.679 × content of NA2FB) + (0.128 × content of NA3Fb).

[0091] The evaluation results are as Figure 3 shown. The abscissa of the survival curve is the observation time of prognosis, and the ordinate is the survival rate. Each point on the curve represents the survival rate of the patient at that time point. It can be seen from the figure that the survival rate of patients predicted as high-risk by the PDAC-GEES model is significantly lower than that of patients predicted as low-risk (P < 0.0001).

[0092] Example 4

[0093] Based on the glycan biomarker combination screened in Example 3 and the training results of the PDAC-GEES model, the model was further verified as follows:

[0094] 1. Test samples

[0095] In this example, a total of 42 pancreatic cancer serum samples from Nanjing Drum Tower Hospital were collected and divided into low-risk and high-risk groups according to the treatment results of pancreatic cancer patients. The above pancreatic cancer samples were all examined according to the recommendations in the clinical practice guidelines for pancreatic cancer. The test results met the clinical diagnostic criteria for pancreatic cancer, and the patients were treated. The following experiments have been filed and approved by the ethics committee.

[0096] 2. Instrument and equipment

[0097] Same as Example 1.

[0098] 3. Detection reagents

[0099] Same as Example 1

[0100] 4. Glycan map detection

[0101] The detection steps are the same as those in Example 1.

[0102] 5. Prognosis status evaluation

[0103] The evaluation results are as Figure 4As shown, the six screened glycan markers were verified in the PDAC-GEES model established in Example 3. When the cutoff value was 2.50, the prognostic model had a significant effect (P = 0.00032).

[0104] The present invention detects the correlation between the changes in glycans on serum glycoproteins and diseases, establishes a predictive model of a glycan marker combination to detect pancreatic cancer, thereby assisting in the diagnosis to distinguish pancreatic cancer from non-pancreatic cancer, and at the same time can evaluate the prognostic status of pancreatic cancer patients. The results of the above examples show that the detection method of the present invention has high sensitivity and high specificity for pancreatic cancer, can help pancreatic cancer patients to be detected and treated early, and predict and evaluate the prognostic status of pancreatic cancer patients, improving the overall cure rate and survival rate of patients.

[0105] The embodiments described above are only a part of the embodiments of the present invention, rather than all embodiments. The detailed description of the embodiments of the present invention is not intended to limit the scope of the present invention claimed, but merely represents the selected embodiments of the present invention. The protection scope of the present invention shall be subject to the scope claimed in the claims. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

Claims

1. Use of a sugar chain marker combination in the preparation of a product for diagnosing and predicting the prognosis of pancreatic cancer, characterized in that: The sugar chain marker combination includes a combination of more than one of the following N-sugar chain markers: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb; wherein the NG1A2F-1 and the NG1A2F-2 are isomers; The PDAC-GEES model is used to predict the prognosis of pancreatic cancer. The PDAC-GEES model uses the content of the sugar chain marker combination as an input variable. The PDAC-GEES model uses the following equation to calculate the predicted value: Predicted value = (0.452 × NGA2F content) - (0.140 × NG1A2F-1 content) - (0.023 × NG1A2F-2 content) + (0.020 × NA2F content) - (0.679 × NA2FB content) + (0.128 × NA3Fb content); When the predicted value is ≥2.50, it indicates a good prognosis, and when the predicted value is <2.50, it indicates a poor prognosis.

2. The use according to claim 1, characterized in that: The product includes a reagent or a kit for detecting the content of the sugar chain marker combination in a sample.

3. The use according to claim 2, characterized in that: The sample is blood, plasma or serum of the subject.

4. A kit for diagnosing and predicting the prognosis of pancreatic cancer, characterized in that: Comprising a substance for detecting the content of a sugar chain marker combination in a sample; the sugar chain marker combination comprises a combination of more than one of the following N-sugar chain markers: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb; wherein the NG1A2F-1 and the NG1A2F-2 are isomers.

5. A system for diagnosing pancreatic cancer, characterized in that: It includes a data analysis module and a result determination module; The data analysis module is used to analyze the relative content of the sugar chain marker combination in the subject sample, perform ROC curve analysis on the obtained relative content, and obtain the cutoff value of the sugar chain marker combination; the sugar chain marker combination includes a combination of more than one of the following N-sugar chain markers: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb; wherein the NG1A2F-1 and the NG1A2F-2 are isomers; The result determination module is used to compare the cutoff value obtained by the data processing module with a preset value, and when the cutoff value ≥ the preset value, it is determined to be positive, that is, the subject suffers from pancreatic cancer; The preset value is 0.

702.

6. A computer device for predicting the prognosis of pancreatic cancer, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: The processor performs the following steps: Step 1) obtaining relative content data of a sugar chain marker combination in a pancreatic cancer patient sample to be tested; the sugar chain marker combination includes a combination of one or more of the following N-sugar chain markers: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3, NA3Fb; wherein the NG1A2F-1 and the NG1A2F-2 are isomers; Step 2) inputting the data into the PDAC-GEES model to calculate the predicted value; the PDAC-GEES model uses the content of the sugar chain marker combination as an input variable, and the PDAC-GEES model calculates the predicted value using the following equation: Predicted value = (0.452 × NGA2F content) - (0.140 × NG1A2F-1 content) - (0.023 × NG1A2F-2 content) + (0.020 × NA2F content) - (0.679 × NA2FB content) + (0.128 × NA3Fb content); When the predicted value is ≥2.50, it indicates a good prognosis, and when the predicted value is <2.50, it indicates a poor prognosis; Step 3) predicting the prognosis status of pancreatic cancer patients according to the predicted value.

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