Use of a combination of sugar chain markers in the preparation of a product for the diagnosis and prognosis of pancreatic cancer

Abstract

The application discloses application of a sugar chain marker combination in preparation of a product for diagnosing and predicting prognosis of pancreatic cancer, and the marker combination comprises the following N-glycan chains: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3 and NA3Fb. The application analyzes the correlation between the change of the sugar chain on the serum glycoprotein and the disease, establishes a prediction model of the sugar chain marker combination to detect the pancreatic cancer, thereby assisting in diagnosing and distinguishing the pancreatic cancer from non-pancreatic cancer, and meanwhile, the prognosis state of the pancreatic cancer patient treatment can be evaluated. The method for detecting the sugar chain marker combination has high sensitivity and high specificity for the pancreatic cancer, can help the pancreatic cancer patient to be found and treated early, and can predict and evaluate the prognosis state of the pancreatic cancer patient, thereby improving the overall cure rate and survival rate of the patient.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to the application of combinations of glycan biomarkers in the preparation of products for the diagnosis and prediction of pancreatic cancer prognosis. Background Technology

[0002] Pancreatic cancer is a highly malignant cancer, often referred to as the "king of cancers." In most countries worldwide, including China, the five-year survival rate for pancreatic cancer is below 10%, far lower than other common malignant tumors such as colorectal cancer, esophageal cancer, and liver cancer. One reason for the extremely low survival rate of pancreatic cancer is that early-stage pancreatic cancer often presents with no obvious clinical symptoms. Once pancreatic cancer progresses to the middle or late stages, the likelihood of patients receiving radical surgical resection is greatly reduced. Furthermore, there is a lack of high-performance biomarkers for screening and diagnosing early and mid-stage pancreatic cancer. Currently, the protein biomarker CA19-9 is the most common and widely used tumor marker in clinical practice for the diagnosis and prognostic monitoring of pancreatic cancer. However, CA19-9 as a tumor marker still has some limitations, such as poor specificity, low expression levels in Lewis-negative phenotypes, and increased false-positive rates in patients with benign conditions such as pancreatitis, cirrhosis, and acute cholangitis. Therefore, developing high-performance tumor markers for screening pancreatic cancer, especially early-stage pancreatic cancer, is a huge unmet clinical need and an important direction that the scientific and clinical oncology communities are committed to.

[0003] Glycoproteins are a class of binding proteins formed through post-translational modification, specifically glycosylation. Glycosylation is one of the most common post-translational modifications of proteins, involving the transfer of sugars to proteins by glycosyltransferases, forming glycosidic bonds with specific amino acid residues. Most glycoproteins are secretory proteins, widely distributed in cell membranes, intercellular matrix, plasma, and mucus. The N-glycans on proteins regulate their structure, stability, and activity through processing and modification; therefore, glycans play a crucial role in maintaining biological functions, thus endowing glycoproteins with diverse biological functions. Understanding changes in glycans helps elucidate the molecular mechanisms of abnormal biological behaviors such as inflammation, tumor cell invasion and metastasis of surrounding tissues.

[0004] Abnormal changes in the N-glycan chains of proteins have been found in various tumors. Abnormalities in the structure and number of N-glycoprotein glycan chains are often closely related to diseases, but targeted research results are still needed. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides the application of a combination of glycan biomarkers in the preparation of products 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 prognostic assessment of pancreatic cancer patients is also performed. This method has the advantages of being non-invasive, highly sensitive, and highly specific, which is beneficial for the early diagnosis of pancreatic cancer and timely assessment of the prognostic status of pancreatic cancer patients.

[0006] This invention is achieved through the following technical solution:

[0007] The application of a combination of glycan biomarkers in the preparation of products for the diagnosis and prediction of pancreatic cancer prognosis, wherein the combination of glycan biomarkers comprises 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 uses the content of the glycan biomarker combination as an input variable. The PDAC-GEES model calculates the predicted value using the following equation:

[0009] 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);

[0010] A predicted value ≥ 2.50 indicates a good prognosis, while a predicted value < 2.50 indicates a poor prognosis.

[0011] Preferably, the product includes reagents or kits for detecting the content of the combination of glycan markers in a sample.

[0012] Preferably, the sample is the subject's blood, plasma, or serum.

[0013] A kit for diagnosing and predicting the prognosis of pancreatic cancer includes a substance for detecting the content of a combination of glycan biomarkers in a sample; the combination of glycan biomarkers includes 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 glycan biomarker combination in the subject sample, perform ROC curve analysis on the obtained relative content, and obtain the cutoff value of the glycan biomarker combination; the glycan biomarker combination includes 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;

[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 is greater than or equal to the preset value, it is determined to be 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 includes a memory and a processor, the memory storing a computer program, and the processor performing the following steps:

[0019] Step 1) Obtain the relative content data of the glycan biomarker combination in the pancreatic cancer patient sample to be tested; the glycan biomarker combination includes 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;

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

[0021] 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);

[0022] A predicted value ≥ 2.50 indicates a good prognosis, while a predicted value < 2.50 indicates a poor prognosis.

[0023] Step 3) Predict the prognosis of pancreatic cancer patients based on the predicted values.

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

[0025] This invention analyzes the correlation between changes in glycan chains on serum glycoproteins and disease, establishing a predictive model for a combination of glycan biomarkers to detect pancreatic cancer. This aids in the diagnosis and differentiation between pancreatic and non-pancreatic cancers, and also allows for the assessment of prognostic outcomes in pancreatic cancer patients. The method using this combination of glycan biomarkers exhibits high sensitivity and specificity for pancreatic cancer, facilitating early detection and treatment, and predicting and assessing prognostic outcomes, thereby improving overall cure and survival rates. This combination of biomarkers for disease detection holds significant potential for applications in screening, diagnosis, prognostic assessment, and glycomics research. Attached Figure Description

[0026] Figure 1 Serum glycan profiles of healthy subjects (A), non-pancreatic cancer subjects (B), and pancreatic cancer subjects (C) in Example 1;

[0027] Figure 2 ROC curves for the glycan marker combinations in Examples 1 and 2;

[0028] Figure 3 This is a graph showing the effect of the combination of glycan biomarkers in Example 3 on the prognostic assessment of pancreatic cancer patients;

[0029] Figure 4 This is a graph showing the effect of the combination of glycan biomarkers in Example 4 on the prognostic assessment of pancreatic cancer patients. Detailed Implementation

[0030] The present invention will now be described in further detail with reference to the accompanying 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, and the experimental methods without specific conditions are all conventional methods in the art.

[0032] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0033] Example 1

[0034] In the early stages of this invention, serum samples were collected from pancreatic cancer patients, non-pancreatic cancer patients, and healthy individuals. The scheme described in this embodiment was used to extract, separate, and detect glycans. Data processing was performed on the detectable glycans, and a t-test between groups revealed significant differences in the eight oligosaccharide chains between groups.

[0035] The eight oligosaccharide chains are specifically: NGA2F (galactosyl 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 biantennary N-glycan), NA3 (galactosylated triantennary N-glycan), and NA3Fb (galactosylated α-1, 3-branched fucosylated triantennary N-glycan), wherein NG1A2F-1 and NG1A2F-2 are isomers.

[0036] A glycan biomarker combination was used to establish a glycan pancreatic cancer prediction model to distinguish between pancreatic cancer and non-pancreatic cancer. This embodiment trains the glycan pancreatic cancer prediction model as follows:

[0037] 1. Test Sample

[0038] This embodiment collected serum samples from 157 pancreatic cancer patients, 163 non-pancreatic cancer patients, and 151 healthy subjects from Jiangsu Provincial People's Hospital. All pancreatic cancer samples underwent the examinations recommended in 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.

[0039] 2. Instruments and equipment

[0040] Capillary electrophoresis analyzer, fully automated biochemical analyzer, PCR machine, centrifuge.

[0041] 3. Test reagents

[0042] Reagent A: NH4HCO3 with a concentration of 2-10 mM is added to a 5% SDS solution;

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

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

[0045] Reagent C: ddH2O;

[0046] Reagent D: A mixture of 2-20 mM fluorescent labeling solution (trisodium 8-aminopyrene-1,3,6-trisulfonic acid) and 1 M DMSO solution.

[0047] 4. Glycan mapping detection

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

[0049] Add 5–10 μL of reagent A to 3–10 μL of sample, heat at 90–100 °C for 10–20 min to denature, 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 add 50 μL of reagent C.

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

[0051] Take 5-10 μL of the sample solution from step (1), dry it at 70-80℃ for 35-40 min, then add 3 μL of reagent D, place it at 80-95℃ 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 an ABI-specific 96-well plate. Detect it using an ABI 3500 sequencer to obtain the glycan map.

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

[0055] (4) Data processing

[0056] The detection results of the eight oligosaccharide chains were quantified. The peak height of each peak was divided by the sum of the heights of all peaks to calculate the relative content of each peak, thus obtaining the pancreatic cancer model.

[0057] (5) Glycan biomarker combination data analysis

[0058] like Figure 1 As shown, health ( Figure 1 (A) Non-pancreatic cancer ( Figure 1 (B) and pancreatic cancer ( Figure 1 Comparative analysis of glycan data from subjects C) showed that human serum glycan maps exhibited eight major glycan peaks. Different glycans showed different migration rates due to differences in charge and molecular size. In other words, different peaks on the glycan map represented different glycans, and their peak heights represented the relative content of glycans.

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

[0060] from Figure 1 As can be seen, compared to healthy individuals ( Figure 1 (A), glycan map of pancreatic cancer ( Figure 1 In the C group, the relative contents of P1 (NGA2F), P5 (NA2F), P6 (NA2FB), and P7 (NA3) all decreased, while the relative contents of other sugar chains such as P2 (NG1A2F-1), P3 (NG1AF-2), P4 (NA2), and P8 (NA3Fb) all increased.

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

[0062] Pancreatic cancer can be detected based on values ​​calculated by an SVM model (random seed 3662) that uses combinations of glycan biomarkers for differentiation. Figure 2 As shown, the prediction model established in this embodiment has an area under the training ROC curve (AUC) of 0.963 when the cutoff value is 0.702, with a sensitivity of 89.7% and a specificity of 93.33%, indicating that the combination of glycan biomarkers in the subject samples can be used as biomarkers for the diagnosis of pancreatic cancer.

[0063] Example 2

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

[0065] 1. Test Sample

[0066] This study collected serum samples from 68 pancreatic cancer patients, 70 non-pancreatic cancer patients, and 65 healthy subjects from Nanjing Drum Tower Hospital. All pancreatic cancer samples underwent the examinations recommended in clinical practice guidelines, and the results met the clinical diagnostic criteria for pancreatic cancer. The following experiments have been filed with and approved by the ethics committee.

[0067] 2. Instruments and equipment

[0068] Same as Example 1.

[0069] 3. Test reagents

[0070] Same as Example 1

[0071] 4. Glycan mapping detection

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

[0073] Test results as follows Figure 2 As shown, the eight glycan biomarkers were validated in the glycan pancreatic cancer prediction model established in Example 1. When the cutoff value was 0.702, the area under the validation ROC curve (AUC) was 0.955, with a sensitivity of 88.7% and a specificity of 90.1%, indicating that the combination of glycan biomarkers in the subject samples can be used as biomarkers for the diagnosis of pancreatic cancer.

[0074] Example 3

[0075] Based on the combination of glycan biomarkers in Example 1, a PDAC-GEES model was established to assess the prognostic status of treated pancreatic cancer patients, as detailed below:

[0076] 1. Test Sample

[0077] This embodiment collected serum samples from 76 pancreatic cancer patients at Jiangsu Provincial People's Hospital, categorizing them into low-risk and high-risk groups based on post-treatment outcomes. All pancreatic cancer samples underwent examinations recommended in clinical practice guidelines, and the test results met the clinical diagnostic criteria for pancreatic cancer. The patients were subsequently treated. The following experiments have been filed with and approved by the ethics committee.

[0078] 2. Instruments and equipment

[0079] Same as Example 1.

[0080] 3. Test reagents

[0081] Same as Example 1

[0082] 4. Glycan mapping detection

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

[0084] 5. Prognostic status assessment

[0085] Eight glycan biomarkers were obtained, and their correlation with prognostic efficacy was screened. The screening results are shown in Table 1 below.

[0086] Table 1 Screening Results

[0087]

[0088]

[0089] Based on the data in Table 1, six peaks with statistical significance (P < 0.05) (NGA2F, NG1A2F-1, NG1A2F-2, NA2F, NA2FB, NA3Fb) were selected. A PDAC-GEES model was established using logistic regression, and the predicted values ​​were calculated using the following equation:

[0090] 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).

[0091] Evaluation results as follows Figure 3 As shown in the figure, the horizontal axis of the survival curve represents the observation time of prognosis, and the vertical axis represents the survival rate. Each point on the curve represents the patient's survival rate at that time point. The figure shows that patients predicted as high-risk by the PDAC-GEES model had significantly lower survival rates than those predicted as low-risk (P < 0.0001).

[0092] Example 4

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

[0094] 1. Test Sample

[0095] This embodiment collected serum samples from 42 pancreatic cancer patients at Nanjing Drum Tower Hospital, categorized as low-risk and high-risk based on post-treatment outcomes. All pancreatic cancer samples underwent examinations recommended in clinical practice guidelines, and the test results met the clinical diagnostic criteria for pancreatic cancer. The patients were subsequently treated. The following experiments have been filed with and approved by the ethics committee.

[0096] 2. Instruments and equipment

[0097] Same as Example 1.

[0098] 3. Test reagents

[0099] Same as Example 1

[0100] 4. Glycan mapping detection

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

[0102] 5. Prognostic status assessment

[0103] Evaluation results as follows Figure 4 As shown, the six selected glycan markers were validated in the PDAC-GEES model established in Example 3. The prognostic model showed significant performance at a cutoff value of 2.50 (P = 0.00032).

[0104] This invention detects pancreatic cancer by establishing a predictive model of glycan biomarker combinations based on changes in glycan chains on serum glycoproteins and their correlation with disease. This aids in the diagnosis and differentiation between pancreatic and non-pancreatic cancers, and also allows for the assessment of prognostic status in pancreatic cancer patients. The results of the above embodiments demonstrate that the detection method of this invention has high sensitivity and specificity for pancreatic cancer, which can help in the early detection and treatment of pancreatic cancer patients, as well as predict and assess their prognostic status, thereby improving the overall cure rate and survival rate of patients.

[0105] 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 the diagnosis and prediction of pancreatic cancer prognosis, characterized in that, The glycan marker combination consists of NGA2F, NG1A2F-1, NG1A2F-2, NA2F, NA2FB, and NA3Fb; wherein NG1A2F-1 and NG1A2F-2 are isomers. The prognosis of pancreatic cancer is predicted using the PDAC-GEES model, which uses the content of the glycan biomarker combination as an input variable. 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); A predicted value ≥ 2.50 indicates a good prognosis, while a predicted value < 2.50 indicates a poor prognosis.

2. The application according to claim 1, characterized in that, The product includes reagents or kits for detecting the content of the combination of glycan markers in a sample.

3. The application according to claim 2, characterized in that, The sample is the subject's blood, plasma, or serum.

4. 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) Obtain the relative content data of the glycan biomarker combination in the pancreatic cancer patient sample to be tested; the glycan biomarker combination consists of NGA2F, NG1A2F-1, NG1A2F-2, NA2F, NA2FB, and NA3Fb; wherein, NG1A2F-1 and NG1A2F-2 are isomers; Step 2) Input the data into the PDAC-GEES model and calculate the predicted value; the PDAC-GEES model uses the content of the glycan biomarker combination as the input variable, and 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); A predicted value ≥ 2.50 indicates a good prognosis, while a predicted value < 2.50 indicates a poor prognosis. Step 3) Predict the prognostic status of pancreatic cancer patients based on the predicted values.

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