Use of glycoprotein tumor biomarkers in the preparation of cancer diagnostic reagents

Abstract

This invention relates to the field of biomedical technology, providing the application of glycoprotein tumor biomarkers in the preparation of cancer diagnostic reagents. The glycoprotein tumor biomarker is a CLU_N_291_HexNAc(4)Hex(5) site-specific glycosylation modification. This invention utilizes CLU_N_291_HexNAc(4)Hex(5) site-specific glycosylation modification as a pan-cancer biomarker, combining it with existing conventional tumor markers (CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or PSA) for early cancer screening, assessment, diagnosis, postoperative monitoring, and / or prognostic analysis, particularly for the diagnosis of pancreatic cancer, cholangiocarcinoma, gastric cancer, lung cancer, breast cancer, or ovarian cancer.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and in particular relates to the application of glycoprotein tumor biomarkers in the preparation of cancer diagnostic reagents. Background Technology

[0002] As we all know, cancer has become a major threat to human life and health. The key to cancer treatment lies in early detection, early diagnosis, and early treatment. Early intervention in tumors is of great significance for improving the prognosis and quality of life of cancer patients, and can even achieve clinical cure for some cancer patients. Currently, tumor diagnosis methods are divided into five levels: 1) Clinical diagnosis: a presumptive diagnosis based on clinical symptoms, signs, and imaging examinations, and referring to the disease progression pattern; 2) Surgical diagnosis: a judgment made solely based on the mass seen by the naked eye after surgery or various endoscopic examinations, without pathological confirmation; 3) Physicochemical diagnosis: a diagnosis that is clinically consistent with cancer and supported by positive physicochemical examination results, such as X-ray, B-ultrasound, CT, and MRI, or carcinoembryonic antigen, alpha-fetoprotein, etc.; 4) Cytopathological diagnosis: a diagnosis made based on various exfoliated cells and puncture cell examinations; 5) Histopathological diagnosis: a diagnosis obtained after pathological analysis of tissue obtained through core needle biopsy.

[0003] Among the five levels of tumor diagnostic methods, the reliability of diagnosis increases sequentially, with level five being the most ideal. However, early-stage tumors often have hidden locations and indistinct clinical features, making it impossible to pinpoint lesions for more precise biopsy. Therefore, liquid biopsy (such as blood and urine) relying on tumor biomarkers is currently the main and commonly used method for early tumor screening and diagnosis in physiochemical diagnostics. However, currently used liquid biopsy tumor biomarkers, such as CEA, have drawbacks such as low sensitivity and weak specificity. Therefore, the development of liquid biopsy biomarkers with higher sensitivity and specificity is of great clinical significance for the early detection and diagnosis of tumors. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides the application of glycoprotein tumor biomarkers in the preparation of cancer diagnostic reagents, with the aim of solving the problems mentioned in the background art.

[0005] Glycosylation is one of the most abundant and heterogeneous types of protein post-translational modifications. It is the process by which carbohydrate molecules are transferred to specific amino acid residues on proteins to form glycosidic bonds under the action of glycosyltransferases. The main types are amino-N glycosylation of asparagine and hydroxy-O glycosylation of threonine / serine. Studies have shown that more than 50% of proteins in human cells can undergo glycosylation modification.

[0006] This invention provides the application of glycoprotein tumor biomarkers in the preparation of cancer diagnostic reagents. The glycoprotein tumor biomarker is a CLU_N291_HexNAc(4)Hex(5) site-specific glycosylation modification, specifically: asparagine at position 291 of the amino acid sequence of CLU is glycosylated, and the glycosylated glycoform is 4 N-acetamidohexose and 5 hexose.

[0007] Furthermore, the reagent includes a specific recognition antibody for the glycoprotein tumor biomarker.

[0008] Furthermore, the reagent also includes at least one of the following: a specific recognition antibody for CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or PSA protein.

[0009] Furthermore, the reagent is used to detect the glycoprotein tumor biomarker, and at least one of the following proteins: CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or PSA, by enzyme-linked immunosorbent assay (ELISA), dot blot assay, Western blot assay, colloidal gold immunochromatography, flow cytometry, mass spectrometry, immunofluorescence, immunoprecipitation, or immunohistochemistry.

[0010] Furthermore, the method for preparing the specific recognition antibody for the glycoprotein tumor biomarker includes the following steps:

[0011] Antigenic peptides were designed based on the glycoprotein tumor biomarkers described.

[0012] The antigenic polypeptide is loaded onto a vector to form an antigen and immunize the host animal;

[0013] Specific recognition antibodies for the glycoprotein tumor biomarkers were obtained through specific affinity purification.

[0014] Furthermore, the reagent is used for the diagnosis of early, intermediate, or late-stage cancer.

[0015] Furthermore, the cancer is pancreatic cancer, bile duct cancer, gastric cancer, lung cancer, breast cancer, or ovarian cancer.

[0016] Furthermore, the sample detected by the reagent is the subject's bodily fluid or exosomes in the bodily fluid.

[0017] Furthermore, the body fluids include: blood, serum, plasma, serous fluid, lymph, urine, cerebrospinal fluid, saliva or secretory tissue; mucosal secretions of organs, vaginal secretions, breast milk, tears or ascites; fluids of the pleura, pericardium, peritoneum or abdomen.

[0018] The present invention has the following technical effects:

[0019] (1) Using CLU_N291_HexNAc(4)Hex(5) site-specific glycosylation modification as a pan-cancer biomarker, in combination with existing conventional tumor markers (CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4 or PSA) for early screening, assessment, diagnosis, postoperative monitoring and / or prognostic analysis of cancer, especially for the diagnosis of pancreatic cancer, bile duct cancer, gastric cancer, lung cancer, breast cancer or ovarian cancer.

[0020] (2) CLU_N291_HexNAc(4)Hex(5) site-specific glycosylation modification, as a pan-cancer biomarker, has high AUC value, high specificity and high sensitivity in the diagnosis of various cancers, reducing misdiagnosis and missed diagnosis. It can accurately capture abnormal glycosylation signals in the early stage of cancer, improve the detection rate and reduce the probability of late-stage cancer; the expression level of glycosylation modification can be used to quantitatively assess the malignancy, invasiveness and stage of tumors, providing a basis for the personalized formulation of clinical diagnosis and treatment plans. Attached Figure Description

[0021] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0022] Figure 1 This is a volcano plot of protein expression differential analysis from Example 1 of the present invention.

[0023] Figure 2 The expression levels of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification of Example 1 of this invention in the blood of healthy controls and patients with malignant tumors (pancreatic cancer, bile duct cancer, gastric cancer, lung cancer, breast cancer, and ovarian cancer) are shown in ***, where p < 0.001.

[0024] Figure 3 The expression level of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification of Example 1 of the present invention in the blood of healthy controls and pancreatic cancer patients is shown in **, where p < 0.05.

[0025] Figure 4The expression level of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification of Example 1 of the present invention in the blood of healthy controls and patients with cholangiocarcinoma is shown in **, where p < 0.05.

[0026] Figure 5 The expression level of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification of Example 1 of the present invention in the blood of healthy controls and gastric cancer patients is shown in **, where p < 0.05.

[0027] Figure 6 The expression levels of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification of Example 1 of this invention in the blood of healthy controls and lung cancer patients are shown in ***, where p < 0.001.

[0028] Figure 7 This refers to the expression level of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification in the blood of healthy controls and breast cancer patients in Example 1 of this invention.

[0029] Figure 8 This refers to the expression level of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification in the blood of healthy controls and ovarian cancer patients in Example 1 of this invention.

[0030] Figure 9 The ROC curves for CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification and CEA levels in the blood of patients with malignant tumors (pancreatic cancer, bile duct cancer, gastric cancer, lung cancer, breast cancer, and ovarian cancer) and healthy controls are from Example 2 of this invention.

[0031] Figure 10 These are the individual and combined ROC curves of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification, CEA, CA19-9, CA242, and CA50 levels in the blood of pancreatic cancer patients and healthy controls in Example 2 of this invention, wherein:

[0032] Figure 10 In this context, A represents a single ROC curve.

[0033] Figure 10 B in the figure represents the joint ROC curve.

[0034] Figure 11These are the individual and combined ROC curves of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification, CEA, CA19-9, and CA50 levels in the blood of bile duct cancer patients and healthy controls in Example 2 of this invention, wherein:

[0035] Figure 11 In this context, A represents a single ROC curve.

[0036] Figure 11 B in the figure represents the joint ROC curve.

[0037] Figure 12 These are the individual and combined ROC curves of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification, CEA, CA72-4, CA199, and CA50 levels in the blood of gastric cancer patients and healthy controls in Example 2 of this invention, wherein:

[0038] Figure 12 In this context, A represents a single ROC curve.

[0039] Figure 12 B in the figure represents the joint ROC curve.

[0040] Figure 13 The figures for Example 2 of this invention are the individual and combined ROC curves of the levels of CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification, CEA, SCC, CA72-4, NSE, CYFRA21-1, and PROGRP in the blood of lung cancer patients and healthy controls, respectively.

[0041] Figure 13 In this context, A represents a single ROC curve.

[0042] Figure 13 B in the figure represents the joint ROC curve.

[0043] Figure 14 These are the individual and combined ROC curves of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification, CEA, and CA153 levels in the blood of breast cancer patients and healthy controls in Example 2 of this invention, wherein:

[0044] Figure 14 In this context, A represents a single ROC curve.

[0045] Figure 14 B in the figure represents the joint ROC curve.

[0046] Figure 15These are the individual and combined ROC curves of the CLU_N291_HexNAc(4)Hex(5) site-specific glycoform modification, CEA, CA72-4, CA125, and HE-4 levels in the blood of ovarian cancer patients and healthy controls in Example 2 of this invention, wherein:

[0047] Figure 15 In this context, A represents a single ROC curve.

[0048] Figure 15 B in the figure represents the joint ROC curve. Detailed Implementation

[0049] To make the technical problems, solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0051] This invention provides the application of glycoprotein tumor biomarkers in the preparation of cancer diagnostic reagents. The glycoprotein tumor biomarker is a specific glycosylation modification of the CLU_N291_HexNAc(4)Hex(5) site, that is, the glycosylation modification of the CLU N291 site, and the specific glycosylation modification is HexNAc(4)Hex(5).

[0052] The site-specific glycosylation modification of CLU_N291_HexNAc(4)Hex(5) is as follows: the asparagine (Asn, N) at position 291 of the amino acid sequence of CLU (Clusterin; Protein accession number: P10909) is glycosylated, and the glycosylated glycoform is 4 N-acetamidohexose and 5 hexose; CLU is a glycoprotein composed of 449 amino acids with a molecular weight of 53 kDa.

[0053] In some embodiments, the reagent includes a specific recognition antibody for glycoprotein tumor biomarkers.

[0054] In some embodiments, the reagent further includes at least one of the following: CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or a specific recognition antibody for the PSA protein.

[0055] It is understandable that CLU_N291_HexNAc(4)Hex(5) site-specific glycosylation modification, as a pan-cancer biomarker, can be used in combination with existing conventional tumor markers (such as CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or PSA, etc.) for early cancer screening, assessment, diagnosis, postoperative monitoring and / or prognostic analysis.

[0056] In some embodiments, the reagent is used to detect glycoprotein tumor biomarkers, and at least one of the following proteins: CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or PSA, by enzyme-linked immunosorbent assay (ELISA), dot blot assay, Western blot assay, colloidal gold immunochromatography, flow cytometry, mass spectrometry, immunofluorescence, immunoprecipitation, or immunohistochemistry.

[0057] It is understandable that common protein quantitative or qualitative analysis methods such as enzyme-linked immunosorbent assay (ELISA), dot blot detection, Western blot detection, colloidal gold immunochromatography, flow cytometry, mass spectrometry, immunofluorescence, immunoprecipitation, or immunohistochemistry are used to detect glycoprotein tumor biomarkers, as well as the presence, expression level, and / or concentration of CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or PSA proteins.

[0058] In some embodiments, a method for preparing a specific recognition antibody for a glycoprotein tumor biomarker includes the following steps:

[0059] Design antigenic peptides based on glycoprotein tumor biomarkers;

[0060] Antigenic peptides are loaded onto a vector to form antigens and immunize the host animal.

[0061] Specific recognition antibodies for glycoprotein tumor biomarkers were obtained through specific affinity purification.

[0062] In some embodiments, the reagent is used for the diagnosis of early, intermediate, or late-stage cancer.

[0063] In some embodiments, the cancer is pancreatic cancer, bile duct cancer, gastric cancer, lung cancer, breast cancer, or ovarian cancer.

[0064] It is understood that this embodiment provides CLU_N291_HexNAc(4)Hex(5) site-specific glycosylation modification as a pan-cancer biomarker for the diagnosis of pancreatic cancer, bile duct cancer, gastric cancer, lung cancer, breast cancer, or ovarian cancer; in addition, cancer may also include oral cancer, oropharyngeal cancer, nasopharyngeal cancer, respiratory system cancer, genitourinary system cancer, gastrointestinal cancer, central or peripheral nervous system tissue cancer, endocrine or neuroendocrine system cancer, hematopoietic system cancer, glioma, sarcoma, epithelial cancer, lymphoma, melanoma, fibroma, meningioma, brain cancer, kidney cancer, biliary system cancer, pheochromocytoma, islet cell carcinoma, livoroma, thyroid cancer, parathyroid cancer, pituitary adenoma, adrenal adenoma, bone sarcoma tumor, neuroendocrine system tumor, head and neck cancer, prostate cancer, esophageal cancer, tracheal cancer, liver cancer, bladder cancer, uterine cancer, cervical cancer, testicular cancer, colon cancer, rectal cancer, or skin cancer.

[0065] In some embodiments, the sample tested by the reagent is the subject's bodily fluids or exosomes in the bodily fluids.

[0066] In some embodiments, body fluids include: blood, serum, plasma, serous fluid, lymph, urine, cerebrospinal fluid, saliva, or secretory tissue; mucosal secretions of organs, vaginal secretions, breast milk, tears, or ascites; fluids of the pleura, pericardium, peritoneum, or abdomen.

[0067] This invention uses a mass spectrometry platform to screen, discover, and identify specific glycoprotein biomarkers in the blood of cancer patients. The experimental samples and methods include:

[0068] 1. Experimental Samples:

[0069] A total of 255 blood samples (serum or plasma) were collected from the Department of Laboratory Medicine of the First Affiliated Hospital of Nanchang University. These included 40 healthy controls (15 males and 25 females) and 215 patients with malignant tumors (26 pancreatic cancers, 24 bile duct cancers, 44 gastric cancers, 81 lung cancers, 26 breast cancers, and 14 ovarian cancers). The ethics code is (2023)CDYFYYLK(02-053). 25% of the samples from each group were randomly selected as the discovery cohort, and the remainder were used as the validation cohort.

[0070] 2. Main experimental methods:

[0071] Intact glycopeptide technology is a method developed in recent years to study protein glycosylation modification. It effectively solves the technical barriers in the field of protein glycosylation modification research. Its advantage is that it can obtain information on protein peptides, glycosylation modification sites and glycosylation modification types at the same time through mass spectrometry, which greatly promotes the study of protein glycosylation function and the development of novel glycoprotein biomarkers.

[0072] This embodiment combines cutting-edge methods such as intact glycopeptide technology and quantitative proteomics technology to conduct quantitative proteomics studies on samples in order to screen, discover and identify novel tumor biomarkers.

[0073] Example 1:

[0074] 1. Detection of expression levels of routine clinical tumor markers:

[0075] The expression levels of routine tumor markers were detected in 255 blood samples collected using a clinical tumor marker diagnostic kit. The routine tumor markers included CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, and HE-4.

[0076] 2. Screening of specific glycoprotein biomarkers:

[0077] (1) Protein extraction

[0078] The remaining plasma (sample) after patient testing was taken from -80℃, and 1% protease inhibitor was added. The sample was then lysed by sonication. After centrifugation at 12000g for 10 min at 4℃, the supernatant was transferred to a new centrifuge tube and the protein concentration was determined using a BCA kit.

[0079] (2) Pancreatic enzyme digestion

[0080] Equal amounts of protein from each sample were digested enzymatically, and the volumes were adjusted to be consistent using lysis buffer. One volume of pre-cooled acetone was added, and the mixture was vortexed and then four volumes of pre-cooled acetone were added. The mixture was precipitated at -20°C for 2 hours. The mixture was centrifuged at 4500g for 5 minutes, the supernatant was discarded, and the precipitate was washed twice with pre-cooled acetone. After drying the precipitate, TEAB (triethylamine bicarbonate) was added to a final concentration of 200 mM. The precipitate was then sonicated and dispersed. Trypsin was added at a mass ratio of 1:50, and the mixture was digested overnight. Dithiothreitol (DTT) was added to a final concentration of 5 mM, and the mixture was reduced at 56°C for 30 minutes. Iodoacetamide (IAA) was then added to a final concentration of 11 mM, and the mixture was incubated at room temperature in the dark for 15 minutes.

[0081] (3) Modification enrichment

[0082] The peptides were dissolved in 200 μL of enrichment buffer (80% acetonitrile / 5% trifluoroacetic acid), and the supernatant was transferred to a hydrophilic microcolumn. Enrichment was completed by centrifugation at 1000g for 15 min. The microcolumn was then washed three times with enrichment buffer. Glycopeptides were then eluted using 0.1% trifluoroacetic acid, 50 mM ammonium bicarbonate solution, and 50% acetonitrile, respectively. The eluates were collected and combined and then freeze-dried under vacuum. After drying, the peptides were reconstituted in 50 μL of 50 mM ammonium bicarbonate buffer dissolved in deionized water, and 2 μL of PNGase F glycosidase was added (for plant samples, the reconstituted samples were in 50 μL of 50 mM sodium citrate buffer dissolved in deionized water, and 2 μL of PNGase A and PNGase F glycosidases were added). The enzyme digestion was performed overnight at 37°C. Finally, the peptides were desalted according to the C18 ZipTips instructions, freeze-dried under vacuum, and then used for LC-MS / MS analysis.

[0083] (4) Liquid chromatography-mass spectrometry analysis

[0084] Peptides were dissolved in mobile phase a of liquid chromatography and then separated using an EASY-nLC 1000 ultra-high performance liquid chromatography system. Mobile phase a was an aqueous solution containing 0.1% formic acid and 2% acetonitrile; mobile phase b was an aqueous solution containing 0.1% formic acid and 90% acetonitrile. The liquid phase gradient settings were: 0-40 min, 5%-20% mobile phase b; 40-52 min, 20%-32% mobile phase b; 52-56 min, 32%-80% mobile phase b; 56-60 min, 80% mobile phase b, with the flow rate maintained at 550 nL / min. After separation by the ultra-high performance liquid chromatography system, the peptides were injected into an NSI ion source for ionization and then injected into Q Exactive™. The analysis was performed using Plus mass spectrometry; the ion source voltage was set to 2.0 kV, and high-resolution Orbitrap was used to detect and analyze peptide precursor ions and their secondary fragments; the primary mass spectrometry scan range was set to 400-1500 m / z, and the scan resolution was set to 60000; the secondary mass spectrometry scan range had a fixed starting point of 100 m / z, and the secondary scan resolution was set to 15000; the data acquisition mode used a data-dependent scanning (DDA) procedure, that is, after the primary scan, the top 20 peptide precursor ions with the highest signal intensity were selected and sequentially entered into the HCD collision cell for fragmentation at 28% of the fragmentation energy, and then the secondary mass spectrometry analysis was performed sequentially; in order to improve the effective utilization of the mass spectrometer, the automatic gain control (AGC) was set to 5E4, the signal threshold was set to 2E4 ions / s, the maximum injection time was set to 100 ms, and the dynamic exclusion time for tandem mass spectrometry scans was set to 30 s to avoid repeated scanning of precursor ions.

[0085] (5) Database search

[0086] Secondary mass spectrometry data were retrieved using MSFragger (v2.3); the database was Homo_sapiens_9606_SP_20220107.fasta (20376 sequences), and a reverse library was added to calculate the false positive rate (FDR) caused by random matching; the restriction enzyme digestion method was set to Trypsin / P; the number of missed cleavage sites was set to 2; the minimum peptide length was set to 7 amino acid residues; the maximum number of peptide modifications was set to 3; the mass error tolerance for primary precursor ions and secondary fragment ions was set to 20 ppm; cysteine ​​alkylation (Carbamidomethyl (C)) was set as a fixed modification, with variable modifications including methionine oxidation and N-terminal acetylation of the protein; mass offsets were set to the glycosylation modification list; and the FDR for both protein identification and PSM identification was set to 1%.

[0087] (6) Protein function annotation

[0088] To gain a thorough understanding of the functional characteristics of different proteins, comprehensive functional annotations are performed on the identified proteins, including detailed annotations on gene ontology (GO), protein domains, KEGG pathways, COG / KOG functional classification, and subcellular localization.

[0089] (7) Quantitative analysis

[0090] The search results provide the signal intensity values ​​(I) of each peptide in different samples. Based on this information, the relative quantification value of the modification site is calculated using the following steps: First, the signal intensity values ​​(I) of the modified peptide in different samples are centered to obtain the relative quantification value (R) of the modified peptide in different samples. The calculation formula is as follows:

[0091] R ij = I ij / Mean (I j );

[0092] Where i represents the i-th sample, j represents the j-th peptide, and R ij This represents the relative quantitative value of the j-th peptide in the i-th sample; I ij Mean (I) represents the signal intensity value of the j-th peptide in the i-th sample; j () represents the average signal intensity of the j-th peptide across all samples;

[0093] (8) Screening of differentially modified sites

[0094] First, select the samples to be compared. The ratio of the relative mean quantification values ​​of the modified sites in multiple replicates is used as the fold change (FC). For example, to calculate the fold change of modified sites between sample group A and sample group B, the formula is as follows:

[0095] FC A / B,k = Mean(R ik , i ∈ A) / Mean(R ik (i ∈ B);

[0096] Where k represents the kth modification site; R ik This represents the relative quantitative value of the k-th modification site in the i-th sample;

[0097] To determine the significance of the differences, a t-test was performed on the relative quantitative values ​​of each modification site in the comparison group samples, and the corresponding p-value was calculated as the significance index, with a default p < 0.05. To ensure that the test data conforms to the normal distribution required for the t-test, the relative quantitative values ​​of the modification sites need to be log2 transformed before the test, and the calculation formula is as follows:

[0098] P k = T.test(Log2(R ik , i ∈ A), Log2(R ik , i ∈ B).

[0099] (9) Biomarker screening

[0100] This embodiment aims to screen specific pan-cancer blood protein biomarkers that can distinguish between cancer patients and healthy controls. Therefore, in the cancer patient group, a protein showing a greater than 1.5-fold increase in site-specific glycosylation modification compared to the healthy control group, with a p-value less than 0.01, was defined as a candidate biomarker. The volcano plot showing the difference in protein expression between the two groups (healthy control group N=10, malignant tumor group T=52) is shown below. Figure 1 As shown, the most significant change is the glycosylation modification of CLU_N291_HexNAc(4)Hex(5) at position 291 of CLU.

[0101] CLU_N291_HexNAc(4)Hex(5) site-specific glycosylation modification is used as a glycoprotein tumor biomarker. Specifically, asparagine at position 291 of the CLU amino acid sequence is glycosylated, and the glycosylated glycoform is 4 N-acetamidohexose and 5 hexose.

[0102] (10) Validation of the expression of CLU_N291_HexNAc(4)Hex(5) in a single cancer type

[0103] To verify whether the CLU_N291_HexNAc(4)Hex(5) site is also specific in a single cancer type, the expression levels of CLU_N291_HexNAc(4)Hex(5) in the blood of patients with malignant tumors and healthy controls were compared. It was found that the cohort of patients with malignant tumors included 6 pancreatic cancers, 6 bile duct cancers, 11 gastric cancers, 20 lung cancers, 6 breast cancers, and 3 ovarian cancers.

[0104] Specific methods: GraphPad software was used to perform statistical analysis on the data comparison between the malignant tumor patient group and the healthy control group. The comparison revealed that the levels of CLU_N291_HexNAc(4)Hex(5) in the blood of the malignant tumor patients in the cohort were compared with those in the healthy control group. The difference between them was tested by the unpaired T test method to see if there was a statistical difference.

[0105] Statistical analysis results as follows Figures 2-8 As shown, the results indicated that in the discovery cohort, the expression levels of CLU_N291_HexNAc(4)Hex(5) in the blood of patients with malignant tumors were significantly higher than those in healthy controls.

[0106] Example 2: Evaluation of the diagnostic efficacy of glycoprotein tumor biomarkers

[0107] The diagnostic efficacy of glycoprotein tumor biomarkers was evaluated in a validation cohort (30 healthy controls, 19 pancreatic cancer, 19 cholangiocarcinoma, 33 gastric cancer, 61 lung cancer, 20 breast cancer and 11 ovarian cancer samples). CLU_N291_HexNAc(4)Hex(5) were used as pan-cancer biomarkers. The diagnostic efficacy of single and combined clinically commonly used tumor biomarkers (CEA, CA19-9, CA50, CA242, CA724, CA125, CA153, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4) was compared with that of existing clinically commonly used tumor biomarkers / combinations.

[0108] Specific methods: The receiver operating characteristic curve (ROC curve) was used to determine the classifier performance index of CLU_N291_HexNAc(4)Hex(5) as a pan-cancer biomarker; ROC curves of the malignant tumor group and the healthy control group were plotted using GraphPad software; a 95% confidence interval was set, and the results were output as a percentage, i.e., AUC (area under the ROC curve and the coordinate axis). When calculating female tumors (breast cancer / ovarian cancer), female samples were used as the healthy control samples.

[0109] The ROC curve calculation results are as follows: Figures 9-15 As shown, the results are as follows:

[0110] CLU_N291_HexNAc(4)Hex(5) in terms of diagnostic efficacy for multiple cancer types ( Figure 9 The AUC of the detection reached 0.897 (sensitivity = 84.7%, specificity = 90%, healthy control group N = 30, malignant tumor group T = 163), which is much higher than the AUC of CEA (0.526);

[0111] CLU_N291_HexNAc(4)Hex(5) showed efficacy in pancreatic cancer (AUC=0.925, sensitivity=94.7%, specificity=90%, N=30, T=19), bile duct cancer (AUC=0.977, sensitivity=100%, specificity=90%, N=30, T=19), gastric cancer (AUC=0.847, sensitivity=81.8%, specificity=83.3%, N=30, T=33), lung cancer (AUC=0.925, sensitivity=88.5%, specificity=90%, N=30, T=61), breast cancer (AUC=0.826, sensitivity=75%, specificity=100%, N=21, T=20), and ovarian cancer (AUC=0.818, sensitivity=81.8%). In a study with a specificity of 100%, N=21, and T=11, it demonstrated superior diagnostic efficacy compared to commonly used clinical tumor biomarkers. Figures 10-15 );

[0112] The combination of CLU_N291_HexNAc(4), Hex(5), CEA, CA19-9, CA242, CA72-4, and CA50 for pancreatic cancer detection achieved an AUC of 0.956 (sensitivity = 89.5%, specificity = 93.3%, N = 30, T = 19), which is superior to existing combinations of pancreatic cancer diagnostic markers. Figure 10 The combination of CLU_N291_HexNAc(4)Hex(5), CEA, CA19-9 and CA-50, etc., was used for the detection of cholangiocarcinoma, and the AUC value reached 0.991 (sensitivity=100%, specificity=93.3%, N=30, T=19), which is superior to the existing combination of diagnostic markers for cholangiocarcinoma. Figure 11 The combination of CLU_N291_HexNAc(4)Hex(5), CEA, CA19-9, CA72-4 and CA50 was used for gastric cancer detection, and the AUC value reached 0.866 (sensitivity=78.8%, specificity=90.0%, N=30, T=33), which is superior to the existing combination of gastric cancer diagnostic markers. Figure 12The combination of CLU_N291_HexNAc(4)Hex(5), CEA, SCC, CA72-4, CYFRA21-1, NSE and PROGRP was used for lung cancer detection, and the AUC value reached 0.982 (sensitivity=90.2%, specificity=96.7%, N=30, T=61), which is superior to existing combinations of lung cancer diagnostic markers. Figure 13 The combination of CLU_N291_HexNAc(4)Hex(5), CEA and CA15-3, etc., for breast cancer detection achieved an AUC value of 0.836 (sensitivity=75%, specificity=100%, N=21, T=20), which is superior to existing combinations of breast cancer diagnostic markers. Figure 14 The combination of CLU_N291_HexNAc(4)Hex(5), CEA, CA72-4, CA125 and HE4 was used for ovarian cancer detection, and the AUC value reached 0.926 (sensitivity=90.9%, specificity=100%, N=21, T=11), which is superior to the existing combination of ovarian cancer diagnostic markers. Figure 15 ).

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. The application of glycoprotein tumor biomarkers in the preparation of cancer diagnostic reagents, characterized by: The glycoprotein tumor biomarker is a CLU_N291_HexNAc(4)Hex(5) site-specific glycosylation modification, specifically: asparagine at position 291 of the CLU amino acid sequence is glycosylated, and the glycosylated glycoform is 4 N-acetamidohexose and 5 hexose; The cancers mentioned are pancreatic cancer, bile duct cancer, stomach cancer, lung cancer, breast cancer, or ovarian cancer; The sample tested by the reagent is the blood of the test subject.

2. The application as described in claim 1, characterized in that: The reagent includes a specific recognition antibody for the glycoprotein tumor biomarker.

3. The application as described in claim 2, characterized in that: The reagent also includes at least one of the following: CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or a specific recognition antibody for PSA protein.

4. The application as described in claim 3, characterized in that: The reagent is used to detect the glycoprotein tumor biomarker, as well as at least one of the following proteins: CEA, CA19-9, CA50, CA242, CA72-4, CA125, CA15-3, AFP, SCC, CYFRA21-1, PROGRP, NSE, HE-4, or PSA, by enzyme-linked immunosorbent assay (ELISA), dot blot assay, Western blot assay, colloidal gold immunochromatography, flow cytometry, mass spectrometry, immunofluorescence, immunoprecipitation, or immunohistochemistry.

5. The application as described in claim 2, characterized in that: The method for preparing the specific recognition antibody for the glycoprotein tumor biomarker includes the following steps: Antigenic peptides were designed based on the glycoprotein tumor biomarkers described. The antigenic polypeptide is loaded onto a vector to form an antigen and immunize the host animal; Specific recognition antibodies for the glycoprotein tumor biomarkers were obtained through specific affinity purification.

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