CN115078726A

CN115078726A - Biomarker for ovarian cancer diagnosis and detection kit

Info

Publication number: CN115078726A
Application number: CN202210786785.4A
Authority: CN
Inventors: 赵阳; 叶华; 王鹏; 史健翔; 段亚茹; 张婧; 高峰; 朱建立
Original assignee: Zhengzhou University
Current assignee: Zhengzhou University
Priority date: 2022-07-04
Filing date: 2022-07-04
Publication date: 2022-09-20

Abstract

The invention belongs to the technical field of medical biology, and particularly discloses a biomarker and a detection kit for ovarian cancer diagnosis. The biomarker for diagnosing ovarian cancer provided by the invention is a combination of autoantibodies of anti-tumor associated antigens GGT5, VCL and TRIM 21. The kit for diagnosing ovarian cancer provided by the invention contains a reagent for detecting the biomarker, wherein the reagent is a reagent for detecting the biomarker in a sample through enzyme-linked immunosorbent assay, a protein chip, immunoblotting or microfluidic immunoassay. According to the invention, by detecting the expression level of autoantibodies of anti-tumor associated antigens GGT5, VCL and TRIM21 in human serum, ovarian cancer patients and healthy people can be effectively distinguished, and the kit can be used for auxiliary diagnosis of ovarian cancer.

Description

Biomarker for ovarian cancer diagnosis and detection kit

Technical Field

The invention belongs to the technical field of medical biology, and particularly discloses a biomarker and a detection kit for ovarian cancer diagnosis.

Background

Ovarian cancer is one of the most fatal gynecological malignant tumors worldwide, seriously threatens the life and health of women, has latent ovarian cancer, unobvious clinical symptoms in early stages and lacks of effective screening and early diagnosis methods, so that about 75 percent of patients are diagnosed in late stages, the treatment effect and prognosis are relatively poor, and the total cure rate is about 30 percent. The 5-year survival rate of patients can reach over 50% by conventional surgery and chemotherapy when the patient is in phase I or II of the clinic, while the long-term survival rate is only about 20% or less when the patient is in phase III or IV of the clinic. Computer simulation studies have shown that confirmed diagnosis of disease in the early preclinical stage can increase patient survival by 10% -30% and is more cost effective.

At present, the clinical common ovarian cancer early screening method comprises pelvic cavity examination, gynecological ultrasonic imaging, tumor markers and the like, because the ovary is deeply located in the pelvic cavity and has small volume, the pelvic cavity examination can only be found when the ovary is obviously affected by a large lesion, the gynecological ultrasonic imaging lacks the capability of distinguishing and distinguishing ovarian cancer and benign ovarian tumor, the false positive rate is easy to appear, and the price is high. The common serum tumor markers comprise CA125 and HE4, but the sensitivity and specificity of the two markers in the aspect of early diagnosis of ovarian cancer still have certain limitations, and the clinical large-scale population screening test cannot be met. Therefore, in order to improve the 5-year survival rate of ovarian cancer patients, the search for an effective and economically practical method for early diagnosis of ovarian cancer is urgently needed.

The tumor development process is often accompanied by the abnormal expression of proteins, these abnormal expression and tumor development related proteins are called Tumor Associated Antigens (TAAs), which can enter the blood circulation at the early stage of cancer and be recognized by the immune system, thereby inducing host immune response to generate anti-tumor associated antigen autoantibodies (TAAb), the anti-TAA autoantibodies are stable in the blood of cancer patients, the existence of high titer makes them more attractive as early diagnosis markers of cancer than tumor associated antigens, and a new approach is brought for the research of early tumor markers. Since the sensitivity of individual autoantibodies for early detection of cancer is low, it has been found that the sensitivity of detection can be improved to some extent by using a specific set of anti-TAA autoantibody combinations. Therefore, screening a group of better marker combinations has important practical significance for early diagnosis of ovarian cancer.

In the past, research methods for searching potential tumor markers mostly focus on serological analysis (SEREX) of recombinant cDNA expression libraries and serological proteome analysis, but the experimental technology of the screening method is complicated and complicated, results are difficult to repeat, certain false positive and false negative exist, and low-abundance proteins cannot be effectively detected. In recent years, protein chip technology has the advantages of high throughput, small usage amount, rapid detection and the like, and has gradually developed into a mainstream tool for proteomics research. The human whole proteome chip is composed of a large number of recombinant proteins arranged on the surface, including kinases, membrane proteins, nucleoproteins, secretory proteins, transcription factors, and proteins involved in functional pathways such as metabolism, signal transduction, cell death, and can be used for biomarker discovery in a disease context in a high throughput manner. Therefore, the application of the human whole proteome chip to screen a large number of potential, novel and higher-sensitivity ovarian cancer-associated antigens and corresponding autoantibodies thereof for large-scale population screening of ovarian cancer is a clinical need to be solved urgently at present.

Disclosure of Invention

In view of the problems and disadvantages of the prior art, it is an object of the present invention to provide a biomarker for diagnosing ovarian cancer, a second object of the present invention is to provide a use of a reagent for detecting the biomarker in the preparation of a product for diagnosing ovarian cancer, and a third object of the present invention is to provide a kit for diagnosing ovarian cancer.

Based on the purpose, the technical scheme adopted by the invention is as follows:

the invention provides a biomarker for ovarian cancer diagnosis, wherein the biomarker is a combination of an anti-tumor associated antigen GGT5 autoantibody, an anti-tumor associated antigen VCL autoantibody and an anti-tumor associated antigen TRIM21 autoantibody. The expression level of the anti-tumor associated antigen GGT5 autoantibody, the anti-tumor associated antigen VCL autoantibody and the anti-tumor associated antigen TRIM21 autoantibody in the serum of the ovarian cancer patient is higher than that of a healthy person, and the difference has statistical significance.

Preferably, the autoantibodies against the tumor associated antigen GGT5, VCL and TRIM21 are the corresponding autoantibodies against the tumor associated antigen in the serum, plasma, interstitial fluid or urine of the subject.

Preferably, the anti-tumor associated antigen GGT5 autoantibody, the anti-tumor associated antigen VCL autoantibody, the anti-tumor associated antigen TRIM21 autoantibody is the anti-tumor associated antigen autoantibody in serum, plasma, interstitial fluid or urine of the subject prior to receiving tumor treatment according to the above biomarker. More preferably, the tumor treatment is chemotherapy, radiation therapy or surgical resection of a tumor.

Preferably, the subject is a mammal, more preferably a primate mammal, according to the above biomarker; most preferably, the subject is a human.

In a second aspect, the present invention provides the use of a reagent for detecting a biomarker according to the first aspect in the manufacture of a product for use in the diagnosis of ovarian cancer.

According to the above application, preferably, the reagent is a reagent for detecting the biomarker in the sample by enzyme-linked immunosorbent, protein chip, immunoblot or microfluidic immunoassay.

Preferably, the sample is serum, plasma, interstitial fluid or urine, according to the above-mentioned use.

Preferably, the reagent is an antigen for detecting the biomarker according to the above-mentioned application. More preferably, the agent is a combination of GGT5 protein, VCL protein, TRIM21 protein.

According to the above-mentioned use, preferably, the product is a protein chip, a kit or a formulation.

According to the above application, preferably, the probability of predicting ovarian cancer of the product is calculated by the formula:

PRE ═ 1/(1+ EXP (- (-3.919+15.112 × GGT5+6.002 × VCL +4.169 × TRIM21))), where PRE represents the probability, GGT5 represents the expression level of anti-tumor associated antigen GGT5 autoantibody, VCL represents the expression level of anti-tumor associated antigen VCL autoantibody, and TRIM21 represents the expression level of anti-tumor associated antigen TRIM21 autoantibody. When the predicted probability PRE is more than or equal to 0.5, preliminarily judging the tested person to be the ovarian cancer patient; when the predicted probability PRE is less than 0.5, the subject is preliminarily determined to be a healthy person.

In a third aspect, the present invention provides a kit for diagnosing ovarian cancer, the kit comprising reagents for detecting the biomarkers of the first aspect.

According to the above kit, preferably, the kit detects the biomarker in a sample by enzyme-linked immunosorbent, protein chip, immunoblot or microfluidic immunoassay. More preferably, the kit detects the biomarker in the sample by antigen-antibody reaction.

According to the kit, preferably, the probability of ovarian cancer prediction of the kit is calculated according to the formula:

According to the above kit, preferably, the kit is an ELISA detection kit. More preferably, the ELISA detection kit comprises a solid phase carrier and an antigen coated on the solid phase carrier; the antigen is GGT5 protein, VCL protein and TRIM21 protein.

Preferably, the sample is serum, plasma, interstitial fluid or urine according to the above-mentioned kit.

According to the kit, preferably, the ELISA detection kit further comprises a sample diluent, a second antibody, an antibody diluent, a washing solution, a developing solution and a stop solution.

The basic information of the tumor-associated antigens GGT5, VCL and TRIM21 in the invention is as follows:

GGT5 is glutamyltransferase, is a member of the gamma-glutamyltranspeptidase gene family, has the ability to cleave the gamma-glutamyl moiety of glutathione, and is involved in a plurality of processes such as redox regulation, drug metabolism, and immune function in vivo. VCL is a actin filament binding protein, which is involved in cell matrix adhesion and cell-cell adhesion, and regulates the expression of cell surface E-cadherin to promote tumorigenesis. TRIM21 is a family member of E3 ubiquitin ligase TRIM protein, plays an important role in regulating cell cycle process, and TRIM21 can negatively regulate NFKB signal pathway and is an important cancer suppressor gene. The sequence number of GGT5 protein in NCBI is: NP-004112.2; the sequence numbers of the VCL proteins are: NP-054706.1; the sequence number of TRIM21 protein is as follows: NP _ 003132.2.

Compared with the prior art, the invention has the following positive beneficial effects:

(1) the invention discovers for the first time that the expression levels of autoantibodies of anti-tumor associated antigens GGT5, VCL and TRIM21 in the serum of an ovarian cancer patient are all significantly higher than those of a healthy person, the differences have statistical significance, and the ovarian cancer can be effectively detected by detecting the expression levels of the autoantibodies of the anti-tumor associated antigens GGT5, VCL and TRIM21 in the human serum; proved by verification, when the ovarian cancer is diagnosed by singly adopting any one marker of autoantibodies of anti-tumor associated antigens GGT5, VCL and TRIM21, the AUC value of the ROC curve is over 0.60; when a plurality of markers are used in a combined mode, the AUC value of the ROC curve is closer to 1 than that of a single index, the distinguishing effect is good, and the diagnosis effect is good. Therefore, the marker for ovarian cancer diagnosis can be used for auxiliary diagnosis of ovarian cancer.

(2) The invention takes the three markers of the anti-tumor associated antigen GGT5 autoantibody, the anti-tumor associated antigen VCL autoantibody and the anti-tumor associated antigen TRIM21 autoantibody as a combination for diagnosing and detecting ovarian cancer, the AUC of its ROC curve was 0.79 (95% CI:0.74-0.84), the detection sensitivity is up to 66.2 percent (namely, the ratio of the ovarian cancer to be correctly diagnosed when the ovarian cancer patient is diagnosed by using the three markers is 66.2 percent), the specificity is up to 77.2 percent (namely, the ratio of healthy people to be determined when the ovarian cancer patient is diagnosed by using the three markers in a healthy control is 77.2 percent, therefore, the marker of the invention has higher sensitivity and specificity, greatly improves the detection rate of ovarian cancer, is beneficial to screening and early discovery of ovarian cancer, thereby greatly reducing the mortality rate of ovarian cancer patients and bringing great welfare to ovarian cancer patients and families.

(3) The kit provided by the invention detects the expression levels of the anti-tumor associated antigen GGT5 autoantibody, the anti-tumor associated antigen VCL autoantibody and the anti-tumor associated antigen TRIM21 autoantibody in human serum by an indirect ELISA method, can accurately distinguish ovarian cancer patients from health control diagnosis, and provides a new reference basis for a clinician to diagnose ovarian cancer.

(4) The detection sample of the kit is serum, so that invasive diagnosis can be avoided, the risk of ovarian cancer can be obtained by obtaining the serum in a minimally invasive manner and detecting the serum, the blood consumption is low, the pain of detected personnel is small, and the compliance is high; moreover, the method is simple to operate, short in detection result time and wide in market prospect and social benefit.

Drawings

FIG. 1 is a schematic diagram showing the principle of screening a marker for ovarian cancer diagnosis using a human proteome chip in example 1 of the present invention;

FIG. 2 is a graph showing the results of the expression levels of 3 kinds of autoantibodies against tumor-associated antigens in serum samples of ovarian cancer groups and healthy control groups in example 2; wherein C represents ovarian cancer group, N represents healthy control group;

FIG. 3 is a ROC graph of the 3 anti-tumor associated antigen autoantibodies of example 2 when diagnosed individually to distinguish ovarian cancer groups from healthy control groups;

FIG. 4 is a graph showing the results of the expression levels of 3 anti-tumor associated antigen autoantibodies in serum samples of the ovarian cancer group and the healthy control group in example 3; wherein C represents ovarian cancer group, N represents healthy control group;

FIG. 5 is a ROC plot of the 3 anti-tumor associated antigen autoantibodies of example 3 when diagnosed individually to distinguish ovarian cancer groups from healthy control groups;

FIG. 6 is a ROC plot of the different combinations of anti-tumor associated antigen autoantibodies in the training set differentiating between ovarian cancer and healthy control in example 4;

FIG. 7 is a ROC plot of the combined diagnosis of the three anti-tumor associated antigen autoantibodies in the validation set of example 4 to distinguish between ovarian cancer and healthy control groups.

Detailed Description

The following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, 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 belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. Furthermore, it will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and/or combinations thereof.

The experimental methods in the following examples, which do not indicate specific conditions, all employ conventional techniques in the art, or follow the conditions suggested by the manufacturers; the reagents or instruments used are not indicated by the manufacturer, and are all conventional products commercially available.

In order to make the technical solutions of the present invention more clearly understood by those skilled in the art, the technical solutions of the present invention will be described in detail below with reference to specific embodiments.

Example 1: screening of human proteome chips for markers for ovarian cancer diagnosis

1. Experimental samples:

collecting 20 serum samples (ovarian cancer group) of ovarian cancer patients from tumor hospital of Henan province, third subsidiary hospital of Zhengzhou university and 20 serum samples (healthy control group) of healthy control from Guangzhou Bo Chong laboratory; of these, 20 ovarian cancer patient sera were from ovarian cancer patients diagnosed pathologically and without any treatment; 20 normal human sera were derived from healthy subjects who were enrolled with the following criteria: no cardiovascular, respiratory, hepatic, renal, gastrointestinal, endocrine, hematological, psychiatric, or neurological disease and history of the above, no acute or chronic disease, no autoimmune disease, no evidence of any tumor association; furthermore, the difference between age in 20 ovarian cancer patients and 20 healthy subjects was not statistically significant. The study was approved by the ethical committee of zheng state university, and all subjects had signed informed consent.

Mixing 2 sera of 20 patients with ovarian cancer into 1 mixed ovarian cancer serum sample to obtain 10 mixed ovarian cancer serum samples; each 2 of 20 healthy control sera were mixed into 1 mixed healthy control serum sample to obtain 10 mixed healthy control serum samples.

Collecting serum: collecting peripheral blood 5ml of the subject in fasting state, standing at room temperature for 1 hr, and centrifuging at 4 deg.C and 3000rpm for 10 min. Then sucking out the serum on the upper layer of the blood collection tube, subpackaging into 1.5ml EP tubes, marking sample numbers on the top and the side of the EP tube, placing the EP tubes in a refrigerator at minus 80 ℃ for freezing storage, and recording the blood collection date and the storage position. Before use, the serum is taken out and put in a refrigerator at 4 ℃ for unfreezing and subpackaging, and repeated freeze thawing of the serum is avoided.

2. Human proteome chip detection

Using Huprot ^TM Human Whole proteome chips (purchased from Guangzhou Bo Chong Biotech Co., Ltd.) were used to detect the expression levels of autoantibodies in 10 mixed ovarian cancer serum samples and 10 mixed healthy control serum samples (collected from Guangzhou Bo Chong Biotech Co., Ltd.)The schematic diagram of the detection using the human proteome chip is shown in FIG. 1). Each chip can detect 14 serum samples at the same time, and the protein fixed on the chip interacts with the specific autoantibody in the serum to be combined.

(1) The experimental method comprises the following steps:

1) rewarming: huprot is ^TM Taking out the human proteome chip from a refrigerator at the temperature of-80 ℃, putting the human proteome chip in the refrigerator at the temperature of 4 ℃ for rewarming for 30min, and then continuing to rewarm for 15min at room temperature;

2) and (3) sealing: placing the rewarmed chip with the right side up in a chip incubation box, adding 10mL of blocking solution (3mL of 10% BSA, adding 7mL of 1 xPBST solution), placing in a side-swinging shaker, and blocking at room temperature for 1h at 50-60 rpm;

3) incubation of serum samples: after the blocking is finished, removing the blocking solution, quickly adding a pre-diluted serum incubation solution (the serum sample is diluted by a diluent according to the proportion of 1:200 to obtain the diluted serum incubation solution, and the diluent is prepared by adding 1ml of 10% BSA into 9ml of 1 xPBST solution), placing in a side shaking table, and incubating at 4 ℃ overnight at 20 rpm;

4) cleaning: after incubation is finished, taking out the chip, placing the chip in a chip cleaning box containing cleaning solution, horizontally shaking the chip at room temperature of 80rpm, and cleaning for 3 times, wherein each time lasts for 10 min;

5) and (3) secondary antibody incubation: after the washing is finished, the chip is transferred into an incubation box, and the mixture is added according to the proportion of 1: the secondary antibody incubation solution diluted by 1000 proportion (the secondary antibody is anti-human IgM and IgG antibodies marked by cy3, the components of the dilution solution are 1g BSA, 100mL1 xPBST solution, the secondary antibody is diluted by the dilution solution according to the proportion of 1:1000 to obtain the secondary antibody incubation solution) is 3mL, the secondary antibody incubation solution is placed on a side shaking table at 40rpm and is protected from light, and the secondary antibody incubation solution is incubated for 1h at room temperature;

6) cleaning: the chip was removed (note that the top surface of the chip was not touched or scratched), placed in a chip washing cassette, and chip washing solution (1 XPBST solution) was added, placed on a horizontal shaker, and washed 3 times for 10min each at 80 rpm. After completion with ddH ₂ Cleaning for 2 times repeatedly for 10min each time;

7) and (3) drying: after the cleaning is finished, placing the chip in a chip drier for centrifugal drying;

8) scanning: performing standard fluorescence scanning on the dried chip according to the use instruction of the scanner and recording a fluorescence signal (the strength of the fluorescence signal has a positive correlation with the affinity and the quantity of a corresponding antibody);

9) data extraction: and opening a corresponding GAL file, aligning each array on the GAL file with the whole chip image, clicking an automatic alignment button, extracting data and storing the data as a GPR.

(2) Data processing:

f532 media refers to the Median of the foreground values of the signal points under the 532nm channel, and B532 media refers to the Median of the background values of the signal points under the 532nm channel. In order to eliminate the signal non-uniformity caused by the non-uniformity of background values among different protein points in the same chip, the signal-to-noise ratio (SNR) is defined as F532 media/B532 media, and the SNR values of 10 mixed ovarian cancer serum samples and 10 mixed healthy control serum samples are obtained by calculating according to the SNR calculation formula. The SNR values of the 10 mixed ovarian cancer serum samples and the 10 mixed healthy control serum samples were subjected to median linear normalization, and for any one autoantibody, a multiple of difference between the ovarian cancer group and the healthy control group (multiple of difference: SNR mean value after median linear normalization in the ovarian cancer group/SNR mean value after median linear normalization in the healthy control group) was calculated to indicate how much the ovarian cancer group is higher than the healthy control group, and the screening conditions were further set: the difference multiple is more than 1.2, the difference value of the positive rates of the ovarian cancer group and the healthy control is more than 60 percent, and the anti-tumor associated antigen autoantibodies meeting the conditions are screened out.

(3) The experimental results are as follows:

through screening, 3 kinds of anti-tumor associated antigen autoantibodies are finally screened out, namely an anti-tumor associated antigen GGT5 autoantibody, an anti-tumor associated antigen VCL autoantibody and an anti-tumor associated antigen TRIM21 autoantibody; wherein, the difference multiple of the autoantibodies of the anti-tumor associated antigen GGT5 is 1.60, the positive rate in the ovarian cancer group is 80%, the positive rate in the healthy control group is 10%, and the difference of the positive rates of the two groups is 70%; the difference multiple of the anti-tumor associated antigen VCL autoantibodies is 4.22, the positive rate in the ovarian cancer group is 60%, the positive rate in the healthy control group is 0%, and the difference of the positive rates of the two groups is 60%; the multiple difference of the anti-tumor associated antigen TRIM21 autoantibody is 3.21, the positive rate in the ovarian cancer group is 100%, the positive rate in the healthy control group is 10%, and the difference of the positive rates is 90%.

Moreover, the expression level of the anti-tumor associated antigen GGT5 autoantibody, the anti-tumor associated antigen VCL autoantibody and the anti-tumor associated antigen TRIM21 autoantibody in the serum of the ovarian cancer group is higher than that of the healthy control group, and the difference has statistical significance.

Example 2: ELISA (enzyme-Linked immuno sorbent assay) is used for detecting the serum expression level of autoantibodies of anti-tumor-associated antigens GGT5, VCL and TRIM21 and evaluating the value of the autoantibodies of the three anti-tumor-associated antigens for diagnosing ovarian cancer

The expression levels of the three anti-tumor associated antigen autoantibodies screened in example 1 in human serum are detected by an indirect enzyme linked immunosorbent assay (ELISA), and the value of the four anti-tumor associated antigen autoantibodies in ovarian cancer diagnosis is evaluated.

1. Experimental samples:

collecting 60 serum samples of ovarian cancer patients from tumor hospital of Henan province (ovarian cancer group) and 60 healthy control serum samples from normal physical examination at third subsidiary hospital of Zheng State university (healthy control group); of these, 60 ovarian cancer patient sera were from ovarian cancer patients diagnosed pathologically and without any treatment; 60 healthy control sera were derived from healthy subjects whose inclusion criteria were: no cardiovascular, respiratory, hepatic, renal, gastrointestinal, endocrine, hematological, psychiatric, or neurological disease and history of such disease, no acute or chronic disease, no evidence of any tumor-related disease; furthermore, the difference between age in 60 ovarian cancer patients and 60 healthy subjects was not statistically significant. The study was approved by the ethical committee of zheng state university, and all subjects signed informed consent.

Collecting serum: collecting peripheral blood 5ml of the subject in fasting state, standing at room temperature for 1 hr, and centrifuging at 4 deg.C and 3000rpm for 10 min. Then sucking out the serum on the upper layer of the blood collection tube, subpackaging into 1.5ml EP tubes, marking sample numbers on the top and the side of the EP tube, placing the EP tubes in a refrigerator at the temperature of 80 ℃ below zero for freezing storage, and recording the blood collection date and the storage position. Before use, the serum is taken out and put in a refrigerator at 4 ℃ for unfreezing and subpackaging, and repeated freeze thawing of the serum is avoided.

2. Experimental materials and reagents:

(1)3 tumor-associated antigen proteins: GGT5 recombinant protein, VCL recombinant protein, TRIM21 recombinant protein purchased from Wuhan Huamei bioengineering Co., Ltd

(2) 96-well enzyme-linked plate (8 rows × 12 columns);

(3) coating liquid: contains 0.15% sodium carbonate (Na) ₂ CO ₃ ) And 0.29% sodium bicarbonate (NaHCO) ₃ ) An aqueous solution of (a);

(4) sealing liquid: PBST buffer containing 2% (v/v) Bovine Serum Albumin (BSA) and 0.2% (v/v) Tween 20;

(5) serum sample diluent: PBST buffer containing 1% (w/v) BSA;

(6) enzyme-labeled secondary antibody: horse Radish Peroxidase (HRP) labeled mouse anti-human immunoglobulin antibody (hereinafter, HRP labeled mouse anti-human IgG antibody);

(7) antibody dilution: PBST buffer containing 1% (w/v) BSA;

(8) washing liquid: PBST buffer containing 0.2% (v/v) Tween 20;

(9) color development liquid: the color developing solution comprises color developing solution A and color developing solution B, wherein the color developing solution A is 20% aqueous solution of tetramethylbenzidine dihydrochloride, and the color developing solution B comprises 0.92g citric acid and 3.7g Na ₂ HPO ₄ ·12H ₂ O and 800. mu.L 0.75% H ₂ O ₂ Dissolving in 100mL deionized water to prepare); when in use, the color development liquid A and the color development liquid B are uniformly mixed according to the equal volume of 1:1, and are prepared at present;

(10) stopping liquid: 10% sulfuric acid.

3. The experimental method comprises the following steps:

(1) preparing 3 tumor-associated antigen-coated ELISA plates:

respectively preparing an ELISA plate coated by a tumor-associated antigen GGT5, an ELISA plate coated by a tumor-associated antigen VCL and an ELISA plate coated by a tumor-associated antigen TRIM 21.

Taking the preparation of an ELISA plate coated with tumor-associated antigen GGT5 as an example, the specific operation steps are as follows:

1) preparing a tumor associated antigen GGT5 protein solution: GGT5 protein was dissolved in the coating solution to prepare a GGT5 protein solution at a concentration of 0.25. mu.g/mL.

2) Coating an enzyme label plate: adding the GGT5 protein solution prepared in the step 1) into each reaction hole of a 96-hole enzyme label plate, wherein the sample adding amount is 50 mu L/hole, coating overnight at 4 ℃, and then throwing out the rest coating solution and beating to dry.

3) And (3) sealing: adding a sealing solution into reaction holes of the coated 96-hole ELISA plate, wherein the sample adding amount is 100 mu L/hole, sealing in water bath at 37 ℃ for 2h, then removing the sealing solution, washing with a washing solution (the sample adding amount is 300 mu L/hole) for 3 times, and drying by beating to obtain the tumor-associated antigen GGT 5-coated ELISA plate.

The preparation steps of the ELISA plate coated by the tumor associated antigen VCL and the ELISA plate coated by the tumor associated antigen TRIM21 are basically the same as the operation steps of the ELISA plate coated by the tumor associated antigen GGT5, and the differences are as follows: the tumor-associated antigens adopted in the step 1) are different, and the concentrations of the prepared tumor-associated antigen solutions are different. Wherein, when preparing the ELISA plate coated by the tumor-associated antigen VCL, the tumor-associated antigen adopted in the step 1) is VCL recombinant protein, and the concentration of the prepared VCL solution of the tumor-associated antigen is 0.125 mug/mL; when preparing the ELISA plate coated by the tumor associated antigen TRIM21, the tumor associated antigen adopted in the step 1) is TRIM21 recombinant protein, and the concentration of the tumor associated antigen TRIM21 solution is 0.25 mug/mL.

(2) Detection of autoantibody expression levels against 3 tumor associated antigens in serum samples:

the autoantibody expression levels of the anti-tumor associated antigens GGT5, VCL and TRIM21 in the serum sample are detected by ELISA method by using the prepared ELISA plate coated by the 3 tumor associated antigens in the same serum sample.

Taking the detection of the expression level of the autoantibody of the anti-tumor associated antigen GGT5 as an example, the specific operation steps are as follows:

1) incubation of serum samples:

diluting the serum sample to be detected with a serum sample diluent according to the volume ratio of 1: 100. Adding the diluted serum sample into the reaction holes of the 1 st to 11 th rows of the 96-hole enzyme label plate coated with GGT5 protein prepared in the step (1), wherein the sample adding amount is 50 mu l/hole; adding quality control serum diluted according to a ratio of 1:100 into the 1 st to 6 th reaction holes of the 12 th column of the 96-hole enzyme label plate coated with GGT5 protein, wherein the sample adding amount is 50 mu l/hole, and the quality control serum is used as quality control to carry out standardization among different enzyme label plates; adding antibody diluent without serum (the sample adding amount is 50 mu l/hole) into the 7 th reaction hole and the 8 th reaction hole of the 12 th column of a 96-hole enzyme label plate coated with GGT5 protein to serve as blank control; the 96-well plate is then incubated in a 37 ℃ water bath for 1h, and then the reaction well is drained, washed 5 times with wash solution (sample amount 300. mu.l/well) and blotted dry.

2) And (3) secondary antibody incubation:

diluting an HRP-labeled mouse anti-human IgG antibody with an antibody diluent according to the proportion of 1:10000(v/v), adding the diluted HRP-labeled mouse anti-human IgG antibody into a reaction hole corresponding to a 96-hole enzyme label plate, placing the diluted HRP-labeled mouse anti-human IgG antibody into a water bath at 37 ℃ for incubation for 1h, then discarding the liquid in the reaction hole, washing with a washing solution (the sample addition is 300 mu l/hole) for 5 times, and patting to dry.

3) Color development and termination reaction:

and (3) uniformly mixing the color development liquid A and the color development liquid B in an equal volume of 1:1, then quickly adding the mixed color development liquid into reaction holes of a 96-hole enzyme label plate, wherein the sample addition amount is 50 mu l/hole, carrying out light-shielding color development reaction at room temperature for 5-15min, then adding 25 mu l of stop solution into each reaction hole, and stopping the color development reaction. The absorbance OD at the wavelength of 450nm and 620nm is read by a microplate reader ₄₅₀ 、OD ₆₂₀ Wherein the absorbance OD at a wavelength of 620nm ₆₂₀ As background value, use OD ₄₅₀ And OD ₆₂₀ As a result of the detected absorbance value, andnull control wells were used for zeroing.

The specific procedures for detecting the expression level of the anti-tumor associated antigens VCL and TRIM21 autoantibodies in the serum sample are substantially the same as those for detecting the anti-tumor associated antigen GGT5 autoantibodies described above, except that: the enzyme label plate adopted in the detection in the step 1) is different. When the expression level of an autoantibody against a tumor-associated antigen VCL in a serum sample is detected, the ELISA plate adopted in the step 1) is an ELISA plate coated by the VCL protein of the tumor-associated antigen; when the expression level of the autoantibody against the tumor associated antigen TRIM21 in the serum sample is detected, the elisa plate adopted in the step 1) is the elisa plate coated by the tumor associated antigen TRIM21 protein.

4. Data processing

A nonparametric test (Mann-Whitney U) was performed on the absorbance values of the serum samples of the ovarian cancer group and the healthy control group, and the difference between the expression levels of the autoantibodies in the ovarian cancer group and the healthy control group was compared.

Further based on the measured expression levels of the anti-tumor associated antigen autoantibodies in the ovarian cancer group and the healthy control group (the expression levels are expressed by the final detected absorbance value results), ROC curves for diagnosing and distinguishing ovarian cancer separately for 3 anti-tumor associated antigen autoantibodies were drawn using GraphPad Prism 8.0; and when the specificity is more than 90%, the OD value corresponding to the maximum Yoden index is taken as a cut-off value, the OD value is judged to be positive when the specificity is higher than the cut-off value, the OD value is judged to be negative when the specificity is lower than the cut-off value, the corresponding AUC and a 95% confidence interval, sensitivity and specificity are calculated simultaneously, and the value of 3 anti-tumor-associated antigen autoantibodies which are independently used for diagnosing ovarian cancer is analyzed. All statistical analyses were performed using SPSS 26.0 software, with P <0.05 as the statistical criterion.

5. Results of the experiment

The distribution of the expression levels of 3 autoantibodies against tumor-associated antigens in serum samples of ovarian cancer groups and healthy control groups is shown in FIG. 2. As shown in FIG. 2, the expression levels of the anti-tumor associated antigen GGT5 autoantibody, the anti-tumor associated antigen VCL autoantibody and the anti-tumor associated antigen TRIM21 autoantibody in the serum samples of the ovarian cancer group were significantly higher than those of the healthy control group, and the differences were statistically significant (P < 0.05). Therefore, 3 anti-tumor associated antigen autoantibodies can be used for auxiliary diagnosis of ovarian cancer.

FIG. 3 is a ROC plot of 3 anti-tumor associated antigen autoantibodies alone diagnosed to distinguish ovarian cancer groups from healthy controls. As can be seen from FIG. 3, when the anti-tumor associated antigen GGT5 autoantibody is used for diagnosing ovarian cancer, the AUC is 0.71 (95% CI: 0.62-0.8); AUC when ovarian cancer was diagnosed using anti-tumor associated antigen VCL autoantibodies was 0.69 (95% CI: 0.59-0.78); the AUC for ovarian cancer using the anti-tumor associated antigen TRIM21 autoantibody was 0.62 (95% CI: 0.52-0.72). Therefore, the anti-tumor associated antigens GGT5, VCL and TRIM21 autoantibodies can be used for assisting diagnosis and distinguishing ovarian cancer patients from healthy people.

Example 3: ELISA is adopted in the serum of a large sample to detect the serum expression level of the autoantibodies of the anti-tumor associated antigens GGT5, VCL and TRIM21, and the value of the autoantibodies of the 3 anti-tumor associated antigens for diagnosing ovarian cancer is evaluated

The expression level of 3 anti-tumor associated antigen autoantibodies is further detected in the serum of a large sample of the human population by adopting an indirect ELISA method, and the capability of distinguishing ovarian cancer patients from healthy controls by the 3 anti-tumor associated antigen autoantibodies is verified.

1. Experimental samples:

collecting 145 serum samples of ovarian cancer patients from tumor hospital of Henan province (ovarian cancer group) and 145 serum samples of healthy control from normal physical examination at third subsidiary hospital of Zheng State university (healthy control group); of these, 145 ovarian cancer patient sera were from pathologically diagnosed and untreated ovarian cancer patients; 145 healthy control sera were derived from healthy subjects whose inclusion criteria were: no cardiovascular, respiratory, hepatic, renal, gastrointestinal, endocrine, hematological, psychiatric, or neurological disease and history of such disease, no acute or chronic disease, no evidence of any tumor-related disease; moreover, the difference between age was not statistically significant in 145 ovarian cancer patients and 145 healthy subjects. The study was approved by the ethical committee of zheng state university, and all subjects had signed informed consent.

Collecting serum: the specific method for serum collection is the same as in example 2 and will not be described herein.

2. Experimental materials and reagents:

the experimental materials and specific reagents were the same as in example 2 and are not described in detail herein.

3. The experimental method comprises the following steps:

the experimental method is the same as that of example 2, and is not described herein again.

4. Data processing

The data analysis method is the same as that of embodiment 2, and is not described herein again.

5. Results of the experiment

The distribution of the expression levels of 3 autoantibodies against tumor-associated antigens in serum samples of ovarian cancer groups and healthy control groups is shown in FIG. 4. As shown in FIG. 4, the expression levels of the anti-tumor associated antigen GGT5 autoantibody, the anti-tumor associated antigen VCL autoantibody and the anti-tumor associated antigen TRIM21 autoantibody in the serum samples of the ovarian cancer group were significantly higher than those of the healthy control group, and the differences were statistically significant (P < 0.05). Therefore, 3 kinds of anti-tumor associated antigen autoantibodies can be used for auxiliary diagnosis of ovarian cancer.

FIG. 5 is a ROC plot of 3 anti-tumor associated antigen autoantibodies alone diagnosed to distinguish ovarian cancer groups from healthy controls. As can be seen from FIG. 5, when the anti-tumor associated antigen GGT5 autoantibody is used for diagnosing ovarian cancer, the AUC is 0.76 (95% CI: 0.70-0.81); AUC of ovarian cancer was 0.71 (95% CI:0.65-0.77) when using anti-tumor associated antigen VCL autoantibodies for diagnosis of ovarian cancer; the AUC for ovarian cancer diagnosis using the anti-tumor associated antigen TRIM21 autoantibody was 0.62 (95% CI: 0.55-0.68). Therefore, the anti-tumor associated antigens GGT5, VCL and TRIM21 autoantibodies can be used for assisting diagnosis and distinguishing ovarian cancer patients from healthy people.

Example 4: diagnostic value of 3 anti-tumor associated antigen autoantibodies combinations on ovarian cancer

Since the sensitivity of diagnosis is relatively low when a single anti-tumor associated antigen autoantibody is used for diagnosing and distinguishing ovarian cancer patients from healthy controls, in order to improve the sensitivity of diagnosis, 145 ovarian cancer patients and 145 healthy control sera, which are included in example 3, are used as training set samples, the present invention combines two or three of anti-tumor associated antigen GGT5 autoantibodies (denoted as anti-GGT 5 autoantibodies), anti-tumor associated antigen VCL autoantibodies (denoted as anti-VCL autoantibodies), and anti-tumor associated antigen TRIM21 autoantibodies (denoted as anti-TRIM 21 autoantibodies), and analyzes the diagnostic value of different anti-tumor associated antigen autoantibody combinations on ovarian cancer according to the expression level results of the anti-tumor associated antigens GGT5, VCL, TRIM21 autoantibodies in each serum sample of the training set.

1. The ability of two anti-tumor associated antigen autoantibodies in combination diagnosis to distinguish ovarian cancer patients from healthy people:

(1) combined diagnosis of anti-GGT 5 autoantibodies and anti-VCL autoantibodies to distinguish ovarian cancer patients from healthy humans

Using the expression levels (expressed as the final measured absorbance value) of the anti-GGT 5 autoantibodies and the anti-VCL autoantibodies in 145 ovarian cancer patients (ovarian cancer group) and 145 healthy control sera (healthy control group) in example 3 as independent variables and whether the expression levels are ovarian cancer events as dependent variables, performing Logistic regression analysis on the expression levels of the anti-GGT 5 autoantibodies and the anti-VCL autoantibodies in the serum samples of the ovarian cancer group and the healthy control group, and constructing a diagnostic model for diagnosing and distinguishing the ovarian cancer patients from the healthy control, wherein the diagnostic model is as follows: PRE (P ═ OC) ═ 1/(1+ EXP (- (-3.203+15.806 × GGT5+5.980 × VCL))), in this diagnostic model: PRE represents the predicted probability, GGT5 represents the expression level of anti-GGT 5 autoantibodies in the subject's serum, and VCL represents the expression level of anti-VCL autoantibodies in the subject's serum. And substituting the expression levels of the anti-GGT 5 autoantibodies and the anti-VCL autoantibodies in each serum sample into the diagnostic model to obtain the prediction probability (namely PRE value) of each serum sample, taking the prediction probability PRE as the optimal cutoff value for diagnosing and distinguishing ovarian cancer patients and healthy people with the prediction probability PRE being 0.5 (when the prediction probability PRE is more than or equal to 0.5, the tested person is judged to be ovarian cancer patients, and when the prediction probability PRE is less than 0.5, the tested person is judged to be healthy people), and calculating the corresponding sensitivity and specificity. And plotting an ROC curve according to the predicted probability, wherein the ROC curve is shown as A in figure 6.

As can be seen from a in fig. 6, the combined diagnosis of anti-GGT 5 autoantibodies and anti-VCL autoantibodies distinguished the area under the ROC curve AUC between ovarian cancer patients and healthy persons of 0.77, corresponding to a sensitivity of 61.4% and a specificity of 75.2%.

(2) Combined diagnosis of anti-GGT 5 autoantibodies and anti-TRIM 21 autoantibodies distinguishes between patients with ovarian cancer and healthy persons

Using the expression levels (expressed as the final measured absorbance value) of the anti-GGT 5 autoantibodies and the anti-TRIM 21 autoantibodies in 145 ovarian cancer patients (ovarian cancer group) and 145 healthy control sera (healthy control group) in example 3 as independent variables, whether the expression levels are ovarian cancer events or not as dependent variables, and performing Logistic regression analysis on the expression levels of the anti-GGT 5 autoantibodies and the anti-TRIM 21 autoantibodies in serum samples of the ovarian cancer group and the healthy control group, a diagnostic model for diagnosing and distinguishing the ovarian cancer patients from the healthy control was constructed, which was: PRE (P ═ OC) ═ 1/(1+ EXP (- (-3.194+19.350 × GGT5+4.167 × TRIM21))), in this diagnostic model: PRE represents the predicted probability, GGT5 represents the expression level of anti-GGT 5 autoantibody in the serum of the subject, and TRIM21 represents the expression level of anti-TRIM 21 autoantibody in the serum of the subject. And substituting the expression levels of the anti-GGT 5 autoantibody and the anti-TRIM 21 autoantibody in each serum sample into the diagnostic model to obtain the prediction probability (namely PRE value) of each serum sample, taking the prediction probability PRE as the optimal cutoff value for diagnosing and distinguishing ovarian cancer patients and healthy people (when the prediction probability PRE is more than or equal to 0.5, the tested person is judged to be ovarian cancer patients, and when the prediction probability PRE is less than 0.5, the tested person is judged to be healthy people), and calculating the corresponding sensitivity and specificity. And (4) plotting an ROC curve according to the predicted probability, wherein the ROC curve is shown as B in figure 6.

From fig. 6B, it can be seen that the combined diagnosis of anti-GGT 5 autoantibodies and anti-TRIM 21 autoantibodies distinguished between ovarian cancer patients and healthy persons by an area under the ROC curve AUC of 0.78, corresponding to a sensitivity of 64.1% and a specificity of 77.2%.

(3) Combined diagnosis of anti-VCL autoantibodies and anti-TRIM 21 autoantibodies to distinguish ovarian cancer patients from healthy humans

Using the expression levels (expressed by final detected absorbance value) of the anti-VCL autoantibody and the anti-TRIM 21 autoantibody in 145 ovarian cancer patients (ovarian cancer group) and 145 healthy control sera (healthy control group) in example 3 as independent variables, whether the expression levels are ovarian cancer events or not as dependent variables, and performing Logistic regression analysis on the expression levels of the anti-VCL autoantibody and the anti-TRIM 21 autoantibody in the serum samples of the ovarian cancer group and the healthy control group, so as to construct a diagnostic model for diagnosing and distinguishing the ovarian cancer patients from the healthy control, wherein the diagnostic model is as follows: PRE (P ═ OC) ═ 1/(1+ EXP (- (-2.799+9.344 × VCL +4.500 × TRIM21))), in this diagnostic model: PRE represents the predicted probability, VCL represents the expression level of anti-VCL autoantibodies in the subject serum, TRIM21 represents the expression level of anti-TRIM 21 autoantibodies in the subject serum. And substituting the expression levels of the anti-VCL autoantibody and the anti-TRIM 21 autoantibody in each serum sample into the diagnostic model to obtain the prediction probability (namely PRE value) of each serum sample, taking the prediction probability PRE as the optimal cut-off value for diagnosing and distinguishing ovarian cancer patients and healthy people with the prediction probability PRE being 0.5 (when the prediction probability PRE is more than or equal to 0.5, the tested person is judged to be ovarian cancer patients, and when the prediction probability PRE is less than 0.5, the tested person is judged to be healthy people), and calculating the corresponding sensitivity and specificity. And plotting an ROC curve according to the predicted probability, wherein the ROC curve is shown as C in figure 6.

As can be seen from C in fig. 6, the combined diagnosis of anti-VCL autoantibodies and anti-TRIM 21 autoantibodies distinguished between ovarian cancer patients and healthy persons by an area under the ROC curve AUC of 0.74, corresponding to a sensitivity of 62.8% and a specificity of 76.6%.

2. The ability of the three anti-tumor associated antigen autoantibodies in combination diagnosis to distinguish ovarian cancer patients from healthy persons:

using the expression levels (expressed as the final absorbance value results) of the anti-GGT 5 autoantibodies, anti-VCL autoantibodies, and anti-TRIM 21 autoantibodies in 145 ovarian cancer patients (ovarian cancer group) and 145 healthy control sera (healthy control group) in example 3 as independent variables, whether the expression levels are ovarian cancer events as dependent variables, and the expression levels of the anti-GGT 5 autoantibodies, anti-VCL autoantibodies, and anti-TRIM 21 autoantibodies in serum samples of the ovarian cancer group and healthy control group as Logistic regression analysis, a diagnostic model was constructed to diagnose and distinguish ovarian cancer patients from healthy controls, the diagnostic model being: PRE (P ═ OC) ═ 1/(1+ EXP (— (-3.919+15.112 × GGT5+6.002 × VCL +4.169 × TRIM21))), in this diagnostic model: PRE represents the predicted probability, GGT5 represents the expression level of anti-GGT 5 autoantibodies in the serum of the subject, VCL represents the expression level of anti-VCL autoantibodies in the serum of the subject, and TRIM21 represents the expression level of anti-TRIM 21 autoantibodies in the serum of the subject. And substituting the expression levels of the anti-GGT 5 autoantibody, the anti-VCL autoantibody and the anti-TRIM 21 autoantibody in each serum sample into the diagnostic model to obtain the predicted probability (namely a PRE value) of each serum sample, taking the predicted probability PRE as 0.5 as an optimal cut-off value for diagnosing and distinguishing ovarian cancer patients and healthy people (when the predicted probability PRE is more than or equal to 0.5, the tested person is judged to be the ovarian cancer patient, and when the predicted probability PRE is less than 0.5, the tested person is judged to be the healthy person), and calculating the corresponding sensitivity and specificity. And drawing an ROC curve according to the prediction probability, wherein the ROC curve is shown as D in figure 6.

As can be seen in FIG. 6D, the combined diagnosis of anti-GGT 5 autoantibodies, anti-VCL autoantibodies, and anti-TRIM 21 autoantibodies distinguished between ovarian cancer patients and healthy persons by an area under the ROC curve AUC of 0.79 (95% CI:0.74-0.84), corresponding sensitivity of 66.2%, and specificity of 77.2%.

For comparison, the results of the ROC curve AUC, sensitivity and specificity for diagnosing and distinguishing ovarian cancer from healthy people by using the single anti-tumor associated antigen autoantibody or the multiple anti-tumor associated antigen autoantibodies in combination are shown in Table 1.

TABLE 1 evaluation results of three anti-tumor-associated antigen autoantibodies for diagnosis of ovarian cancer patients and healthy persons

As can be seen from Table 1, compared with a single anti-tumor associated antigen autoantibody, when any two anti-tumor associated antigen autoantibodies are used for combined diagnosis and differentiation of ovarian cancer patients and healthy people, the AUC of the ROC curve can reach more than 0.7, which is obviously higher than that of the single anti-tumor associated antigen autoantibody; moreover, when the combination diagnosis of the three anti-tumor associated antigen autoantibodies distinguishes ovarian cancer patients from healthy people, the AUC of the ROC curve reaches the maximum value, and is 0.79; the diagnostic sensitivity and specificity of the kit reach the highest, respectively 66.2% and 77.2%. Therefore, the combined diagnosis effect of the three anti-tumor associated antigens and the autoantibodies is optimal.

3. Capability of verifying combination diagnosis of three anti-tumor associated antigens and autoantibodies by using verification set to distinguish ovarian cancer patients from healthy people

The ability of the anti-GGT 5 autoantibody, anti-VCL autoantibody, and anti-TRIM 21 autoantibody in combination to diagnostically distinguish ovarian cancer patients from healthy controls was verified using the serum samples of 60 ovarian cancer patients (ovarian cancer group) and 60 healthy control serum samples (healthy control group) included in example 2 as validation set samples.

By substituting the expression levels (expressed by the final detected absorbance value results) of the anti-GGT 5 autoantibodies, the anti-VCL autoantibodies, and the anti-TRIM 21 autoantibodies in 60 ovarian cancer patients (ovarian cancer group) and 60 healthy control sera (healthy control group) in example 2 into the diagnostic model PRE (P ═ OC) ═ 1/(1+ EXP (- (-3.919+15.112 × GGT5+6.002 × VCL +4.169 × TRIM21))) constructed in step 2, the prediction probability of each serum sample can be obtained, the prediction probability PRE ═ 0.5 is the optimal cutoff value for diagnosing and distinguishing ovarian cancer patients and healthy controls (when the prediction probability PRE is not less than 0.5, the subject is determined to be an ovarian cancer patient, when the prediction probability PRE is not less than 0.5, the subject is determined to be a healthy person, and the corresponding sensitivity and specificity are calculated. And (4) plotting an ROC curve according to the predicted probability, wherein the ROC curve is shown in FIG. 7.

As can be seen from FIG. 7, the combined diagnosis of anti-GGT 5 autoantibodies, anti-VCL autoantibodies, and anti-TRIM 21 autoantibodies distinguished between ovarian cancer patients and healthy persons by an area under the ROC curve AUC of 0.76 (95% CI:0.68-0.85), corresponding sensitivity of 63.3%, and specificity of 76.7%.

According to the verification, the capability of the anti-GGT 5 autoantibody, the anti-VCL autoantibody and the anti-TRIM 21 autoantibody in the verification set for diagnosing and distinguishing ovarian cancer is basically consistent with that in the training set, so that the model for diagnosing ovarian cancer by combining the three anti-tumor related antigen autoantibodies constructed by the invention has better stability.

The above-described embodiments are intended to illustrate the substance of the present invention, but are not intended to limit the scope of the present invention. It will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the true spirit and scope of the invention.

Claims

1. A biomarker for diagnosis of ovarian cancer, wherein the biomarker is a combination of anti-tumor associated antigen GGT5 autoantibodies, anti-tumor associated antigen VCL autoantibodies, anti-tumor associated antigen TRIM21 autoantibodies.

2. The biomarker of claim 1, wherein the autoantibodies to the tumor associated antigen GGT5, VCL, and TRIM21 are all the corresponding autoantibodies to the tumor associated antigen in the serum, plasma, interstitial fluid, or urine of the subject.

3. Use of a reagent for detecting a biomarker according to claim 1 or 2 in the manufacture of a product for the diagnosis of ovarian cancer.

4. The use of claim 3, wherein the reagent is a reagent for the detection of the biomarker in the sample by enzyme-linked immunosorbent, protein chip, immunoblot or microfluidic immunoassay.

5. The use of claim 4, wherein the sample is serum, plasma, interstitial fluid or urine.

6. The use of claim 5, wherein the reagent is an antigen for detecting the biomarker.

7. The use according to claim 6, wherein the product is a protein chip, a kit or a formulation.

8. A kit for diagnosis of ovarian cancer, comprising reagents for detecting the biomarkers of claim 1 or 2.

9. The kit of claim 8, wherein the kit detects the biomarker in a sample by enzyme-linked immunosorbent, protein chip, immunoblot, or microfluidic immunoassay.

10. The kit according to claim 8, wherein the probability that the kit predicts ovarian cancer is calculated according to the formula:

PRE =1/(1 + EXP (- (-3.919+15.112 × GGT5+6.002 × VCL +4.169 × TRIM21))), where PRE represents probability, GGT5 represents the expression level of anti-tumor-associated antigen GGT5 autoantibody, VCL represents the expression level of anti-tumor-associated antigen VCL autoantibody, and TRIM21 represents the expression level of anti-tumor-associated antigen TRIM21 autoantibody.