Marker group for screening and diagnosing ovarian cancer and application thereof

Abstract

The invention discloses a marker group for screening and diagnosing ovarian cancer and application of the marker group, and belongs to the technical field of molecular biology. The marker group comprises at least four of phospholipids and polypeptides as shown in sequences SEQ ID NO. 1-41. When being used for auxiliary diagnosis and early screening of ovarian cancer, the marker combination constructed by the invention has more excellent sensitivity and specificity, and the auxiliary diagnosis results of the marker combination are partially or completely superior to those of an ROMA index ovarian cancer risk prediction model recommended by a current clinical guide. The early screening result is also partially or completely superior to that of tumor marker CA125 detection and HE4 detection with the highest ovarian cancer early screening value at present. The kit is expected to be applied to diagnosis and treatment of ovarian cancer.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of molecular biology technology, and particularly relates to a marker group for screening and diagnosing ovarian cancer and use thereof. BACKGROUND

[0002] Ovarian cancer is a common cancer among women worldwide. The current first-line treatment for ovarian cancer involves a combination of cytoreductive surgery and platinum-based chemotherapy. Targeted therapies, including anti-VEGF antibodies and PARP inhibitors, can be applied to certain patients. Due to limited understanding of the early mechanisms of ovarian cancer, existing methods are difficult to effectively diagnose early ovarian cancer.

[0003] Currently, there are two commonly used methods for the auxiliary diagnosis of ovarian cancer: CT and blood CA125 and HE4 content. However, when the tumor tissue is too small or the location is hidden, the sensitivity of CT detection is less than 30%. Blood CA125 and human epididymis protein 4 (HE4) are the most valuable tumor markers in ovarian epithelial cancer and can be used for auxiliary diagnosis, efficacy monitoring and recurrence monitoring. The "Ovarian Cancer Diagnosis and Treatment Guidelines 2022 Edition" released by the National Cancer Center indicates that the ROMA index is an evaluation model that combines the serum concentrations of CA125 and HE4 with the menopausal status of patients. Its value depends on the serum concentrations of CA125 and HE4, hormones and menopausal status. Studies have shown that for premenopausal patients, the sensitivity of the ROMA index in diagnosing ovarian cancer is an average of 76.0% (70.2%-81.0%), and the specificity is about 85.1% (80.4%-88.8%), while in postmenopausal patients, its sensitivity is about 90.6% (87.4%-93.0%), and the specificity is about 79.4% (73.7%-84.2%), which cannot achieve satisfactory auxiliary diagnostic effect. Therefore, the method for distinguishing the benign and malignant of ovarian cancer still needs further exploration.

[0004] Liquid biopsy, as a new diagnostic technique, can overcome the influence of tumor heterogeneity, provide more comprehensive tumor molecular information, and provide strong support for guiding clinical treatment and evaluating prognosis. In the past 3 years, protein biomarkers in the serum of ovarian cancer patients have been extensively studied, and more than 100 potential biomarkers have been evaluated. Among them, folate receptor alpha (FOLR1) is a membrane protein, its expression is limited to the luminal surface of epithelial cells in healthy people, but it is highly expressed in many epithelial cancers, including breast cancer, ovarian cancer, clear cell kidney cancer, endometrial cancer and endometrial cancer and lung cancer. Compared with healthy and benign tumor populations, serum FOLR1 levels in ovarian cancer patients are significantly elevated. Compared with CA125, serum FOLR1 also shows higher specificity, which shows better diagnostic performance. However, the level of FOLR1 is affected by tumor tissue type, clinical grade, stage and tumor size. Most patients with elevated FOLR1 levels have tumors of the serous subtype and are in the advanced stage of the disease. In mucinous tumors and early tumor stages, FOLR1 levels are much lower.

[0005] CA72-4 is a tumor-associated glycoprotein. It is a unique epitope of MUC1 mucin, and its abnormal elevation has been detected in ovarian cancer. Its level is not affected by pregnancy, menstrual cycle or endometriosis, and is only slightly affected by inflammatory conditions. Therefore, the combined detection of CA72-4 and CA125 can increase diagnostic specificity, but at the cost of decreased sensitivity. In addition, overexpression of CA72-4 has been detected in many cases of ovarian clear cell carcinoma and mucinous tumors, while CA125 and HE4 levels are usually not elevated in these two histological types, which means that CA72-4 may be able to detect cases missed by CA125 and HE4. However, the sensitivity of CA72-4 as a single marker is still limited. SUMMARY

[0006] In view of the above problems in the prior art, the present application provides a marker group for screening and diagnosing ovarian cancer and its use. In the auxiliary diagnosis and early screening of ovarian cancer, it has excellent sensitivity and specificity, and is expected to be applied to the diagnosis and treatment of ovarian cancer.

[0007] To achieve the above-mentioned purpose, the technical scheme adopted by the present application to solve its technical problems is: A marker group for screening and diagnosing ovarian cancer, comprising at least four of phospholipids and polypeptides as shown in SEQ ID NO. 1-41, and the specific sequence is shown in Table 1.

[0008] Table 1 Marker sequence and phospholipid

[0009] wherein the fourth amino acid G in polypeptide 10 has a Phospho modification; the second amino acid K in polypeptide 15 has an Acetyl modification; the third amino acid G in polypeptide 22 has a Phospho modification; the fourth amino acid G in polypeptide 32 has a Dehydrated modification; the first amino acid Q in polypeptide 40 has a Gln->pyro-Glu modification, and the sixth amino acid N has a Dehydrated modification.

[0010] Further, the marker panel comprises the polypeptides of SEQ ID NO: 1 and SEQ ID NO: 2, and the following polypeptide combinations: the polypeptide combinations are one of SEQ ID NO: 3 and SEQ ID NO: 4; SEQ ID NO: 5 and SEQ ID NO: 6; SEQ ID NO: 15 and SEQ ID NO: 16; SEQ ID NO: 30 and SEQ ID NO: 40; SEQ ID NO: 3, SEQ ID NO: 4 and SEQ ID NO: 5; SEQ ID NO: 5, SEQ ID NO: 6 and SEQ ID NO: 7; SEQ ID NO: 15, SEQ ID NO: 16 and SEQ ID NO: 20; SEQ ID NO: 9, SEQ ID NO: 30 and SEQ ID NO: 35.

[0011] Further, the marker panel comprises the polypeptides of SEQ ID NO: 7 and SEQ ID NO: 8, and the following polypeptide combinations: the polypeptide combinations are one of SEQ ID NO: 3 and SEQ ID NO: 4; SEQ ID NO: 9 and SEQ ID NO: 10; SEQ ID NO: 3, SEQ ID NO: 4 and SEQ ID NO: 9; SEQ ID NO: 9, SEQ ID NO: 10 and SEQ ID NO: 11.

[0012] Further, the marker panel comprises the polypeptides of SEQ ID NO: 1, SEQ ID NO: 10 and SEQ ID NO: 30, and the polypeptides of SEQ ID NO: 40; SEQ ID NO: 20; SEQ ID NO: 20 and SEQ ID NO: 28; or SEQ ID NO: 28 and SEQ ID NO: 40.

[0013] Further, the marker panel comprises the polypeptides of SEQ ID NO: 5 and SEQ ID NO: 18, and the following polypeptide combinations: the polypeptide combinations are one of SEQ ID NO: 2 and SEQ ID NO: 19; or SEQ ID NO: 2, SEQ ID NO: 19 and SEQ ID NO: 20.

[0014] Further, the marker panel comprises the polypeptides of SEQ ID NO: 4 and SEQ ID NO: 24, and the following polypeptide combinations: the polypeptide combinations comprise the polypeptides of SEQ ID NO: 1 and SEQ ID NO: 27; SEQ ID NO: 8, SEQ ID NO: 20 and SEQ ID NO: 25; or SEQ ID NO: 8 and SEQ ID NO: 25.

[0015] Further, the marker panel comprises the polypeptides of SEQ ID NO: 7, SEQ ID NO: 10, SEQ ID NO: 17, SEQ ID NO: 18 and / or SEQ ID NO: 20.

[0016] Further, the marker panel comprises the polypeptides of SEQ ID NO: 5, SEQ ID NO: 15, SEQ ID NO: 25, and SEQ ID NO: 40, or SEQ ID NO: 28 and SEQ ID NO: 30.

[0017] Further, the marker group comprises polypeptides as shown in SEQ ID NO. 1~41 and 1-Linoleoyl-2-Hydroxy-sn-glycero-3-PC.

[0018] The use of the marker group in the preparation of a preparation for the screening and diagnosis of ovarian cancer.

[0019] The use of the marker group in medical basic research for non-diagnostic / therapeutic purposes.

[0020] Further, the medical basic research is Western Blot, immunohistochemistry or flow cytometry, etc.

[0021] The beneficial effects of the present application: The marker combination constructed by the present application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of ovarian cancer, and the auxiliary diagnosis results are partially or completely superior to the ROMA index ovarian cancer risk prediction model recommended by the current clinical guidelines (the sensitivity and specificity of premenopausal patients are 76.0% and 85.1%, respectively, and the sensitivity and specificity of postmenopausal patients are 90.6% and 79.4%, respectively). The early screening results are also partially or completely superior to the current ovarian cancer early screening value of the highest tumor marker CA125 detection (the sensitivity and specificity of premenopausal patients are 69.8%~87.5% and 63.3%~85.7%, respectively, and the sensitivity and specificity of postmenopausal patients are 79.1%~90.7% and 79.1%~89.8%, respectively) and HE4 detection (the sensitivity and specificity are 78% and 86%, respectively). It is expected to be applied to the diagnosis and treatment of ovarian cancer. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 MRM verification result graph of phospholipids in serum; Figure 2 ROC curve graph of marker combination 1; Figure 3 ROC curve graph of marker combination 2; Figure 4 ROC curve graph of marker combination 3; Figure 5 ROC curve graph of marker combination 4; Figure 6 ROC curve graph of marker combination 5; Figure 7 ROC curve graph of marker combination 6; Figure 8 ROC curve graph of marker combination 7; Figure 9ROC curve for marker combination 8; Figure 10 ROC curve for marker combination 9; Figure 11 ROC curve for marker combination 10; Figure 12 ROC curve for marker combination 11; Figure 13 ROC curve for marker combination 12; Figure 14 ROC curve for marker combination 13; Figure 15 ROC curve for marker combination 14; Figure 16 ROC curve for marker combination 15; Figure 17 ROC curve for marker combination 16; Figure 18 ROC curve for marker combination 17; Figure 19 ROC curve for marker combination 18; Figure 20 ROC curve for marker combination 19; Figure 21 ROC curve for marker combination 20; Figure 22 ROC curve for marker combination 21; Figure 23 ROC curve for marker combination 22; Figure 24 ROC curve for marker combination 23; Figure 25 ROC curve for marker combination 24; Figure 26 ROC curve for marker combination 25; Figure 27 ROC curve for marker combination 26. Detailed Implementation

[0023] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0024] The patient samples used in the present application are all from Zhongshan Hospital affiliated to Fudan University, and have passed the ethical review.

[0025] The experimental methods used in the present application are as follows: I. Serum sample collection 1) Sample type: serum 2) Collection requirements: fasting collection, 5 mL of venous blood is drawn using a coagulation tube, and after standing for 30 min, 3000 rpm centrifugation for 15 min, about 1 mL of serum is taken out and placed in a cryopreservation tube.

[0026] 3) Sample storage: Used on the same day, the storage condition is 2-8°C; If it cannot be used on the same day, it is stored at-20°C and can be stored for 30 days; If stored for a long time (more than one month), it needs to be stored at-80°C.

[0027] The repeated freezing and thawing should not exceed 3 times.

[0028] II. Extraction of the analyte in serum 1) After calibrating the mass spectrometer instrument, open the solid-phase biological automatic sample analysis system instrument SPS1000 / SPS4000, put in consumables, matching reagent kit and sample to be tested Select the program method "solid-phase sample addition"; Run the program: a. Open the hole; b. Take not less than 10 µL of serum sample and activated reagent, mix them in a ratio of 1:1, and then place them in the G row reserved hole for later use; c. Wash the customized suction head in cleaning reagent 1 and cleaning reagent 2 in order. When washing, take not less than 10 µL of liquid each time, and repeat the suction and beating not less than 3 times; d. Use the cleaned customized suction head to process the serum mixture in the G row hole. When processing, take not less than 10 µL of solution each time, and repeat the suction and beating not less than 3 times; e. Use cleaning reagent 3 to clean the customized suction head after adsorbing the serum mixture. When cleaning, take not less than 10 µL of liquid each time, and repeat the suction and beating not less than 3 times; f. Transfer not less than 10 µL of buffer reagent to the H row reserved hole, and place the customized suction head after using cleaning reagent 3 in the liquid to take not less than 10 µL of liquid, and repeat the suction and beating not less than 3 times; g. Transfer not less than 10 µL of sample matrix solution to the H row reserved hole to complete the sample processing; h. Take 2.0 µL of the solution in the H hole and spot it on the hydrophobic coating biological chip (Wuxi Pimico Technology Co., Ltd.); i. Vacuum dry for 240 s.

[0029] The main components of each reagent are shown in Table 2.

[0030] Table 2 Reagent components

[0031] III. Mass Spectrometry Data Collection and Upload Place the vacuum-dried and hydrophobic-coated biochip (Tinpeimo Technology Co., Ltd.) into the mass spectrometer; Use the set SP1 voltage (target high voltage), SP2 voltage (pulse high voltage), focusing voltage (lens high voltage), detector voltage (MCP voltage), pulse delay time, acquisition card range, target point diameter, laser frequency, calibration method, and laser intensity to collect data.

[0032] IV. Quality Control 1) After data collection, upload the data to the Mass Spectrometry Data Analysis Software; 2) The software reads the sample information and signal spectrum, and determines whether the sample and sample pretreatment are qualified according to the quality control model. Unqualified quality control may include multiple possibilities, including non-ovarian lesion samples, and signal spectrum intensity not meeting standards.

[0033] 3) If the quality control is unqualified, according to the quality control results, correct the corresponding parameters and re-extract the analyte from the serum; 4) If the quality control is qualified, proceed to the next process.

[0034] V. Establishment of Positive Judgment Value and Result Analysis The study of positive judgment value uses ovarian cancer samples with clear diagnostic information and normal samples, wherein the ovarian benign samples cover ovarian benign tumors, ovarian borderline tumors, ovarian cysts, adnexal inflammation, and at least 2 samples of non-ovarian cancer interference samples. The core algorithm is based on the known ovarian cancer sample atlas for supervised learning, through a series of processes such as smoothing denoising baseline, screening characteristic peaks, constructing a classification model, and calculating the similarity between the hormone signal atlas and the known hormone signal atlas stored in the software (Cannataro M, Guzzi P H, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data [J]. 2005.). Finally, the maximum Youden index method is used to determine the similarity score positive judgment value of the kit. When the similarity score < positive judgment value, the detection result of the sample is negative; when the similarity score ≥ positive judgment value, the detection result of the sample is positive. The maximum similarity score is used as the positive judgment value to assist in the diagnosis of ovarian cancer or early screening of ovarian cancer, the sensitivity = true positive number / (true positive number + false negative number) x 100%, and the specificity = true negative number / (true negative number + false positive number) x 100%.

[0035] Example 1 Screening and identification of markers The application obtains 42 polypeptides or metabolite substances in blood with ovarian cancer diagnostic ability by time-of-flight mass spectrometry on 400 normal samples (all female, age distribution range of 20 to 65 years old, average age of 43.8±12.3 years old. The specific age distribution is: 73 cases of 20-29 years old, 108 cases of 30-39 years old, 119 cases of 40-49 years old, 78 cases of 50-59 years old, and 22 cases of 60-65 years old), 400 ovarian cancer samples (all female, age distribution range of 28 to 72 years old, average age of 52.6±11.4 years old. The specific age distribution is: 41 cases of 28-37 years old, 97 cases of 38-47 years old, 143 cases of 48-57 years old, 86 cases of 58-67 years old, and 33 cases of 68-72 years old), the relative abundance difference of the characteristic peak data in normal people and ovarian cancer patients is considered, the statistical difference (p<0.05, t test) of the data, the influence factor sorting of the feature screening by machine learning algorithm (random forest), and the matching degree of the data in the database, and 42 polypeptides or metabolite substances in blood with ovarian cancer diagnostic ability are found. The mass-to-charge ratio (m / z) of these polypeptides, the relative abundance and the influence factor are shown in Table 1. The relative abundance is normalized based on the normal sample, that is, ln (average signal intensity of cancer patient sample / average signal intensity of normal sample). The screening of characteristic peaks in machine learning is based on the feature importance evaluation of ensemble learning. By constructing multiple decision trees, the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction is quantified. The feature importance score, that is, the influence factor, is obtained by calculating the average reduction of impurity brought by the feature in all tree node splitting (Biau, G., Scornet, E. A random forest guided tour. TEST 25, 197-227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0036] The specific primary mass spectrometry parameters are as follows: Ionization method: matrix-assisted laser desorption ionization (MALDI), and the matrix is α-cyano-4-hydroxycinnamic acid (CHCA).

[0037] Mass range: 100-4000 Da.

[0038] Resolution: 20000 (full mass range).

[0039] Laser energy: 30-40%.

[0040] Acquisition mode: positive ion mode.

[0041] Calibration: external mass calibration is performed using peptide standard (Bruker Peptide Calibration Standard).

[0042] Subsequently, the sequences of the 42 substances in the clinical serum were confirmed by secondary mass spectrometry (secondary mass spectrometry (MS / MS or TOF / TOF) is recommended by the China Food and Drug Administration and the U.S. Food and Drug Administration (FDA) guidelines for polypeptide identification). The secondary mass spectrometry data analysis process is as follows: Data analysis method: database search using Mascot software (version 2.8), database is UniProt human proteome database (published in 2023). Search parameters: enzyme setting is "no enzyme cutting", parent ion mass error is allowed ±0.5 Da, fragment ion mass error is allowed ±0.3 Da, fixed modification is cysteine urea methylation, variable modification is methionine oxidation.

[0043] Sequence confirmation standard: the confirmation of polypeptide sequence is based on the matching of fragment ion spectrum (b- and y-ions) with theoretical spectrum, and Mascot score higher than 30 (p<0.05) is considered significant.

[0044] False positive exclusion: verify specificity by reverse database search, false positive rate is controlled below 1%.

[0045] Secondary mass spectrometry parameters: Collision-induced dissociation (CID).

[0046] Collision energy: 30 eV.

[0047] Fragment ion mass range: 100-3500 Da.

[0048] Data acquisition: at least 1000 laser scans per sample to improve signal-to-noise ratio.

[0049] The sequences and specificities of the 42 markers were confirmed by secondary mass spectrometry, and the false positive problem was excluded, and the specific sequences are shown in Table 1.

[0050] Example 2 Verification of marker combination According to the 41 polypeptide and phospholipid markers identified and confirmed in Example 1 (sequences and mass-to-charge ratios are shown in Table 1), different marker combinations shown in Table 3 were formed, and the sensitivity and specificity of the marker combinations in the auxiliary diagnosis and early screening of ovarian cancer were verified. The analysis process is as follows: for all validation cohort samples, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain mass spectrometric peak intensity data of all markers in Table 1. The entire detection process was carried out in a blind manner, i.e., the experimental operator was unaware of the grouping information of the samples. The raw mass spectrometric data were processed by baseline correction, smoothing and normalization (using the internal standard peak intensity as the reference), and then the peak area or intensity value of each marker was extracted. Statistical analysis was performed using R software (version 4.0.2). The preprocessed marker intensity data were input into the logistic regression (Logistic Regression) model. For auxiliary diagnosis verification, ten-fold cross-validation was performed using all samples in the cohort (Sun T, Liu J, Yuan H, Li X, Yan H. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm. Front Oncol. 2024 Aug 9;14:1403392. doi: 10.3389 / fonc.2024.1403392. PMID: 39184040; PMCID: PMC11341396.). For early screening verification, due to the uneven sample size, the SMOTE (Synthetic Minority Over-sampling Technique) technique was used to process the data before model training and testing (van den Goorbergh R, van Smeden M, Timmerman D, Van Calster B. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J Am Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093. PMID: 35686364; PMCID: PMC9382395.).The data analysis procedure referred to general guidelines for clinical prediction model construction (Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993 Apr;39(4):561-77. Erratum in: Clin Chem 1993 Aug;39(8):1589. PMID: 8472349.). Sensitivity, Specificity and Area under the Receiver Operating Characteristic Curve (AUC) were calculated for each marker combination. The detailed calculation results of the performance indicators are shown in Table 3, and the corresponding ROC curves are shown in Figures 2-27 .

[0051] 1) The auxiliary diagnosis of ovarian cancer by each marker combination was verified by 600 ovarian cancer patients (all female, age range 31-74 years old, average age 53.8±10.7 years old. The specific distribution is: 58 cases of 31-39 years old, 127 cases of 40-47 years old, 168 cases of 48-55 years old, 157 cases of 56-63 years old, and 90 cases of 64-74 years old) and 600 healthy people samples (all female, age range 25-68 years old, average age 45.3±11.2 years old. The specific distribution is: 72 cases of 25-33 years old, 128 cases of 34-41 years old, 146 cases of 42-49 years old, 135 cases of 50-57 years old, and 119 cases of 58-68 years old).

[0052] 2) The early screening of ovarian cancer by each marker combination was verified by 500 ovarian cancer patients (all female, age range 29-72 years old, average age 52.4±10.9 years old. The specific distribution is: 43 cases of 29-37 years old, 108 cases of 38-45 years old, 147 cases of 46-53 years old, 126 cases of 54-61 years old, and 76 cases of 62-72 years old) and 4000 healthy people samples (all female, age range 23-70 years old, average age 44.6±12.1 years old. The specific distribution is: 423 cases of 23-31 years old, 687 cases of 32-39 years old, 812 cases of 40-47 years old, 798 cases of 48-55 years old, 714 cases of 56-63 years old, and 566 cases of 64-70 years old).

[0053] Table 3 Sensitivity and Specificity of Marker Combination for Auxiliary Diagnosis and Early Screening

[0054] Continue the table above

[0055] According to the detection results of Table 3 and Figures 2-27 The results show that the auxiliary diagnosis results of the selected peak set (marker combination) of the present application are all partially or completely superior to the ROMA index ovarian cancer risk prediction model recommended by the current clinical guidelines (the sensitivity and specificity of premenopausal patients are 76.0% and 85.1%, respectively, and the sensitivity and specificity of postmenopausal patients are 90.6% and 79.4%, respectively).

[0056] The early screening results are also partially or completely superior to the detection of the most valuable tumor marker CA125 for ovarian cancer early screening (the sensitivity and specificity of premenopausal patients are 69.8%~87.5% and 63.3%~85.7%, respectively, and the sensitivity and specificity of postmenopausal patients are 79.1%~90.7% and 79.1%~89.8%, respectively) and the detection of HE4 (the sensitivity and specificity are 78% and 86%, respectively).

[0057] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to examples, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A biomarker set for ovarian cancer screening and diagnosis, characterized in that, The biomarker group includes at least four of the following: phospholipids and polypeptides such as those shown in SEQ ID NO. 1-41.

2. The marker set according to claim 1, characterized in that, The biomarker set includes the peptides shown in Sequence 1 and Sequence 2, as well as the following combinations of peptides: The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 15 and sequence 16; sequence 30 and sequence 40; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 15, sequence 16 and sequence 20; sequence 9, sequence 30 and sequence 35.

3. The marker set according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides: The polypeptide combination is one of the following: sequence 3 and sequence 4; sequence 9 and sequence 10; sequence 3, sequence 4 and sequence 9; sequence 9, sequence 10 and sequence 11.

4. The marker set according to claim 1, characterized in that, The biomarker set includes polypeptides represented by sequences 1, 10, and 30, as well as polypeptides represented by sequences 40, 20, 20, and 28, or 28 and 40.

5. The set of markers according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 5 and 18, as well as the following combinations of peptides: The polypeptide combination is sequence 2 and sequence 19; or sequence 2, sequence 19 and sequence 20.

6. The set of markers according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequence 4 and sequence 24, as well as the following combinations of peptides: The polypeptide combination includes sequences 1 and 27; sequences 8, 20 and 25; or the polypeptides shown in sequences 8 and 25.

7. The set of markers according to claim 1, characterized in that, The biomarker set includes the polypeptides shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 20.

8. The set of markers according to claim 1, characterized in that, The group of markers includes sequence 5, sequence 15, sequence 25, and sequence 40, or sequence 28 and sequence 30.

9. The set of markers according to claim 1, characterized in that, The biomarker group includes phospholipids and polypeptides as shown in SEQ ID NO. 1~41.

10. Use of the biomarker group according to any one of claims 1 to 9 in the preparation of a formulation for ovarian cancer screening and diagnosis.

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