A marker panel for ovarian cancer screening, diagnosis and uses thereof

Abstract

The application discloses a marker group for ovarian cancer screening and diagnosis and application thereof, and belongs to the field of molecular biological technology.The marker group comprises at least four of phospholipids and polypeptides shown in sequences SEQ ID NO.1-41.The marker group constructed by the 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 all partially or completely superior to the ROMA index ovarian cancer risk prediction model recommended by the current clinical guideline.The early screening result is also partially or completely superior to the detection of the tumor marker CA125 and the detection of HE4, which are the most valuable tumor markers for the early screening of ovarian cancer, and the application is expected to be applied to the diagnosis and treatment of ovarian cancer.

Description

Technical Field

[0001] This invention belongs to the field of molecular biology technology, specifically relating to a biomarker group for ovarian cancer screening and diagnosis and its uses. Background Technology

[0002] Ovarian cancer is one of the most common cancers among women worldwide. Current first-line treatments for ovarian cancer involve a combination of cytoreductive surgery and platinum-based chemotherapy. Targeted therapies, including anti-VEGF antibodies and PARP inhibitors, can be used in some patients. Due to limited understanding of the early mechanisms of ovarian cancer, existing methods struggle to effectively diagnose early-stage ovarian cancer.

[0003] Currently, there are two commonly used methods for the auxiliary diagnosis of ovarian cancer: CT scans and blood CA125 and HE4 levels. However, when the tumor tissue is too small or located in a hidden area, the sensitivity of CT scans is less than 30%. Blood CA125 and human epididymis protein 4 (HE4) are the most valuable tumor markers for ovarian epithelial cancer, and can be used for auxiliary diagnosis, efficacy monitoring, and recurrence monitoring. The "Guidelines for the Diagnosis and Treatment of Ovarian Cancer 2022 Edition" published by the National Cancer Center points out that the ROMA index is an assessment model that combines the serum concentrations of CA125 and HE4 with the patient's menopausal status; its value depends on the serum concentrations of CA125 and HE4, hormone levels, and menopausal status. Studies show that for premenopausal patients, the ROMA index has an average sensitivity of 76.0% (70.2%–81.0%) and a specificity of approximately 85.1% (80.4%–88.8%) for diagnosing ovarian cancer. However, in postmenopausal patients, the sensitivity is approximately 90.6% (87.4%–93.0%) and the specificity is approximately 79.4% (73.7%–84.2%), neither of which achieves satisfactory auxiliary diagnostic results. Therefore, further exploration is needed to determine the benign or malignant nature of ovarian cancer.

[0004] Liquid biopsy, as an emerging diagnostic technique, can overcome the impact of tumor heterogeneity, providing more comprehensive molecular information about tumors and offering strong support for guiding clinical treatment and assessing prognosis. Over the past three years, extensive research has been conducted on serum protein biomarkers in ovarian cancer patients, evaluating more than 100 potential biomarkers. Among them, folate receptor α (FOLR1) is a membrane protein whose expression is limited to the luminal surface of epithelial cells in healthy individuals, but is highly expressed in many epithelial carcinomas, including breast cancer, ovarian cancer, clear cell renal cell carcinoma, endometrial cancer, and lung cancer. Serum FOLR1 levels are significantly elevated in ovarian cancer patients compared to healthy and benign tumor populations. Serum FOLR1 also shows higher specificity compared to CA125, demonstrating better diagnostic performance. However, FOLR1 levels are influenced by tumor histology, clinical grade, stage, and tumor size. Most patients with elevated FOLR1 levels have serous subtype tumors and are in advanced stages of disease. In mucinous tumors and early-stage tumors, FOLR1 levels are much lower.

[0005] CA72-4 is a tumor-associated glycoprotein. It is a unique epitope of the MUC1 mucin, and its abnormally elevated levels have been detected in ovarian cancer. Its levels are unaffected by pregnancy, the menstrual cycle, or endometriosis, and are only slightly influenced by inflammatory conditions. Therefore, combined detection of CA72-4 with CA125 can increase diagnostic specificity, but at the cost of decreased sensitivity. Furthermore, CA72-4 overexpression has been detected in many cases of clear cell ovarian carcinoma and mucinous neoplasms, in which CA125 and HE4 levels are typically not elevated, meaning that CA72-4 may be able to detect cases missed by CA125 and HE4. However, the sensitivity of CA72-4 remains limited when used as a single marker. Summary of the Invention

[0006] In view of the above-mentioned shortcomings in the prior art, the present invention provides a biomarker set for ovarian cancer screening and diagnosis and its uses. It has excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of ovarian cancer, and is expected to be applied to the diagnosis and treatment of ovarian cancer.

[0007] To achieve the above objectives, the technical solution adopted by the present invention to solve its technical problem is as follows:

[0008] A biomarker group for ovarian cancer screening and diagnosis includes phospholipids and at least four of the polypeptides represented by sequences SEQ ID NO. 1-41, the specific sequences of which are shown in Table 1.

[0009] Table 1. Biomarker sequences and phospholipids

[0010]

[0011] Among them, the fourth amino acid G in peptide 10 is modified with Phospho; the second amino acid K in peptide 15 is modified with Acetyl; the third amino acid G in peptide 22 is modified with Phospho; the fourth amino acid G in peptide 32 is modified with Dehydrated; and the first amino acid Q in peptide 40 is modified with Gln->pyro-Glu, and the sixth amino acid N is modified with Dehydrated.

[0012] Furthermore, the biomarker set includes the peptides shown in sequences 1 and 2, as well as the following combinations of peptides:

[0013] 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.

[0014] Furthermore, the biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides:

[0015] 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; or sequence 9, sequence 10 and sequence 11.

[0016] Furthermore, the biomarker group includes peptides shown in sequences 1, 10, and 30, as well as peptides shown in sequences 40, 20, 20, and 28; or sequences 28 and 40.

[0017] Furthermore, the biomarker set includes the peptides shown in sequences 5 and 18, as well as the following combinations of peptides:

[0018] The polypeptide combination is sequence 2 and sequence 19; or sequence 2, sequence 19 and sequence 20.

[0019] Furthermore, the biomarker set includes the peptides shown in sequences 4 and 24, as well as the following combinations of peptides:

[0020] The polypeptide combination includes sequences 1 and 27; sequences 8, 20 and 25; or the polypeptides shown in sequences 8 and 25.

[0021] Furthermore, the biomarker set includes the polypeptides shown in sequences 7, 10, 17, 18 and / or 20.

[0022] Furthermore, the marker set includes sequence 5, sequence 15, sequence 25, and sequence 40, or sequence 28 and sequence 30.

[0023] Furthermore, the biomarker group includes the polypeptides shown in SEQ ID NO. 1~41 and 1-linoleoyl-2-Hydroxy-sn-glycero-3-PC.

[0024] The use of the above biomarkers in the preparation of formulations for ovarian cancer screening and diagnosis.

[0025] The above biomarkers may be used in basic medical research for non-diagnostic / therapeutic purposes.

[0026] Further, basic medical research includes Western blotting, immunohistochemistry, or flow cytometry.

[0027] The beneficial effects of this invention are:

[0028] The biomarker combination constructed in this invention exhibits superior sensitivity and specificity in the auxiliary diagnosis and early screening of ovarian cancer. Its auxiliary diagnostic results are partially or completely superior to the ROMA index ovarian cancer risk prediction model currently recommended in clinical guidelines (sensitivity and specificity of 76.0% and 85.1% for premenopausal patients, and 90.6% and 79.4% for postmenopausal patients, respectively). The early screening results are also partially or completely superior to the currently most valuable tumor markers for early ovarian cancer screening: CA125 detection (sensitivity and specificity of 69.8%–87.5% and 63.3%–85.7% for premenopausal patients, and 79.1%–90.7% and 79.1%–89.8% for postmenopausal patients, respectively) and HE4 detection (sensitivity and specificity of 78% and 86%, respectively). It holds promise for applications in the diagnosis and treatment of ovarian cancer. Attached Figure Description

[0029] Figure 1 The image shows the MRM validation results of phospholipids in serum.

[0030] Figure 2 ROC curve for marker combination 1;

[0031] Figure 3 ROC curve for marker combination 2;

[0032] Figure 4 ROC curve for marker combination 3;

[0033] Figure 5 ROC curve for marker combination 4;

[0034] Figure 6 ROC curve for marker combination 5;

[0035] Figure 7 ROC curve for marker combination 6;

[0036] Figure 8 ROC curve for marker combination 7;

[0037] Figure 9 ROC curve for marker combination 8;

[0038] Figure 10 ROC curve for marker combination 9;

[0039] Figure 11 ROC curve for marker combination 10;

[0040] Figure 12 ROC curve for marker combination 11;

[0041] Figure 13 ROC curve for marker combination 12;

[0042] Figure 14 ROC curve for marker combination 13;

[0043] Figure 15 ROC curve for marker combination 14;

[0044] Figure 16 ROC curve for marker combination 15;

[0045] Figure 17 ROC curve for marker combination 16;

[0046] Figure 18 ROC curve for marker combination 17;

[0047] Figure 19 ROC curve for marker combination 18;

[0048] Figure 20 ROC curve for marker combination 19;

[0049] Figure 21 ROC curve for marker combination 20;

[0050] Figure 22 ROC curve for marker combination 21;

[0051] Figure 23 ROC curve for marker combination 22;

[0052] Figure 24 ROC curve for marker combination 23;

[0053] Figure 25 ROC curve for marker combination 24;

[0054] Figure 26 ROC curve for marker combination 25;

[0055] Figure 27 ROC curve for marker combination 26. Detailed Implementation

[0056] 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.

[0057] The patient samples used in this invention are all from Zhongshan Hospital affiliated with Fudan University and have passed ethical review.

[0058] The experimental methods used in this invention are as follows:

[0059] I. Serum Sample Collection

[0060] 1) Sample type: serum

[0061] 2) Collection requirements: Fasting is required. Use a coagulation tube to draw 5 mL of venous blood, let it stand for 30 min, centrifuge at 3000 rpm for 15 min, and take out about 1 mL of serum and put it into a cryopreservation tube.

[0062] 3) Sample storage:

[0063] Use on the same day; store at 2-8℃.

[0064] If not used on the same day, store at -20℃ for up to 30 days;

[0065] If stored for an extended period (more than one month), it should be kept at -80°C.

[0066] The freeze-thaw cycle should not exceed 3 times.

[0067] II. Extraction of analytes from serum

[0068] 1) After calibrating the mass spectrometer, turn on the SPS1000 / SPS4000 fully automated solid-liquid biological sample analysis system, and put in the consumables, reagent kits, and samples to be tested.

[0069] Select the procedure method "Concentrated loading";

[0070] Run the program:

[0071] a. Opening a hole;

[0072] b. Take at least 10µL of serum sample and activation reagent, mix them in a 1:1 ratio, and place them in the G-row pre-reserved well for later use;

[0073] c. Clean the custom pipette tip in cleaning reagent 1 and cleaning reagent 2 in sequence. Each time, aspirate at least 10µL of liquid and repeat the aspiration and dispensing process at least 3 times.

[0074] d. Process the serum mixture in the G-row wells using the cleaned custom pipette tips. Aspirate at least 10 µL of solution each time, repeating the process at least three times.

[0075] e. Clean the custom pipette tip after adsorbing the serum mixture using cleaning reagent 3. During cleaning, aspirate at least 10µL of liquid each time, repeating the aspiration and dispensing process at least 3 times.

[0076] f. Transfer no less than 10µL of buffer reagent into the H-row pre-reserved hole, and place the customized pipette tip after using cleaning reagent 3 into the liquid to draw no less than 10µL of liquid. Repeat the suction and aspiration at least 3 times.

[0077] g. Transfer at least 10µL of sample matrix solution into the H-row pre-reserved well to complete sample processing;

[0078] h. Spot 2.0 µL of the solution from well H onto the hydrophobic-coated biochip (Wuxi Pimo Technology Co., Ltd.).

[0079] i. Vacuum drying for 240 seconds.

[0080] The main components of each reagent are shown in Table 2.

[0081] Table 2 Reagent Composition

[0082]

[0083] III. Mass Spectrometry Data Acquisition and Upload

[0084] The hydrophobic coated biochip (Wuxi Pimo Technology Co., Ltd.) was vacuum dried and placed into a mass spectrometer;

[0085] Data acquisition is performed using the pre-defined 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 diameter, laser frequency, calibration method, and laser intensity.

[0086] IV. Quality Control

[0087] 1) After data collection, the data will be uploaded to the "Mass Spectrometry Data Analysis Software";

[0088] 2) The software reads the sample information and signal spectrum, and judges whether the sample and sample pretreatment are qualified according to the quality control model; quality control failure may include a variety of possibilities, including the sample is not an ovarian lesion sample, the signal spectrum intensity is not up to standard, etc.

[0089] 3) If the quality control fails, adjust the corresponding parameters according to the quality control results and repeat the serum analyte extraction process;

[0090] 4) If the quality control is qualified, proceed to the next process.

[0091] V. Establishment of Positive Criterion Value and Result Analysis

[0092] The study of positive cutoff values ​​used ovarian cancer samples with clear diagnostic information and normal human samples. Benign ovarian samples covered benign ovarian tumors, borderline ovarian tumors, ovarian cysts, and adnexal inflammation. At least two interfering samples were from non-ovarian cancers. The core algorithm was based on supervised learning using known ovarian cancer sample atlases. Through a series of processes including smoothing, noise reduction, and baseline removal, characteristic peaks were selected, a classification model was constructed, and the similarity between the hormone signal atlas and known hormone signal atlases stored in the software was calculated (Cannataro M, Guzzi PH, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data[J]. 2005.). Finally, the similarity score positive cutoff value of the kit was determined using the Youden index maximization method. When the similarity score < positive cutoff value, the sample test result was negative; when the similarity score ≥ positive cutoff value, the sample test result was positive. When using the maximum similarity score as the positive cutoff value to assist in the diagnosis of ovarian cancer or to perform early screening for ovarian cancer, the sensitivity is calculated as: Sensitivity = (Number of true positives / (Number of true positives + Number of false negatives)) × 100%, and Specificity is calculated as: (Number of true negatives / (Number of true negatives + Number of false positives)) × 100%.

[0093] Example 1: Screening and Identification of Biomarkers

[0094] This invention analyzed 400 normal human samples (all female, aged 20 to 65 years, with a mean age of 43.8 ± 12.3 years; specifically, the age distribution was: 73 cases aged 20-29, 108 cases aged 30-39, 119 cases aged 40-49, 78 cases aged 50-59, and 22 cases aged 60-65) and 400 ovarian cancer samples (all female, aged 28 to 72 years, with a mean age of 52.6 ± 11.4 years; specifically, the age distribution was: 41 cases aged 28-37, 41 cases aged 30-49, 119 cases aged 40-49, 78 cases aged 50-59, and 22 cases aged 60-65) and ovarian cancer samples (all female, aged 28 to 72 years, with a mean age of 52.6 ± 11.4 years; specifically, the age distribution was: 41 cases aged 28-37, 41 cases aged 30-49, 119 cases aged 40-49, 78 cases aged 50-59, and 22 cases aged 60-65). Time-of-flight mass spectrometry (TOF-MS) was performed on 97 patients aged 47-47, 143 patients aged 48-57, 86 patients aged 58-67, and 33 patients aged 68-72. Through first-level mass spectrometry testing, the relative abundance differences of characteristic peak data in normal individuals and ovarian cancer patients were comprehensively considered, along with statistical differences (p<0.05, t-test). A machine learning algorithm (random forest) was used to rank the impact factors for feature selection and to assess the matching degree of data in the database. This identified 42 blood peptides or metabolites with diagnostic capabilities for ovarian cancer. The mass-to-charge ratio (m / z), relative abundance, and impact factors of these peptides are shown in Table 1. The relative abundance was normalized to ln(mean signal intensity of cancer patient samples / mean signal intensity of normal samples) based on the normal human sample. The feature peak selection in machine learning was based on feature importance assessment using ensemble learning. Multiple decision trees were constructed to quantify the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction. The feature importance score, or influence factor, is obtained by calculating the mean reduction in impurity caused by the feature when splitting across all tree nodes (Biau, G., Scornet, E. A random forest guided tour. TEST 25, 197–227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0095] The specific parameters for first-order mass spectrometry are as follows:

[0096] Ionization method: Matrix-assisted laser desorption / ionization (MALDI), with α-cyano-4-hydroxycinnamic acid (CHCA) as the matrix.

[0097] Quality range: 100-4000 Da.

[0098] Resolution: 20000 (full quality range).

[0099] Laser energy: 30-40%.

[0100] Acquisition mode: Positive ion mode.

[0101] Calibration: External quality calibration was performed using the Bruker Peptide Calibration Standard.

[0102] Subsequently, the sequences of these 42 substances in the clinical serum were confirmed using secondary mass spectrometry (MS / MS or TOF / TOF is a peptide identification method recommended by the guidelines of the China Food and Drug Administration and the U.S. Food and Drug Administration). The secondary mass spectrometry data analysis process is as follows:

[0103] Data analysis methods: Mascot software (version 2.8) was used for database searching, with the UniProt Human Proteome Database (released in 2023) as the target database. Search parameters: Enzyme was set to "no digestion", parent ion mass error was allowed ±0.5 Da, fragment ion mass error was allowed ±0.3 Da, fixed modification was cysteine ​​urea methylation, and variable modification was methionine oxidation.

[0104] Sequence confirmation criteria: The confirmation of peptide sequences is based on the matching of fragment ion spectra (b- and y- ions) with theoretical spectra. A Mascot score higher than 30 (p<0.05) is considered significant.

[0105] False positive exclusion: Specificity was verified through reverse database search, and the false positive rate was controlled to below 1%.

[0106] Secondary mass spectrometry parameters:

[0107] Collision-induced dissociation (CID).

[0108] Collision energy: 30 eV.

[0109] Fragment ion mass range: 100-3500 Da.

[0110] Data acquisition: Each sample is scanned at least 1000 times with lasers to improve the signal-to-noise ratio.

[0111] The sequences and specificity of these 42 biomarkers were confirmed by secondary mass spectrometry, ruling out false positives. The specific sequences are shown in Table 1.

[0112] Example 2: Validation of Marker Combinations

[0113] Based on the 41 polypeptide and phospholipid biomarkers identified and confirmed in Example 1 (sequences and mass-to-charge ratios are shown in Table 1), different biomarker combinations were formed as shown in Table 3, and the sensitivity and specificity of the biomarker 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 the mass spectrometry peak intensity data of all biomarkers in Table 1. The entire detection process was blinded, meaning that the experimental operators were unaware of the sample grouping information. The raw mass spectrometry data were processed by baseline correction, smoothing, and normalization (based on the internal standard peak intensity), and then the peak area or intensity value of each biomarker was extracted. Statistical analysis was performed using R software (version 4.0.2). The preprocessed biomarker intensity data were input into a logistic regression model. For auxiliary diagnostic validation, ten-fold cross-validation was performed using all samples from this 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 validation, given the imbalanced sample size, oversampling (SMOTE) 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 riskprediction models: illustration and simulation using logistic regression. JAm Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093.PMID: 35686364; PMCID: PMC9382395.).The data analysis process referenced general guidelines for constructing clinical predictive models (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 (AUC) curve were calculated for each biomarker combination. The specific calculation results for the performance indicators are shown in Table 3, and the corresponding ROC curves are shown in [Table data missing]. Figures 2-27 .

[0114] 1) The use of various biomarker combinations for the auxiliary diagnosis of ovarian cancer was validated in 600 ovarian cancer patients (all female, aged 31 to 74 years, mean age 53.8 ± 10.7 years; specifically distributed as follows: 58 cases aged 31-39, 127 cases aged 40-47, 168 cases aged 48-55, 157 cases aged 56-63, and 90 cases aged 64-74) and 600 healthy individuals (all female, aged 25 to 68 years, mean age 45.3 ± 11.2 years; specifically distributed as follows: 72 cases aged 25-33, 128 cases aged 34-41, 146 cases aged 42-49, 135 cases aged 50-57, and 119 cases aged 58-68).

[0115] 2) The early screening efficacy of various biomarker combinations for ovarian cancer was validated using a sample of 500 ovarian cancer patients (all female, aged 29 to 72 years, mean age 52.4 ± 10.9 years; specific distribution: 43 cases aged 29-37, 108 cases aged 38-45, 147 cases aged 46-53, 126 cases aged 54-61, and 76 cases aged 62-72) and 4000 healthy individuals (all female, aged 23 to 70 years, mean age 44.6 ± 12.1 years; specific distribution: 423 cases aged 23-31, 687 cases aged 32-39, 812 cases aged 40-47, 798 cases aged 48-55, 714 cases aged 56-63, and 566 cases aged 64-70).

[0116] Table 3 Sensitivity and specificity of biomarker combinations for assisted diagnosis and early screening

[0117]

[0118] (Continued from the table above)

[0119]

[0120] According to Table 3 and Figures 2-27 The test results show that the differential peak set (biomarker combination) selected in this invention is partially or completely superior to the ROMA index ovarian cancer risk prediction model recommended by current clinical guidelines (sensitivity and specificity of 76.0% and 85.1% for premenopausal patients, and 90.6% and 79.4% for postmenopausal patients, respectively).

[0121] Early screening results are also partially or completely superior to the most valuable tumor markers for early ovarian cancer screening, namely CA125 (sensitivity and specificity of 69.8%~87.5% and 63.3%~85.7% for premenopausal patients, and 79.1%~90.7% and 79.1%~89.8% for postmenopausal patients) and HE4 (sensitivity and specificity of 78% and 86%, respectively).

[0122] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A biomarker set for ovarian cancer screening and diagnosis, characterized in that, The biomarker group is selected from one of the following combinations of peptides: A) Sequence 1, Sequence 2, Sequence 3 and Sequence 4; B) Sequences 1, 2, 5, and 6; C) Sequence 1, Sequence 2, Sequence 15 and Sequence 16; D) Sequences 1, 2, 30, and 40; E) Sequence 1, Sequence 2, Sequence 3, Sequence 4 and Sequence 5; F) Sequence 1, Sequence 2, Sequence 5, Sequence 6 and Sequence 7; G) Sequence 1, Sequence 2, Sequence 15, Sequence 16 and Sequence 20; H) Sequence 1, Sequence 2, Sequence 9, Sequence 30 and Sequence 35; I) Sequences 1, 10, 30, and 40; J) Sequences 1, 10, 30, and 20; K) Sequence 1, Sequence 10, Sequence 30, Sequence 20 and Sequence 28; L) Sequences 1, 4, 24, and 27; M) sequences 1 to 41 and phospholipids; The amino acid sequences of sequences 1 to 41 are shown in SEQ ID NO. 1 to 41; Specifically, the fourth amino acid G in sequence 10 is modified with Phospho; the second amino acid K in sequence 15 is modified with Acetyl; the third amino acid G in sequence 22 is modified with Phospho; the fourth amino acid G in sequence 32 is modified with Dehydrated; the first amino acid Q in sequence 40 is modified with Gln->pyro-Glu, and the sixth amino acid N is modified with Dehydrated; the phospholipid is 1-linoleyl-2-hydroxy-SN-glycerol-3-lecithin.

2. Use of the reagent for detecting the biomarker group of claim 1 in the preparation of preparations for ovarian cancer screening and diagnosis.

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