KR102402428B1 - Multiple biomarkers for diagnosing ovarian cancer and uses thereof - Google Patents

Abstract

The present invention relates to multiple biomarkers for ovarian cancer diagnosis and a use thereof. The present invention provides a biomarker for diagnosing ovarian cancer or a combination thereof, a composition for diagnosing ovarian cancer, and a kit for diagnosing ovarian cancer. The biomarkers and combination thereof of the present invention can diagnose ovarian cancer with significantly excellent sensitivity and specificity than previously reported ovarian cancer biomarkers, and in particular, can detect stage 1 and/or stage 2 ovarian cancers to diagnose ovarian cancer in early stages. Furthermore, the biomarker of the present invention performs diagnosis based on plasma protein levels and has excellent sensitivity and specificity even in a very small amount of blood. Compared to other ovarian cancer diagnosis methods involving a biopsy, the diagnosis procedure is simple, and a patient's pain and burden can be remarkably reduced. The biomarker for diagnosing ovarian cancer of the present invention is different from previously reported ovarian cancer biomarkers and can be used together with conventional biomarkers or as a substitute therefor. Also, the biomarker can be used as a marker for a therapeutic effect after drug administration, and thus can be very usefully used in screening a clinical therapeutic agent for ovarian cancer in patients.

Description

Multiple biomarkers for diagnosing ovarian cancer and uses thereof

The present invention relates to multiple biomarkers for diagnosing ovarian cancer and uses thereof, and a biomarker combination for diagnosing ovarian cancer comprising two or more proteins selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ, the biomarker A composition or kit for diagnosis of ovarian cancer comprising a marker protein or an agent capable of measuring the level of a gene encoding the same, and a method for diagnosing ovarian cancer using the biomarker combination or a drug screening method for preventing or treating ovarian cancer .

Ovarian cancer is a malignant tumor that occurs in the ovary and occurs frequently in postmenopausal women over the age of 50. It is the most common gynecological cancer among women along with cervical cancer. According to data from the Health Insurance Review and Assessment Service in 2019, 47% of female patients who die from cancer died from ovarian cancer, which has a significantly higher mortality rate than other female cancers such as cervical cancer, breast cancer, and thyroid cancer. According to the NIH report (NIH, Cancer Stat Facts: Ovarian Cancer), more than 1.2% of women suffer from ovarian cancer, and the 5-year survival rate of ovarian cancer patients is only 49.1%.

In particular, ovarian cancer is often in the metastatic stage when symptoms appear, and according to actual reports, more than 70% of patients are diagnosed at the advanced stage (Holschneider and Berek, 2000), and about 60% is reported to be diagnosed after metastasis (Siegel et al., 2017; and NIH, SEER program). On the other hand, when diagnosed at an early stage (localized), the 5-year survival rate is 92.6%, which increases to a very high level. Therefore, although the most effective strategy to increase the survival rate and treatment potential of ovarian cancer is early diagnosis of ovarian cancer, the currently used clinical diagnosis of ovarian cancer is to confirm the tumor through ultrasound or palpation, and then Malignancy is determined through tests or blood tests (such as CA-125), and currently, it is the earliest method to detect ovarian cancer. The method through ultrasound and palpation can be confirmed only after the tumor has grown to a certain extent, and since a biopsy is essential for an accurate diagnosis of a malignant tumor, it is very invasive and causes pain and burden on the patient. . Therefore, a blood test has been devised as a diagnostic method to replace this method, and biomarkers such as CA125, HE4, and CEA have been reported through a lot of investment and research for discovering blood biomarkers for diagnosing ovarian cancer, but each Biomarkers of ovarian cancer are used only for reference in clinical judgment due to their low sensitivity and specificity, and it is difficult to replace existing methods for confirming ovarian cancer.

The most common biomarker for early diagnosis of ovarian cancer is CA125. However, in addition to ovarian cancer, CA125 is also detected in ovarian adenomyosis, uterine myoma, endometrial pathology, and endometriosis, limiting its use as a single biomarker ( Kim et al., 2019). In addition, as early diagnostic markers, HE4, IL-2R, prolactin, CA 15-3, CA 19-9, CA 72-4, Cyfra 21-1, TNFR1, TNFR2, IL-6, IL-7, IL-10 , TNF-α, TSH, IGFBP1, MMP-7, VCAM-1, eotaxin-1, FSH, LH, ErbB2, ApoA1, TTR, adiponectin, and CD40L have been reported, but the biomarkers other than CA125 are sensitive and The specificity is very low (Baron et al., 2003; Perkins et al., 2003). As such, a single biomarker can be increased in expression in various diseases except ovarian cancer, and ovarian that does not express the biomarker Diagnosis of cancer patients is impossible Therefore, diagnosis of ovarian cancer using a single biomarker has very low specificity and sensitivity, making it difficult to use for accurate diagnosis.

As an analysis method to overcome the limitations of the single biomarker, a multivariate index assay using a combination of multiple biomarkers has been developed. Jacobs and group have proposed a “Risk of Malignancy Index” (RMI) algorithm by combining CA125 with ultrasound and menopause (Jacobs et al., 1990). RMI is a method for differentiating ovarian cancer patients from women with pelvic masses. In addition, Ova1 is an FDA-approved multivariate index assay, consisting of a panel of five plasma protein markers (CA125-II, transferrin, beta-2 microglobulin, apolipoprotein A-1, and transthyretin). Ova1 has a low specificity of 35% and a sensitivity of 96% (Ueland et al., 2011; Miller et al., 2011). Another multivariate index analysis method for predicting epithelial cancer in women with a pelvic mass is the Moore group's Risk of Ovarian Malignancy Algorithm (ROMA). The FDA-approved ROMA score in 2011 has a specificity of about 75% for the combination of HE4, CA125, and menopause. Another biomarker-based index, called the Copenhagen index (CPH-I), using a combination of HE4, CA125 and age, was developed by Karlsen et al. and is similar to ROMA and RMI, but does not take into account ultrasound and menopause status. The multi-biomarker-based analysis method currently used in clinical practice has low specificity and sensitivity, and, in addition to plasma markers, confirmation of pelvic mass through ultrasound, menopause status, age, etc. are included as markers. It cannot be used as a diagnostic method for ovarian cancer, and therefore, the above methods are not ideal diagnostic methods, but are used as primary classification and reference test methods for patients. Therefore, in order to confirm the diagnosis of ovarian cancer, conventional biopsy, etc. must be accompanied, and thus, pain and burden imposed on the patient are still accompanied.

As a result, biomarkers that can diagnose ovarian cancer with high specificity and sensitivity, especially early diagnosis in stage I and II, are virtually non-existent, despite the high need. In addition, the combination of biomarkers does not always entail enhancement of sensitivity and specificity, and even if one of sensitivity and specificity is improved, it is often the case that the other decreases. Therefore, it is required to discover biomarkers capable of early diagnosis of ovarian cancer with high sensitivity and specificity and to develop a diagnostic method through an optimal combination thereof (Heliyon 5 (2019) e02826).

As a result of diligent efforts to discover biomarkers capable of ovarian cancer diagnosis, especially early diagnosis, with high specificity and sensitivity, the present inventors have derived new protein biomarkers for early diagnosis of ovarian cancer, including MBL2, APOL1 and SERPINA5. , in the case of combining these, it is possible to diagnose ovarian cancer with significantly higher specificity and sensitivity than previously reported methods for ovarian cancer using biomarkers or a combination thereof, even in a very small amount of sample of several to several tens of μL, especially in stage 1 and The present invention was completed by confirming that early diagnosis of stage 2 ovarian cancer is possible.

The information described in the background section is only for improving the understanding of the background of the present invention, and it does not include information forming prior art known to those of ordinary skill in the art to which the present invention pertains. it may not be

An object of the present invention is to provide a biomarker capable of diagnosing ovarian cancer with high specificity and sensitivity, and a combination thereof.

Another object of the present invention is to provide a diagnostic use of the biomarker and a combination thereof for ovarian cancer.

Another object of the present invention is to provide a composition for diagnosing ovarian cancer capable of diagnosing ovarian cancer with high specificity and sensitivity.

Another object of the present invention is to provide a diagnostic kit for ovarian cancer capable of diagnosing ovarian cancer with high specificity and sensitivity.

Another object of the present invention is to provide a method for diagnosing ovarian cancer capable of diagnosing ovarian cancer with high specificity and sensitivity.

In order to achieve the above object, the present invention provides a biomarker for diagnosis of ovarian cancer comprising two or more proteins selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ, or a combination thereof:

The present invention also provides a composition for diagnosis of ovarian cancer, comprising an agent capable of measuring the level of two or more proteins selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ or a gene encoding the same .

The present invention also provides a kit for diagnosing ovarian cancer, comprising an agent capable of measuring the level of two or more proteins selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ or a gene encoding the same .

The present invention also provides a method for diagnosing or providing information for diagnosing ovarian cancer, comprising the following steps:

(A) from the sample isolated from the subject, MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2, and measuring the expression level of two or more proteins selected from the group consisting of CPN2 and ADIPOQ or a gene encoding the same;

(b) comparing the protein level or the expression level of the gene encoding the same with a control.

The present invention also provides a method for screening a drug for preventing or treating ovarian cancer, comprising the steps of:

(a) treating a sample isolated from a subject or a candidate drug to an animal model of cancer; and

(b) In the sample or cancer disease animal model treated with the candidate drug, the level of two or more proteins selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ, or the expression level of a gene encoding the same step.

The biomarker of the present invention and a combination thereof can diagnose ovarian cancer with significantly superior sensitivity and specificity than previously reported ovarian cancer biomarkers. Early diagnosis is possible. Furthermore, the biomarker of the present invention can be diagnosed based on plasma protein level, and has excellent sensitivity and specificity even in a very small amount of blood, so the procedure is simple compared to other ovarian cancer diagnostic methods involving biopsy, and the patient's It can significantly reduce pain and burden. Since the ovarian cancer biomarker of the present invention is different from the previously reported ovarian cancer biomarker, it has the advantage that it can be used together with or instead of the existing biomarker, and can also be used as a marker for the therapeutic effect after drug administration, so that the patient It can also be very usefully used in screening clinical therapeutic agents for ovarian cancer.

1 is a result of analyzing the amount and peak area of a plasma protein normalized based on the median value (Median centering normalization).
Figure 2 shows the number of differentially expressed proteins analyzed in total plasma proteomic bodies of three groups (normal, stage 1-2, stage 3-4). In the Van diagram, green shows the differential expression of proteins between normal and stage 1-2 ovarian cancer patients, blue shows normal and stage 3-4 ovarian cancer patients, and pink shows stage 1-2 and stage 3-4 ovarian cancer patients.
3 shows the results of a feature selection process performed to select an optimal marker protein candidate for diagnosis of an early stage ovarian cancer patient (stage 1-2, 3a) or a total ovarian cancer patient (3b). Markers with a Probability of less than 0.50 were excluded from the combination.
4 is a box plot comparing 7 plasma protein levels in normal controls and stage 1-2 ovarian cancer patients.
5 is a box plot comparing 7 plasma protein levels in normal controls and total ovarian cancer patients.
6 schematically shows training information of a random forest model for diagnostic validation of ovarian cancer patient groups (stages 1-2) for three protein markers.
7 schematically shows training information of a support vector machine model for diagnostic validation of ovarian cancer patient groups (stages 1-2) for three protein markers.
8 is a ROC analysis result in the training set and validation set of the normal group and ovarian cancer patients (stage 1 and 2) using a random forest analysis model including three protein panels of the early ovarian cancer prediction model.
9 is a ROC analysis in the training set and validation set of the normal group and ovarian cancer patients (stages 1 and 2) using a sub vector machine (SVM) analysis model including three protein panels of the early ovarian cancer prediction model. is the result
10 is an ROC analysis result when ovarian cancer is diagnosed independently using three proteins (a: APOL1, b: SERPINA5, C: MBL2) of an early ovarian cancer prediction model.
11 schematically shows training information of a random forest model for diagnostic validation of ovarian cancer patient groups (all stages) for 7 protein markers.
12 schematically shows training information of a support vector machine model for diagnostic validation of an ovarian cancer patient group (full stage) for three protein markers.
13 is a ROC analysis analysis result in the training set and validation set of the normal group and ovarian cancer patients (stage 1, 2, 3, 4) using a random forest analysis model including 7 protein panels of the ovarian cancer prediction model. to be.
14 is a ROC analysis result in the training set and validation set of the normal group and ovarian cancer patients (stage 1, 2, 3, 4) using a support vector machine analysis model including 7 protein panels of the ovarian cancer prediction model. to be.
15 is an ovarian cancer prediction model using 7 proteins (a: APOL1, b: SERPINA5, c: MBL2, d: ECM1, e: SERPINA7, f: CPN2, g: ADIPOQ) of the ovarian cancer prediction model, respectively, to diagnose ovarian cancer. It is the result of ROC analysis in the case of

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. In general, the nomenclature used herein is those well known and commonly used in the art.

In the concentration range described herein, “to” is used to include (above and below) both critical ranges, and when not including both critical ranges, the concentration ranges are described as “over” and “less than”. . “About” as used in a numerical value in this specification is used to include a range expected to have an effect substantially equivalent to a numerical value described by a person skilled in the art, for example, ±20% of the numerical value described , ±10%, ±5%, etc., but is not limited thereto.

In contrast to the incidence of ovarian cancer in about 1 to 2% of women, 47% of female patients who die from cancer are ovarian cancer, which has a significantly higher mortality rate than other female cancers. The 5-year survival rate is also very low, less than 50%. The cause of the low mortality rate is that, in the case of ovarian cancer, most of the cases are in the metastatic stage when symptoms appear, and the diagnosis has already progressed considerably.

Therefore, although the most effective strategy to increase the survival rate and treatment potential of ovarian cancer is early diagnosis of ovarian cancer, the currently used clinical diagnosis of ovarian cancer is to confirm the tumor through ultrasound or palpation, and then Malignancy is determined through tests or blood tests (such as CA-125), and currently, it is the earliest method to detect ovarian cancer. Diagnosis of ovarian cancer through a blood test is non-invasive compared to a biopsy, and has characteristics that reduce the burden on the patient. However, currently approved blood test methods such as the RMI method (RMI I, II, III), ROMA, CPH-1, and Ova1 are not independent diagnostic methods, but mostly to screen for malignancy from a patient with a mass found. , its sensitivity and specificity are low, and it has disadvantages that it can be applied only to patients with specific conditions, including factors other than blood biomarkers such as age or menopause.

In one embodiment of the present invention, using SWATH (Sequential Window Acquisition of all Theoretical Mass Spectra), which is a quantitative plasma proteomic analysis technique based on QTOF LC-MS, for plasma samples, plasma proteins of normal controls and ovarian cancer patients are quantitatively analyzed. was analyzed. A significant difference was identified between the normal control group and ovarian cancer patients, and biomarker candidate groups (MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2, and ADIPOQ) for diagnosis of ovarian cancer patients were derived from about 133 candidate proteins.

In another embodiment of the present invention, when evaluating a quantified biomarker with various predictive statistical models such as a random forest model and a support vector machine using a combination of derived biomarker candidate groups, the previously reported ovarian cancer patients It was confirmed that ovarian cancer patients can be diagnosed with significantly higher sensitivity and specificity compared to diagnostic methods. and specificity for diagnosis.

Accordingly, in one aspect, the present invention relates to a combination of biomarkers for diagnosis of ovarian cancer comprising two or more proteins of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ.

In the present invention, it may preferably include two or more proteins among MBL2, APOL1 and SERPINA5, and more preferably, it may include MBL2, APOL1 and SERPINA5 proteins.

In the present invention, it may be characterized in that it further comprises any one or more proteins of SERPINA7, ECM1, CPN2 and ADIPOQ together with MBL2, APOL1 and SERPINA5 proteins.

In the present invention, in addition to two or more of the above-described MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ proteins, it may be characterized in that it is composed in combination with various plasma biomarkers known as ovarian cancer markers.

In the present invention, the ovarian cancer may be characterized as stage 1 and stage 2 ovarian cancer, but is not limited thereto.

In another aspect, the present invention relates to the use of a combination of two or more proteins of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ for the diagnosis of ovarian cancer.

In another aspect, the present invention provides the level of two or more proteins selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ, or the expression level of a gene encoding the same. It relates to a diagnostic composition.

In the present invention, the composition for diagnosis of ovarian cancer may include an agent capable of measuring the protein level of MBL2, APOL1, and SERPINA5 or the expression level of a gene encoding the same.

In the present invention, the composition for diagnosis of ovarian cancer preferably measures the protein level of MBL2 and APOL1, MBL2 and SERPINA5, or APOL1 and SERPINA5, most preferably MBL2, APOL1 and SERPINA5 or the expression level of a gene encoding the same. In this case, the diagnosis of ovarian cancer may be characterized as stage 1 and/or stage 2 ovarian cancer.

In the present invention, the composition for diagnosis of ovarian cancer is together with an agent capable of measuring the protein level of MBL2, APOL1 and SERPINA5 or the expression level of a gene encoding it, and the protein level of any one or more of SERPINA7, ECM1, CPN2 and ADIPOQ Or it may be characterized by including an agent capable of measuring the expression level of the gene encoding it.

In the present invention, the composition for diagnosis of ovarian cancer, MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ protein level or the expression level of the gene encoding the same can be characterized by comprising an agent capable of measuring have.

In the present invention, the composition for diagnosis of ovarian cancer can measure the level of various plasma biomarker proteins known as conventional ovarian cancer markers, or the expression level of genes encoding the same, in addition to the disclosed biomarker protein for ovarian cancer diagnosis of the present invention. It may be characterized in that it further comprises an agent.

terms of the present invention. “MBL2 (mannose binding lectin 2)” is also called Mannose binding protein C (MBP-C). MBL2 can be present in plasma and activates the complement pathway by recognizing and binding to mannose and N-acetyl glucosamine in many microorganisms including bacteria, yeast, and viruses. In the present invention, a representative sequence of human MBL2 is the same as SEQ ID NO: 1 (UniProtKB-P11226), but is not limited thereto, and a protein comprising a peptide sequence determined to be substantially homologous to SEQ ID NO: 1 or a mutant thereof includes For example, at least about 80% homology to SEQ ID NO: 1, preferably at least 85% homology, more preferably at least 90% homology, more preferably at least 95% homology, even more preferably at least 97% homology It may include, but is not limited to, sequences having more than homology, and most preferably, more than 99% homology.

terms of the present invention. “APOL1 (Apolipoprotein L1)” is an apolipoprotein component included in HDL that circulates through the blood and is involved in inflammatory reactions. APOL1 is known to be associated with diseases such as sleep disease, kidney disease, and FSGS. In the present invention, a representative human APOL1 sequence is the same as SEQ ID NO: 2 (UniProtKB-O14791), but is not limited thereto, and a protein comprising a peptide sequence determined to be substantially homologous to SEQ ID NO: 2 or a mutant thereof includes For example, at least about 80% homology to SEQ ID NO: 1, preferably at least 85% homology, more preferably at least 90% homology, more preferably at least 95% homology, even more preferably at least 97% homology It may include, but is not limited to, sequences having more than homology, and most preferably, more than 99% homology.

As used herein, the term “SERPINA 5 (Serpin Family A Member 5)” is also called plasma serine protease inhibitor or protein C inhibitor (PCI). SERPINA5 inhibits the activity of protein C, and is a protein known as a plasma biomarker of prostate cancer, a male tumor in particular. Representative human SERPINA5 sequence is the same as SEQ ID NO: 3 (UniProtKB: P05154), but is not limited thereto, and includes a protein comprising a peptide sequence determined to be substantially homologous to SEQ ID NO: 3 or a mutant thereof. For example, at least about 80% homology with SEQ ID NO: 3, preferably at least 85% homology, more preferably at least 90% homology, more preferably at least 95% homology, even more preferably at least 97% homology It may include, but is not limited to, sequences having more than homology, and most preferably, more than 99% homology.

As used herein, the term “SERPINA7 (Serpin Family A Member 7)” is also called thyroxine binding globulin (TBG). SERPINA7 is reported as a plasma transport protein capable of binding to thyroid hormone. A representative human SERPINA7 sequence is the same as, but not limited to, SEQ ID NO: 4 (UniProtKB: P05543), and includes a protein comprising a peptide sequence determined to be substantially homologous to SEQ ID NO: 4 or a mutant thereof. For example, at least about 80% homology with SEQ ID NO: 4, preferably at least 85% homology, more preferably at least 90% homology, more preferably at least 95% homology, even more preferably at least 97% homology It may include, but is not limited to, sequences having more than homology, and most preferably, more than 99% homology.

As used herein, the term “ECM1 (Extracellular matrix protein 1)” is a secreted glycoprotein known to have cell proliferation, angiogenesis and differentiation functions, and is associated with promotion of tumor development in carcinomas such as breast cancer, thyroid cancer, and hepatocellular carcinoma. It is known that there are (Atlas of Genetics and Cytogenetics in Oncology and Haematology). A representative sequence of human ECM1 is the same as, but not limited to, SEQ ID NO: 5 (UniProtKB: Q16610), and includes a protein comprising a peptide sequence determined to be substantially homologous to SEQ ID NO: 5 or a mutant thereof. For example, at least about 80% homology with SEQ ID NO: 5, preferably at least 85% homology, more preferably at least 90% homology, more preferably at least 95% homology, even more preferably at least 97% homology It may include, but is not limited to, sequences having more than homology, and most preferably, more than 99% homology.

As used herein, the term “CPN2 (Carboxypeptidase N Subunit 2)” is a human plasma protein known to bind to and stabilize the catalytic subunit. CPN2 is also reported to be upregulated in liver cancer tissue (The Human protein atlas). Representative human CPN2 sequence is the same as SEQ ID NO: 6 (UniProtKB: P22792), but is not limited thereto, and includes a protein comprising a peptide sequence determined to be substantially homologous to SEQ ID NO: 6 or a mutant thereof. For example, at least about 80% homology with SEQ ID NO: 6, preferably at least 85% homology, more preferably at least 90% homology, more preferably at least 95% homology, even more preferably at least 97% homology It may include, but is not limited to, sequences having more than homology, and most preferably, more than 99% homology.

As used herein, the term “ADIPOQ (adiponectin)” is also referred to as GBP-28, apM1, and Acrp30. ADIPOQ is a protein hormone that regulates metabolic processes such as glucose regulation and fatty acid oxidation, and is abundantly present in plasma. Mutations in ADIPOQ are reported to be associated with breast cancer (Cancers 2021, 13(10), 2261). A representative human ADIPOQ sequence is the same as, but not limited to, SEQ ID NO: 7 (UniProtKB: Q15848), and includes a protein comprising a peptide sequence determined to be substantially homologous to SEQ ID NO: 7 or a mutant thereof. For example, at least about 80% homology with SEQ ID NO: 7, preferably at least 85% homology, more preferably at least 90% homology, more preferably at least 95% homology, even more preferably at least 97% homology It may include, but is not limited to, sequences having more than homology, and most preferably, more than 99% homology.

In the present invention, the "diagnosis" means accurately grasping the condition of a subject for a specific disease or disorder. For example, a subject's condition for a particular disease or condition can be determined by susceptibility to the particular disease or condition, determining the condition the subject currently suffers from, as well as the subject's prognosis, identification of a cancer state, and diagnosis of cancer. Evidence of appropriate treatment according to the patient's disease and condition, such as determining the stage or determining the characteristics of a disease, such as predicting the susceptibility and responsiveness of cancer to treatment, and confirming the condition of a subject to confirm the therapeutic effect of a specific drug It is used in a broad sense including prediction and confirmation of recurrence in a subject who has been cured from a specific disease or condition. In the present invention, preferably, the diagnosis is to confirm whether or not the disease is onset or the possibility of the onset.

In the present invention, the term "prognosis" refers to the prediction of medical outcomes (eg, long-term viability, disease-free survival rate, etc.), and includes a positive prognosis (positive prognosis) or a negative prognosis (negative prognosis), and the negative prognosis The prognosis includes disease progression or mortality such as recurrence, tumor growth, metastasis, drug resistance, etc., and a positive prognosis includes remission of disease such as disease-free state, improvement or stabilization of disease such as tumor regression ( stabilization).

In the present invention, the term "prediction" means to guess in advance for medical reasons, and for the purpose of the present invention, the course of the disease of a patient diagnosed with ovarian cancer (progression, improvement, recurrence of gastric cancer, tumor growth) , drug resistance) in advance.

In the present invention, the disease or condition to be diagnosed is ovarian cancer. The ovarian cancer refers to a malignant tumor occurring in the ovary or surrounding organs of the ovary, that is, cancer. Specific examples, the ovarian cancer includes epithelial cell carcinoma, germ cell tumor, and gonad stromal tumor in the ovary, and more specifically, serous carcinoma, mucinous ovarian cancer, endometrial cancer Endometroid carcinoma, clear cell carcinoma, Malignant brenner tumor, undifferentiated cell carcinoma, Unclassified Carcinoma.

In the present invention, the ovarian cancer may be classified according to the stage, specifically, it may be divided into stages 1 to 4 ovarian cancer, and stages 1 and 2 are early stages, and stages 3 and 4 Stage is classified as advanced stage (FIGO classification criteria).

As used herein, the stage of ovarian cancer can be classified according to the following criteria (TNM and FIGO classification criteria, 2019, urogenital imaging diagnostic gynecological imaging, urogenital radiology society):

The composition for diagnosing ovarian cancer of the present invention may be characterized in that it is used for diagnosing stage 1 to stage 4 total ovarian cancer, and preferably used for diagnosing stage 1 to stage 2 early ovarian cancer, but limited thereto it's not going to be

In the present invention, the agent capable of measuring the protein level is, for example, an antibody, oligopeptide, ligand, PNA (peptide nucleic acid) and app that specifically binds to each ovarian cancer diagnostic biomarker protein of the present invention. It may be any one or more of aptamers, but is not limited thereto.

In the present invention, the antibody includes all "antibodies" such as polyclonal antibodies, monoclonal antibodies, and recombinant antibodies, and the antibody refers to a specific protein molecule directed against an antigenic site. The polyclonal antibody can be produced by a method well known in the art for obtaining a serum containing an antibody by injecting the ovarian diagnostic biomarker protein of the present invention into an animal and collecting blood from the animal. Such polyclonal antibodies can be prepared from any animal species host, such as goat, rabbit, sheep, monkey, horse, pig, rat, rat, cow, and dog. Monoclonal antibodies can be prepared by hybridoma methods well known in the art (see Kohler and Milstein (1976) European Jounral of Immunology 6:511-519), or phage antibody libraries (Clackson et al, Nature, 352:624-628, 1991). ; Marks et al, J. Mol. Biol., 222:58, 1-597, 1991). The antibody prepared by the above method may be separated and purified using methods such as gel electrophoresis, dialysis, salt precipitation, ion exchange chromatography, and affinity chromatography. In addition, the antibody of the present invention includes functional fragments of antibody molecules as well as complete forms having two full-length light chains and two full-length heavy chains. A functional fragment of an antibody molecule refers to a fragment having at least an antigen-binding function, and includes Fab, F(ab'), F(ab') 2 and Fv.

In general, such an antibody is a secondary antibody conjugated with an enzyme such as alkaline phosphatase (AP) or horseradish peroxidase (HRP) and a substrate thereof. Quantitative analysis can also be carried out by directly using a protein monoclonal antibody conjugated with AP or HRP enzyme.

In the present invention, the "PNA (Peptide Nucleic Acid)" is a polymer similar to artificially synthesized DNA or RNA, and has an N-(2-aminoethyl)-glycine backbone linked by a peptide bond. PNA has improved binding ability and stability to DNA or RNA, and is used in diagnostic analysis.

In the present invention, the "aptamer (aptamer)" is an oligonucleic acid or peptide molecule, it may be characterized in that it specifically binds to a target. In the present invention, the aptamer may be characterized in that it specifically binds to any one or more of the ovarian cancer diagnostic biomarker proteins of the present invention.

In the present invention, the agent capable of measuring the expression level of the gene may be characterized in that it is selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to genes encoding each protein, but is limited thereto. it's not going to be

As used herein, the term "primer" is a nucleic acid sequence having a short free 3' hydroxyl group, which can form a base pair with a complementary template and is a starting point for copying the template. refers to a short nucleic acid sequence that functions. In the present invention, the prognosis of gastric cancer can be predicted based on whether a desired product is generated by PCR amplification using the sense and antisense primers of the marker polynucleotide of the present invention. PCR conditions, the length of the sense and antisense primers can be modified based on what is known in the art.

The "probe" of the present invention means a nucleic acid fragment such as RNA or DNA corresponding to several bases to several hundred bases as short as possible to achieve specific binding to mRNA, and is labeled to determine the presence or absence of a specific mRNA have. The probe may be manufactured in the form of an oligonucleotide probe, a single-stranded DNA probe, a double-stranded DNA probe, an RNA probe, or the like.

In the present invention, the measurement of the protein level is a protein selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ, which are markers for ovarian cancer diagnosis of the present invention in a biological sample in order to diagnose the disease of the present invention. It means to check the presence and level of expression.

In the present invention, the protein level measurement or comparative analysis method is Western blotting (western blotting), ELISA (enzyme linked immunosorbent assay), radioimmunoassay (Radioimmunoassay), radioimmunodiffusion method (Radioimmunodiffusion), Ouchterlony (Ouchterlony) Immune diffusion method, Rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complete fixation assay, FACS, protein chip, mass spectrometry, etc. However, the present invention is not limited thereto.

In the present invention, the mass spectrometry (Mass spectrometry) is according to the ionization method, for example, FAB, CI, APCI, ESI, DESI, MALDI, SELDI, ICP, DESI, SESI, LAESI, FD, FAB, DIOS, DART , SIMS, TIMS, etc., depending on the mass selection method, with a qaudrupole mass filter, ion trap, electron multiplier, etc., depending on the combination with the separation technique, gas chromatography It can be classified into gas chromatography (GS), liquid chromatography (LC), etc., and data derived from mass spectrometry can be displayed using methods such as SIM, TIC, and SRM, and each classification is combined is named For specific examples, Matrix Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF) analysis, Surface Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry (SELDITOF) analysis, liquid chromatography-Mass spectrometry Spectrometry, LC-MS), liquid chromatography-Mass Spectrometry/Mass Spectrometry (LCMS/MS), etc., but are not limited thereto.

In another aspect, the present invention provides a level of two or more proteins selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ, or ovarian cancer comprising an agent capable of measuring the expression level of a gene encoding the same of the diagnostic kit.

In the present invention, the ovarian cancer diagnostic kit may include an agent capable of measuring the protein level of MBL2, APOL1, and SERPINA5 or the expression level of a gene encoding the same.

In the present invention, the ovarian cancer diagnostic kit preferably measures the protein level of MBL2 and APOL1, MBL2 and SERPINA5, or APOL1 and SERPINA5, most preferably MBL2, APOL1 and SERPINA5 or the expression level of a gene encoding the same. In this case, the diagnosis of ovarian cancer may be characterized as stage 1 and/or stage 2 ovarian cancer.

In the present invention, the kit for diagnosis of ovarian cancer, together with a formulation capable of measuring the protein level of MBL2, APOL1 and SERPINA5 or the expression level of a gene encoding the same, the protein level of any one or more of SERPINA7, ECM1, CPN2 and ADIPOQ Or it may be characterized by including an agent capable of measuring the expression level of the gene encoding it.

In the present invention, the ovarian cancer diagnostic kit may include an agent capable of measuring MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ protein levels or the expression level of a gene encoding the same. .

In the present invention, the ovarian cancer diagnostic kit can measure the level of various plasma biomarker proteins known as conventional ovarian cancer markers, or the expression level of genes encoding the same, in addition to the biomarker protein for ovarian cancer diagnosis of the present invention disclosed above. It may be characterized in that it further comprises an agent.

In the context of the present invention, definitions and embodiments of terms not described may share the same characteristics as those described in the context of the composition for diagnosis of ovarian cancer, unless otherwise specified.

In the present invention, the kit for diagnosing ovarian cancer may include the composition for diagnosing ovarian cancer of the present invention.

In the present invention, the kit for diagnosing ovarian cancer may include one or more other component compositions, solutions, or devices suitable for the analysis method. For example, it may be an RT-PCR kit, a DNA chip kit, a protein chip kit, a rapid kit, or a selected reaction monitoring (SRM)/multiple reaction monitoring (MRM) kit.

The RT-PCR kit includes, in addition to each primer pair specific for a marker gene, a test tube or other suitable container, reaction buffer, deoxynucleotides (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNase, RNase inhibitors , DEPC-water, sterile water, and the like. In addition, a primer pair specific for a gene used as a quantitative control may be included. The DNA chip kit may include a substrate to which cDNA corresponding to a gene or fragment thereof is attached as a probe, and the substrate may include cDNA corresponding to a quantitative structural gene or fragment thereof.

In addition, the kit according to the present invention may be a diagnostic kit including an agent for measuring the protein level, wherein the agent for measuring the protein level may preferably be an antibody specific for the protein. Accordingly, a diagnostic kit including an agent for measuring the protein level may be, for example, a kit for detecting a diagnostic marker including essential elements necessary for performing ELISA, and such a kit may be an "antigen-antibody complex". It may also contain reagents capable of detecting an antibody, such as a labeled secondary antibody, chromophores, an enzyme (eg, conjugated to an antibody) and a substrate thereof. In addition, an antibody specific for the quantitative control protein may be included.

In addition, the amount of formation of the antigen-antibody complex can be quantitatively measured through the magnitude of the signal of the detection label. The detection label may be selected from the group consisting of an enzyme, a fluorescent substance, a ligand, a luminescent substance, a microparticle, a redox molecule, and a radioisotope, but is not necessarily limited thereto.

The selected reaction monitoring (SRM), also called multiple reaction monitoring (MRM), is a method used in tandem mass spectrometry, and is used for target quantitative proteomic analysis. The SRM used for targeted quantitative proteomic analysis is Nature Methods. 9 (6): 555-566.

In another aspect, (a) from a sample isolated from a subject, MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2, and the expression level of two or more proteins selected from the group consisting of CPN2 and ADIPOQ or the expression level of a gene encoding the same measuring;

(b) relates to a method for diagnosing ovarian cancer and/or a method for providing information for diagnosis, comprising comparing the protein level or the expression level of a gene encoding the same with a control.

In the present invention, the subject may be an animal including a human, preferably a mammal, more preferably a human.

In the present invention, the sample may be a solid or non-solid sample isolated from a subject to be diagnosed with ovarian cancer, for example, an organ, tissue, cell or whole blood obtained from a subject isolated from the subject. , leukocytes, peripheral blood mononuclear cells, buffy coat, plasma, serum, sputum, tears, mucus, cecum Nasal washes, nasal aspirate, breath, urine, semen, saliva, peritoneal washings, ascites, cystic fluid, meningeal fluid, amniotic fluid, glandular fluid, pancreatic fluid, lymph fluid, pleural fluid, nipple aspirate, bronchial aspiration It may be bronchial aspirate, synovial fluid, joint aspirate, organ secretions, cell, cell extract or cerebrospinal fluid, but is not limited thereto. it is not

In an embodiment of the present invention, a biomarker for ovarian cancer diagnosis and a combination thereof having high sensitivity and specificity were developed based on plasma protein levels. Therefore, in the present invention, the sample is most preferably obtained from a subject. It may be whole blood, plasma or serum.

In the present invention, step (a) may be characterized in measuring the protein level.

In the present invention, the measurement of the protein level is a protein selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ, which are markers for ovarian cancer diagnosis of the present invention in a biological sample in order to diagnose the disease of the present invention. It may be to confirm the presence and level of expression.

In the present invention, the method for measuring the protein level in step (a) is western blotting, ELISA (enzyme linked immunosorbent assay), radioimmunoassay, radioimmunoassay, radioimmunodiffusion, Oukreroni ( Ouchterlony immunodiffusion method, Rocket immunoelectrophoresis, tissue immunostaining, immunoprecipitation assay, complete fixation assay, FACS, protein chip, and mass spectrometry and the like, but are not limited thereto, and any known method capable of measuring a protein level, preferably a plasma protein level, may be used.

In the present invention, step (a) may be preferably performed by mass spectrometry.

In the present invention, the mass spectrometry (Mass spectrometry) is according to the ionization method, for example, FAB, CI, APCI, ESI, DESI, MALDI, SELDI, ICP, DESI, SESI, LAESI, FD, FAB, DIOS, DART , SIMS, TIMS, etc., depending on the mass selection method, with a qaudrupole mass filter, ion trap, electron multiplier, etc., depending on the combination with the separation technique, gas chromatography It can be classified into gas chromatography (GS), liquid chromatography (LC), etc., and data derived from mass spectrometry can be displayed using methods such as SIM, TIC, and SRM, and each classification is combined can be named As a specific example, the mass spectrometry is MALDI-TOF (Matrix Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry) analysis, SELDITOF (Sulface Enhanced Laser Desorption/Ionization Time of Flight Mass Spectrometry) analysis, liquid chromatography-mass spectrometry ( It may be liquid chromatography-Mass Spectrometry, LC-MS), or liquid chromatography-Mass Spectrometry/Mass Spectrometry (LCMS/MS), but is not limited thereto.

In the present invention, the step (a) may be characterized by measuring the MBL2, APOL1 and SERPINA5 protein levels or the expression level of the gene encoding them.

In the present invention, the step (a) preferably measures the protein level of MBL2 and APOL1, MBL2 and SERPINA5, or APOL1 and SERPINA5, most preferably MBL2, APOL1 and SERPINA5 or the expression level of a gene encoding the same. It may be characterized in that, in this case, the diagnosis of ovarian cancer may be characterized in that the diagnosis of stage 1 and/or stage 2 ovarian cancer.

In the present invention, the step (a) together with MBL2, APOL1 and SERPINA5, SERPINA7, ECM1, CPN2, and ADIPOQ any one or more protein level or the expression level of the gene encoding it can be characterized by further measuring have.

In the present invention, the step (a) may be characterized by measuring the MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ protein levels or the expression level of the gene encoding them.

In the present invention, the composition for diagnosis of ovarian cancer includes measuring the level of various plasma biomarker proteins known as conventional ovarian cancer markers, or the expression level of genes encoding the same, in addition to the disclosed biomarker protein for ovarian cancer diagnosis of the present invention. can be characterized.

In the present invention, the control in step (b) is MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ protein levels of a sample isolated from a subject not suffering from ovarian cancer, or the expression level of a gene encoding the same. can be done with

In the present invention, the control in step (b) may be provided by direct measurement, or may be characterized as a reference value provided by measuring previously.

In an embodiment of the present invention, in quantitative plasma proteomic analysis comparing two groups of ovarian cancer patients (group 1: stage 1-2, group 2: stage 3-4) and a normal control group of each stage, respectively , a total of 31 DEPs (differential expressed protein) showing significant differences were identified. The DEP common to the 3 groups was Alpolipoprotein L1, and 10 proteins were found to be common DEPs for the control group and the ovarian cancer stage 1-2, and the control group and the ovarian cancer stage 3-4 group. In addition, 7 proteins were found to be common DEPs between the control group and the stage 1-2 ovarian cancer group, the stage 1-2 ovarian cancer group and the ovarian cancer stage 3-4 group. In particular, among the biomarkers for ovarian cancer diagnosis of the present invention, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ decreased in all ovarian cancer patients, and MBL2 increased. In particular, in the case of stage 1-2 ovarian cancer patients, not only the normal control group but also when compared with stage 3-4 ovarian cancer patients, APOL1 and SERPINA5 were significantly decreased, and MBL2 was significantly increased.

Accordingly, in the present invention, (C) when a change in the level of any one or more of the following proteins or the expression level of a gene encoding the same is detected, further comprising the step of confirming that the subject is likely to have ovarian cancer can be characterized.

i) an increase in the level of MBL2 protein and/or the expression level of a gene encoding it;

ii) a decrease in the level of APOL1 protein and/or the expression level of the gene encoding it;

iii) a decrease in the level of the SERPINA5 protein and/or the expression level of the gene encoding it;

iv) a decrease in the level of the SERPINA7 protein and/or the expression level of the gene encoding it;

v) a decrease in the level of ECM1 protein and/or the expression level of the gene encoding it;

vi) a decrease in the level of the CPN2 protein and/or the expression level of the gene encoding it; and

vii) a decrease in the ADIPOQ protein level and/or the expression level of the gene encoding it.

In the present invention, i) the MBL2 protein level and / or the increase in the expression level of the gene encoding it is, for example, about 1.1 times or more, preferably about 1.2 times or more, more preferably about 1.3 times or more compared to the control group. , more preferably about 1.4 times or more, more preferably about 1.5 times or more, and most preferably about 1.53 times or more.

In the present invention, the ii) APOL1 protein level and/or the decrease in the expression level of the gene encoding the same is, for example, about 0.01-fold to about 0.9-fold, more preferably about 0.1-fold to about 0.85-fold, more than that of the control group Preferably from about 0.4 to about 0.8 times, most preferably from about 0.7 to about 0.8 times.

In the present invention, the iii) SERPINA5 protein level and/or the decrease in the expression level of the gene encoding it is, for example, about 0.01-fold to about 0.9-fold compared to the control, more preferably about 0.1-fold to about 0.85-fold, more Preferably from about 0.4 to about 0.8 times, most preferably from about 0.7 to about 0.8 times may be characterized.

In the present invention, the iv) SERPINA7 protein level and / or the decrease in the expression level of the gene encoding it is, for example, from about 0.01 times to about 0.95 times compared to the control, more preferably from about 0.1 times to about 0.9 times, more Preferably, it may be characterized in that it is reduced by about 0.5 times to about 0.9 times, and most preferably from 0.8 times to 0.9 times.

In the present invention, v) the decrease in the level of ECM1 protein and / or the expression level of the gene encoding it is, for example, about 0.01-fold to about 0.95-fold, more preferably about 0.1-fold to about 0.9-fold, more preferably about 0.01-fold to about 0.9-fold compared to the control. Preferably, it may be characterized in that it is reduced by about 0.5 times to about 0.9 times, and most preferably from 0.7 times to 0.9 times.

In the present invention, vi) the CPN2 protein level and/or the decrease in the expression level of the gene encoding it is, for example, about 0.01-fold to about 0.95-fold, more preferably about 0.1-fold to about 0.9-fold, more preferably about 0.01-fold to about 0.9-fold compared to the control. Preferably, it may be characterized in that it is reduced by about 0.5 times to about 0.9 times, and most preferably from 0.8 times to 0.9 times.

In the present invention, vii) the ADIPOQ protein level and / or the decrease in the expression level of the gene encoding it is, for example, about 0.01-fold to about 0.95-fold, more preferably about 0.1-fold to about 0.9-fold, more preferably about 0.01-fold to about 0.9-fold compared to the control. Preferably, it may be characterized in that it is reduced by about 0.5 times to about 0.9 times, and most preferably from 0.8 times to 0.9 times.

In the present invention, (C) when a change in the level of the following protein or a gene encoding the same is detected, further comprising the step of confirming that the subject is likely to suffer from stage 1 or stage 2 ovarian cancer can be characterized.

i) an increase in the level of the MBL2 protein or the gene encoding it;

ii) a decrease in the level of the APOL1 protein or the gene encoding it; and

iii) a decrease in the level of the SERPINA5 protein or the gene encoding it.

In the present invention, i) the MBL2 protein level and / or the increase in the expression level of the gene encoding the same is, for example, about 1.1 times or more, preferably about 1.2 times or more, more preferably about 1.4 times or more compared to the control group. , more preferably about 1.5 times or more, more preferably about 1.6 times or more, and most preferably about 1.7 times or more.

In the present invention, the ii) APOL1 protein level and/or the decrease in the expression level of the gene encoding the same is, for example, about 0.01-fold to about 0.9-fold, more preferably about 0.1-fold to about 0.85-fold, more than that of the control group Preferably from about 0.4 to about 0.8 times, most preferably from about 0.6 to about 0.7 times.

In the present invention, the iii) SERPINA5 protein level and/or the decrease in the expression level of the gene encoding it is, for example, about 0.01-fold to about 0.9-fold compared to the control, more preferably about 0.1-fold to about 0.85-fold, more Preferably from about 0.4 to about 0.8 times, most preferably from about 0.6 to about 0.7 times.

In the present invention, examples of the increase or decrease of the expression level of each protein level and / or the gene encoding it are derived based on the values identified and verified in 113 human subjects in the Examples of the present invention, It will be apparent to those skilled in the art that the decrease or increase in the level may be different for each individual subject.

Therefore, in the present invention, in order to provide information for an accurate diagnosis or diagnosis of ovarian cancer, the step of interpreting the change (increase or decrease) of the protein level and/or the expression level of the gene encoding it may be further included. can

In the present invention, the step of interpreting the change (increase or decrease) of the protein level and / or the expression level of the gene encoding it is interpreted by a scoring algorithm based on the importance of each biomarker. .

In the present invention, the step of interpreting the change in the expression level of the protein level and / or the gene encoding it may be characterized in that it is interpreted by a prediction or classification model. In the present invention, the prediction or classification model may be characterized in that it is learned by a known data analysis method. For example, the prediction or classification model may be a linear regression, logistic regression, ridge regression, lasso regression, jackknife regression, decision tree (Decision) Tree, Random Forest, K-means Clustering, Cross-Validation, Artificial Neural Network, Ensemble Learning, Naive Bayes Classification Bayesian Classifier), collaborative filtering, principal component analysis (PCA), and support vector machine (SVM), etc., preferably learning with a random forest or support vector machine. may be, but is not limited thereto. In the present invention, the prediction or classification model may be learned by a supervised or unsupervised algorithm newly designed for diagnosis of ovarian cancer by a person skilled in the art based on an embodiment of the present invention in addition to a known model.

In an embodiment of the present invention, a prediction or classification model based on a random forest or support vector machine method was used for the level change of the biomarker of the present invention, and significantly high sensitivity and specificity in both the prediction or classification models trained by both methods It has been proven that it can diagnose total ovarian cancer and stage 1-2 ovarian cancer patients.

The biomarker protein for ovarian cancer diagnosis of the present invention can be used to diagnose whether a subject has ovarian cancer, and based on this, it can also be used to improve or treat ovarian cancer in patients already diagnosed with ovarian cancer It is obvious to a person skilled in the art.

Accordingly, in another aspect, the present invention provides the steps of: (a) treating a sample isolated from a subject or a candidate drug to an animal model of cancer; and

(b) In the sample or cancer disease animal model treated with the candidate drug, the level of two or more proteins selected from the group consisting of MBL2, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ, or the expression level of a gene encoding the same It relates to a method of screening a drug for the prevention or treatment of ovarian cancer, comprising the steps.

The method for screening a drug for preventing or treating ovarian cancer of the present invention may be characterized by having the same characteristics as those described in the method for providing information for diagnosing and/or diagnosing ovarian cancer.

Example

Hereinafter, the present invention will be described in more detail through examples. These examples are only for illustrating the present invention, and it will be apparent to those of ordinary skill in the art that the scope of the present invention is not to be construed as being limited by these examples.

Example 1: Preparation of blood samples

For the development of ovarian cancer diagnostic marker biomarkers, normal group and ovarian cancer patients were recruited and plasma samples were collected. A total of 113 subjects participated in the experiment, and the number, average age, and stage of each group are shown in Table 1 below. Plasma samples from normal and ovarian cancer patients were provided by Yonsei University Gangnam Severance Hospital.

Characteristic normal group ovarian cancer sample water 30 83 mean age (standard deviation) 47.26 (8.71) 51.29 (10.57) ordnance 1st term 0 34 2nd term 0 5 3rd term 0 36 4th 0 32 Grade One 14 2 19 3 50

Example 2: Quantitative Plasma Proteomic Analysis

To screen for ovarian cancer and early diagnostic markers from plasma samples, plasma proteins were quantitatively analyzed. In order to quantitatively analyze plasma proteins from a total of 113 plasma samples, SWATH (Sequential Window Acquisition of all Theoretical Mass Spectra) was used, a quantitative plasma proteomic analysis technique based on Quadrupole Time of Flight mass spectrometry. did Using the mixed sample, DPHL: A pan-human protein mass spectrometry library (Reference paper: https://pubmed.ncbi.nlm.nih.gov/32795611/) (spectrum library), 40 μL of small amount of blood Samples were pretreated with cleaved peptides, and plasma proteins were quantitatively analyzed through LC/MS SWATH analysis.

High-concentration plasma protein removal column MARS14 column (100 x 4.6 mm; Agilent Technology, Palo Alto) coupled to high-performance liquid chromatography (HPLC) for reproducible analysis of 113 plasma samples prior to being subjected to SWATH analysis , CA, USA) was used to separate low-concentration proteins, and a pretreatment process for preparing peptides was performed using an automated liquid dispensing system (Bravo Automated Liquid Handling Platform - Agilent). After the peptide preparation, 113 different samples were analyzed by SWATH-MS in the same amount based on the values quantified with NanoDrop.

More specifically, 40 μL of plasma was injected into the MARS14 column to inject the top 14 abundant proteins (albumin, IgA, IgG, IgM, α1-antitrypsin, α1-acid glycoprotein, apolipoprotein A1, apolipoprotein A2, complement C3, transferrin, α2- macroglobulin, transthyretin, haptoglobin and fibrinogen) were removed. For this, the mixture was diluted 4-fold with Agilent's buffer A (part number: 5185-5987) and loaded onto a MARS14 column of a Shimadzu HPLC LC20AT system. A fraction of unbound plasma protein was reabsorbed after lyophilization to prepare peptides by the suspension trap method. Specifically, reabsorbed with 5% SDS (50 mM TEAB) buffer, 1,4-dithiothreitol (1,4-Dithiothreitol) is added to 10 mM, and reacted at 95 ° C for 10 minutes to reduce disulfide bonds. made it Then, for alkylation, iodoacetamide was added so as to become 20 mM, and the reaction was carried out at room temperature under dark conditions for 30 minutes, followed by decomposition with 6 μg of a trypsin/rice seed mixture at 37°C for 18 hours. Peptides were eluted, lyophilized with a cold trap (CentriVap Cold Traps, LABCONCO), and stored at −80° C. until use. Prior to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, the dried peptide samples were dissolved in buffer A (0.1 % formic acid in HPLC water) and the total peptide concentration was measured using a UV/Vis spectrophotometer (NanoDrop One, Thermo Fisher Scientific). ) to measure the absorbance. Wavelength 280 nm, sample type option set to "1 Abs = 1 mg/mL". The iRT standard provided by the iRT-Kit (Biognosys AG, Schlieren, Switzerland) was then added to the sample at 1/10 volume by the vendor instructions (reference: https://pubmed.ncbi.nlm.nih.gov/22577012) /), the retention time was corrected. The total amount of each sample was 40 μg and was dissolved in 40 μL. Samples of 4 μL injected were analyzed using a SCIEX TripleTOF 5600+ system mass spectrometer with the following LC-MS/MS settings. For LC separation, nanoLC 425 (Eksigent, Dublin, CA, USA) was combined with an Eksigent micro-trap C18 column (ChromXP C18CL, 5 μm, 120 Å) and an Eksigent column (C18-CL, 0.3 x 150 mm, particle size 3) as an analytical column. μm, pore size 120 Å) together such that the column temperature was maintained at 40°C. Samples were loaded onto the trap column using buffer A at a flow rate of 10 μL/min. After 10 min, the peptide mixture was separated by gradient using buffer A and buffer B (0.1 % formic acid in HPLC acetonitrile) at a flow rate of 5 μL/min by 57 min. Buffer B 3-25% change for 38 min, 25-32% change for 5 min, 32-80% change for 2 min, hold 80% for 3 min, 80-3% change for 3 min changed and held at 3% for 8 minutes. For individual samples, all mass spectrometry runs were operated in sequential window acquisition of all theoretical mass spectra (SWATH) modes using 100 variable windows according to the SCIEX technical note. The SWATH parameter was set as follows: Lower m/z limit 400; m/z upper limit 1250; window overlap (Da) 1.0; CES was 5 for the small window, 8 for the large window, and 10 for the largest window. MS2 spectra were collected in the range of 100–1500 m/z for 2.5 ms in high sensitivity mode, with a total cycle time of 2.8 s. Other MS parameters were set as follows: Ion Source Gas 1 (GS1) 15 psi; Ion Source Gas 2 (GS2) 20 psi; Curtain gas (CUR) 30 psi; temperature (TEM) 250 °C; Ion Spray Voltage Floating (ISVF) 5500V.

SWATH LC-MS analysis was sequentially performed over 5 days by randomly mixing 113 samples of plasma protein prepared with peptides in order to minimize the bias for each batch in analysis by LC-MS. SWATH LC-MS analysis was performed on 113 samples to obtain 113 spectral results. The resulting spectra were qualitatively analyzed using DIA-NN software and Pan-human proteome library, and a total of 831 proteins from 113 samples could be analyzed qualitatively/quantitatively. As shown in FIG. 1 , it was confirmed that reproducibility was shown as a result of analyzing the amount information of normalized proteins based on the median value for a total of 113 samples in the normal group and the ovarian cancer patient group on a log scale. In addition, statistical analysis was performed using quantitative information on a total of 133 proteins that were detected over 80% in 113 plasma samples from two groups and were not related to MARS-14 high abundant protein depletion.

As a result of quantitative plasma proteomics analysis comparing the 2 groups of ovarian cancer patients (group 1: 1-2, group 2: 3-4) and the normal control group of each stage, the total showing a significant difference 31 DEPs (differential expressed protein) were identified. The DEP common to the 3 groups was Alpolipoprotein L1, and 10 proteins were found to be the common DEPs of the control group and the stage 1-2 ovarian cancer group, and the control group and the ovarian cancer stage 3-4 group. In addition, 7 proteins were found to be common DEPs between the control group and the stage 1-2 ovarian cancer group, the stage 1-2 ovarian cancer group and the ovarian cancer stage 3-4 group. In particular, among the biomarkers for ovarian cancer diagnosis of the present invention, APOL1, SERPINA5, SERPINA7, ECM1, CPN2 and ADIPOQ decreased in all ovarian cancer patients, and MBL2 increased. In particular, in the case of stage 1-2 ovarian cancer patients, not only the normal control group, but also when compared with stage 3-4 ovarian cancer patients, APOL1 and SERPINA5 were significantly decreased, and MBL2 was significantly increased (Fig. 2). ).

By performing a feature selection process based on the total plasma proteome detected in the group of ovarian cancer patients (group 1: stage 1-2, group 2: stage 3-4) and the normal control group, the The optimal marker protein candidates for deriving ovarian cancer patients were selected. 4 proteins (Fig. 3a) for the stage 1-2 group and 10 proteins for the whole stage were derived as candidates (Fig. 3b), but in order to improve the sensitivity and accuracy of detection, biomarker candidates with a Probability of less than 0.5 were excluded. Thus, 7 types of plasma biomarkers including 3 types of biomarkers for the diagnosis of stage 1-2 ovarian cancer patients and the 3 types of biomarkers for the diagnosis of total ovarian cancer patients were selected.

The changes in the expression level compared to the normal group of 7 biomarkers for ovarian cancer diagnosis of the present invention to be described below are shown in Table 2 and FIGS. 4 and 5 below. 7 biomarkers selected as shown in Table 2 and FIGS. 4-5 show significant differences in the level of stage 1-2 ovarian cancer patients or total ovarian cancer patients compared to the normal control group.

APOL1 SERPINA5 MBL2 SERPINA7 ECM1 CPN2 ADIPOQ Ovarian Cancer/Normal (pear) 0.76 0.71 1.53 0.85 0.83 0.88 0.81 Ovarian Cancer Stage 1-2/Normal (2x) 0.70 0.65 1.70 0.84 0.79 0.86 0.81

Example 3: Model construction and validation using plasma protein data

A model capable of predicting early ovarian cancer disease and predicting ovarian cancer disease was constructed using quantitative information on plasma proteomic proteins of the normal group and ovarian cancer patients collected in Example 2. As a first set, a disease prediction model was created for 39 patients with stage 1-2 among 83 patients with ovarian cancer, including 30 normal patients for early stage ovarian cancer disease prediction. In the second set, the prediction of the ovarian cancer disease model included 30 normal subjects and 83 total ovarian cancer patients. Among 133 proteins, for feature selection to be used as a biomarker, the same decision tree structure is used in both sets to use quantitative protein information as a variable and a model at the stage of selecting characteristic variables. An embedded method, which includes variable selection itself, was applied. 10,000 decision trees were formed using a random forest algorithm (RF), and a variable with high predictive power was first selected based on the AUC result value of each tree. This 10-fold cross-validation was repeated 3 times to finally select a variable with a probability value of 50% or more of the selected variable (Table 3). Using the three protein panels selected as a result of the initial ovarian cancer prediction set through the above process, the entire sample is divided into 3 groups and 2/3 of the information is used to construct the model (training set) and the remaining 1/3 of the information was composed of a test set that verifies the completeness of the model (Table 4). Two types of predictive statistical models were used: a random forest algorithm and a support vector machine (SVM). Seven protein panels (Table 5) were used in the ovarian cancer prediction set, and samples were constructed with the above three-fold cross-validation to generate and verify SVM and RF models (Table 6).

number Uniprot Accession ID gene name protein name importance Percentage to be selected (%) final variable selection One O14791 APOL1 Apolipoprotein L1 1.405 86.7 Yes 2 P05154 SERPINA5 Plasma serine protease inhibitor 0.906 50.0 Yes 3 P11226 MBL2 Mannose-binding protein C 0.847 56.7 Yes 4 P06396 GSN Gelsolin 0.834 23.3 no

normal group ovarian cancer total sample training set 20 26 46 validation set 10 13 23 total sample 30 39 69

number Uniprot Accession ID gene name protein name importance Percentage to be selected (%) final variable selection One P11226 MBL2 Mannose-binding protein C 0.859 60.0 Yes 2 O14791 APOL1 Apolipoprotein L1 0.815 60.0 Yes 3 P05543 SERPINA7 Thyroxine-binding globulin 0.802 60.0 Yes 4 Q16610 ECM1 Extracellular matrix protein 1 0.786 60.0 Yes 5 P05154 SERPINA5 Plasma serine protease inhibitor 0.731 53.0 Yes 6 P00746 CFDs Complement factor D 0.674 43.0 no 7 P22792 CPN2 Carboxypeptidase N subunit 2 0.621 50.0 Yes 8 Q15848 ADIPOQ Adiponectin 0.613 50.0 Yes 9 P01031 C5 Complement C5 0.603 37.0 no 10 P06396 GSN Gelsolin 0.587 27.0 no

normal group ovarian cancer total sample training set 20 56 76 validation set 10 27 37 total sample 30 83 113

For early ovarian cancer diagnosis prediction, it was verified whether it was possible to distinguish the normal group from the ovarian cancer patient group (stage 1-2) using a model consisting of the three proteins (APOL1, SERPINA5, MBL2) panel. For verification, a random forest model (RF model) and a support vector machine model (SVM model) were used. The training of each model for diagnostic validation of the ovarian cancer patient group (stage 1-2) using three protein markers is shown in FIGS. 6 and 7, respectively.

The area under curve (AUC) of the receiver operating characteristics (ROC) indicating the clinical usefulness of the training set and the validation set to which the random forest model is applied is 1.0 (training set), 0.886 (validation set) appeared very well. When the clinical cut value was set to 0.5284, which is the Youden index, based on the validation set, the specificity was 83.33% and the sensitivity was 90.90% (FIG. 8). On the other hand, as a result of applying the support vector machine model, the area under the surface of the receiving motion characteristic is 0.857 (training set) and 0.939 (validation set), and the clinical cut value is set to 0.6852, the Youden index, based on the validation set. , it was found that the specificity was 100% and the sensitivity was 90.90% (FIG. 9). This is significantly superior to the AUC value in the case of diagnosing early ovarian cancer using each biomarker independently, meaning that the early ovarian cancer diagnosis method of the present invention exhibits remarkably excellent diagnostic accuracy (FIG. 10) ).

The training of each model for diagnostic validation of the ovarian cancer patient group (total stage) using 7 protein markers is shown in FIGS. 11 and 12, respectively.

For predicting ovarian cancer diagnosis, using a model consisting of the 7 proteins (MBL2, APOL1, SERPINA7, ECM1, SERPINA5, CPN2, ADIPOQ) panel, it is possible to distinguish between the normal group and the total ovarian cancer patient group (stages 1-4). verified. The area under curve (AUC) of the receiver operating characteristics (ROC) indicating the clinical usefulness of the training set and the validation set to which the random forest model is applied is 1.0 (training set), 0.881 (validation set) appeared very well. When the clinical cut value was set to 0.6289, which is the Youden index, based on the validation set, the specificity was 90.0% and the sensitivity was 77.77% (FIG. 13). On the other hand, as a result of applying the support vector machine model, the area under the surface of the receiving motion characteristic is 0.858 (training set) and 0.800 (validation set), and the clinical cut value based on the validation set is set to the Youden index, 0.666. , it was found that the specificity was 80.0% and the sensitivity was 74.07% (FIG. 14). Seven biomarkers for diagnosing total ovarian cancer are also significantly superior to the area of the surface area (AUC) when diagnosing early ovarian cancer using each biomarker independently, so the method for diagnosing early ovarian cancer of the present invention is significantly better It was confirmed that excellent diagnostic accuracy was exhibited ( FIGS. 15A to 15G ).

As described above in detail a specific part of the content of the present invention, for those of ordinary skill in the art, it is clear that this specific description is only a preferred embodiment, and the scope of the present invention is not limited thereby. will be. Accordingly, it is intended that the substantial scope of the present invention be defined by the appended claims and their equivalents.

<110> REGION Co., Ltd. <120> Multiple biomarkers for diagnosing ovarian cancer and uses thereof <130> P21-B089 <160> 7 <170> KoPatentIn 3.0 <210> 1 <211> 248 <212> PRT <213> Artificial Sequence <220> <223> Human MBL2 (UniProtKB: P11226) <400> 1 Met Ser Leu Phe Pro Ser Leu Pro Leu Leu Leu Leu Ser Met Val Ala 1 5 10 15 Ala Ser Tyr Ser Glu Thr Val Thr Cys Glu Asp Ala Gln Lys Thr Cys 20 25 30 Pro Ala Val Ile Ala Cys Ser Ser Pro Gly Ile Asn Gly Phe Pro Gly 35 40 45 Lys Asp Gly Arg Asp Gly Thr Lys Gly Glu Lys Gly Glu Pro Gly Gln 50 55 60 Gly Leu Arg Gly Leu Gln Gly Pro Pro Gly Lys Leu Gly Pro Pro Gly 65 70 75 80 Asn Pro Gly Pro Ser Gly Ser Pro Gly Pro Lys Gly Gln Lys Gly Asp 85 90 95 Pro Gly Lys Ser Pro Asp Gly Asp Ser Ser Leu Ala Ala Ser Glu Arg 100 105 110 Lys Ala Leu Gln Thr Glu Met Ala Arg Ile Lys Lys Lys Trp Leu Thr Phe 115 120 125 Ser Leu Gly Lys Gln Val Gly Asn Lys Phe Phe Leu Thr Asn Gly Glu 130 135 140 Ile Met Thr Phe Glu Lys Val Lys Ala Leu Cys Val Lys Phe Gln Ala 145 150 155 160 Ser Val Ala Thr Pro Arg Asn Ala Ala Glu Asn Gly Ala Ile Gln Asn 165 170 175 Leu Ile Lys Glu Glu Ala Phe Leu Gly Ile Thr Asp Glu Lys Thr Glu 180 185 190 Gly Gin Phe Val Asp Leu Thr Gly Asn Arg Leu Thr Tyr Thr Asn Trp 195 200 205 Asn Glu Gly Glu Pro Asn Asn Ala Gly Ser Asp Glu Asp Cys Val Leu 210 215 220 Leu Leu Lys Asn Gly Gln Trp Asn Asp Val Pro Cys Ser Thr Ser His 225 230 235 240 Leu Ala Val Cys Glu Phe Pro Ile 245 <210> 2 <211> 398 <212> PRT <213> Artificial Sequence <220> <223> Human APOL1 (UniProtKB: O14791) <400> 2 Met Glu Gly Ala Ala Leu Leu Arg Val Ser Val Leu Cys Ile Trp Met 1 5 10 15 Ser Ala Leu Phe Leu Gly Val Gly Val Arg Ala Glu Glu Ala Gly Ala 20 25 30 Arg Val G ln Gln Asn Val Pro Ser Gly Thr Asp Thr Gly Asp Pro Gln 35 40 45 Ser Lys Pro Leu Gly Asp Trp Ala Ala Gly Thr Met Asp Pro Glu Ser 50 55 60 Ser Ile Phe Ile Glu Asp Ala Ile Lys Tyr Phe Lys Glu Lys Val Ser 65 70 75 80 Thr Gln Asn Leu Leu Leu Leu Leu Leu Thr Asp Asn Glu Ala Trp Asn Gly 85 90 95 Phe Val Ala Ala Ala Glu Leu Pro Arg Asn Glu Ala Asp Glu Leu Arg 100 105 110 Lys Ala Leu Asp Asn Leu Ala Arg Gln Met Ile Met Lys Asp Lys Asn 115 120 125 Trp His Asp Lys Gly Gln Gln Tyr Arg Asn Trp Phe Leu Lys Glu Phe 130 135 140 Pro Arg Leu Lys Ser Glu Leu Glu Asp Asn Ile Arg Arg Leu Arg Ala 145 150 155 160 Leu Ala Asp Gly Val Gln Lys Val His Lys Gly Thr Thr Ile Ala Asn 165 170 175 Val Val Ser Gly Ser Leu Ser Ile Ser Ser Gly Ile Leu Thr Leu Val 180 185 190 Gly Met Gly Leu Ala Pro Phe Thr Glu Gly Gly Se r Leu Val Leu Leu 195 200 205 Glu Pro Gly Met Glu Leu Gly Ile Thr Ala Ala Leu Thr Gly Ile Thr 210 215 220 Ser Ser Thr Met Asp Tyr Gly Lys Lys Trp Trp Thr Gln Ala Gln Ala 225 230 235 240 His Asp Leu Val Ile Lys Ser Leu Asp Lys Leu Lys Glu Val Arg Glu 245 250 255 Phe Leu Gly Glu Asn Ile Ser Asn Phe Leu Ser Leu Ala Gly Asn Thr 260 265 270 Tyr Gln Leu Thr Arg Gly Ile Gly Lys Asp Ile Arg Ala Leu Arg Arg 275 280 285 Ala Arg Ala Asn Leu Gln Ser Val Pro His Ala Ser Ala Ser Arg Pro 290 295 300 Arg Val Thr Glu Pro Ile Ser Ala Glu Ser Gly Glu Gln Val Glu Arg 305 310 315 320 Val Asn Glu Pro Ser Ile Leu Glu Met Ser Arg Gly Val Lys Leu Thr 325 330 335 Asp Val Ala Pro Val Ser Phe Phe Le u Val Leu Asp Val Val Tyr Leu 340 345 350 Val Tyr Glu Ser Lys His Leu His Glu Gly Ala Lys Ser Glu Thr Ala 355 360 365 Glu Glu Leu Lys Lys Val Ala Gln Glu Leu Glu Glu Lys Leu Asn Ile 370 375 380 Leu Asn Asn Asn Tyr Lys Ile Leu Gln Ala Asp Gln Glu Leu 385 390 395 <210> 3 <211> 406 <212> PRT <213> Artificial Sequence <220> <223> Human SERPINA5(UniProtKB: P05154) <400> 3 Met Gln Leu Phe Leu Leu Leu Cys Leu Val Leu Leu Ser Pro Gln Gly 1 5 10 15 Ala Ser Leu His Arg His His Pro Arg Glu Met Lys Lys Arg Val Glu 20 25 30 Asp Leu His Val Gly Ala Thr Val Ala Pro Ser Ser Arg Arg Asp Phe 35 40 45 Thr Phe Asp Leu Tyr Arg Ala Leu Ala Ser Ala Ala Pro Ser Gln Ser 50 55 60 Ile Phe Phe Ser Pro Val Ser Ile Ser Met Ser Leu Ala Met Leu Ser 65 70 75 80 Leu Gly Ala Gly Ser Ser Thr Lys Met Gln Ile Leu Glu Gly Leu Gly 85 90 95 Leu Asn Leu Gln Lys Ser Ser Glu Lys Glu Leu Hi s Arg Gly Phe Gln 100 105 110 Gln Leu Leu Gln Glu Leu Asn Gln Pro Arg Asp Gly Phe Gln Leu Ser 115 120 125 Leu Gly Asn Ala Leu Phe Thr Asp Leu Val Val Asp Leu Gln Asp Thr 130 135 140 Phe Val Ser Ala Met Lys Thr Leu Tyr Leu Ala Asp Thr Phe Pro Thr 145 150 155 160 Asn Phe Arg Asp Ser Ala Gly Ala Met Lys Gln Ile Asn Asp Tyr Val 165 170 175 Ala Lys Gln Thr Lys Gly Lys Ile Val Asp Leu Leu Lys Asn Leu Asp 180 185 190 Ser Asn Ala Val Val Ile Met Val Asn Tyr Ile Phe Phe Lys Ala Lys 195 200 205 Trp Glu Thr Ser Phe Asn His Lys Gly Thr Gln Glu Gln Asp Phe Tyr 210 215 220 Val Thr Ser Glu Thr Val Val Arg Val Pro Met Met Ser Arg Glu Asp 225 230 235 240 Gln Tyr His Tyr Leu Leu Asp Arg As n Leu Ser Cys Arg Val Val Gly 245 250 255 Val Pro Tyr Gln Gly Asn Ala Thr Ala Leu Phe Ile Leu Pro Ser Glu 260 265 270 Gly Lys Met Gln Gln Val Glu Asn Gly Leu Ser Glu Lys Thr Leu Arg 275 280 285 Lys Trp Leu Lys Met Phe Lys Lys Arg Gln Leu Glu Leu Tyr Leu Pro 290 295 300 Lys Phe Ser Ile Glu Gly Ser Tyr Gln Leu Glu Lys Val Leu Pro Ser 305 310 315 320 Leu Gly Ile Ser Asn Val Phe Thr Ser His Ala Asp Leu Ser Gly Ile 325 330 335 Ser Asn His Ser Asn Ile Gln Val Ser Glu Met Val His Lys Ala Val 340 345 350 Val Glu Val Asp Glu Ser Gly Thr Arg Ala Ala Ala Ala Thr Gly Thr 355 360 365 Ile Phe Thr Phe Arg Ser Ala Arg Leu Asn Ser Gln Arg Leu Val Phe 370 375 380 Asn Arg Pro Phe Leu Met Phe Ile Val Asp As n Asn Ile Leu Phe Leu 385 390 395 400 Gly Lys Val Asn Arg Pro 405 <210> 4 <211> 415 <212> PRT <213> Artificial Sequence <220> <223> Human SERPINA7(UniProtKB: P05543) <400> 4 Met Ser Pro Phe Leu Tyr Leu Val Leu Leu Val Leu Gly Leu His Ala 1 5 10 15 Thr Ile His Cys Ala Ser Pro Glu Gly Lys Val Thr Ala Cys His Ser 20 25 30 Ser Gln Pro Asn Ala Thr Leu Tyr Lys Met Ser Ser Ile Asn Ala Asp 35 40 45 Phe Ala Phe Asn Leu Tyr Arg Arg Phe Thr Val Glu Thr Pro Asp Lys 50 55 60 Asn Ile Phe Phe Ser Pro Val Ser Ile Ser Ala Ala Leu Val Met Leu 65 70 75 80 Ser Phe Gly Ala Cys Cys Ser Thr Gln Thr Glu Ile Val Glu Thr Leu 85 90 95 Gly Phe Asn Leu Thr Asp Thr Pro Met Val Glu Ile Gln His Gly Phe 100 105 110 Gln His Leu Ile Cys Ser Leu Asn Phe Pro Lys Lys Glu Leu Glu Leu 115 120 125 Gln Ile Gly Asn Ala Leu Phe Ile Gly Lys His Leu Lys Pro Leu Ala 130 135 14 0 Lys Phe Leu Asn Asp Val Lys Thr Leu Tyr Glu Thr Glu Val Phe Ser 145 150 155 160 Thr Asp Phe Ser Asn Ile Ser Ala Ala Lys Gln Glu Ile Asn Ser His 165 170 175 Val Glu Met Gln Thr Lys Gly Lys Val Val Gly Leu Ile Gln Asp Leu 180 185 190 Lys Pro Asn Thr Ile Met Val Leu Val Asn Tyr Ile His Phe Lys Ala 195 200 205 Gln Trp Ala Asn Pro Phe Asp Pro Ser Lys Thr Glu Asp Ser Ser Ser 210 215 220 Phe Leu Ile Asp Lys Thr Thr Thr Val Gln Val Pro Met Met His Gln 225 230 235 240 Met Glu Gln Tyr Tyr His Leu Val Asp Met Glu Leu Asn Cys Thr Val 245 250 255 Leu Gln Met Asp Tyr Ser Lys Asn Ala Leu Ala Leu Phe Val Leu Pro 260 265 270 Lys Glu Gly Gln Met Glu Ser Val Glu Ala Ala Met Ser Ser Lys Thr 275 280 285 Leu Lys Lys Trp Asn Arg Leu Leu Gln Lys Gly Trp Val Asp Leu Phe 290 295 300 Val Pro Lys Phe Ser Ile Ser Ala Thr Tyr Asp Leu Gly Ala Thr Leu 305 310 315 320 Leu Lys Met Gly Ile Gln His Ala Tyr Ser Glu Asn Ala Asp Phe Ser 325 330 335 Gly Leu Thr Glu Asp Asn Gly Leu Lys Leu Ser Asn Ala Ala His Lys 340 345 350 Ala Val Leu His Ile Gly Glu Lys Gly Thr Glu Ala Ala Ala Val Pro 355 360 365 Glu Val Glu Leu Ser Asp Gln Pro Glu Asn Thr Phe Leu His Pro Ile 370 375 380 Ile Gln Ile Asp Arg Ser Phe Met Leu Leu Ile Leu Glu Arg Ser Thr 385 390 395 400 Arg Ser Ile Leu Phe Leu Gly Lys Val Val Asn Pro Thr Glu Ala 405 410 415 <210> 5 <211> 540 <212> PRT <213> Artificial Sequence <220> <223> Human ECM1(UniProtKB : Q16610) <400> 5 Met Gly Thr Thr Ala Arg Ala Ala Leu Val Leu Thr Tyr Leu Ala Val 1 5 10 15 Ala Ser Ala Ala Ser Glu Gly Gly Phe Thr Ala Thr Gly Gln Arg Gln 20 25 30 Leu Arg Pro Glu His Phe Gln Glu Val Gly Tyr Ala Ala Pro Ser 35 40 45 Pro Pro Leu Ser Arg Ser Leu Pro Met Asp His Pro Asp Ser Ser Gln 50 55 60 His Gly Pro Pro Phe Glu Gly Gln Ser Gln Val Gln Pro Pro Pro Ser 65 70 75 80 Gln Glu Ala Thr Pro Leu Gln Gln Glu Lys Leu Leu Pro Ala Gln Leu 85 90 95 Pro Ala Glu Lys Glu Val Gly Pro Pro Leu Pro Gln Glu Ala Val Pro 100 105 110 Leu Gln Lys Glu Leu Pro Ser Leu Gln His Pro Asn Glu Gln Lys Glu 115 120 125 Gly Thr Pro Ala Pro Phe Gly Asp Gln Ser His Pro Glu Pro Glu Ser 130 135 140 Trp Asn Ala Ala Gln His Cys Gln Gln Asp Arg Ser Gln Gly Gly Trp 145 150 155 160 Gly His Arg Leu Asp Gly Phe Pro Pro Gly Arg Pro Ser Pro Asp Asn 165 170 175 Leu Asn Gln Ile Cys Leu Pro Asn Arg Gln His Val Val Tyr Gly Pro 180 185 190 Trp Asn Leu Pro Gln Ser Ser Tyr Ser His Leu Thr Arg Gln Gly Glu 195 200 205 Thr Leu Asn Phe Leu Glu Ile Gly Tyr Ser Arg Cys Cys His Cys Arg 210 215 220 Ser His Thr Asn Arg Leu Glu Cys Ala Lys Leu Val Trp Glu Glu Ala 225 230 235 240 Met Ser Arg Phe Cys Glu Ala Glu Phe Ser Val Lys Thr Arg Pro His 245 250 255 Trp Cys Cys Thr Arg Gln Gly Glu Ala Arg Phe Ser Cys Phe Gln Glu 260 265 270 Glu Ala Pro Gln Pro His Tyr Gln Leu Arg Ala Cys Pro Ser His Gln 275 280 285 Pro Asp Ile Ser Ser Ser Gly Leu Glu Leu Pro Phe Pro Pro Gly Val Pro 290 295 300 Thr Leu Asp Asn Ile Lys Asn Ile Cys His Leu Arg Arg Phe Arg Ser 305 310 315 320 Val Pro Arg Asn Leu Pro Ala Thr Asp Pro Leu Gln Arg Glu Leu Leu 325 330 335 Ala Leu Ile Gln Leu Glu Arg Glu Phe Gln Arg Cys Cys Arg Gln Gly 340 345 350 Asn Asn His Thr Cys Thr Trp Lys Ala Trp Glu Asp Thr Leu Asp Lys 355 360 365 Tyr Cys Asp Arg Glu Tyr Ala Val Lys Thr His His His Leu Cys Cys 370 375 380 Arg His Pro Pro Ser Pro Thr Arg Asp Glu Cys Phe Ala Arg Arg Ala 385 390 395 400 Pro Tyr Pro Asn Tyr Asp Arg Asp Ile Leu Thr Ile Asp Ile Gly Arg 405 410 415 Val Thr Pro Asn Leu Met Gly His Leu Cys Gly Asn Gln Arg Val Leu 420 425 430 Thr Lys His Lys His Ile Pro Gly Leu Ile His Asn Met Thr Ala Arg 435 440 445 Cys Cys Asp Leu Pro Phe Pro Glu Gln Ala Cys Cys Ala Glu Glu Glu 450 455 460 Lys Leu Thr Phe Ile Asn Asp Leu Cys Gly Pro Arg Arg Asn Ile Trp 465 470 475 480 Arg Asp Pro Ala Leu Cys Cys Tyr Leu Ser Pro Gly Asp Glu Gln Val 485 490 495 Asn Cys Phe Asn Ile Asn Tyr Leu Arg Asn Val Ala Leu Val Ser Gly 500 505 510 Asp Thr Glu Asn Ala Lys Gly Gln Gly Glu Gln Gly Ser Thr Gly Gly 515 520 525 Thr Asn Ile Ser Ser Thr Ser Glu Pro Lys Glu Glu 530 535 540 <210> 6 <211 > 545 <212> PRT <213> Artificial Sequence <220> <223> Human CPN2(UniProtKB: P22792) <400> 6 Met Leu Pro Gly Ala Trp Leu Leu Trp Thr Ser Leu Leu Leu Leu Ala 1 5 10 15 Arg Pro Ala Gln Pro Cys Pro Met Gly Cys Asp Cys Phe Val Gln Glu 20 25 30 Val Phe Cys Ser Asp Glu Glu Leu Ala Thr Val Pro Leu Asp Ile Pro 35 40 45 Pro Tyr Thr Lys Asn Ile Ile Phe Val Glu Thr Ser Phe Thr Thr Leu 50 55 60 Glu Thr Arg Ala Phe Gly Ser Asn Pro Asn Leu Thr Lys Val Val Phe 65 70 75 80 Leu Asn Thr Gln Leu Cys Gln Phe Arg Pro Asp Ala Phe Gly Gly Leu 85 90 95 Pro Arg Leu Glu Asp Leu Glu Val Thr Gly Ser Ser Phe Leu Asn Leu 100 105 110 Ser Thr Asn Ile Phe Ser Asn Leu Thr Ser Leu Gly Lys Leu Thr Leu 115 120 125 Asn Phe Asn Met Leu Glu Ala Leu Pro Glu Gly Leu Phe Gln His Leu 130 135 140 Ala Ala Leu Glu Ser Leu His Leu Gln Gly Asn Gln Leu Gln Ala Leu 145 150 155 160 Pro Arg Arg Leu Phe Gln Pro Leu Thr His Leu Lys Thr Leu Asn Leu 165 170 175 Ala Gln Asn Leu Leu Ala Gln Leu Pro Glu Glu Leu Phe His Pro Leu 180 185 190 Thr Ser Leu Gln Thr Leu Lys Leu Ser Asn Asn Ala Leu Ser Gly Leu 195 200 205 Pro Gln Gly Val Phe Gly Lys Leu Gly Ser Leu Gln Glu Leu Phe Leu 210 215 220 Asp Ser Asn Asn Ile Ser Glu Leu Pro Pro Gln Val Phe Ser Gln Leu 225 230 235 240 Phe Cys Leu Glu Arg Leu Trp Leu Gln Arg Asn Ala Ile Thr His Leu 245 250 255 Pro Leu Ser Ile Phe Ala Ser Leu Gly Asn Leu Thr Phe Leu Ser Leu 260 265 270 Gln Trp Asn Met Leu Arg Val Leu Pro Ala Gly Leu Phe Ala His Thr 275 280 285 Pro Cys Leu Val Gly Leu Ser Leu Thr His Asn Gln Leu Glu Thr Val 290 295 300 Ala Glu Gly Thr Phe Ala His Leu Ser Asn Leu Arg Ser Leu Met Leu 305 310 315 320 Ser Tyr Asn Ala Ile Thr His Leu Pro Ala Gly Ile Phe Arg Asp Leu 325 330 335 Glu Glu Leu Val Lys Leu Tyr Leu Gly Ser Asn Asn Leu Thr Ala Leu 340 345 350 His Pro Ala Leu Phe Gln Asn Leu Ser Lys Leu Glu Leu Leu Ser Leu 355 360 365 Ser Lys Asn Gln Leu Thr Thr Leu Pro Glu Gly Ile Phe Asp Thr Asn 370 375 380 Tyr Asn Leu Phe Asn Leu Ala Leu His Gly Asn Pro Trp Gln Cys Asp 385 390 395 400 Cys His Leu Ala Tyr Leu Phe Asn Trp Leu Gln Gln Tyr Thr Asp Arg 405 410 415 Leu Leu Asn Ile Gln Thr Tyr Cys Ala Gly Pro Ala Tyr Leu Lys Gly 420 425 430 Gln Val Val Pro Ala Leu Asn Glu Lys Gln Leu Val Cys Pro Val Thr 435 440 445 Arg Asp His Leu Gly Phe Gln Val Thr Trp Pro Asp Glu Ser Lys Ala 450 455 460 Gly Gly Ser Trp Asp Leu Ala Val Gln Glu Arg Ala Ala Arg Ser Gln 465 470 475 480 Cys Thr Tyr Ser Asn Pro Glu Gly Thr Val Val Leu Ala Cys Asp Gln 485 490 495 Ala Gln Cys Arg Trp Leu Asn Val Gln Leu Ser Pro Gln Gln Gly Ser 500 505 510 Leu Gly Leu Gln Tyr Asn Ala Ser Gln Glu Trp Asp Leu Arg Ser Ser 515 520 525 Cys Gly Ser Leu Arg Leu Thr Val Ser Ile Glu Ala Arg Ala Ala Gly 530 535 540 Pro 545 <210> 7 <211> 244 <212> PRT <213> Artificial Sequence <220> <223> Human ADIPOQ(UniProtKB: Q15848) <400> 7 Met Leu Leu Leu Gly Ala Val Leu Leu Leu Leu Ala Leu Pro Gly His 1 5 10 15 Asp Gln Glu Thr Thr Thr Gln Gly Pro Gly Val Leu Leu Pro Leu Pro 20 25 30 Lys Gly Ala Cys Thr Gly Trp Met Ala Gly Ile Pro Gly His Pro Gly 35 40 45 His Asn Gly Ala Pro Gly Arg Asp Gly Arg Asp Gly Thr Pro Gly Glu 50 55 60 Lys Gly Glu Lys Gly Asp Pro Gly Leu Ile Gly Pro Lys Gly Asp Ile 65 70 75 80 Gly Glu Thr Gly Val Pro Gly Ala Glu Gly Pro Arg Gly Phe Pro Gly 85 90 95 Ile Gln Gly Arg Lys Gly Glu Pro Gly Glu Gly Ala Tyr Val Tyr Arg 100 105 110 Ser Ala Phe Ser Val Gly Leu Glu Thr Tyr Val Thr Ile Pro Asn Met 115 120 125 Pro Ile Arg Phe Thr Lys Ile Phe Tyr Asn Gln Gln Asn His Tyr Asp 130 135 140 Gly Ser Thr Gly Lys Phe His Cys Asn Ile Pro Gly Leu Tyr Tyr Phe 145 150 155 160 Ala Tyr His Ile Thr Val Tyr Met Lys Asp Val Lys Val Ser Leu Phe 165 170 175 Lys Lys Asp Lys Ala Met Leu Phe Thr Tyr Asp Gln Tyr Gln Glu Asn 180 185 190 Asn Val Asp Gln Ala Ser Gly Ser Val Leu Leu His Leu Glu Val Gly 195 200 205 Asp Gln Val Trp Leu Gln Val Tyr Gly Glu Gly Glu Arg Asn Gly Leu 210 215 220 Tyr Ala Asp Asn Asp Asn Asp Ser Thr Phe Thr Gly Phe Leu Leu Tyr 225 230 235 240His Asp Thr Asn

Claims (20)

of MBL2, APOL1 and SERPINA5 A composition for diagnosis of ovarian cancer, comprising an agent capable of measuring the protein level or the expression level of a gene encoding the same.
delete The composition for diagnosis of ovarian cancer according to claim 1, further comprising an agent capable of measuring the level of any one or more proteins selected from the group consisting of SERPINA7, ECM1, CPN2 and ADIPOQ or the expression level of a gene encoding the same.
The method of claim 1, wherein the agent capable of measuring the protein level is selected from the group consisting of an antibody, oligopeptide, ligand, PNA (peptide nucleic acid) and aptamer that specifically binds to each protein. A composition for diagnosis of ovarian cancer, characterized in that.
The diagnosis of ovarian cancer according to claim 1, wherein the agent capable of measuring the expression level of the gene is selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to genes encoding each protein. composition.
A kit for diagnosing ovarian cancer, comprising an agent capable of measuring the protein level of MBL2, APOL1, and SERPINA5 or the expression level of a gene encoding the same.
delete [Claim 7] The kit for diagnosing ovarian cancer according to claim 6, further comprising an agent capable of measuring the level of any one or more proteins selected from the group consisting of SERPINA7, ECM1, CPN2 and ADIPOQ or the gene expression level encoding the same.
The method of claim 6, wherein the agent capable of measuring the protein level is selected from the group consisting of an antibody, oligopeptide, ligand, PNA (peptide nucleic acid) and aptamer that specifically binds to each protein. A diagnostic kit for ovarian cancer.
[Claim 7] The diagnosis of ovarian cancer according to claim 6, wherein the agent capable of measuring the expression level of the gene is selected from the group consisting of primers, probes and antisense nucleotides that specifically bind to genes encoding each protein. kit.
(a) measuring the protein level of MBL2, APOL1 and SERPINA5 or the expression level of a gene encoding the same from the sample isolated from the subject;
(b) a method for providing information for diagnosing ovarian cancer, comprising comparing the protein level or the expression level of a gene encoding the same with a control.
The method of claim 11, wherein the sample in step (a) is blood, serum or plasma.
The method of claim 11, wherein in step (a), the protein level is measured through mass spectrometry.
delete The method of claim 11, wherein step (a) further measures the level of any one or more proteins selected from the group consisting of SERPINA7, ECM1, CPN2 and ADIPOQ or the expression level of a gene encoding the same. How to provide information for diagnosis.
The method of claim 11, wherein the ovarian cancer is stage 1 or stage 2 ovarian cancer.
16. The method of claim 15,
(C) When a change in the level of any one or more of the following proteins or the expression level of a gene encoding the same is detected, the method of providing information for diagnosing ovarian cancer further comprising the step of confirming that there is a possibility of developing ovarian cancer:
i) an increase in the level of MBL2 protein and/or the expression level of a gene encoding it;
ii) a decrease in the level of APOL1 protein and/or the expression level of the gene encoding it;
iii) a decrease in the level of the SERPINA5 protein and/or the expression level of the gene encoding it;
iv) a decrease in the level of the SERPINA7 protein and/or the expression level of the gene encoding it;
v) a decrease in the level of ECM1 protein and/or the expression level of the gene encoding it;
vi) a decrease in the level of the CPN2 protein and/or the expression level of the gene encoding it; and
vii) a decrease in the ADIPOQ protein level and/or the expression level of the gene encoding it.
12. The method of claim 11,
(C) When a change in the level of the following protein or a gene encoding the same is detected, a method of providing information for diagnosing ovarian cancer, comprising the step of confirming that there is a possibility of developing stage 1 or stage 2 ovarian cancer:
i) an increase in the level of the MBL2 protein or the gene encoding it;
ii) a decrease in the level of the APOL1 protein or the gene encoding it; and
iii) a decrease in the level of the SERPINA5 protein or the gene encoding it.
(a) treating a sample isolated from a subject or a candidate drug to an animal model of cancer; and
(b) screening for a drug for preventing or treating ovarian cancer, comprising measuring the protein level of MBL2, APOL1 and SERPINA5 or the expression level of a gene encoding the same in a sample treated with the candidate drug or in an animal model of cancer disease How to.
The method of claim 19, wherein step (b) further measures the level of any one or more proteins selected from the group consisting of SERPINA7, ECM1, CPN2 and ADIPOQ or the expression level of a gene encoding the same. A method of screening for prophylactic or therapeutic drugs.

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