Application of blood extracellular vesicle miRNA in the diagnosis of ovarian cancer

Abstract

The present invention discloses one or more blood extracellular vesicle miRNAs that can serve as biomarkers for the diagnosis and early diagnosis of ovarian cancer, particularly for differentiating between benign and malignant ovarian tumors. These biomarkers can be used to prepare chips, detection reagents, or detection kits for the diagnosis and early diagnosis of ovarian cancer, particularly for differentiating between benign and malignant ovarian tumors. Using these one or more miRNAs as biomarkers can improve the specificity of ovarian cancer diagnosis and early diagnosis, reduce false positives, and thus have potential clinical application value in differentiating between benign and malignant ovarian lesions and in the diagnosis and early diagnosis of ovarian cancer.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine. Specifically, the present invention relates to a combination of blood extracellular vesicle miRNA for diagnosing ovarian cancer and its application. Background Art

[0002] Ovarian cancer (OC) is one of the three major malignant tumors of the female reproductive system. Its incidence is relatively low, but its mortality rate ranks first among gynecological tumors, making it a serious threat to women's health. The five-year survival rate for patients with early-stage ovarian cancer is 80-95%, while that for patients with advanced ovarian cancer is only 10-30%. The ovaries are located deep in the pelvic cavity, and early-stage ovarian cancer lacks specific symptoms, making it difficult to detect. More than two-thirds of ovarian cancer patients have already progressed to the advanced stage at the time of diagnosis. If detected early, cytoreductive surgery combined with platinum-based combination chemotherapy can increase the five-year survival rate of ovarian cancer to 40-50%. Therefore, early diagnosis of ovarian cancer is crucial to prolonging patient survival.

[0003] The main diagnostic methods for ovarian cancer include imaging, tumor marker measurement, cytology, and histopathology. Because the ovaries are entirely intraperitoneal organs, ovarian cancer cannot be diagnosed without surgery. Currently, a histopathological biopsy is the gold standard for confirming ovarian cancer. Microscopic examination of a tissue sample taken from a suspicious area is the only way to confirm the diagnosis. However, needle biopsies should be avoided for early-stage ovarian tumors because cancer cells can easily spread to the peritoneal cavity, which can promote peritoneal metastasis. Therefore, needle biopsies are reserved for patients with advanced cancer or other serious medical conditions who are not suitable for surgery. For patients with abdominal fluid accumulation, abdominal fluid can be analyzed for the presence of cancer cells. Currently, various diagnostic methods for ovarian cancer have their advantages and disadvantages, and a definitive diagnosis requires a comprehensive assessment based on the patient's medical history. There is an urgent need for a less invasive diagnostic biomarker to enable early detection and diagnosis of ovarian cancer and to explore its potential use in screening tests. Summary of the Invention

[0004] This invention aims to develop an independent liquid biopsy technology for the diagnosis of ovarian cancer by discovering and validating a highly accurate serum exosomal miRNA diagnostic biomarker model, thereby making up for the shortcomings of existing imaging examinations, serum tumor marker determinations, and tissue biopsies in the diagnosis of ovarian cancer, providing the clinic with a less invasive diagnostic technology, reducing patients' suffering, and improving their quality of life.

[0005] To this end, the present invention provides a combination of biomarkers, which can be used to clinically diagnose ovarian cancer, especially to distinguish between benign and malignant ovarian tumors.

[0006] The present invention also provides a chip and a kit that can be used to clinically diagnose ovarian cancer, especially to distinguish between benign and malignant ovarian tumors.

[0007] The present invention also provides a method for diagnosing ovarian cancer using the biomarker, especially for distinguishing benign and malignant ovarian tumors.

[0008] In a first aspect, the present invention provides a use of blood extracellular vesicle miRNA in preparing a chip, a detection reagent or a detection kit for diagnosing ovarian cancer, wherein the miRNA is

[0009] (i) one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p, and hsa-miR-429; or

[0010] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0011] In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7, 8 or 9.

[0012] In a specific embodiment, the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is the differentiation of benign and malignant ovarian tumors.

[0013] In a specific embodiment, the miRNA is one or more miRNAs selected from the group consisting of hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.

[0014] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6 or 7.

[0015] In a second aspect, the present invention provides a miRNA chip, comprising:

[0016] a solid support; and

[0017] Oligonucleotide probes are sequentially fixed on the solid support, wherein the oligonucleotide probes specifically bind to miRNA;

[0018] Wherein, the miRNA is the miRNA described in the first aspect.

[0019] In a specific embodiment, the oligonucleotide probe comprises:

[0020] complementary binding region; and / or

[0021] A linker region connected to a solid support.

[0022] In a specific embodiment, the miRNA chip is used for early diagnosis of ovarian cancer; preferably for distinguishing between benign and malignant ovarian tumors.

[0023] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0024] In a third aspect, the present invention provides a use of the miRNA chip described in the second aspect for preparing a detection kit for diagnosing ovarian cancer.

[0025] In a specific embodiment, the diagnosis of ovarian cancer is early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is differentiation between benign and malignant ovarian tumors.

[0026] In a fourth aspect, the present invention provides a detection kit comprising a detection reagent for detecting miRNA;

[0027] Wherein, the miRNA is the miRNA described in the first aspect;

[0028] Alternatively, the detection kit is equipped with the miRNA chip described in the second aspect.

[0029] In a specific embodiment, the detection kit is used for early diagnosis of ovarian cancer; preferably, it is used to distinguish between benign and malignant ovarian tumors.

[0030] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0031] In a fifth aspect, the present invention provides a miRNA isolated from extracellular vesicles of blood for use in the diagnosis of ovarian cancer:

[0032] Wherein, the miRNA is:

[0033] (i) one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p, and hsa-miR-429; or

[0034] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0035] In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7, 8 or 9.

[0036] In a preferred embodiment, the diagnosis of ovarian cancer is early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is differentiation between benign and malignant ovarian tumors.

[0037] In a preferred embodiment, the miRNA is one or more miRNAs selected from the group consisting of hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.

[0038] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6 or 7.

[0039] In a preferred embodiment, the diagnosis of ovarian cancer is early diagnosis of ovarian cancer; preferably, it is to distinguish between benign and malignant ovarian tumors.

[0040] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0041] In a sixth aspect, the present invention provides a method for diagnosing ovarian cancer, comprising the steps of:

[0042] (a) Based on the miRNA expression data of the training cohort and combined with the pathological test results, a benign / malignant classification model was constructed;

[0043] (b) The miRNA expression level of the test subject is used as a variable, combined with the reference value, and when the risk value is less than or equal to the reference value, the subject is judged to be benign, otherwise it is malignant ovarian cancer;

[0044] The miRNA is:

[0045] (i) one or more miRNAs selected from the group consisting of miRNAs: hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p, and hsa-miR-429; or

[0046] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0047] In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7, 8 or 9.

[0048] In a preferred embodiment, the diagnosis of ovarian cancer is early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is differentiation between benign and malignant ovarian tumors.

[0049] In a preferred embodiment, the miRNA is one or more miRNAs selected from the group consisting of hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.

[0050] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6 or 7.

[0051] In a preferred embodiment, the reference value is 0.2.

[0052] In a preferred embodiment, the diagnosis of ovarian cancer is early diagnosis of ovarian cancer; preferably, it is to distinguish between benign and malignant ovarian tumors, wherein the control sample is a benign ovarian lesion sample.

[0053] In a preferred embodiment, the miRNA is blood extracellular vesicle miRNA.

[0054] In a seventh aspect, the present invention provides a miRNA isolated from extracellular vesicles of blood, wherein the miRNA is:

[0055] (i) one or more miRNAs selected from the group consisting of miRNAs: hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p, and hsa-miR-429; or

[0056] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0057] In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7, 8 or 9.

[0058] In a preferred embodiment, the miRNA is one or more miRNAs selected from the group consisting of hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.

[0059] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6 or 7.

[0060] In an eighth aspect, the present invention provides an isolated or artificially constructed precursor miRNA, wherein the precursor miRNA can be cleaved and expressed into the miRNA in human cells.

[0061] In a ninth aspect, the present invention provides an isolated polynucleotide, wherein the polynucleotide can be transcribed into a precursor miRNA by a human cell, and the precursor miRNA can be cleaved and expressed into the miRNA in the human cell.

[0062] In a preferred embodiment, the polynucleotide has a structure shown in Formula I:

[0063] Seq 正向 -X-Seq 反向 Formula I

[0064] In Formula I,

[0065] Seq 正向 A nucleotide sequence that can be expressed as the miRNA in human cells;

[0066] Seq 反向 is a nucleotide sequence that is substantially complementary or completely complementary to Seq in the forward direction;

[0067] X is located in Seq 正向 and Seq 反向 The spacer sequence between the two sequences is the same as Seq 正向 and Seq 反向 Not complementary;

[0068] After the structure shown in Formula I is introduced into human cells, it forms the secondary structure shown in Formula II:

[0069]

[0070] In Formula II, Seq 正向 、Seq 反向 and X is defined as above,

[0071] || means in Seq 正向and Seq 反向 The base pairing relationship formed between them.

[0072] In a tenth aspect, the present invention provides a vector comprising the miRNA or the polynucleotide.

[0073] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features described in detail below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be listed here one by one. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 The results of transmission electron microscopy identification of exosomes are shown;

[0075] Figure 2 The expression of characteristic exosome proteins is shown;

[0076] Figure 3 The ROC curve for the training cohort is shown;

[0077] Figure 4 The ROC curves for the test cohort are shown;

[0078] Figure 5 ROC curves for the validation cohort are shown. DETAILED DESCRIPTION

[0079] Transvaginal ultrasound (TVS) is quick, economical, non-invasive, and repeatable, making it the preferred method for diagnosing ovarian cancer. However, the morphology, internal structure, and relationship of smaller ovarian masses to surrounding tissues are often unclear, making it difficult to detect solid tumors with a diameter of less than 1 cm. The clinical application of computed tomography (CT) technology has greatly improved the spatial resolution of images. It can clearly show the degree of tumor infiltration into the surrounding area and the presence or absence of pelvic and abdominal metastases. However, there is no significant difference in the CT appearance of primary ovarian tumors and metastases. Magnetic resonance imaging (MRI) has high soft tissue resolution, is capable of multi-plane imaging, and is non-invasive. However, MRI is also more expensive than CT, and patients with intrauterine devices must have MRI removed before undergoing MRI. PET-CT has low sensitivity and specificity for ovarian tumors and is generally not recommended for initial diagnosis. However, its imaging can reflect changes in tumor cell metabolism. When ovarian cancer recurrence is suspected clinically, PET-CT should be the preferred imaging method.

[0080] Serum tumor marker testing has been used clinically for the early diagnosis of ovarian cancer. Currently, the US Food and Drug Administration (FDA)-approved serum markers for ovarian cancer include tumor antigen 125 (CA125) and human epididymis protein 4 (HE4). Both are highly valuable in assessing the efficacy of postoperative treatment for ovarian cancer patients, but lack sufficient sensitivity and specificity for early diagnosis.

[0081] In this regard, the present invention uses serum samples from clinical patients and a small RNA sequencing method to disclose a liquid biopsy technology for detecting serum exosomal microRNA (miRNA) for ovarian cancer diagnosis. Specifically, the present invention makes the following two contributions to the field:

[0082] (1) To study serum exosomal miRNA biomarkers and their combinations for ovarian cancer diagnosis and to construct a highly accurate diagnostic model;

[0083] (2) Further verify the diagnostic effect of the discovered diagnostic model.

[0084] Through (1) and (2), we discovered and validated a liquid biopsy technology based on a combination of serum exosomal miRNA biomarkers that can be used for the diagnosis of ovarian cancer.

[0085] definition

[0086] The scientific and technical terms used herein are consistent with those commonly understood by those skilled in the art. To facilitate understanding of the present invention, the following explanations and definitions of the relevant terms are provided:

[0087] Exosomes are a type of small extracellular vesicles (EVs), membranous vesicles with a diameter of approximately 30-150 nm. They originate from vesicles of late endosomes (multivesicular bodies, MVBs). These vesicles are formed by the inward indentation of the endosome membrane, forming multivesicular bodies containing multiple small vesicles. These multivesicular bodies fuse with the cell membrane and are released into the extracellular matrix. Extracellular vesicles carry contents such as proteins, lipids, mRNA, rRNA, and miRNA. Cells can secrete extracellular vesicles under both normal and pathological conditions, and they can participate in the transmission of information between cells.

[0088] The contents of extracellular vesicles can characterize certain physiological and pathological conditions. In various diseases (such as malignant tumors and immune diseases), free exosomes in peripheral blood have received widespread attention and in-depth research as an important form of liquid biopsy. Other studies have identified differences in blood exosomal miRNAs between healthy people and ovarian cancer patients. However, there are few studies based on second-generation sequencing technology to explore blood exosomal miRNAs as diagnostic biomarkers in ovarian cancer, and no biomarkers have been found and verified that can be used to improve the specificity of early diagnosis of ovarian cancer.

[0089] This study employed a comprehensive approach using various clinical techniques to examine serum samples from patients suspected of having ovarian cancer. Exosomes were extracted using the exosome extraction reagent L3525, independently developed by Shanghai Silidi Biomedical Technology Co., Ltd. Next-generation sequencing (nrSmall RNA sequencing) was then used to detect the expression of serum exosomal miRNAs. A diagnostic model for serum exosomal miRNA biomarkers for ovarian cancer was discovered, tested, and validated in three independent cohorts.

[0090] Blood extracellular vesicle miRNA of the present invention

[0091] Currently, imaging examinations and laboratory serological tests used in the clinical diagnosis of ovarian cancer have certain limitations and poor accuracy. The only definitive way to confirm ovarian cancer is a histopathological biopsy. However, puncture biopsy should be avoided for early-stage ovarian tumors because cancer cells can easily spread to the peritoneal cavity and puncture can promote peritoneal metastasis. Therefore, it is only suitable for patients with advanced cancer or other serious illnesses who are not suitable for surgery.

[0092] The advantage of the present invention lies in the development of a liquid biopsy technology based on serum exosomal miRNA biomarkers that can be used for the diagnosis of ovarian cancer. The technology is non-invasive and avoids the side effects of histopathological biopsy due to sampling.

[0093] Current research on diagnostic biomarkers for ovarian cancer has the following shortcomings: a. Few studies truly focus on the differential diagnosis of benign and malignant ovarian tumors; b. Few studies are based on next-generation sequencing platforms; c. Few studies use exosome contents as diagnostic biomarkers and validate them with two other data sets; and d. No liquid biopsy technology with high diagnostic accuracy has been found.

[0094] To address the shortcomings of current research, the present invention uses the independently developed extraction reagent L3525 to extract serum exosomes. Furthermore, small RNA sequencing is used to detect the expression of serum exosomal miRNAs, ultimately identifying and validating a highly effective serum exosomal miRNA diagnostic biomarker. This invention is the first to propose and validate serum exosomal miRNAs as biomarkers for the diagnosis of ovarian cancer, avoiding the risks associated with biopsy.

[0095] Specifically, the inventors have discovered a class of blood extracellular vesicle miRNAs that can be used to diagnose ovarian cancer, especially for early diagnosis of ovarian cancer. In a specific embodiment, the blood extracellular vesicle miRNAs of the present invention are:

[0096] (i) one or more miRNAs selected from the group consisting of hsa-miR-1-3p (UGGAAUGUAAAGAAGUAUGUAU; SEQ ID NO: 1), hsa-miR-1246 (AAUGGAUUUUUGGAGCAGG; SEQ ID NO: 2), hsa-miR-141-3p (UAACACUGUCUGGUAAAGAUGG; SEQ ID NO: 3), hsa-miR-200a-3p (UAACACUGUCUGGUAACGAUGU; SEQ ID NO: 4), hsa-miR-200b-3p (UAAUACUGCCUGGUAAUGAUGA; SEQ ID NO: 5), hsa-miR-200c-3p (UAAUACUGCCGGGUAAUGAUGGA; SEQ ID NO: 6), hsa-miR-203a-3p (GUGAAAUGUUUAGGACCACUAG; SEQ ID NO: 7), hsa-miR-204a-3p (UAACACUGUCUGGUAACGAUGU; SEQ ID NO: 8), hsa-miR-205a-3p (UAACACUGUCUGGUAACGAUGU; SEQ ID NO: 9), hsa-miR-206a-3p (UAACACUGUCUGGUAACGAUGU; SEQ ID NO: 10), hsa-miR-207b-3p (UAAUACUGCCUGGUAAUGAUGA; SEQ ID NO: 11), hsa-miR-208a-3p (UAACACUGUCUGGUAACGAUGU; SEQ ID NO: 12), hsa-miR-209 NO:7), hsa-miR-3679-5p (UGAGGAUAUGGCAGGGAAGGGGA; SEQ ID NO:8), and hsa-miR-429 (UAAUACUGUCUGGUAAAACCGU; SEQ ID NO:9); or

[0097] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0098] In a preferred example, the “plurality” is 2, 3, 4, 5, 6, 7, 8 or 9.

[0099] Currently, there are no reports on the use of next-generation sequencing to identify the expression of exosomal miRNAs in blood as diagnostic biomarkers for distinguishing benign from malignant ovarian tumors. The inventors used small RNA sequencing technology to confirm for the first time in three independent populations (training group, test group, and validation group) that the discovered serum exosomal miRNA combination has a good effect in distinguishing benign from malignant ovarian tumors, with an AUC of 0.913 (training group), 0.973 (test group), and 0.924 (validation group). In a specific embodiment, to distinguish between benign and malignant ovarian tumors, one or more miRNAs selected from the following group are preferably used: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, and hsa-miR-429. In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6 or 7.

[0100] In further embodiments, the blood extracellular vesicle miRNA can be used to prepare chips, detection reagents, or detection kits for diagnosing ovarian cancer, particularly for early diagnosis of ovarian cancer, preferably for differentiating between benign and malignant ovarian tumors. For example, the miRNA chip can include a solid support; and oligonucleotide probes systematically immobilized on the solid support, wherein the oligonucleotide probes specifically bind to the miRNA. In preferred embodiments, the oligonucleotide probes contain a complementary binding region and / or a linker region connected to the solid support.

[0101] In a specific embodiment, the present invention further provides a detection kit, wherein the detection kit is equipped with a detection reagent for detecting the miRNA, or the detection kit is equipped with the miRNA chip.

[0102] The present invention selects miRNA, one of the contents of serum exosomes, for identification of diagnostic biomarkers and construction of an ovarian cancer diagnostic model. However, exosomes contain many other contents, such as proteins, mRNA, and lncRNA, which can also serve as biomarkers and achieve similar effects as miRNA. In addition, patient serum contains a variety of components, such as ctDNA (circulating tumor DNA), CTCs (circulating tumor cells), and proteins. These substances may be discovered as diagnostic biomarkers for ovarian cancer and developed into independent liquid biopsy diagnostic technologies.

[0103] Method of the present invention

[0104] Based on the blood extracellular vesicle miRNA of the present invention, the present invention also provides various methods for utilizing the miRNA.

[0105] For example, using the miRNA, the present invention provides a method for diagnosing ovarian cancer, comprising the steps of:

[0106] (a) Based on the miRNA expression data of the training cohort and combined with the pathological test results, a benign / malignant classification model was constructed;

[0107] (b) The miRNA expression level of the test subject is used as a variable, combined with the reference value. When the risk value is less than or equal to the reference value, the subject is judged to be benign, otherwise it is malignant ovarian cancer.

[0108] In a preferred embodiment, the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer, preferably differentiating between benign and malignant ovarian tumors. When differentiating between benign and malignant ovarian tumors, the control sample is a benign ovarian lesion sample.

[0109] In a preferred embodiment, the reference value is 0.2.

[0110] Advantages of the present invention:

[0111] (1) The present invention proposes to use serum exosomal miRNA combination as a biomarker to establish a high-accuracy diagnostic model to distinguish between benign and malignant ovarian tumors, thereby improving the accuracy of ovarian cancer diagnosis.

[0112] (2) The present invention uses a variety of real-world benign ovarian disease samples as control samples. The benign controls are highly heterogeneous, which is different from the healthy population or mixed samples of healthy population and benign diseases used as controls in published screening studies. The results obtained by the design adopted by the present invention can better reflect the real-world situation.

[0113] (3) The present invention uses the exosome (exosome) extraction reagent L3525 independently developed by Shanghai Silidi Biomedical Technology Co., Ltd. to extract serum exosomes and conduct subsequent exosomal miRNA detection.

[0114] (4) The present invention used a multivariate logistic regression model statistical analysis method to discover 7 serum exosomal miRNAs, including: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429, and constructed a high-accuracy risk prediction model for benign and malignant ovarian tumors.

[0115] (5) The present invention used data from two other independent cohorts (test group and validation group) to confirm the high accuracy of the risk prediction model of 7 serum exosomal miRNAs.

[0116] The present invention will be further described below with reference to specific examples. It should be understood that these examples are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The experimental methods in the following examples, for which specific conditions are not specified, were generally performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (Cold Spring Harbor Laboratory Press, 2001), or according to the conditions recommended by the manufacturer. Unless otherwise stated, percentages and parts are calculated by weight.

[0117] Example

[0118] Material

[0119] The materials used in the following examples are all commercially available.

[0120] method

[0121] 1. Study cohort and clinical information

[0122] This study included three cohorts totaling 79 patients suspected of ovarian cancer based on imaging (e.g., ultrasound, abdominal and pelvic CT scans), tumor markers (e.g., CA125, HE4), cytology, and histopathology. Blood samples were collected from these patients before surgery. Each enrolled patient received an accurate diagnosis based on postoperative pathological examination results.

[0123] 2. Extraction and Characterization of Serum Exosomes

[0124] 1) Blood collection and serum exosome extraction

[0125] All blood samples from patients with benign and malignant ovarian tumors in this study were collected before surgery or medical treatment into 5 ml red-capped vacutainer tubes (REF367814, BD, USA). The tubes were gently inverted several times and allowed to stand upright at room temperature for 1–2 hours. After the clots had solidified and shrunken, they were centrifuged at low speed (2000 g) for 10 minutes at room temperature. After centrifugation, the blood samples were assessed for hemolysis grade; samples with hemolysis grade less than 4 were processed. The supernatant was transferred to 1.5 ml centrifuge tubes and centrifuged again at 8000 g at 4°C for 10 minutes. The supernatant was aliquoted into 1 ml tubes and stored in a -80°C refrigerator. Serum samples were removed and incubated in a 37°C metal bath until completely thawed. The samples were then centrifuged at 12,000 g for 10 min at 4°C. 500 μl of the supernatant was transferred to a 0.45 μm tube filter (Costar, CLS8163-100EA, Corning, USA) and centrifuged at 12,000 g for 5 min at 4°C. The filtrate was then transferred to a 0.22 μm tube filter (Costar, CLS8161-100EA, USA) and centrifuged at 12,000 g for 5 min at 4°C. The filtrate was transferred to a 1.5 ml centrifuge tube and 1 / 4 volume of L-type exosome precipitation reagent (L3525, 3DMed, Shanghai, China) was added. The tubes were mixed and incubated at 4°C for 30 min. The supernatant was discarded and 200 μl of phosphate buffered saline (PBS) was added to suspend the exosome pellet.

[0126] 2) Characterization of serum exosomes

[0127] In order to detect the characteristics of serum exosomes in patients with benign and malignant ovarian tumors, the present invention uses transmission electron microscopy to detect exosome morphology, and further uses immunoblotting to detect the expression level of exosome characteristic proteins. Exosome morphological characteristics identification: First, the isolated extracellular vesicles are resuspended in PBS, and then 4% paraformaldehyde is added to fix the extracellular vesicles. After that, the exosomes are transferred to a carbon-coated 200-mesh electron microscope copper grid. Secondly, the copper grid is washed twice with PBS, freshly prepared PBS containing glycine (50mM) and washed for 3 minutes, and freshly prepared PBS containing 0.5% BSA is washed again for 10 minutes. Finally, the copper grid is stained with 2% uranyl acetate. After staining, the exosome morphology is characterized by transmission electron microscopy (H-7650, Hitachi High-Technologies, Japan).

[0128] Detection of Exosome Characteristic Proteins: Exosomes were precipitated with N-type exosome precipitation reagent (N3525, 3DMed, Shanghai, China) and then lysed with RIPA lysis buffer (P0013B, Beyotime, Shanghai, China) on ice for 30 min. Electrophoresis was performed on a 4%-20% SDS-PAGE gel (#4561095, Bio-Rad, USA) at constant voltage for approximately 1 h. The membranes were then transferred to a PVDF membrane (Millipore) at constant current for 45 min and blocked overnight with 5% skim milk powder. Primary antibody information: TSG101(1:1000diluted,ab125011,Abcam,England), CD63(1:1000diluted,ab216130,Abcam,England), CD9(1:1000diluted,ab92726,Abcam,England), Alix(1:1000diluted,2171,Cell Signaling Technology, Danvers, MA, USA), Syntenin (1:1000diluted, ab19903, Abcam, England) and Calnexin (1:1000diluted, 2679, Cell Signaling The cells were incubated with the rabbit secondary antibody (A0208, Beyotime) or the mouse secondary antibody (A0216, Beyotime) at room temperature for 1 h, washed four times with TBST for 10 min each, and developed with a chemiluminescence system (Tanon-5200 Multi, Shanghai, China).

[0129] The present invention uses the exosome extraction reagent L3525, independently developed by Shanghai Silidi Biomedical Technology Co., Ltd., to extract serum exosomes. However, those skilled in the art will recognize other alternatives, including but not limited to: a. ultracentrifugation; b. density gradient centrifugation; c. ultrafiltration centrifugation; d. immunomagnetic bead method; e. other commercially available exosome extraction reagents.

[0130] 3. Exosomal miRNA extraction and expression detection

[0131] 1) Extraction of serum exosome miRNA

[0132] Serum exosomal miRNAs were isolated using the miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai, China), following the product instructions. MiRNA quantification and fragment distribution were performed using an Agilent 2100 analyzer equipped with a corresponding chip (5067-1548, Agilent, USA).

[0133] 2) Expression detection of serum exosomes

[0134] The present invention uses small RNA sequencing to detect the expression level of serum exosomal miRNA. The library was constructed using the NEBNext, Multiplex Small RNA Library Prep Set for Illumina (E7300L, NEB, USA) kit, and the specific operation procedures were in accordance with the product instructions. The amount of miRNA loaded in each serum sample was 100 ng, and the total volume did not exceed 6 μl. The 3' adapter was connected, the reverse transcription primer was hybridized, the 5' adapter was connected, reverse transcription was performed, and Illumina index primers were added to mark the PCR amplification for 18 cycles. The PCR product was purified using the NucleoSpin Gel and PCR Clean-up (740609.50, MACHERY-NAGEL, Germany) kit, and the DNA was eluted with 30 μl NE buffer. GX Touch TM DNA quantification and fragment distribution were determined using the HT nucleic acid analyzer and its accompanying chip (CLS138948, PerkinElmer, USA) and reagents (CLS760672, PerkinElmer, USA). Typically, 20–25 libraries were mixed in equal molar ratios and sequenced in lanes using an Illumina HiSeq PE150 analyzer.

[0135] The present invention utilizes small RNA sequencing, a second-generation sequencing technology, to detect serum exosomal miRNA expression. However, those skilled in the art will recognize other alternatives, including but not limited to: a. Q-PCR detection; b. microarray detection; c. other second-generation sequencing methods; d. third-generation sequencing methods.

[0136] 4. Sequencing Data Analysis Process

[0137] Based on small RNA sequencing technology, the expression level of miRNA in patient serum exosomes was obtained. The analysis process of sequencing data is as follows:

[0138] 1) Sequencing data alignment. After removing the sequencing adapters from the small RNA sequencing data, the sequencing data were aligned to the human reference genome hg19 (genome download link: http: / / hgdownload.soe.ucsc.edu / goldenPath / hg19 / bigZips / ) using BWA software (version: 0.7.12-r1039), and the number of reads aligned to the miRNA was counted.

[0139] 2) miRNA annotation. MiRNAs were annotated using the Gencode v25 and miRBase v21 databases, and those annotated as known mature miRNAs were retained for subsequent analysis.

[0140] 3) miRNA filtering. For the training cohort, mature miRNAs with a length of 30 nt or less and covered by at least two reads per sample in the training cohort data were retained for subsequent analysis. For the test and validation cohorts, miRNAs screened from the training cohort and covered by at least two reads per sample in the test and validation cohort data were retained for subsequent analysis.

[0141] 4) Normalization of miRNA expression levels. The trimmed mean of M-values (TMM) method and the limma-voom method in the limma analysis package in R were used to normalize miRNA expression levels in the training cohort, test cohort, and validation cohort samples, respectively.

[0142] 5. Discovery of biomarkers

[0143] Based on the expression levels of miRNAs in the training cohort, the samples were grouped according to the pathological test results, and statistical methods were used to discover miRNAs that can be used to distinguish between benign and malignant ovarian tumors as biomarkers. The process is as follows:

[0144] 1) Grouping of the training cohort. Based on the pathological examination results, the patients in the training cohort were divided into two groups: a benign tumor group and a malignant tumor group.

[0145] 2) Candidate molecular markers. In the training cohort, the linear model fitting (limma-voom) method in the R language limma analysis package was used to analyze miRNAs with differential expression between the benign and malignant groups. MiRNAs with expression levels greater than or equal to 4, a change of more than 2-fold between the two groups, and a test result P value less than or equal to 0.05 were selected as candidate molecular markers. Subsequently, GSE53829 from the NCBI GEO public database was searched and downloaded (download link: https: / / www.ncbi.nlm.nih.gov / geo / query / acc.cgi? acc=GSE53829 Whole-genome quantitative real-time polymerase chain reaction (qRT-PCR) miRNA expression profiling data were collected. Data from GSE53829 were divided into two groups based on tissue type: 15 normal ovarian tissue samples and 48 malignant ovarian cancer tissue samples. The Student's t test in R was used to analyze miRNAs with differential expression between the two groups. Those with a difference of more than 2-fold between the two groups and a P value of 0.05 or less were selected as candidate molecular markers. A total of 296 candidate molecular markers were identified and used for subsequent analysis.

[0146] 3) Molecular markers. The intersection of candidate molecular markers in the training cohort and those in the GSE53829 dataset was selected as molecular markers. Ultimately, seven miRNAs were selected as molecular markers for the subsequent construction of risk scoring models for benign and malignant ovarian tumors.

[0147] The present invention uses the least absolute shrinkage and selection operator (LASSO) model as the analytical method. However, those skilled in the art will appreciate other alternatives, including but not limited to: a. linear regression; b. support vector machine; c. Bayesian classifier; d. neural network.

[0148] 6. Ovarian tumor benign and malignant risk scoring model

[0149] Using miRNA expression data from the training cohort, seven molecular markers were used as variables, and a multivariate logistic regression model was used in conjunction with pathological examination results to construct a risk score model for benign and malignant ovarian tumors. The model consists of three parts: parameters, model formula, and reference values. The process is as follows:

[0150] 1) Model parameters. In the training cohort, seven molecular markers were used as variables, and 100 repetitions of 10-fold cross-validation were used to obtain model calibration parameters and model coefficients for molecular markers.

[0151] 2) Risk scoring model. The risk scoring model formula is as follows:

[0152] Risk-Score=∑Gi*βi+α

[0153] Where Risk-Score is the risk prediction value for benign or malignant disease, Gi represents the expression value of the i-th miRNA, βi represents the risk score model coefficient for the i-th miRNA, i = 1, 2, ..., n, where n is the total number of molecular markers predicting benign or malignant disease, and α represents the model correction value. Using the risk score model and the expression levels of the seven molecular markers in each sample, a risk value can be obtained for each sample. The high or low risk value reflects the benign or malignant nature of the sample.

[0154] 3) Reference value. When the risk value is less than or equal to the reference value, the sample is predicted to be benign; otherwise, it is predicted to be malignant. Based on the risk value and pathological test results of each patient in the training cohort, a receiver operating characteristic (ROC) curve is plotted for the training cohort. Based on the ROC curve results, the reference value is determined while ensuring that the specificity and sensitivity are greater than 0.5.

[0155] 4) Model performance evaluation. Using a reference value of 0.2 as the standard, the training cohort samples were divided into a low-risk group (i.e., predicted to be benign lesions) and a high-risk group (i.e., predicted to be malignant tumors). The model's predictive performance was evaluated using the pathological test results as the true value. Model predictive performance was evaluated using methods including AUC (range 0–1), specificity (range 0–1), and sensitivity (range 0–1), with higher values indicating better performance.

[0156] 7. Testing and Validation of the Predictive Effectiveness of Risk Scoring Models

[0157] In the test cohort and validation cohort, the model's ability to predict benign and malignant tumors was verified based on the risk score model and reference values determined in the training cohort. The process is as follows:

[0158] 1) Risk value: Calculate the risk value for each sample in the test and validation cohorts.

[0159] 2) Model performance verification. Using a reference value of 0.2, patients in the test and validation cohorts were divided into a low-risk group (same as the training cohort) and a high-risk group, respectively. Using the pathology test results as the true value, receiver operating characteristic (ROC) curves were plotted for the test and validation cohorts to evaluate the model's predictive performance, including area under the curve (AUC), specificity, and sensitivity. Higher values indicate better performance.

[0160] 8. Application of the Benign and Malignant Ovarian Tumor Risk Scoring Model

[0161] 1) Peripheral blood was collected from patients with suspected ovarian cancer, and exosomes were obtained. Biomarker expression was determined using small RNA sequencing.

[0162] 2) Use the risk scoring model to obtain the risk value for each patient;

[0163] 3) Compare the risk value with the model reference value to give a predicted result of each patient's risk of ovarian cancer.

[0164] Example 1.

[0165] Three study cohorts were recruited in this example. The specific study cohorts and clinical information are as follows:

[0166] The training cohort consisted of 29 patients, including 13 patients with benign tumors and 16 patients with malignant tumors (Table 1). The test cohort consisted of 20 patients, including 5 patients with benign tumors and 15 patients with malignant tumors (Table 1). The validation cohort consisted of 30 patients, including 15 patients with benign tumors and 15 patients with malignant tumors (Table 1). The types of malignant tumor samples included low-grade and high-grade serous carcinoma and mucinous carcinoma. The types of benign tumor samples included ovarian serous cystadenoma, ovarian mucinous cystadenoma, ovarian fallopian tube abscess, endometrial atypical hyperplasia, etc. Table 1 shows the clinical information of the patients' age and pathological diagnosis. The analysis results showed that there was no significant difference in the age and proportion of benign and malignant patients between the two groups of patients.

[0167] Table 1. Clinical information of ovarian cancer patients.

[0168]

[0169] Example 2. Extraction and characterization of serum exosomes

[0170] In this example, exosome extraction reagents L3525 and N3525 independently developed by Shanghai Silidi Biomedical Technology Co., Ltd. were used to extract exosomes from the serum of patients with ovarian cancer. In order to detect the characteristics of serum exosomes from patients with benign and malignant ovarian tumors, the morphology of exosomes was detected by transmission electron microscopy, and the expression levels of exosome characteristic proteins were further detected by immunoblotting. The results of transmission electron microscopy showed that the exosomes had a typical "horseshoe-shaped" morphology (see Figure 1 The test results showed that the characteristic exosome proteins TSG101, CD63, CD9, Alix and Syntenin were expressed in the representative samples extracted from this patent, and the negative exosome protein Calnexin was not expressed (see Figure 2 ).

[0171] Example 3. Biomarker Discovery

[0172] In this example, small RNA sequencing was used to detect the expression levels of serum exosomal miRNAs in patients with benign and malignant ovarian tumors. Based on the expression levels of miRNAs in the training cohort and the pathological results of the samples, statistical methods were used to identify miRNAs that can be used to distinguish between benign and malignant ovarian tumors as biomarkers. Nine candidate molecular markers, including hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p, and hsa-miR-429, were used for subsequent analysis. Subsequently, qRT-PCR miRNA expression profiling data from GSE53829 in the NCBI GEO public database were used. The GSE53829 dataset was divided into two groups based on tissue type. Statistical methods were used to identify candidate miRNAs that could distinguish normal ovarian tissue from malignant ovarian cancer tissue. The intersection of candidate markers in the training cohort and those in the GSE53829 dataset was selected as molecular markers. Ultimately, seven miRNAs were selected as molecular markers: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, and hsa-miR-429. These markers were then used to construct a risk score model for benign and malignant ovarian tumors.

[0173] Example 4. Ovarian tumor benign and malignant risk scoring model

[0174] In this embodiment, the miRNA expression data of the training cohort was used, 7 molecular markers were used as variables, and based on the multivariate logistic regression model, combined with the pathological test results, a risk scoring model for benign and malignant ovarian tumors was constructed. The model consists of three parts: parameters, model formulas, and reference values. Using the 7 molecular markers as variables, a 100-fold cross-validation method was repeated 10 times to obtain the model correction parameters and model coefficients of the molecular markers (Table 2) and the risk scoring model formula. Using the risk scoring model and the expression levels of the 7 molecular markers in each sample, the risk value of each sample can be obtained, and the level of the risk value can reflect the benign or malignant nature of the sample.

[0175] According to the risk value and pathological test results of each patient in the training cohort, the receiver operating characteristic curve (ROC curve) of the training cohort was drawn ( Figure 3 ). According to the ROC curve results, the reference value is determined to be 0.2 while ensuring that the specificity value is greater than 0.5 and the sensitivity value is greater than 0.5. When the risk value is less than or equal to the reference value, the sample is predicted to be benign; otherwise it is predicted to be malignant. The pathological test results are used as the true value to evaluate the model prediction efficiency. The model prediction efficiency evaluation method, including AUC (value range 0 to 1), specificity (value range 0 to 1) and sensitivity (value range 0 to 1), are 0.913 ( Figure 3 ), 92.3% and 87.5% (Table 3). The results showed that in the training cohort, this risk prediction model had high AUC, specificity and sensitivity, and had excellent predictive efficiency.

[0176] Table 2. Parameters of the risk scoring model constructed using 7 miRNAs as markers.

[0177]

[0178] Example 5. Risk scoring model predictive efficacy testing and validation

[0179] In this embodiment, the effectiveness of the model in predicting benign and malignant is verified in the test cohort and validation cohort based on the risk score model and reference value determined in the training cohort. Based on the reference value of 0.2, the patients in the test cohort and validation cohort are divided into a low-risk group (same as the training cohort) and a high-risk group. In the test cohort, the pathological test results are used as the true value, and the ROC curve is drawn ( Figure 4 ), evaluate the model prediction performance, including AUC ( Figure 4 ), specificity, and sensitivity were 0.973%, 100%, and 86.7%, respectively (Table 3). In the validation cohort, the ROC curve was drawn with the pathological test results as the true value ( Figure 5 ), evaluate the model prediction performance, including AUC ( Figure 5 ), specificity, and sensitivity were 0.924%, 93.3%, and 86.7%, respectively (Table 3). The results showed that this risk prediction model had high AUC, specificity, and sensitivity in both the test and validation cohorts, indicating that the model had excellent predictive efficacy.

[0180] Table 3. Effectiveness evaluation of seven molecular marker models

[0181]

[0182]

[0183] All documents mentioned in this application are incorporated herein by reference, just as if each document were incorporated herein by reference individually. It should also be understood that after reading the above teachings of the present invention, those skilled in the art may make various changes or modifications to the present invention, and that such equivalents also fall within the scope of the claims appended hereto.

Claims

1. Use of blood extracellular vesicle miRNA in the preparation of a chip, a detection reagent or a detection kit for diagnosing ovarian cancer, wherein the miRNA is (i) a combination of hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429; or (ii) a miRNA that is complementary to the miRNA sequence described in (i).

2. The use according to claim 1, characterized in that The diagnosis of ovarian cancer is an early diagnosis of ovarian cancer.

3. The use according to claim 2, characterized in that The early diagnosis of ovarian cancer is to distinguish between benign and malignant ovarian tumors.

4. A miRNA chip, comprising: solid phase carrier; as well as Oligonucleotide probes are sequentially fixed on the solid support, wherein the oligonucleotide probes specifically bind to miRNA; Wherein, the miRNA is the miRNA described in any one of claims 1-3.

5. The miRNA chip according to claim 4, wherein The oligonucleotide probe contains: complementary binding region; and / or A linker region connected to a solid support.

6. The miRNA chip according to claim 5, wherein The miRNA chip is used for early diagnosis of ovarian cancer 7. The miRNA chip according to claim 6, wherein The miRNA chip is used to distinguish benign and malignant ovarian tumors.

8. Use of the miRNA chip according to any one of claims 4 to 7 for preparing a detection kit for diagnosing ovarian cancer.

9. The use according to claim 8, characterized in that The diagnosis of ovarian cancer is an early diagnosis of ovarian cancer.

10. The use according to claim 9, characterized in that The early diagnosis of ovarian cancer is to distinguish between benign and malignant ovarian tumors.

11. A detection kit containing a detection reagent for detecting miRNA; in, The miRNA is the miRNA according to any one of claims 1 to 3; Alternatively, the detection kit is equipped with the miRNA chip according to any one of claims 4 to 7.

12. The detection kit according to claim 11, wherein The detection kit is used for early diagnosis of ovarian cancer.

13. The detection kit according to claim 12, wherein The kit is used for distinguishing benign and malignant ovarian tumors.

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