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

Abstract

The present invention discloses one or more blood small extracellular vesicle miRNAs (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 small extracellular vesicle miRNA for diagnosing ovarian cancer and its application. Background Art

[0002] Ovarian cancer (OC) is one of the three major tumors of the female reproductive system. Although the incidence of ovarian cancer is relatively low, its mortality rate ranks first among gynecological tumors, making it one of the malignant diseases that seriously threaten women's health. The five-year survival rate of patients with early-stage (stage I and II) ovarian cancer is 80-95%, while the five-year survival rate of patients with late-stage (including stage III and IV) ovarian cancer is only 10-30%. The ovaries are located deep in the pelvic cavity. Due to the lack of specific symptoms in the early stages of ovarian cancer, early lesions are difficult to detect. More than two-thirds of ovarian cancer patients have already progressed to the late stage at the time of diagnosis. If it is detected early, tumor cell reduction surgery and combined chemotherapy with platinum-based chemotherapy can increase the five-year survival rate of ovarian cancer to 40%-50%.

[0003] The main diagnostic methods for ovarian cancer include imaging, tumor marker testing, cytology, and histopathology. Because the ovaries are entirely intraperitoneal organs, it is impossible to diagnose ovarian cancer without surgery. Currently, a histopathological biopsy is the gold standard for confirming ovarian cancer. This involves taking a tissue sample from a suspicious area and examining it under a microscope. This is the only way to confirm the diagnosis of ovarian cancer. However, a needle 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, a needle biopsy is suitable for patients with advanced cancer or other serious illnesses who are not suitable for surgery. For patients with abdominal fluid accumulation, the abdominal fluid can be analyzed to determine whether it contains cancer cells.

[0004] Currently, various ovarian cancer screening methods have their own advantages and disadvantages, and a definitive diagnosis requires a comprehensive assessment based on the patient's medical history. Therefore, a low-invasive diagnostic biomarker is urgently needed to enable early detection and diagnosis of ovarian cancer and explore its potential use in screening tests. Summary of the Invention

[0005] The present invention aims to develop an independent liquid biopsy technology for the diagnosis of ovarian cancer by discovering and validating a highly accurate blood small extracellular vesicle miRNA diagnostic biomarker model, thereby making up for the shortcomings of existing imaging examinations, serum tumor marker determination and tissue biopsy in the diagnosis of ovarian cancer, providing the clinic with a less invasive diagnostic technology, reducing patients' pain and improving their quality of life.

[0006] 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.

[0007] 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.

[0008] The present invention also provides a method for diagnosing ovarian cancer using the biomarker, especially for distinguishing benign and malignant ovarian tumors.

[0009] In a first aspect, the present invention provides a use of blood small extracellular vesicle miRNA in the preparation of a chip, a detection reagent or a detection kit for diagnosing ovarian cancer, wherein the miRNA is

[0010] (i) one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-196b-5p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p, hsa-miR-374a-5p, hsa-miR-374b-5p, hsa-miR-542-3p, and hsa-miR-96-5p; or

[0011] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0012] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14.

[0013] 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.

[0014] In a specific embodiment, the miRNA is one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p and hsa-miR-542-3p.

[0015] In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7 or 8.

[0016] In a second aspect, the present invention provides a miRNA chip, comprising:

[0017] a solid support; and

[0018] Oligonucleotide probes are sequentially fixed on the solid support, wherein the oligonucleotide probes specifically bind to miRNA;

[0019] Wherein, the miRNA is the miRNA described in the first aspect.

[0020] In a specific embodiment, the oligonucleotide probe comprises:

[0021] complementary binding region; and / or

[0022] A linker region connected to a solid support.

[0023] In a specific embodiment, the miRNA chip is used for early diagnosis of ovarian cancer; preferably for distinguishing between benign and malignant ovarian tumors.

[0024] In a preferred embodiment, the miRNA is a blood small extracellular vesicle miRNA.

[0025] 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.

[0026] 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.

[0027] In a fourth aspect, the present invention provides a detection kit comprising a detection reagent for detecting miRNA;

[0028] Wherein, the miRNA is the miRNA described in the first aspect;

[0029] Alternatively, the detection kit is equipped with the miRNA chip described in the second aspect.

[0030] 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.

[0031] In a preferred embodiment, the miRNA is a blood small extracellular vesicle miRNA.

[0032] In a fifth aspect, the present invention provides a miRNA isolated from small extracellular vesicles in blood for use in the diagnosis of ovarian cancer:

[0033] Wherein, the miRNA is:

[0034] (i) one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-196b-5p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p, hsa-miR-374a-5p, hsa-miR-374b-5p, hsa-miR-542-3p, and hsa-miR-96-5p; or

[0035] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0036] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14.

[0037] 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.

[0038] In a preferred embodiment, the miRNA is one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p and hsa-miR-542-3p.

[0039] In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7 or 8.

[0040] 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.

[0041] In a preferred embodiment, the miRNA is a blood small extracellular vesicle miRNA.

[0042] In a sixth aspect, the present invention provides a method for diagnosing ovarian cancer, comprising the steps of:

[0043] (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;

[0044] (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;

[0045] The miRNA is:

[0046] (i) one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-196b-5p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p, hsa-miR-374a-5p, hsa-miR-374b-5p, hsa-miR-542-3p, and hsa-miR-96-5p; or

[0047] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0048] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14.

[0049] 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.

[0050] In a preferred embodiment, the miRNA is one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p and hsa-miR-542-3p.

[0051] In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7 or 8.

[0052] In a preferred embodiment, the reference value is 0.562.

[0053] In a preferred embodiment, the diagnosis of ovarian cancer is an 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.

[0054] In a preferred embodiment, the miRNA is a blood small extracellular vesicle miRNA.

[0055] In a seventh aspect, the present invention provides a miRNA isolated from small extracellular vesicles in blood, wherein the miRNA is:

[0056] (i) one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-196b-5p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p, hsa-miR-374a-5p, hsa-miR-374b-5p, hsa-miR-542-3p, and hsa-miR-96-5p; or

[0057] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0058] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14.

[0059] In a preferred embodiment, the miRNA is one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p and hsa-miR-542-3p.

[0060] In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7 or 8.

[0061] 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.

[0062] 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.

[0063] In a preferred embodiment, the polynucleotide has a structure shown in Formula I:

[0064] Seq 正向 -X-Seq 反向 Formula I

[0065] In Formula I,

[0066] Seq 正向 A nucleotide sequence that can be expressed as the miRNA in human cells;

[0067] Seq 反向 is a nucleotide sequence that is substantially complementary or completely complementary to Seq in the forward direction;

[0068] 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;

[0069] After the structure shown in Formula I is introduced into human cells, it forms the secondary structure shown in Formula II:

[0070]

[0071] In Formula II, Seq 正向 、Seq 反向 and X is defined as above,

[0072] || means in Seq 正向 and Seq 反向 The base pairing relationship formed between them.

[0073] In a tenth aspect, the present invention provides a vector comprising the miRNA or the polynucleotide.

[0074] 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

[0075] Figure 1 Transmission electron microscopy identification of small extracellular vesicles is shown;

[0076] Figure 2 The size distribution of small extracellular vesicles is shown;

[0077] Figure 3 The ROC curve for the training cohort is shown;

[0078] Figure 4 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 used as 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] To this end, the present invention discloses a liquid biopsy technology for detecting microRNA (miRNA) in small extracellular vesicles in blood for ovarian cancer diagnosis. Specifically, the present invention contributes to the field in the following two aspects:

[0082] (1) To study blood small extracellular vesicle miRNA biomarkers 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 blood small extracellular vesicle 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] Extracellular vesicles (EVs), also known as exosomes, are membrane-bound vesicles approximately 30-150 nm in diameter. They originate from vesicles in 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 a variety of contents, including proteins, lipids, mRNA, rRNA, and miRNA. Cells can secrete extracellular vesicles under both normal and pathological conditions, and they can participate in intercellular communication.

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

[0089] This study used a variety of clinical techniques to comprehensively test the serum of patients suspected of ovarian cancer as research samples. The exosome extraction reagent L3525, independently developed by Shanghai Silidi Biomedical Technology Co., Ltd., was used to extract serum exosomes. Next-generation sequencing (nrDNA) was then used to detect the expression of serum small extracellular vesicle (miRNA) microRNAs (small RNA sequencing). A serum small extracellular vesicle (miRNA) biomarker diagnostic model for ovarian cancer was discovered and validated in two independent cohorts.

[0090] Blood extracellular vesicle miRNA of the present invention

[0091] Currently, clinical imaging and serological tests used for ovarian cancer diagnosis have limitations and poor accuracy. The only definitive diagnosis of ovarian cancer is a histopathological biopsy. However, puncture biopsy should be avoided for early-stage ovarian tumors because cancer cells easily spread to the peritoneal cavity, which can promote peritoneal metastasis. Therefore, it is only suitable for patients with advanced cancer or other serious illnesses who are not suitable for surgery. The advantage of the present invention lies in the development of a liquid biopsy technology based on blood-derived small extracellular vesicle miRNA biomarkers for ovarian cancer diagnosis. This technology is non-invasive and avoids the side effects of histopathological biopsy sampling.

[0092] 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 additional data; and d. No liquid biopsy technology with high diagnostic accuracy has been found.

[0093] To address the shortcomings of current research, the present invention uses the independently developed extraction reagent L3525 to extract serum small extracellular vesicles (SECs). Furthermore, small RNA sequencing is used to detect the expression of SEC miRNAs in serum SECs. Ultimately, this discovery and validation demonstrates a highly effective SEC miRNA diagnostic biomarker. This is the first study to propose and validate SEC miRNAs as biomarkers for the diagnosis of ovarian cancer, avoiding the risks associated with biopsy.

[0094] Specifically, the inventors have discovered a class of blood small extracellular vesicle miRNAs that can be used to diagnose ovarian cancer, especially for early diagnosis of ovarian cancer. In a specific embodiment, the blood small extracellular vesicle miRNAs of the present invention are:

[0095] (i) one or more miRNAs selected from the group consisting of hsa-miR-1-3p (UGGAAUGUAAAGAAGUAUGUAU; SEQ ID NO: 1), hsa-miR-106a-5p (AAAAGUGCUUACAGUGCAGGUAG; SEQ ID NO: 2), hsa-miR-130a-3p (CAGUGCAAUGUUAAAAGGGCAU; SEQ ID NO: 3), hsa-miR-196b-5p (UAGGUAGUUUCCUGUUGUUGGG; SEQ ID NO: 4), hsa-miR-199a-3p (ACAGUAGUCUGCACAUUGGUUA; SEQ ID NO: 5), hsa-miR-199b-3p (ACAGUAGUCUGCACAUUGGUUA; SEQ ID NO: 6), hsa-miR-200c-3p (UAAUACUGCCGGGUAAUGAUGGA; SEQ ID NO: 7), hsa-miR-304a-3p (UAAUACUGCCGGGUAAUGAUGGA; SEQ ID NO: 8), hsa-miR-305a-3p (UAAUACUGCCGGGUAAUGAUGGA; SEQ ID NO: 9), hsa-miR-306b-3p (UAAUACUGCCGGGUAAUGAUGGA; SEQ ID NO: 10), hsa-miR-307 NO: 7), hsa-miR-21-5p (UAGCUUAUCAGACUGAUGUUGA; SEQ ID NO: 8), hsa-miR-335-5p (UCAAGAGCAAUAACGAAAAAUGU; SEQ ID NO: 9), hsa-miR-340-5p (UUAUAAAGCAAUGAGACUGAUU; SEQ ID NO: 10), hsa-miR-374a-5p (UUAUAAUACAACCUGAUAAGUG; SEQ ID NO: 11), hsa-miR-374b-5p (AUAUAAUACAACCUGCUAAGUG; SEQ ID NO: 12), hsa-miR-542-3p (UGUGACAGAUUGAUAACUGAAA; SEQ ID NO: 13) and hsa-miR-96-5p (UUUGGCACUAGCACAUUUUUGCU; SEQ ID NO: 14); or

[0096] (ii) a miRNA that is complementary to the miRNA sequence described in (i).

[0097] In a preferred embodiment, the “plurality” is 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14.

[0098] Currently, there are no reports in the prior art of using next-generation sequencing to identify the expression of small extracellular vesicle miRNAs in blood as diagnostic biomarkers for distinguishing benign from malignant ovarian tumors. However, the small extracellular vesicle miRNAs in blood of the present invention can be used to distinguish between benign and malignant ovarian tumors. In specific embodiments, to distinguish between benign and malignant ovarian tumors, one or more miRNAs selected from the group consisting of hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p, and hsa-miR-542-3p are preferably used. In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, 7, or 8, with 8 being most preferred.

[0099] 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.

[0100] 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.

[0101] In addition, those skilled in the art should be aware that the present invention selects one of the contents of small extracellular vesicles in peripheral blood, miRNA, for identification of diagnostic biomarkers and construction of an ovarian cancer diagnostic model. However, there are many other contents of small extracellular vesicles, such as proteins, mRNA, LncRNA, etc. These contents can also be used as biomarkers and achieve similar effects as miRNA. In addition, there are many components in patient serum, such as ctDNA (Circulating tumor DNA), CTC (Circulating tumor cell), protein and other substances. These substances may be found to be diagnostic biomarkers for ovarian cancer and developed into independent liquid biopsy diagnostic technologies.

[0102] Method of the present invention

[0103] Based on the blood small extracellular vesicle miRNA of the present invention, the present invention also provides various methods for utilizing the miRNA.

[0104] For example, using the miRNA, the present invention provides a method for diagnosing ovarian cancer, comprising the steps of:

[0105] (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;

[0106] (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.

[0107] 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.

[0108] In a preferred embodiment, the reference value is 0.562.

[0109] Advantages of the present invention:

[0110] (1) The present invention proposes to use blood small extracellular vesicle miRNA 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;

[0111] (2) The present invention uses a variety of real-world benign ovarian disease samples as control samples. The benign controls are highly heterogeneous. Unlike published screening studies that use healthy people or mixed samples of healthy people and benign diseases as controls, the results obtained by the design adopted by the present invention are more reflective of real-world conditions.

[0112] (3) The present invention uses the small extracellular vesicle (exosome) extraction reagent L3525 independently developed by Shanghai Silidi Biomedical Technology Co., Ltd. to extract serum small extracellular vesicles, and then conducts subsequent small extracellular vesicle miRNA detection;

[0113] (4) The present invention uses the least absolute shrinkage and selection operator (LASSO) model statistical analysis method to discover 8 blood small extracellular vesicle miRNAs, including: hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p and hsa-miR-542-3p, and constructs a risk prediction model with high accuracy;

[0114] (5) The present invention uses another independent cohort data to verify the high accuracy of the risk prediction model of 8 blood small extracellular vesicle miRNAs.

[0115] 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.

[0116] Example

[0117] Material

[0118] The materials used in the following examples are all commercially available.

[0119] method

[0120] 1. Study cohort and clinical information

[0121] This study included two cohorts of 49 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.

[0122] 2. Extraction and Characterization of Small Extracellular Vesicles from Blood

[0123] 1) Blood collection and extraction of small extracellular vesicles

[0124] 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 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 at −80°C. 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 buffer saline (PBS) was added to suspend the small extracellular vesicle pellet.

[0125] 2) Characterization of blood small extracellular vesicles

[0126] To characterize small extracellular vesicles (SECs) in the blood of patients with benign and malignant ovarian tumors, the present invention employed transmission electron microscopy to examine SEC morphology and further employed nanoparticle tracking analysis (NTA) to determine SEC size distribution. SEC morphological characterization: The isolated SECs were first resuspended in PBS and fixed with 4% paraformaldehyde. The SECs were then transferred to a carbon-coated 200-mesh electron microscope copper grid. The grids were then washed twice with PBS, freshly prepared with 50 mM glycine and washed for 3 minutes, and then washed again with fresh 0.5% BSA in PBS for 10 minutes. Finally, the grids were stained with 2% uranyl acetate. After staining, SEC morphology was characterized using a transmission electron microscope (H-7650, Hitachi High-Technologies, Japan).

[0127] Detection of the size distribution of small extracellular vesicles: First, separate the small extracellular vesicles and dilute them with PBS to 1*10^7-1*10^9 / ml, then pipette and mix thoroughly. Subsequently, the NTA (NanoSight NS300, Malvern, UK) was activated and 1 ml of the diluted sample was injected into the sample chamber. Using a 488 nm excitation module, the camera lens parameters were set to a shutter value of 890, a gain of 146, and a detection threshold of 7. At least 200 complete tracks were analyzed and acquired for each video. Finally, the nanoparticle tracking data for the small extracellular vesicle sample was analyzed using NTA analysis software (version 2.3).

[0128] The present invention uses the small extracellular vesicle (exosome) extraction reagent L3525, independently developed by Shanghai Silidi Biomedical Technology Co., Ltd., to extract serum small extracellular vesicles. However, those skilled in the art will recognize various 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.

[0129] 3. Extraction and Expression Detection of Small Extracellular Vesicles miRNA

[0130] 1) Extraction of miRNA from blood small extracellular vesicles

[0131] Blood SV-miRNAs were isolated using the miRNeasy Serum / Plasma Kit (217184, QIAGEN, Shanghai, China) according to the product instructions. MiRNA quantification and fragment distribution were performed using an Agilent 2100 analyzer equipped with a corresponding chip (5067-1548, Agilent, USA).

[0132] 2) Expression detection of small extracellular vesicles in blood

[0133] The present invention uses small RNA sequencing to detect the expression level of blood small extracellular vesicle miRNA. The library construction uses the NEBNext, Multiplex Small RNA Library Prep Set for Illumina (E7300L, NEB, USA) kit, and the specific operation process is in accordance with the product instructions. The amount of miRNA loaded in each serum small extracellular vesicle sample is 100ng, and the total volume does not exceed 6μl. Connect the 3' adapter, hybridize the reverse transcription primer, connect the 5' adapter, reverse transcribe, add Illumina index primers, and mark PCR amplification for 18 cycles. Use the NucleoSpin Gel and PCR Clean-up (740609.50, MACHERY-NAGEL, Germany) kit to purify the PCR product, and elute the DNA with 30μl NE buffer. Use 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.

[0134] The present invention utilizes small RNA sequencing, a second-generation sequencing technology, to detect the expression of serum small extracellular vesicle miRNAs. 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.

[0135] 4. Sequencing Data Analysis Process

[0136] Based on small RNA sequencing technology, we obtained the expression levels of miRNAs in small extracellular vesicles in the patient's blood. The analysis process of sequencing data is as follows:

[0137] 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.

[0138] 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.

[0139] 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 validation cohort, miRNAs screened from the training cohort and covered by at least two reads per sample in the validation cohort data were retained for subsequent analysis.

[0140] 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 and validation cohort samples, respectively.

[0141] 5. Discovery of biomarkers

[0142] 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:

[0143] 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.

[0144] 2) Candidate molecular markers. The limma-voom method in the R language limma analysis package was used to analyze miRNAs with differential expression between the benign and malignant groups. Those with expression levels greater than 50, a difference of more than 1.5 times between the two groups, and a P value less than or equal to 0.01 were selected as candidate molecular markers.

[0145] 3) Molecular markers. In the training cohort, the Least Absolute Shrinkage and Selection operator (LASSO) model was used to calculate the contribution of 14 candidate molecular markers to the prediction of benign and malignant disease. Candidate molecular markers with a contribution of 0 were removed. Ultimately, eight miRNAs were selected as molecular markers for the subsequent construction of benign and malignant disease risk scoring models.

[0146] The analysis method used in the present invention is the Least Absolute Shrinkage and Selection operator (LASSO) model. 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.

[0147] 6. Ovarian tumor benign and malignant risk scoring model

[0148] Using the training cohort miRNA expression data, eight molecular markers as variables, and combined with pathological test results, a benign and malignant risk scoring model was constructed. The model consists of three parts: parameters, model formula, and reference values. The process is as follows:

[0149] 1) Model parameters. In the training cohort, eight 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.

[0150] 2) Risk scoring model. The risk scoring model formula is as follows:

[0151] Risk-Score=∑Gi*βi+α

[0152] 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 eight 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.

[0153] 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 value is greater than 0.5 and the sensitivity value is greater than 0.5.

[0154] 4) Model performance evaluation. Based on the reference value of 0.562, 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 lesions). The pathological test results were used as the true value to evaluate the model's predictive performance. Model predictive performance evaluation methods include the area under the receiver operating characteristic (ROC) curve (AUC, Area Under Curve, range 0-1), positive predictive value (PPV, Positive Predictive Value, range 0-1), specificity (range 0-1), and sensitivity (range 0-1). Higher values indicate better performance.

[0155] 7. Verification of the predictive efficacy of the risk scoring model

[0156] In the 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:

[0157] 1) Risk value. In the validation cohort, a risk value is calculated for each sample.

[0158] 2) Model performance verification. Using the reference value of 0.562, the validation cohort was divided into a low-risk group (same as the training cohort) and a high-risk group. Using the pathology test results as the true value, receiver operating characteristic (ROC) curves were plotted to evaluate the model's predictive performance, including area under the curve (AUC), predictive value per volume (PPV), specificity, and sensitivity. Higher values indicate better performance.

[0159] 8. Application of the Benign and Malignant Ovarian Tumor Risk Scoring Model

[0160] 1) Peripheral blood was collected from patients with suspected ovarian cancer, small extracellular vesicles were obtained from the peripheral blood, and biomarker expression was determined using small RNA sequencing;

[0161] 2) Use the risk scoring model to obtain the risk value for each patient;

[0162] 3) Compare the risk value with the model reference value to give a predicted result of each patient's risk of ovarian cancer.

[0163] Example 1.

[0164] Two study cohorts were recruited in this example. The specific study cohorts and clinical information are as follows:

[0165] The training cohort consisted of 25 patients, including 9 with benign tumors and 16 with malignant tumors (Table 1). The validation cohort consisted of 24 patients, including 9 with benign tumors and 15 with malignant tumors (Table 1). The types of malignant tumor samples included low-grade and high-grade serous carcinomas and mucinous carcinomas. The types of benign tumor samples included ovarian serous cystadenoma, ovarian mucinous cystadenoma, ovarian fallopian tube abscess, and endometrial atypical hyperplasia. 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.

[0166] Table 1. Clinical information of ovarian cancer patients.

[0167]

[0168]

[0169] Example 2. Extraction and Characteristics of Small Extracellular Vesicles from Blood

[0170] The present invention uses the small extracellular vesicle (exosome) extraction reagent L3525 independently developed by Shanghai Silidi Biomedical Technology Co., Ltd. to extract small extracellular vesicles from the serum of ovarian cancer patients. In order to detect the characteristics of small extracellular vesicles in the blood of patients with benign and malignant ovarian tumors, the present invention uses transmission electron microscopy to detect the morphology of small extracellular vesicles, and further uses NTA to detect the particle size distribution of small extracellular vesicles. The transmission electron microscopy results show that the small extracellular vesicles have a typical "horseshoe-shaped" morphology (see Figure 1 The NTA test results showed that the average particle size of the small extracellular vesicles in the representative samples extracted by the present invention was 117.2 nm, which was consistent with the particle size distribution of small extracellular vesicles (see Figure 2 ).

[0171] Example 3. Biomarker Discovery

[0172] The present invention uses small RNA sequencing to detect the expression level of small extracellular vesicle miRNA in the blood of patients with benign and malignant ovarian tumors. Based on the expression levels of miRNAs in the training cohort and the pathological test results, the samples were grouped. Statistical methods were used to discover miRNAs that can be used to distinguish benign from malignant ovarian tumors as biomarkers. A total of 14 candidate molecular markers, including hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-196b-5p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p, hsa-miR-374a-5p, hsa-miR-374b-5p, hsa-miR-542-3p, and hsa-miR-96-5p, were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) model was then used to calculate the contribution of 14 candidate molecular markers to the prediction of benign and malignant diseases. The candidate molecular markers hsa-miR-196b-5p, hsa-miR-199a-3p, hsa-miR-199b-3p, hsa-miR-374a-5p, hsa-miR-374b-5p and hsa-miR-96-5p with a contribution of 0 were removed. Finally, eight miRNAs were selected as molecular markers, including hsa-miR-1-3p, hsa-miR-106a-5p, hsa-miR-130a-3p, hsa-miR-200c-3p, hsa-miR-21-5p, hsa-miR-335-5p, hsa-miR-340-5p and hsa-miR-542-3p, for the subsequent construction of benign and malignant risk scoring models.

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

[0174] To construct a risk classification model for benign and malignant ovarian tumors, a benign and malignant risk score model was constructed using miRNA expression data from the training cohort, eight molecular markers as variables, and pathological examination results. The model consists of three components: parameters, model formula, and reference values. Using 100 repetitions of 10-fold cross-validation with the eight molecular markers as variables, the model calibration parameters and model coefficients for the molecular markers (Table 2) and the risk score model formula were obtained. Using the risk score model and the expression levels of the eight molecular markers in each sample, a risk value was obtained for each sample, which reflects the benign and 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 was determined to be 0.562 while ensuring that the specificity value was greater than 0.5 and the sensitivity value was greater than 0.5. When the risk value was less than or equal to the reference value, the sample was predicted to be benign; otherwise, it was predicted to be malignant. The pathological test results were used as the true value to evaluate the predictive efficacy of the model. The model predictive efficacy evaluation method, including the area under the ROC curve (AUC, range of 0 to 1), positive predictive value (PPV, Positive Predictive Value, range of 0 to 1), specificity (range of 0 to 1) and sensitivity (range of 0 to 1), were 0.993 ( Figure 3 ), 100%, 93.8%, and 100% (Table 3). The results showed that in the training cohort, this risk prediction model had high AUC, PPV, specificity, and sensitivity, and had excellent predictive efficacy.

[0176] Table 2. Parameters of the risk scoring model constructed using 8 miRNAs as markers.

[0177]

[0178] Example 5. Validation of the predictive efficacy of the risk scoring model

[0179] In order to verify the effectiveness of the risk score model in predicting benign and malignant tumors, another independent cohort was selected as the validation cohort. The model's effectiveness in predicting benign and malignant tumors was verified based on the risk score model and reference value determined in the training cohort. Based on the reference value of 0.562, the patients in the validation cohort were divided into a low-risk group (same as the training cohort) and a high-risk group. The ROC curve was drawn with the pathological test results as the true value ( Figure 4 ), evaluate the model prediction performance, including AUC ( Figure 4 ), PPV, specificity and sensitivity were 0.881( Figure 4 ), 93.3%, 88.9%, and 93.3% (Table 3). The results showed that in the validation cohort, this risk prediction model also had high AUC, PPV, specificity, and sensitivity, indicating that the model had excellent predictive efficacy.

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

[0181]

[0182] 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 a reagent for detecting the expression level of serum exosomal miRNA in the preparation of a chip for distinguishing benign and malignant ovarian tumors, wherein the miRNA is a combination of hsa-miR-1-3p as shown in SEQ ID NO: 1, hsa-miR-106a-5p as shown in SEQ ID NO: 2, hsa-miR-130a-3p as shown in SEQ ID NO: 3, hsa-miR-200c-3p as shown in SEQ ID NO: 7, hsa-miR-21-5p as shown in SEQ ID NO: 8, hsa-miR-335-5p as shown in SEQ ID NO: 9, hsa-miR-340-5p as shown in SEQ ID NO: 10, and hsa-miR-542-3p as shown in SEQ ID NO:

13.

2. Use of a reagent for detecting the expression level of serum exosomal miRNA in the preparation of a detection reagent for distinguishing benign and malignant ovarian tumors, wherein the miRNA is a combination of hsa-miR-1-3p as shown in SEQ ID NO: 1, hsa-miR-106a-5p as shown in SEQ ID NO: 2, hsa-miR-130a-3p as shown in SEQ ID NO: 3, hsa-miR-200c-3p as shown in SEQ ID NO: 7, hsa-miR-21-5p as shown in SEQ ID NO: 8, hsa-miR-335-5p as shown in SEQ ID NO: 9, hsa-miR-340-5p as shown in SEQ ID NO: 10, and hsa-miR-542-3p as shown in SEQ ID NO:

13.

3. Use of a reagent for detecting the expression level of serum exosomal miRNA in the preparation of a detection kit for distinguishing benign and malignant ovarian tumors, wherein the miRNA is a combination of hsa-miR-1-3p as shown in SEQ ID NO: 1, hsa-miR-106a-5p as shown in SEQ ID NO: 2, hsa-miR-130a-3p as shown in SEQ ID NO: 3, hsa-miR-200c-3p as shown in SEQ ID NO: 7, hsa-miR-21-5p as shown in SEQ ID NO: 8, hsa-miR-335-5p as shown in SEQ ID NO: 9, hsa-miR-340-5p as shown in SEQ ID NO: 10, and hsa-miR-542-3p as shown in SEQ ID NO:

13.

4. The use according to claim 1, wherein The chip includes: a solid support; and Oligonucleotide probes are sequentially fixed on the solid support, wherein the oligonucleotide probes specifically bind to miRNA; Wherein, the miRNA is the miRNA according to claim 1.

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