WO2023219447A1 - Composition for diagnosing ovarian cancer comprising agent for detecting extracellular vesicle-derived mirna - Google Patents

Abstract

The present invention relates to a composition for diagnosing ovarian cancer. More specifically, the present invention comprises an agent that detects the expression level of at least one extracellular vesicle-derived miRNA selected from the group consisting of miR-1246, miR-1290, miR-483-5p, miR-429, miR-34b-3p, miR-34c-5p, miR-449a, and miR-145-5p, thereby more accurately and conveniently diagnosing ovarian cancer by means of a non-invasive method.

Description

Composition for diagnosing ovarian cancer comprising an agent for detecting extracellular vesicle-derived MIRNA

The present invention relates to a composition for diagnosing ovarian cancer, a kit for diagnosing ovarian cancer, and a method of providing information for diagnosing ovarian cancer.

Ovarian cancer has the highest mortality rate among gynecological cancers and is one of the cancers with a poor prognosis, with more than 50% of patients dying. In addition, even if early treatment is successful, recurrence may occur in 60 to 85% of patients. The high mortality and recurrence rates of ovarian cancer are due to the lack of early diagnostic biomarkers and effective screening strategies, and it is important to diagnose ovarian cancer early and continuously monitor it. Meanwhile, existing invasive cancer diagnosis methods using tissue have limitations in repeated testing, making continuous monitoring of ovarian cancer difficult. Therefore, there is a need to develop a non-invasive ovarian cancer diagnostic technique that can replace this.

The purpose of the present invention is to provide a composition for diagnosing ovarian cancer.

The purpose of the present invention is to provide a kit for diagnosing ovarian cancer.

The purpose of the present invention is to provide a method of providing information for diagnosing ovarian cancer.

1. At least one cell selected from the group consisting ofmiR-1246,miR-1290,miR-483-5p,miR-429,miR-34b-3p,miR-34c-5p,miR-449a andmiR-145-5p A composition for diagnosing ovarian cancer, comprising an agent for detecting the expression level of exoplasmic reticulum-derived miRNA.

2. In 1 above, the miR-1246 consists of the sequence of SEQ ID NO: 1, the miR-1290 consists of the sequence of SEQ ID NO: 2, and the miR-483-5p consists of the sequence of SEQ ID NO: 3 , themiR-429 consists of the sequence of SEQ ID NO: 4, themiR-34b-3p consists of the sequence of SEQ ID NO: 5, themiR-34c-5p consists of the sequence of SEQ ID NO: 6, and themiR- 449a consists of the sequence of SEQ ID NO: 7, and the miR-145-5p consists of the sequence of SEQ ID NO: 8. A composition for diagnosing ovarian cancer.

3. The composition for diagnosing ovarian cancer according to 1 above, wherein the extracellular vesicles are separated from blood, serum, or plasma.

4. The composition for diagnosing ovarian cancer according to 1 above, wherein the agent contains a primer or probe.

5. The method of 1 above, wherein the composition is an agent for detecting the expression level of extracellular endoplasmic reticulum-derived miRNAs of miR-1246, miR-1290, and miR-483-5p; Agents that detect the expression levels of extracellular vesicle-derived miRNAs: miR-1246, miR-1290, miR-483-5p, and miR-429; Agents for detecting the expression levels of extracellular vesicle-derived miRNAs:miR-1246,miR-1290,miR-483-5p,miR-429 andmiR-34b-3p; or an agent for detecting the expression level of extracellular vesicle-derived miRNAs: Composition for cancer diagnosis.

6. The method of 1 above, wherein the composition contains the following compounds: A composition for diagnosing ovarian cancer, comprising an agent for detecting the expression level of extracellular vesicle-derived miRNA.

7. A kit for diagnosing ovarian cancer comprising the composition of any one of items 1 to 6 above.

8. (a) From the subject's sample, there is the following: A method of providing information for diagnosing ovarian cancer, comprising: comparing the expression level of at least one extracellular vesicle-derived miRNA selected from the group consisting of the expression level of the corresponding extracellular vesicle-derived miRNA in a control sample.

9. In item 8 above, the expression level of at least one of the miR-1246, miR-1290, miR-483-5p and miR-429 is higher than the control group, or the expression level of the miR-34b-3p, miR-34c-5p, A method of providing information for diagnosing ovarian cancer, further comprising: determining that the subject has ovarian cancer when the expression level of at least one of miR-449a and miR-145-5p is lower than that of the control group.

10. The method of providing information for diagnosing ovarian cancer according to item 8 above, wherein the sample is blood, serum, or plasma.

11. The method of 8 above, wherein the sample is extracellular vesicles isolated from blood, extracellular vesicles isolated from serum, or extracellular vesicles isolated from plasma.

Using the composition for diagnosing ovarian cancer, the kit for diagnosing ovarian cancer, or the method for providing information for diagnosing ovarian cancer of the present invention, ovarian cancer can be diagnosed more accurately and conveniently in a non-invasive manner.

Figures 1A to 1E schematically illustrate the process of separating extracellular vesicles from ascites and plasma, and show the results of identifying the separated extracellular vesicles.

Figures 2A to 2M show the process of constructing an OCEM (Ovarian cancer EV miRNA) signature based on miRNA expression profiling of biofluid-derived EVs and the results of verifying the ovarian cancer diagnosis effect of the OCEM signature.

Figure 3 shows the results of verifying the OCEM signature of various combinations of miRNAs and the ovarian cancer diagnostic effect of eight miRNAs alone.

Figures 4A to 4J show the results of verifying the ovarian cancer diagnosis effect of the OCEM signature using various public data sets (in Figure 4e, the thin solid line graph closer to the y-axis is the validation set AUC, and the thicker solid line graph inside it is the validation set AUC) training set AUC graph).

Figures 5A to 5G show the results confirming changes in the ovarian cancer malignant phenotype according to treatment with MA-EVs.

Figures 6A to 6L show the results confirming the effect of EV miR-1246 and miR-1290 on the metastatic potential of ovarian cancer cells.

Figures 7A to 7I show the results of identifying targets of miR-1246 and miR-1290 related to ovarian cancer metastasis and patient prognosis.

Figure 8 shows an experimental process that confirmed that 8 types of EV-derived miRNAs were selected and their expression profiles could effectively diagnose or predict ovarian cancer, according to an example.

Figures 9A to 9D show the process of selecting eight EV-derived miRNAs for constructing an ovarian cancer diagnostic signature.

Figures 10A to 10F show the results confirming the effects of EV miR-1246 and miR-1290 on the invasion and migration of ovarian cancer cells.

Hereinafter, the present invention will be described in detail.

The present invention relates to a method of discovering an ovarian cancer diagnostic marker and effectively diagnosing ovarian cancer using a substance capable of specifically binding to the marker. The method using the miRNA marker of the present invention enables early diagnosis of ovarian cancer, thereby enabling early treatment and improvement of prognosis. In the present invention, we selected 8 types of miRNAs whose expression levels were significantly changed in samples from normal groups and ovarian cancer patients and confirmed that ovarian cancer can be effectively diagnosed or predicted through their expression profiles.

The present invention relates to at least one selected from the group consisting of: A composition for diagnosing ovarian cancer comprising an agent for detecting the expression level of miRNA is provided. The miRNA to be detected may be an extracellular vesicle-derived miRNA.

The term “miRNA” or “microRNA” or “miR” means that its precursor RNA transcript can form a small stem-loop, from which the mature “miRNA” is processed by the endonuclease Dicer. refers to RNA (or RNA analogue) that contains the product of an endogenous, non-coding gene that is cleaved. MiRNAs are encoded within genes that are distinct from the mRNAs whose expression they regulate.

In one example, the term “miRNA” or “microRNA” refers to a single-stranded RNA molecule of at least 10 nucleotides and up to 35 nucleotides covalently linked together. The miRNA or its complementary strand molecule includes, but is not limited to, 15 to 50, 15 to 30, or 18 to 25 nucleic acids.

The miRNAs of the present specification include specific nucleotide sequences of the above-mentioned miR-1246, MiR-1290, MiR-483-5p, MiR-429, MiR-34b-3p, MiR-34c-5p, MiR-449a, and MiR-145-5p ( or sequence number), as well as precursors (pre-miRNA, pri-miRNA) of the above-mentioned miRNA, miRNAs with equivalent biological functions, such as homologs (i.e., homologs or orthologs), genetic polymorphisms, etc. Also includes variants and derivatives of. Such precursors, homologs, variants or derivatives can be specifically identified using miRBase release 20 (http://www.mirbase.org/).

The miRNA of the present specification may be a gene product of a miR gene, and this gene product may include a mature miRNA or a miRNA precursor (e.g., pre-miRNA or pri-miRNA as described above).

The base sequence of the miRNA mentioned in this specification may be SEQ ID NO: 1 to 8 (see Table 1 below).

sequence number	miRNA name	Sequence (5'->3')
One	miR-1246	AAUGGAUUUUUGGAGCAGG
2	miR-1290	UGGAUUUUUGGAUCAGGGGA
3	miR-483-5p	AAGACGGGAGGAAAGAAGGGAG
4	miR-429	UAAUACUGUCUGGUAAAACCGU
5	miR-34b-3p	CAAUCACUAACUCCCACUGCCAU
6	miR-34c-5p	AGGCAGUGUAGUUAGCUGAUUGC
7	miR-449a	UGGCAGUGUAUUGUUAGCUGGU
8	miR-145-5p	GUCCAGUUUUCCCAGGAAUCCCU

The present invention relates to at least one selected from the group consisting of: A composition for diagnosing ovarian cancer can be provided, including an agent for detecting the expression level of extracellular vesicle-derived miRNA.

The term “extracellular vesicle (EV)” refers to the endoplasmic reticulum secreted by cells into the external environment for information exchange between cells. Extracellular vesicles may be exosomes.

The composition of the present invention diagnoses ovarian cancer by measuring the expression level of miRNA derived from extracellular vesicles (e.g., extracellular vesicles isolated from blood, extracellular vesicles isolated from plasma, or extracellular vesicles isolated from serum) of the subject. can do. Detection of extracellular vesicle-derived miRNA can usually be performed by extracting extracellular vesicles from a sample isolated from a subject and measuring the level of miRNA in the extracellular vesicles. Detection of such extracellular vesicle miRNAs can be measured by hybridization reaction and amplification reaction, but is not limited thereto and can be easily performed using various techniques known in the art.

Extracellular vesicles may be isolated from blood, serum, or plasma.

The composition of the present invention can detect the above-mentioned types of miRNAs from extracellular vesicles isolated from blood, serum, or plasma, and can exhibit an accurate ovarian cancer diagnosis effect in a non-invasive manner.

The ovarian cancer may be, but is not limited to, high-grade serous ovarian cancer.

The present inventors identified miR-1246, miR-1290, miR-483-5p, miR-429, miR-34b-3p, miR-34c-5p, miR-449a, and miR-145-5p as biomarkers for ovarian cancer diagnosis. It was confirmed that some combinations showed better efficacy.

Specifically, the composition for diagnosing ovarian cancer of the present invention includes miR-1246, miR-1290, and miR-483-5p; miR-1246,miR-1290,miR-483-5p andmiR-429; miR-1246,miR-1290,miR-483-5p,miR-429 andmiR-34b-3p; miR-1246,miR-1290,miR-483-5p,miR-429,miR-34b-3p andmiR-34c-5p; Alternatively, expression levels for miR-1246, miR-1290, miR-483-5p, miR-429, miR-34b-3p, miR-34c-5p, miR-449a, and miR-145-5p can be measured. It may contain agents.

According to one embodiment, the composition for diagnosing ovarian cancer may include an agent for detecting extracellular vesicle-derived miR-1246, miR-1290, and miR-483-5p. According to another embodiment, the composition for diagnosing ovarian cancer may further include an agent for detecting extracellular vesicle-derived miR-1246, miR-1290, and miR-483-5p, as well as an agent for detecting extracellular vesicle-derived miR-429. You can. According to another embodiment, the composition for diagnosing ovarian cancer detects extracellular vesicle-derived miR-429 and miR-34b-3p along with an agent that detects extracellular vesicle-derived miR-1246, miR-1290, and miR-483-5p. Additional agents may be included. According to another embodiment, the composition for diagnosing ovarian cancer includes an agent for detecting extracellular vesicle-derived miR-1246, miR-1290, and miR-483-5p, along with extracellular vesicle-derived miR-429, miR-34b-3p, and miR It may further include an agent that detects -34c-5p.

According to another embodiment, the composition for diagnosing ovarian cancer includes extracellular endoplasmic reticulum-derived miR-1246, miR-1290, miR-483-5p, miR-429, miR-34b-3p, miR-34c-5p, miR-449a, and It may include an agent that detects miR-145-5p.

The agent may be a substance that specifically binds to the at least one miRNA, and may include, for example, a primer or probe.

Detection of nucleic acids can be performed by an amplification reaction using one or more oligonucleotide primers that hybridize to the nucleic acid molecule or the complement of the nucleic acid molecule. For example, detection of extracellular vesicle miRNAs using primers can be performed by amplifying the gene sequence using an amplification method such as PCR and then confirming whether the gene is amplified by a method known in the art.

A primer is a short nucleic acid sequence that has a short free 3' hydroxyl group that can form a base pair with a complementary template and serves as a starting point for copying the template strand. means. Primers can initiate DNA synthesis in the presence of four different nucleoside triphosphates and a reagent for polymerization (i.e., DNA polymerase or reverse transcriptase) in an appropriate buffer solution and temperature. In the present invention, ovarian cancer can be diagnosed by performing PCR amplification using sense and antisense primers that specifically bind to the at least one miRNA and confirming the expression level. PCR conditions and lengths of sense and antisense primers can be modified based on those known in the art.

A probe refers to a nucleic acid fragment such as RNA or DNA that is as short as a few bases or as long as several tens of bases and is labeled. Probes may be manufactured in the form of oligonucleotide probes, single stranded DNA probes, double stranded DNA probes, RNA probes, etc. In the present invention, ovarian cancer can be diagnosed by confirming the expression level by performing hybridization using a probe complementary to one or more of the above miRNAs. Selection of appropriate probes and hybridization conditions can be modified based on those known in the art.

Primers or probes can be appropriately designed by those skilled in the art based on known sequences. For example, primers or probes can be chemically synthesized using the phosphoramidite solid support method, or other well-known methods. These nucleic acid sequences can also be modified using many means known in the art. Non-limiting examples of such modifications include methylation, capping, substitution of a native nucleotide with one or more homologs, and modifications between nucleotides, such as uncharged linkages (e.g., methyl phosphonates, phosphotriesters, phosphorylates). amidate, carbamate, etc.) or charged linkages (e.g. phosphorothioate, phosphorodithioate, etc.).

The expression level of miRNA can be measured according to methods commonly used in the field, such as reverse transcriptase polymerase reaction (RT-PCR), competitive reverse transcriptase polymerase reaction (Competitive RT-PCR), and real-time reverse transcriptase polymerase reaction. (RealtimeRT-PCR), RNase protection assay (RPA), Northern blotting, or gene chip, etc., but are not limited to these.

Additionally, the present invention provides a kit for diagnosing ovarian cancer comprising the above-described composition.

The kit may further include ingredients, tools, reagents, etc. commonly used in the art to be suitable for use as an ovarian cancer diagnostic kit, as well as an agent for measuring the expression level of the above-described miRNA or extracellular vesicle-derived miRNA. .

The components, tools or reagents may be carriers, labeling substances capable of generating detectable signals, chromophores, solubilizers, detergents, buffers, stabilizers, etc. If the labeling substance is an enzyme, it may include a substrate that can measure enzyme activity and a reaction stopper. The carrier may be a soluble carrier or an insoluble carrier, and the soluble carrier includes physiologically acceptable buffers known in the art, such as PBS, and examples of the insoluble carrier include polystyrene, polyethylene, polypropylene, polyester, and polyacrylic. It may be ronitrile, fluororesin, cross-linked dextran, polysaccharide, polymers such as magnetic fine particles plated with metal on latex, other paper, glass, metal, agarose, and combinations thereof.

Additionally, the present invention provides a method of providing information for diagnosing ovarian cancer.

The method of providing information of the present invention is (a) from a sample of a subject. And comparing the expression level of at least one extracellular vesicle-derived miRNA selected from the group consisting of miR-145-5p with the expression level of the corresponding extracellular vesicle-derived miRNA in the control sample.

“Comparison with the expression level of the corresponding extracellular vesicle-derived miRNA in the control sample” means comparing the expression level of a specific miRNA derived from the extracellular vesicles isolated from the sample of the individual subject to diagnosis and the same type of control miRNA.

The method of providing information of the present invention is (b) the expression level of at least one of the miR-1246, miR-1290, miR-483-5p and miR-429 is high compared to the control group, or the expression level of the miR-34b-3p and the miR-429 is high compared to the control group. The method may further include determining that the subject has ovarian cancer when the expression level of at least one of -34c-5p, miR-449a, and miR-145-5p is lower than that of the control group.

The control group may be a normal control group.

The subject may be an animal that currently has ovarian cancer, has previously had ovarian cancer, or is suspected of having ovarian cancer.

The sample may be selected from the group consisting of blood, serum, plasma, ascites, or urine.

The sample may be selected from the group consisting of extracellular vesicles isolated from blood, extracellular vesicles isolated from serum, or extracellular vesicles isolated from plasma.

In step (b), the subject may be judged to have ovarian cancer if the expression levels of miR-1246, miR-1290, and miR-483-5p are higher than those of the control group.

In step (b), the subject may be determined to have ovarian cancer if the expression levels of miR-1246, miR-1290, miR-483-5p, and miR-429 are higher than the control group.

In step (b), if the expression level of miR-1246, miR-1290, miR-483-5p, and miR-429 is high compared to the control group, and the expression level of miR-34b-3p is low compared to the control group, the subject is It may be considered ovarian cancer.

In step (b), the expression levels of miR-1246, miR-1290, miR-483-5p, and miR-429 are higher than the control group, and the expression levels of miR-34b-3p and miR-34c-5p are higher than the control group. If it is high, the subject may be judged to have ovarian cancer.

In stage (b), the expression levels of miR-1246, miR-1290, miR-483-5p, and miR-429 are higher than the control group, and the expression levels of miR-34b-3p, miR-34c-5p, miR-449a, and miR-429 are higher than those of the control group. If the expression level of -145-5p is high compared to the control group, the subject may be judged to have ovarian cancer.

The method of providing information of the present invention may further include the step of isolating extracellular vesicles from a sample of a subject before performing step (a).

Additionally, the present invention provides a composition for predicting ovarian cancer metastasis, comprising an agent for detecting the expression level of at least one miRNA selected from the group consisting of miR-1246 or miR-1290. The miRNA to be detected may be an extracellular vesicle-derived miRNA.

Extracellular endoplasmic reticulum, miRNA, and ovarian cancer have been described above, so detailed explanations will be omitted.

Additionally, the present invention provides a kit for predicting ovarian cancer metastasis comprising the above-described composition.

In addition, the present invention (i) compares the expression level of at least one extracellular vesicle-derived miRNA selected from the group consisting of miR-1246 and miR-1290 from the sample of the subject to the expression level of the corresponding extracellular vesicle-derived miRNA in the control sample. A method of providing information for predicting ovarian cancer metastasis is provided, including the step of comparing.

The method of providing information of the present invention may further include (ii) determining that the subject has ovarian cancer when the expression level of at least one of the miR-1246 and the miR-1290 is higher than that of the control group. .

The method of providing information of the present invention may further include the step of isolating extracellular vesicles from a sample of a subject before performing step (i).

Since the details of the control group, subjects, and samples have been described above, detailed descriptions are omitted.

Hereinafter, the present invention will be described in detail with reference to examples.

Experimental method

1. Clinical sample collection

This study was approved by the Medical Research Ethics Review Committee of Seoul National University Hospital (IRB No. 2004-128-1117) and was conducted in accordance with the Declaration of Helsinki. After obtaining written consent from patients with high-grade serous ovarian cancer (HGSOC), malignant ascites samples (N=28) were collected. As a control group, benign peritoneal fluids (N=21) from patients with benign gynecologic diseases were used. Additionally, plasma samples were obtained from patients with HGSOC (N=109) and patients with benign gynecological diseases (N=96).

2. Ascites and plasma preparation

The collected ascites samples were centrifuged at 2500 rpm and 4°C for 10 minutes, separated into cell fractions and cell-free fractions, and used and stored in experiments. The cell-free supernatant was filtered through a Falcon 70 μm filter (Corning, USA) and stored at -70°C for future use. Whole blood was collected from an EDTA-treated tube and centrifuged at 2500 g for 15 minutes at 4°C to remove cells. The supernatant was then collected and centrifuged again to remove platelets and stored at -70°C for future use.

3. Isolation and identification of extracellular vesicles

Before isolation of extracellular vesicles (EV), ascites, plasma, and cell culture medium stored at -70°C were thawed and filtered through a 0.2 μm syringe filter (Sartorius, Gottingen, Germany). To separate ascites EV and plasma EV, ExoQuick (System Biosciences, USA), a commercial EV isolation kit, was used according to the manufacturer's protocol.

4. Nanoparticle tracking analysis

The EV pellet was suspended in PBS and diluted to the appropriate concentration. EV concentration and size distribution were measured by nanoparticle tracking analysis (NTA) using the NanoSight NS300 system (Malvern Technologies, Malvern, UK).

5. Transmission electron microscopy

10 μL of EV suspended in PBS was dispensed onto a 200 mesh formvar-coated copper grid and dried. The grid was then negatively stained with 10 μL of 2% phosphotungstic acid. Image analysis confirmed the morphology of EVs using a JEM 1400 transmission electron microscope (Jeol Ltd, Peabody, USA).

6. Western blotting

After cell culture, the obtained cells were lysed in lysis buffer and incubated at 4°C for 30 minutes. The lysed cells were centrifuged at 13,000 rpm at 4°C for 20 minutes to obtain protein from the supernatant, and the obtained protein concentration was confirmed using the BCA Protein Assay kit (Thermo Scientific, Waltham, MA, USA). 5X loading buffer was added to the protein and boiled at 99.9°C for 5 minutes to denature the protein structure for Western blotting. Proteins were loaded on SDS-PAGE, separated by size through electrophoresis, and then transferred to a nitrocellulose membrane. The nitrocellulose membrane onto which the protein was transferred was incubated in 5% skim milk for 1 hour and incubated with primary and secondary antibodies of the protein to be identified. Finally, the signal was detected using ECL Western Blotting Detection Reagent (Amersham, UK).

7. Small RNA sequencing

Extracted RNA was quantified using the NanoDrop 2000 Spectrophotometer system (Thermo Fisher Scientific, Waltham, MA, USA). The library was constructed using the NEBNext Multiplex Small RNA Library Prep kit New England Bio Labs, Inc. according to the manufacturer's instructions. For each sample, 1 μg of total RNA was used to conjugate adapters, and cDNA synthesis was performed using reverse transcriptase with adapter-specific primers. PCR was performed to amplify the library and purified using QIAquick PCR Purification Kit (Qiagen, Hilden, Germany) and AMPure XP beads (Beckmancoulter, CA, USA). For highly sensitive DNA analysis, the yield and size distribution of the small RNA library were measured using the Agilent 2100 Bioanalyzer instrument (Agilent Technologies, Santa Clara, CA, USA). Then, single-end 75 sequencing was performed on the generated high-throughput sequences on the NextSeq500 system (Illumina, SanDiego, CA, USA).

8. Bioinformatics analysis

We divided our data set into training data set and validation data set. EdgeR N+1 normalization was performed and 8 miRNAs were selected using EdgeR from the training data. A logistic regression model was set up based on the OCEM (Ovarian cancer EV miRNA) signature. ROC curve (receiver operating characteristic) and risk-probability plot were generated. The signature refers to a collection of multiple selected extracellular vesicle-derived miRNAs.

Additionally, several public data sets spanning various patient populations were used for validation of the OCEM signature. Due to batch effects between public datasets, each dataset was trained and validated independently using OCEM signatures.

9. Cell culture

Human ovarian cancer cell lines SKOV3 and KURAMOCHI were used in this study. The medium composition was RPMI1640 (WelGENE, Seoul, Korea) supplemented with 10% fetal bovine serum (FBS; Gibco, MD, USA) and 100 μg/mL penicillin-streptomycin (Invitrogen, Carlsbard, CA, USA) to support cells. Cultured. Culture conditions were carried out in an incubator set at 37°C and 5% CO ₂ .

10. EV uptake by ovarian cancer cells

According to the manufacturer's protocol, the lipid membrane of isolated EVs was stained and labeled using the PKH67 green fluorescent cell linker mini kit (Sigma, USA), and the cell membrane was labeled with the PKH26 red fluorescent cell linker mini kit (Sigma, USA). Ovarian cancer cells were treated with labeled EVs and co-cultured for 6 hours. EV uptake by ovarian cancer was confirmed using a confocal microscope LSM800 (EVOS, USA).

11. Invasion and migration assay

To investigate the invasion and migration potential of ovarian cancer cells, 8 μm-sized Transwell inserts (BD Biosciences, CA, USA) were used. For invasion assays, inserts were pre-coated with Matrigel (BD Biosciences, CA, USA). Ovarian cancer cells were dispensed into the upper chamber in serum-free medium and maintained for 24 hours. Then, medium containing 10% FBS was added to the lower chamber and incubated for 24 hours. Transwell inserts were washed with PBS, and infiltrated and migrated cells were stained with 0.5% crystal violet. Then, the remaining cells on the upper surface of the inserts were removed, and the infiltrated and migrated cells were confirmed under a microscope and further analyzed with Image J software.

12. Organoid establishment

Malignant ascites-derived cells were used to establish ovarian cancer organoids. Cells were seeded with phenol red-free Matrigel Growth Factor Reduced Basement Membrane Matrix (BD Bioscience, CA, USA) and cultured in culture medium for organoids. Culture media for organoids include Penicillin/Streptomycin (Gibco), HEPES (Gibco), GlutaMax (Gibco), b-Estradiol (Sigma), Nicotinamide (Sigma), recombinant human Noggin (Peprotech), recombinant R-Spondin1 (RSPO1; Peprotech). , B27 (Invitrogen), EGF (Invitrogen), FGF10 (Peprotech), HeregulinB-1 (Peprotech), Forskolin (bio-techne), Hydrocortisone (Sigma), A83-10 (bio-techne), Y-27632 dihydrochloride (Sigma) ), N-acetylcysteine (Sigma), and Priomcin (InvivoGen) were used.

13. 3D invasion assay

Primary cancer cells (A37) from malignant ascites were used for tumor spheroid formation and 3D invasion analysis. Cells were seeded in an Ultra-Low Attachment 96 well-plate (Corning, USA) to form spheroids. After 3 days, spheroids were treated with EVs along with Matrigel for 3D invasion analysis. After 72 hours, the extent of 3D invasion was imaged and analyzed using ImageJ.

14. RNA extraction and quantitative real-time PCR

RNA was extracted from EVs using an RNA extraction kit (Qiagen, Hilden, Germany) and transcribed into cDNA using the miRCURY LNA RT kit (Qiagen, Hilden, Germany) supplemented with spike-in control Unisp6 according to the manufacturer's instructions. did. Quantitative real-time PCR was performed using the Mir-X ^TM miRNA qRT-PCR TB Green® Kit (Takara, Tokyo, Japan). Cells were cultured in culture medium for 24 or 48 hours and then collected for further analysis.

15. Transformation

Lipofectamine RNA iMAX (Invitrogen, Carlsbad, CA, USA) was used to transform miRNA mimics or inhibitors, and Lipofectamine 3000™ reagent (Invitrogen, Carlsbad, CA, USA) was used to transform plasmids. converted. The culture medium was replaced 6 hours after transformation. Cells were cultured in culture medium for 24 or 48 hours and then collected for further analysis.

16. Luciferase reporter assay

pGL-3-RORα 3'UTR reporter plasmid (Origene, Rockvile, USA) containing wild-type or mutated miR-1246 or miR-1290 binding sequences and miR-1246 or miR-1290 using Lipofectamine3000 (Invitrogen, USA). Cells were cotransfected with mimics or negative controls. Firefly luciferase was detected using the Luciferase Reporter Gene Detection Kit (MilliporeSigma, Missouri, USA). Firefly luciferase activity was calculated for red fluorescent protein (RFP) intensity.

17. Immunohistochemistry

Immunohistochemistry (IHC) analysis was performed using 4-μm-thick tissue microarray (TMA) sections with a Benchmark autostainer (Ventana, Tucson, AZ, USA) according to the manufacturer's instructions. The intensity of the immune response was scored for further analysis.

18. Statistical analysis

Data were expressed as mean ± SEM using GraphPad Prism 9. Statistical comparisons were performed using Student's t-test and ANOVA analysis with Bonferroni's post hoc test. All statistical analyzes were two-sided with a p-value <0.05 considered statistically significant.

A flow chart summarizing the experimental method of the above-described embodiment is shown in Figure 8.

Experiment result

1. Isolation of EVs from ascites and plasma and confirmation of isolated EVs

During surgery, malignant ascites obtained from a patient with high-grade serous ovarian cancer (HGSOC) was separated into a cell fraction and a cell-free fraction for future use and stored at -70°C until used in the experiment (Figure 1A). In this study, benign peritoneal fluid collected from female patients with benign gynecological diseases was used as a control. Likewise, malignant and benign plasma samples were obtained from patients suffering from HGSOC or benign disease. EVs were isolated from benign peritoneal fluids (BA), malignant ascites (MA), benign plasma (BP), and malignant plasma (MP) using the commercial kit ExoQuic ( Figure 1B). EVs isolated from benign peritoneal fluid were expressed as BA-EV, EVs isolated from malignant ascites were expressed as MA-EV, EVs isolated from benign plasma were expressed as BP-EV, and EVs isolated from malignant plasma were expressed as MP-EV. The typical morphology of EVs as nano-sized double-membrane circular particles was confirmed by TEM (Figure 1C; scale bar 200 nm). Western blotting demonstrated positive expression of EV markers CD9 and CD81 (Figure 1D). Additionally, the size distribution of EVs ranging from 30 nm to 200 nm was evaluated by NTA (Figure 1E; Figures 1ea to 1ed).

Figure 1A schematically illustrates the process of sampling ascites, and specifically shows the process of processing malignant ascites obtained from an HGSOC patient into cell fraction and cell-free fraction. Figure 1B schematically illustrates the process of separating EVs from ascites and plasma. It shows the process of filtering the ascites cell-free fraction and plasma and isolating EVs by applying the ExoQuick kit. Figure 1C shows the results of confirming the morphology of BA-EV, MA-EV, BP-EV, and MP-EV through TEM. Figure 1D shows the results confirming that BA-EV, MA-EV, BP-EV, and MP-EV express EV markers through Western blotting. Figure 1E shows the results of confirming the size distribution and concentration of BA-EV, MA-EV, BP-EV, and MP-EV through NTA. BA-EV: refers to benign peritoneal fluid-derived EV, MA-EV: refers to malignant ascites-derived EV, BP-EV: refers to benign plasma-derived EV, MP-EV: refers to malignant plasma-derived EV.

2. Construction and verification of ovarian cancer EV miRNA (OCEM) signature

(1) Signature construction and verification

Small RNA sequencing was performed to investigate the miRNA profiling of MP-EVs (N=31) and BP-EVs (N=24), as well as MA-EVs (N=10) and BA-EVs (N=9). . miRNAs whose P value was less than 0.05 and whose expression differed at least 4-fold between the benign and malignant groups were considered differentially expressed miRNAs (DEmiRs). According to this criterion, 93 miRNAs were upregulated and 117 miRNAs were downregulated in MA-EVs compared to BA-EVs (Figures 2A and 2B). Likewise, 21 miRNAs were upregulated and 45 miRNAs were downregulated in MP-EVs compared to BP-EVs (Figure 2C, Figure 2D). Pathway analysis showed that a large portion of DEmiRs were closely involved in oncogenesis, cancer progression, several cancer hallmarks, and EV development-related pathways.

Next, to select candidate miRNAs for constructing diagnostic signatures, our data set was divided into training and validation sets (Figure 9A). DEmiRs were analyzed only in the training set (Figures 9B and 9C). Analysis results showed that 17 DEmiRs overlapped in the ascites and plasma datasets (Figure 2E). Univariate logistic regression analysis was performed to confirm the diagnostic ability of each miRNA using the AUC (Area Under the Curve) of the ROC curve. The 17 DEmiRs were ranked according to their AUC for detecting ovarian cancer patients in the ascites and plasma training sets (Figures 2F and 2G). Eight miRNAs showed fine discriminant effects in ascites and plasma regression analyzes (miR-1246, MiR-1290, MiR-483-5p, MiR-429, MiR-34b-3p, MiR-34c-5p, MiR-449a, MiR) -145-5p) was selected (Figure 9D). The expression of these eight miRNAs in ascites and plasma subsets was shown as a heatmap (Figure 2H, Figure 2I).

A validation set of our own data set was applied to verify the performance of the OCEM signature containing all eight types of miRNAs. Results demonstrate that the OCEM signature exhibits good diagnostic ability in both ascites (training set: AUC=1, validation set: AUC=1) and plasma (training set: AUC=0.9762, validation set: AUC=0.9375). (FIG. 2J, FIG. 2L; the thick solid line graph closer to the y-axis in FIG. 2J and the dark solid line graph closer to the dotted line in FIG. 2L are validation set AUC, the inner thin solid line graph farther from the y-axis in FIG. The thin solid line graph farthest from the dotted line is the training set AUC graph). Risk-probability plots distinguished between benign peritoneal fluid and malignant ascites (Figure 2K) and benign and malignant plasma (Figure 2M).

Figure 2A is a volcano plot showing DEmiRs in the ascites subset (up-regulation: N=93; down-regulation: N=117). Figure 2B (Figures 2Ba-2BG) is a heatmap showing the expression patterns of DEmiRs in multiple subsets. Figure 2C is a volcano plot showing DEmiRs in the plasma subset (upregulation: N=21; downregulation: N=45). Figure 2D (Figures 2da-2dd) is a heatmap showing the expression pattern of DEmiRs in plasma subsets. Figure 2E is a Venn diagram showing the overlap of 17 DEmiRs in the ascites and plasma training sets (up-regulation: N=7; down-regulation: N=10). Figure 2F shows the results of univariate logistic regression analysis demonstrating the AUC for whether the 17 DEmiRs can identify cancer patients in multiple training sets. Figure 2G shows the results of univariate logistic regression analysis demonstrating the AUC for whether the 17 DEmiRs can identify cancer patients in the plasma training set. Figure 2H is a heatmap showing the expression of eight miRNAs selected to build the OCEM signature in multiple subsets. Figure 2I is a heatmap showing the expression of eight miRNAs selected to build the OCEM signature in the plasma subset. Figure 2J is an ROC curve showing the diagnostic ability of OCEM signatures on multiple training and validation sets. Figure 2K is a risk probability plot showing predicted risk probabilities in multiple training and validation sets. Figure 2L is an ROC curve showing the diagnostic ability of OCEM signatures on plasma training and validation sets. Figure 2M is a risk probability plot showing predicted risk probabilities in plasma training and validation sets.

In addition, five additional OCEM signatures were constructed by various combinations of the eight miRNAs selected above, and the ovarian cancer diagnosis effect of each of these signatures was verified.

Table 2 below lists the OCEM signatures and verification results of various combinations used for verification, and Table 3 below shows the verification results for each of the eight selected miRNAs. Additionally, a graph related to this result is shown in Figure 3.

Signature classification	Selected miRNAs markers to build signatures	Diagnostic effectiveness verification result (AUC)
OCEM-3	hsa-miR-1246, hsa-miR-1290, hsa-miR-483-5p	0.884
OCEM-4	hsa-miR-1246, hsa-miR-1290, hsa-miR-483-5p, hsa-miR-429	0.89
OCEM-5	hsa-miR-1246, hsa-miR-1290, hsa-miR-483-5p, hsa-miR-429, hsa-miR-34b-3p	0.964
OCEM-6	hsa-miR-1246, hsa-miR-1290, hsa-miR-483-5p, hsa-miR-429, hsa-miR-34b-3p, hsa-miR-34c-5p	0.97
OCEM-8	miR-1246,miR-1290,miR-483-5p,miR-429,miR-34b-3p,miR-34c-5p,miR-449a,miR-145-5p	0.976

Classification of miRNAs	Diagnostic effectiveness verification result (AUC)
miR-429	0.625
miR-34c-5p	0.688
miR-1246	0.688
miR-145-5p	0.688
miR-483-5p	0.75
miR-449a	0.813
miR-34b-3p	0.866
miR-1290	0.884

3. Confirming the diagnostic effectiveness of OCEM signatures in diverse populations

We verified the accuracy and clinical usability of the OCEM signature, which includes all eight types of miRNAs, using multiple public data sets.

First, GSE106817 (cancer: N = 333; non-cancer: N = 2788) and GSE113486 (cancer: N = 40; non-cancer: N = 100), which contain serum extracellular miRNA profiling data by microarray, were applied for verification. . Considering the heterogeneity of miRNA detection platforms and batch effects between datasets, we retrained OCEM signatures independently on each dataset. OCEM signatures of all stages in GSE106817 (training set: AUC=0.9557; validation set: AUC=0.9515) (Figure 4A) and GSE113486 (training set: AUC=0.904; validation set: AUC=0.095) (Figure 4C) It showed great discriminatory performance in detecting ovarian cancer patients. Risk probability plots showed that the OCEM signature discriminated well between cancer and non-cancer sera (Figures 4B and 4D).

Additionally, based on the hypothesis that EV miRNA signatures detected in ascites and plasma may reflect the characteristics of cancer tissues, tissue miRNA profiling datasets GSE83693 (cancer: N=80, non-cancer: N=7) and GSE65819 (cancer: N=7) =16, non-cancer: N=4), the OCEM signature was verified. The OCEM signatures in GSE83693 (training set: AUC=1, validation set: AUC=1) and GSE65819 (training set: AUC=1, validation set: AUC=1) both showed good diagnostic ability (Figure 4G; y The solid line graph closer to the axis is the validation set AUC, and the solid line graph inside is the training set AUC graph). The risk probability plot demonstrated that the OCEM signature could almost perfectly distinguish between cancer and normal tissue (Figure 4F and Figure 4H).

Additionally, urine dataset GSE58517 (cancer: N=80; non-cancer: N=7) was also used for validation. Interestingly, the OCEM signature could also be applied to urine samples (training set: AUC=1, validation set: AUC=1) (Figures 4I and 4J; the solid line graph closer to the y-axis in Figure 4I indicates the validation set AUC, than the validation set AUC). The solid line graph inside is the training set AUC graph). These results highlighted the clinical applicability of the OCEM signature constructed from eight types of miRNAs in diverse patient populations across multiple sample types.

Although we considered the high detectability of miRNAs on various platforms when selecting candidate miRNAs for establishing diagnostic signatures, some data sets were identified that failed to detect all eight miRNAs simultaneously in the OCEM signature. Interestingly, strong diagnostic efficacy (training set: AUC=0.9633 and validation set: AUC=0.9003) was observed. Additionally, another tissue dataset GSE127873 containing stage I serous cancer tissue (N=22) and normal tissue (N=9) was also applied for validation. In particular, six miRNAs among the OCEM signatures showed high AUC and accuracy in identifying stage I ovarian cancer patients (training set: AUC = 1; validation set: AUC = 0.9091).

Figures 4A and 4B show OCEM signature validation results using serum dataset GSE106817 (cancer: N=333; non-cancer: N=2788). Figures 4C and 4D show OCEM signature validation results using serum dataset GSE113486 (cancer: N=40, non-cancer: N=100). Figures 4G and 4H show OCEM signature verification results using dataset GSE65819 (Cancer: N=16; Non-cancer: N=4). Figures 4I and 4J show OCEM signature verification results using urine dataset GSE58517 (cancer: N=80, non-cancer: N=7).

4. Confirmation of the effect of malignant ascites-derived EV in promoting the possibility of ovarian cancer metastasis

EV concentrations were compared between benign peritoneal fluid and malignant ascites of ovarian cancer patients by counting particle numbers using NTA. As a result, EV concentration was significantly increased in malignant ascites compared to benign peritoneal fluid (Figure 5A). We also observed higher MA-EV concentrations in late-stage patients (stages III and IV) than in early-stage patients (stage I+) (Figure 5B). The results showed that the concentration of MA-EV was higher in relapsed patients who experienced recurrence due to the use of chemotherapy compared to patients with a primary diagnosis (Figure 5C). These data suggest that MA-EVs concentration may be of significant help in monitoring cancer progression.

To investigate the effect of MA-EVs on the ovarian cancer malignant phenotype, ovarian cancer cell lines SKOV3 and KURAMOCHI were treated with MA-EVs. Cancer cell membranes were stained with red PKH26, and MA-EVs were stained with green PKH-67. Uptake of MA-EVs by cancer cells was detected after 6 hours of co-culture (Figure 5D). As a result, MA-EV treatment significantly promoted the invasion and migration of SKOV3 and KURAMOCHI cells, whereas BA-EV showed no significant effect (Figure 5E). Isolated ovarian cancer cells floating in the ascites usually cluster at the metastatic site as either single cells or multicellular spheroids. Therefore, a 3D spheroid invasion assay mimicking tumor micro-metastasis was performed to investigate the effect of MA-EVs on tumor spheroids. 3D spheroids treated with MA-EVs had increased invasion ability compared to spheroids treated with BA-EVs or PBS (Figure 5F; scale bar 200 μm).

Organoid models derived from human samples have emerged as a unique model that bridges the gap between in vitro and in vivo preclinical cancer models. We developed ascites cell-derived organoids and tested the effects of MA-EVs in this in vivo-like in vitro model. As a result, treatment with MA-EVs significantly increased the number and size of organoids, while organoids treated with BA-EVs maintained similar levels to the PBS control group (Figure 5G).

Figure 5A shows the concentrations of BA-EVs and MA-EVs determined by NTA. Figure 5B shows the concentration of MA-EVs (stage I+II) and MA-EVs (stage III+IV). Figure 5C shows the concentration of MA-EVs (primary diagnosis) and MA-EVs (relapse). Figure 5D shows EV uptake by cancer cells. Cancer cell membranes were stained with PKH26 in red, while MA-EVs were stained with PKH-67 in green. Uptake of MA-EVs by SKOV3 and KURAMOCHI was detected by confocal microscopy. Figure 5E (Figures 5ea to 5ec) shows that ovarian cells were treated with MA-EVs or BA-EVs and their invasion and migration abilities were confirmed by transwell analysis. Figure 5F (Figures 5fa-5fc) shows the results of 3D invasion analysis. Cancer cells were observed to infiltrate Matrigel under a microscope. Figure 5G (Figure 5ga to Figure 5gc; in Figure 5gb, the scale bar is 500 μm in the upper row and 100 μm in the lower row), to evaluate the effect of MA-EVs, an ascites cell-derived organoid model was established and ascites cells were spread on Matrigel. This is a schematic diagram of the process of forming an organoid model. After continuously treating the 14-day-old organoid model with PBS, BA-EVs, or MA-EVs for 2 weeks, the number and size of organoids formed were evaluated. *, P<0.05; **, P<0.01; ***, P<0.001.

5. Confirmation of the effects of miR-1246 and miR-1290 on ovarian cancer cell invasion and migration

EV miRNA signatures may not only serve as clinical biomarkers in liquid biopsies but also influence cancer characteristics. To identify the miRNA functions of MA-EVs involved in altering ovarian cancer metastatic ability, we referred to the results of miRNA sequencing of ascites and plasma-derived EVs. MiR-1246 and miR-1290 were selected as target miRNAs for posterior analysis because they are among the most upregulated miRNAs in the malignant group and the absolute expression levels of these two miRNAs were significantly high (Figures 10A and 10B; The part below -2 on the x axis is Down, and the part above 2 is UP; in Figure 10a, the up arrow is for miR-1246 and the down arrow is for miR-1290; in Figure 10b, the up arrow is for miR-1290 and the down arrow is for miR-1246. lim). The expression of miR-1246 and miR-1290 in ascites and plasma subsets was shown in the heatmap (Figures 6A and 6B). To further investigate the expression pattern of miR-1246 and miR-1290, additional ascites (MA-EVs: N=18, BA-EVs: N=12) and plasma samples (MP-EVs: N=78; BP-EVs: N=72) was used to perform RT-qPCR. The expression levels of these two miRNAs were significantly elevated in MA-EVs and MP-EVs compared to benign EVs (Figures 6C and 6D). In particular, upregulation of miR-1246 and miR-1290 was also observed in patients with early (stage I+II) cancer (Figures 6E and 6F). As a result of confirming the correlation between the expression of miR-1246 and miR-1290 and patient survival through public data sets, it was confirmed that higher expression of miR-1246 and miR-1290 was associated with a poor prognosis of patients (Figure 10D; Figure 10D) 10da to 10db). Herein, survival analysis in our study showed that plasma EV miR-1290 levels were strongly associated with patients' overall survival and progression-free survival, whereas EV miR-1246 expression did not correlate with patient survival ( Figures 10E and Figure 10F; In Figure 10e, the dark line is the Low graph, and the light line is the High graph. In Figure 10f, the Low graph is farther from the origin than the High graph), indicating the potential prognostic value of miR-1290.

As the expression of miR-1246 and miR-1290 increased in malignant biofluids, we wondered whether this trend resulted from differential secretion between cancer cells and normal epithelial cells. We used the miRNA profiling data of GSE103708 to analyze the expression of miR-1246 and miR-1290 in cancer cells (N=13) and normal epithelial cells (N=3) and each EV. However, no significant differences were observed between the normal and cancer groups at both the cell level and EV level. Meanwhile, no differences in miR-1246 or miR-1290 expression were detected between HGSOC and non-HGSOC cell lines or their respective EVs. Notably, the expression of miR-1246 and miR-1290 in cells and respective EVs was not correlated, indicating that a complex sorting mechanism may be involved in the secretion process of these two miRNAs.

As hypoxia is one of the pivotal hallmarks of cancer, we investigated whether hypoxia could cause changes in the expression of miR-1246 and miR-1290 in EVs. SKOV3 and KURAMOCHI cells were cultured in medium supplemented with EV-free FBS under normoxic and hypoxic conditions. EVs were isolated after 72 h of incubation and identified as double-membrane particles ranging from 30 nm to 200 nm with CD63 and CD9 expression by TEM, NTA, and Western blotting. Interestingly, the expression of miR-1246 and miR-1290 was dramatically increased in hypoxic EVs (H-EVs) than in normoxic EVs (N-EVs) (Figures 6I and 6J). Afterwards, we investigated the effects of these two miRNAs on ovarian cancer metastatic potential. As shown in Figure 6K (Figure 6ka to Figure 6kb; the graph on the right sequentially shows the relative cell number in each case on the left), inhibition of miR-1246 and miR-1290 inhibits the invasion and migration of SKOV3 and KURAMOCHI cells. While significantly suppressed, overexpression of these two miRNAs had the opposite effect. These results demonstrated that miR-1246 and miR-1290 encapsulated in MA-EVs play an important role in promoting ovarian cancer metastasis. The main metastatic route for ovarian cancer is transcolomic, with metastasis occurring along the peritoneum and to various organs in the abdominal cavity. Therefore, in this study, we created a new in vivo model to investigate the effect of miR-1246 and miR-1290 on the metastatic ability of ovarian cancer cells using reticular adipose tissue derived from ovarian cancer patients.miR-1246 or miR-1290 inhibitors The number of cases of SKOV3 ^luc administered with SKOV3 luc migrating and colonizing retinal tissue pieces was reduced compared to the control group, whereas migration increased when the MiR-1246 or miR-1290 mimic was administered (Figure 6L).

Figure 6A is a heatmap showing the expression of miR-1246 and miR-1290 in ascites subsets. Figure 6B is a heatmap showing the expression of miR-1246 and miR-1290 in plasma subsets. Figure 6C shows the results of measuring the expression of miR-1246 and miR-1290 in MA-EVs (N=18) and BA-EVs (N=12) by RT-qPCR. Figure 6D shows the results of measuring the expression of miR-1246 and miR-1290 in MP-EVs (N=78) and BP-EVs (N=72) by RT-qPCR. Figure 6E shows the results of comparing the expression of MiR-1246 and miR-1290 in MP-EVs (N=16) and BP-EVs (N=72) of early stage (stage I+II) cancer patients. Figure 6F shows the results of TEM to confirm the morphology of EVs derived from cancer cell lines. Figure 6G shows the results of confirming whether EV markers CD63 and CD9 are expressed in EVs derived from cancer cell lines through Western blotting. Figure 6H shows the results of confirming the size contribution and concentration of EVs derived from cancer cell lines using NTA. Figure 6I shows the results showing the expression of miR-1246 in oxygenated and normoxic EVs. Figure 6J shows the results showing the expression of miR-1290 in hypoxic and normoxic EVs. Figure 6K shows the results of investigating the effect of treating ovarian cancer cells with a miRNA inhibitor or mimic on ovarian cancer invasion and migration. Figure 6L shows retinal adipose tissue derived from an ovarian cancer patient co-cultured with SKOV3 ^luc cells treated with an inhibitor or mimic of miRNA for 5 days. The number of SKOV3 ^luc cells that migrated and colonized the adventitial tissue pieces was determined by luminescence intensity measured with an IVIS 100 imaging system. (H-EV: hypoxic EV; N-EV: normoxic EV; IN: inhibition; OV: overexpression; *, P<0.05; **, P<0.01; ***, P<0.001).

6. Confirmation of the effect of miR-1246/miR-1290-RORα on ovarian cancer metastasis and survival of ovarian cancer patients

Because miR-1246 and miR-1290 share similar sequences, it was assumed that they could bind to some common targets. Common targets of miR-1246 and miR-1290 were predicted using an online tool (Figure 7A). Among the 10 predicted targets, Retinoid orphan receptor alpha (RORa) was the only target that was clearly downregulated in ovarian cancer tissues compared to normal tissues in GEPIA (Figure 7B). RORα was selected as a potential target. It was confirmed that inhibition of miR-1246 and miR-1290 could significantly increase the expression of RORα in SKOV3 and KURAMOCHI cells (Figure 7C). Partial 3′-UTR sequences of RORα containing the putative miRNA target region of wild type (WT) or a mutated form with an altered putative binding region (MT) were constructed (Figure 7D). Luciferase activity was significantly reduced only in cells cotransfected with RORα WT and either miR-1246 or miR-1290 mimics, indicating that miR-1246 and miR-1290 can directly regulate RORα by binding to the 3'-UTR (Figure 7E). We further investigated the function of RORa in ovarian cancer. Overexpression of RORα in ovarian cancer cells significantly disrupted their invasion and migration abilities (Figure 7F; the graph on the right sequentially shows the relative cell number for each case on the left). In addition, it was confirmed that this inhibitory effect was antagonized by overexpression of miR-1246 or miR-1290 (Figure 7G; the graph on the right sequentially shows the relative cell number in each case on the left). Additionally, IHC analysis (HGSOC tissue: N=58) was performed to determine the clinical significance of RORα. Higher expression of RORα was associated with longer overall survival, whereas lower expression was associated with shorter overall survival in HGSOC patients (Figures 7H and 7I; the dark line graph close to the origin in Figure 7I indicates RORα Low; A light line graph that is far from the origin is a RORα High graph). These results suggest that RORα may act as a tumor suppressor gene in ovarian cancer.

Figure 7A shows the results of predicting common targets of miR-1246 and miR-1290 using an online tool. Figure 7B shows the results of GEPIA confirming RORα expression in ovarian cancer tissues and normal tissues. Figure 7C shows the results of Western blotting measuring changes in RORα expression in SKOV3 and KURAMOCHI cells following inhibition of miR-1246 and miR-1290. Figure 7D shows the results of constructing a putative miRNA target region of RORa 3'-UTR wild type (WT) or a mutant with an altered putative binding region (MT). Figure 7E (Figures 7ea to 7eb) shows the results of verifying that the putative binding region of miR-1246 and miR-1290 is RORa 3'-UTR through dual luciferase reporter gene analysis. Figure 7F shows the results of measuring the invasion and migration abilities of ovarian cancer cells overexpressed with RORa. Figure 7G (Figure 7ga to Figure 7gd) shows the results of a rescue experiment to determine whether miR-1246 or miR-1290 can restore the effect of ovarian cancer invasion and migration by RORα overexpression. Figure 7H shows the results of analysis of RORa in ovarian cancer tissues and normal tissues. Figure 7I presents the results of examining the relationship between tissue RORα expression and overall survival in HGSOC patients through IHC analysis. (OV: overexpression. *, P<0.05; **, P<0.01; ***, P<0.001.)

In line with the above-mentioned experimental results, we investigated the miRNA profiling of ascites and plasma-derived EVs and established eight miRNA diagnostic signatures in ovarian cancer liquid biopsies. This signature demonstrated strong diagnostic ability across a variety of samples from proprietary and public datasets. Additionally, signatures combining various combinations of the above 8 miRNAs were established and their diagnostic ability was confirmed. In addition, it was confirmed that malignant ascites-derived EVs can promote ovarian cancer metastasis by delivering functional miRNAs such as miR-1246 and miR-1290.

Claims (11)

1. At least one extracellular vesicle selected from the group consisting of miR-1246, miR-1290, miR-483-5p, miR-429, miR-34b-3p, miR-34c-5p, miR-449a, and miR-145-5p A composition for diagnosing ovarian cancer, comprising an agent for detecting the expression level of derived miRNA.
2. The method of claim 1, wherein the miR-1246 consists of the sequence of SEQ ID NO: 1, the miR-1290 consists of the sequence of SEQ ID NO: 2, the miR-483-5p consists of the sequence of SEQ ID NO: 3, and The miR-429 consists of the sequence of SEQ ID NO: 4, the miR-34b-3p consists of the sequence of SEQ ID NO: 5, the miR-34c-5p consists of the sequence of SEQ ID NO: 6, and the miR-449a A composition for diagnosing ovarian cancer, which consists of the sequence of SEQ ID NO: 7, and wherein the miR-145-5p consists of the sequence of SEQ ID NO: 8.
3. The composition for diagnosing ovarian cancer according to claim 1, wherein the extracellular vesicles are isolated from blood, serum, or plasma.
4. The composition for diagnosing ovarian cancer according to claim 1, wherein the agent includes a primer or a probe.
5. The method according to claim 1, wherein the composition is an agent for detecting the expression level of extracellular vesicle-derived miRNAs of miR-1246, miR-1290, and miR-483-5p; Agents that detect the expression levels of extracellular vesicle-derived miRNAs: miR-1246, miR-1290, miR-483-5p, and miR-429; Agents for detecting the expression levels of extracellular vesicle-derived miRNAs:miR-1246,miR-1290,miR-483-5p,miR-429 andmiR-34b-3p; or an agent for detecting the expression level of extracellular vesicle-derived miRNAs: Composition for cancer diagnosis.
6. The method of claim 1, wherein the composition is an extracellular inhibitor of miR-1246, miR-1290, miR-483-5p, miR-429, miR-34b-3p, miR-34c-5p, miR-449a, and miR-145-5p. A composition for diagnosing ovarian cancer, comprising an agent for detecting the expression level of endoplasmic reticulum-derived miRNA.
7. A kit for diagnosing ovarian cancer comprising the composition of any one of claims 1 to 6.
8. (a) A group consisting of miR-1246, MiR-1290, MiR-483-5p, MiR-429, MiR-34b-3p, MiR-34c-5p, MiR-449a, and MiR-145-5p from the subject's sample. A method of providing information for diagnosing ovarian cancer, comprising: comparing the expression level of at least one extracellular vesicle-derived miRNA selected from the expression level of the corresponding extracellular vesicle-derived miRNA in a control sample.
9. The method of claim 8, wherein the expression level of at least one of the miR-1246, miR-1290, miR-483-5p, and miR-429 is higher than the control group, or the expression level of the miR-34b-3p, miR-34c-5p, and miR- A method of providing information for diagnosing ovarian cancer, further comprising: predicting that the subject has ovarian cancer when the expression level of at least one of 449a and miR-145-5p is lower than that of the control group.
10. The method of claim 8, wherein the sample is blood, serum, or plasma.
11. The method of claim 8, wherein the sample is extracellular vesicles isolated from blood, extracellular vesicles isolated from serum, or extracellular vesicles isolated from plasma.

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