CN117802235A - Application of blood extracellular vesicle miRNA in diagnosis of ovarian cancer - Google Patents
Application of blood extracellular vesicle miRNA in diagnosis of ovarian cancer Download PDFInfo
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Abstract
The invention discloses a chip, a detection reagent or a detection kit for preparing diagnosis and early diagnosis of ovarian cancer, especially for distinguishing benign and malignant ovarian tumor by using one or more blood extracellular vesicle miRNAs as biomarkers for diagnosing and early diagnosing ovarian cancer, especially for distinguishing benign and malignant ovarian tumor. The specificity of the diagnosis and early diagnosis of the ovarian cancer can be improved and the false positive of the ovarian cancer can be reduced by using the miRNA or miRNAs as biomarkers, so that the kit has potential good clinical application value for the benign and malignant differentiation of ovarian lesions, the diagnosis and early diagnosis of the ovarian cancer.
Description
Technical Field
The invention belongs to the field of biological medicine. In particular, the invention relates to a combination of blood extracellular vesicle miRNAs for diagnosing ovarian cancer and application thereof.
Background
Ovarian Cancer (OC) is one of the three major malignant tumors of the female reproductive system, with relatively low incidence, but the mortality rate is the first to locate in gynaecological tumors, and has become one of the malignant diseases that severely threatens female health. The five-year survival rate of early ovarian cancer patients is 80-95%, while the five-year survival rate of late ovarian cancer patients is only 10-30%. The ovaries are positioned in the deep part of the pelvis, and early-stage ovarian cancer is difficult to find due to lack of specific symptoms, so that more than 2/3 of ovarian cancer patients are advanced to a late stage in diagnosis. If early detection can be carried out, the 5-year survival rate of the ovarian cancer can be improved to 40-50% by applying tumor cell debulking and assisting platinum-based combined chemotherapy. Early diagnosis of ovarian cancer is therefore critical to extending patient survival.
The diagnosis method of ovarian cancer mainly comprises imaging examination, tumor marker determination, cytology and histopathological examination. Since the ovaries are entirely intraperitoneal organs, it is not possible to diagnose ovarian cancer without surgery. Currently, histopathological biopsies are the gold standard for definitive diagnosis of ovarian cancer. Taking tissue samples from suspicious regions and observing them under a microscope is the only method for definitive diagnosis of ovarian cancer. However, for early ovarian tumors, needle biopsies should be avoided, as cancer cells readily spread into the peritoneal cavity, and needle sticks promote peritoneal metastasis. Thus, aspiration biopsies are suitable for patients who are not suitable for surgery due to advanced cancer or other serious diseases. For patients with abdominal dropsy, abdominal dropsy may be taken and analyzed to see if cancer cells are present. At present, various examination means of ovarian cancer are beneficial and disadvantageous, and diagnosis needs to be combined with medical history and comprehensively evaluated. Diagnosis of ovarian cancer there is an urgent need for a low invasive diagnostic biomarker to enable early detection and diagnosis of ovarian cancer and to explore the possibilities for screening assays.
Disclosure of Invention
The invention aims to develop an independent liquid biopsy technology capable of being used for diagnosing ovarian cancer by finding and verifying a high-accuracy serum exosome miRNA diagnosis biomarker model so as to make up for the defects of the existing imaging examination, serum tumor marker determination and tissue biopsy on diagnosing ovarian cancer, provide a diagnosis technology with low invasiveness for clinic, reduce pain of patients and improve life quality of the patients.
For this purpose, the invention provides a combination of biomarkers which can be used for clinically diagnosing ovarian cancer, in particular for distinguishing benign and malignant ovarian tumors.
The invention also provides a chip and a kit capable of clinically diagnosing ovarian cancer, in particular distinguishing benign and malignant ovarian tumors.
The invention also provides a method for diagnosing ovarian cancer, especially distinguishing benign and malignant ovarian tumors by using the biomarker.
In a first aspect, the present invention provides the use of a blood extracellular vesicle miRNA for the preparation of a chip, test reagent or test kit for the diagnosis of ovarian cancer, wherein said miRNA is
(i) One or more mirnas selected from the group consisting of: hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p and hsa-miR-429; or alternatively
(ii) A miRNA complementary to the miRNA sequence of (i).
In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, 7, 8, or 9.
In specific embodiments, the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is to distinguish between benign and malignant ovarian tumors.
In particular embodiments, the miRNA is one or more mirnas selected from the group consisting of: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.
In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, or 7.
In a second aspect, the present invention provides a miRNA chip comprising:
a solid phase carrier; and
an oligonucleotide probe orderly immobilized on the solid support, the oligonucleotide probe specifically binding to miRNA;
wherein the miRNA is the miRNA of the first aspect.
In a specific embodiment, the oligonucleotide probe comprises:
a complementary binding region; and/or
A junction region attached to the solid support.
In specific embodiments, the miRNA chip is used for early diagnosis of ovarian cancer; preferably, the ovarian tumor is distinguished.
In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.
In a third aspect, the invention provides the use of the miRNA chip of the second aspect for preparing a detection kit for diagnosing ovarian cancer.
In specific embodiments, the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is to distinguish between benign and malignant ovarian tumors.
In a fourth aspect, the present invention provides a detection kit comprising a detection reagent for detecting miRNA;
wherein the miRNA is the miRNA of the first aspect;
alternatively, the detection kit is provided with the miRNA chip according to the second aspect.
In specific embodiments, the detection kit is used for early diagnosis of ovarian cancer; preferably for differentiating between benign and malignant ovarian tumors.
In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.
In a fifth aspect, the invention provides a miRNA isolated from a blood extracellular vesicle for use in the diagnosis of ovarian cancer:
wherein, miRNA is:
(i) One or more mirnas selected from the group consisting of: hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p and hsa-miR-429; or alternatively
(ii) A miRNA complementary to the miRNA sequence of (i).
In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, 7, 8, or 9.
In a preferred embodiment, the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is to distinguish between benign and malignant ovarian tumors.
In preferred embodiments, the miRNA is one or more mirnas selected from the group consisting of: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.
In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, or 7.
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.
In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.
In a sixth aspect, the present invention provides a method for diagnosis of ovarian cancer, comprising the steps of:
(a) Constructing a benign/malignant classification model according to miRNA expression data of the training queue and combining a pathology detection result;
(b) Taking the miRNA expression quantity of the object to be detected as a variable, combining a reference value, and judging that the object is benign when the risk value is smaller than or equal to the reference value, otherwise, judging that the object is malignant ovarian cancer;
the miRNA is as follows:
(i) One or more mirnas selected from the group consisting of: miRNA: hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p and hsa-miR-429; or alternatively
(ii) A miRNA complementary to the miRNA sequence of (i).
In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, 7, 8, or 9.
In a preferred embodiment, the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is to distinguish between benign and malignant ovarian tumors.
In preferred embodiments, the miRNA is one or more mirnas selected from the group consisting of: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.
In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, or 7.
In a preferred embodiment, the reference value is 0.2.
In a preferred embodiment, the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; preferably, the ovarian tumor benign malignancy is distinguished, wherein the control sample is an ovarian benign lesion sample.
In a preferred embodiment, the miRNA is a blood extracellular vesicle miRNA.
In a seventh aspect, the invention provides a miRNA isolated from a blood extracellular vesicle, said miRNA being:
(i) One or more mirnas selected from the group consisting of: miRNA: hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p and hsa-miR-429; or alternatively
(ii) A miRNA complementary to the miRNA sequence of (i).
In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, 7, 8, or 9.
In preferred embodiments, the miRNA is one or more mirnas selected from the group consisting of: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.
In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, or 7.
In an eighth aspect, the invention provides an isolated or artificially constructed precursor miRNA that is capable of being sheared and expressed in human cells as said miRNA.
In a ninth aspect, the invention provides an isolated polynucleotide that is transcribed by a human cell into a precursor miRNA that is cleaved and expressed in the human cell into the miRNA.
In a preferred embodiment, the polynucleotide has the structure shown in formula I:
Seq forward direction -X-Seq Reverse direction I is a kind of
In the formula I, the compound (I),
Seq forward direction A nucleotide sequence for enabling expression of said miRNA in a human cell;
Seq reverse direction A nucleotide sequence that is substantially complementary or fully complementary to the forward direction of Seq;
x is at Seq Forward direction And Seq Reverse direction A spacer sequence therebetween, and said spacer sequence is identical to Seq Forward direction And Seq Reverse direction Are not complementary;
after the structure shown in the formula I is transferred into human cells, a secondary structure shown in the formula II is formed:
in formula II, seq Forward direction 、Seq Reverse direction And X is as defined above,
the expression is shown in Seq Forward direction And Seq Reverse direction Complementary base pairing relationship formed between them.
In a tenth aspect, the present invention provides a vector comprising said miRNA or said polynucleotide.
It is understood that within the scope of the present invention, the above-described technical features of the present invention and technical features specifically described below (e.g., in the examples) may be combined with each other to constitute new or preferred technical solutions. And are limited to a space, and are not described in detail herein.
Drawings
FIG. 1 shows the identification results of exosome transmission electron microscopy;
FIG. 2 shows the expression of proteins characteristic of exosomes;
FIG. 3 shows the ROC curve of a training queue;
FIG. 4 shows the ROC curve of the test queue;
fig. 5 shows ROC curves for validation queues.
Detailed Description
Transvaginal ultrasonic examination (TVS) is a rapid, economical, noninvasive, repeatable, and preferred method of diagnosis of ovarian cancer. However, the morphology, internal structure and relationship with surrounding tissues of smaller ovarian tumors often show unclear, and solid tumors with diameters < 1cm are not easily detected. Clinical application of electronic Computed Tomography (CT) technology has resulted in a significant increase in spatial resolution of images. It can clearly show the infiltration degree of tumor to the surrounding area and the metastasis of the pelvic cavity and the abdominal cavity. But the ovarian primary tumor and metastasis show no significant difference in CT. Magnetic Resonance Imaging (MRI) soft tissue has high resolution, can image in multiple planes, and is noninvasive. However, the cost of MRI is also higher than CT, and the patient with intrauterine device can take out the device to do MRI. PET-CT has low sensitivity and specificity to ovarian tumors and is generally not recommended for primary diagnosis. However, the imaging can reflect the metabolic change of tumor cells, and PET-CT is considered to be the first imaging examination method when ovarian cancer recurrence is clinically suspected.
Detection of serum tumor markers has been used in clinical early diagnosis of ovarian Cancer, and currently, clinical serum markers for ovarian Cancer approved by the U.S. food and drug administration (Food and Drug Administration, FDA) include tumor antigen 125 (Cancer anti 125, ca 125) and human epididymal protein 4 (Humanepididymis protein, he 4). Both have high value in terms of post-operation efficacy detection of ovarian cancer patients, but lack sufficient sensitivity and specificity for early diagnosis of ovarian cancer.
In contrast, the invention discloses a liquid biopsy technique for detecting serum exosomes microRNA (miRNA) for ovarian cancer diagnosis by adopting a serum sample derived from a clinical patient and a small RNA sequencing detection method. Specifically, the present invention contributes to the art in two parts:
(1) Research on serum exosome miRNA biomarkers and combinations thereof for ovarian cancer diagnosis, and construct a diagnosis model with high accuracy;
(2) The diagnostic effect of the discovered diagnostic model was further validated.
Liquid biopsy techniques based on serum exosome miRNA biomarker combinations useful for ovarian cancer diagnosis were discovered and validated by (1), (2).
Definition of the definition
The scientific and technical terms used herein are consistent with the general understanding of those skilled in the art. For the convenience of understanding the present invention, the relevant terms are now explained and defined as follows:
exosomes are a class of small extracellular vesicles (Extracellular vesicles, EVs) that are membranous vesicles about 30-150nm in diameter. It originates from vesicles of late endocytosis (multivesicular bodies, MVB), where the endocytic vesicle membrane is depressed inward to form a multivesicular body containing multiple vesicles, which fuse with the cell membrane and are released into the extracellular matrix. The extracellular vesicles carry protein, lipid, mRNA, rRNA, miRNA, etc. Cells secrete extracellular vesicles under both normal and pathological conditions, which can be involved in intercellular information transfer.
The content of extracellular vesicles can characterize certain physiological and pathological conditions. Free exosomes in the peripheral blood have been of great interest and intense research as an important liquid biopsy modality in a variety of diseases (e.g., malignant tumors, immune diseases, etc.). Additional studies identified differences in blood exosome mirnas in healthy and ovarian cancer patients. However, the search for blood exosome mirnas as diagnostic biomarkers based on second generation sequencing technology was few in ovarian cancer, nor was a biomarker found and validated that could be used to improve the early diagnosis specificity of ovarian cancer.
The invention adopts various technologies in clinic to comprehensively detect serum of a patient suspected to be ovarian cancer as a research sample, applies an exosome extraction reagent L3525 independently developed by Shanghai thought Di biomedical science and technology Co, further adopts a second-generation sequencing technology small RNA sequencing to detect the expression of miRNA of the serum exosome, and discovers, tests and verifies a diagnosis model of miRNA biomarkers of the serum exosome, which can be used for diagnosis of ovarian cancer, in three independent queues.
The blood extracellular vesicle miRNA of the invention
At present, imaging examination and laboratory serological detection applied to ovarian cancer diagnosis clinically have certain limitations and have poor accuracy. The only way to diagnose ovarian cancer is histopathological biopsy, but for early stage ovarian tumors, needle biopsy should be avoided because cancer cells readily spread to the peritoneal cavity, and needle penetration can promote peritoneal metastasis, only in patients with advanced cancer or other serious diseases who are not suitable for surgery.
The invention has the advantages that the liquid biopsy technology based on serum exosome miRNA biomarker for ovarian cancer diagnosis is developed, the liquid biopsy technology is non-invasive, and the side effect of histopathological biopsy caused by sampling is avoided.
In the prior art, the research on ovarian cancer diagnosis biomarkers has the following defects: a. the research quantity of true focus benign and malignant ovarian tumor differential diagnosis is rare; b. the research amount based on the second-generation sequencing platform is small; c. exosome content is used as a diagnosis biomarker and is rarely studied through the verification of two other groups of data; d. no liquid biopsy technique with high diagnostic accuracy is seen.
Aiming at the defects of the current research, the invention adopts an independently developed extraction reagent L3525 to extract serum exosomes, further utilizes small RNA sequencing to detect the expression of the serum exosome miRNA, and finally discovers and verifies the serum exosome miRNA diagnostic biomarker with high efficiency. The invention provides and verifies that serum exosome miRNA as a biomarker can be used for diagnosing ovarian cancer for the first time, and avoids the risk brought by puncture biopsy.
In particular, the inventors have discovered a class of blood extracellular vesicle mirnas that can be used for diagnosing ovarian cancer, in particular for early diagnosis of ovarian cancer. In a specific embodiment, the blood extracellular vesicle miRNA of the present invention is:
(i) One or more mirnas selected from the group consisting of: hsa-miR-1-3p (UGGAAUGUAAAGAAGUAUGUAU; SEQ ID NO: 1), hsa-miR-1246 (AAUGGAUUUUUGGAGCAGG; SEQ ID NO: 2), hsa-miR-141-3p (UAACACUGUCUGGUAAAGAUGG; SEQ ID NO: 3), hsa-miR-200a-3p (UAACACUGUCUGGUAACGAUGU; SEQ ID NO: 4), hsa-miR-200b-3p (UAAUACUGCCUGGUAAUGAUGA; SEQ ID NO: 5), hsa-miR-200c-3p (UAAUACUGCCGGGUAAUGAUGGA; SEQ ID NO: 6), hsa-miR-203a-3p (GUGAAAUGUUUAGGACCACUAG; SEQ ID NO: 7), hsa-miR-3679-5p (UGAGGAUAUGGCAGGGAAGGGGA; SEQ ID NO: 8) and hsa-miR-429 (UAAUACUGUCUGGUAAAACCGU; SEQ ID NO: 9); or alternatively
(ii) A miRNA complementary to the miRNA sequence of (i).
In a preferred embodiment, the "plurality" is 2, 3, 4, 5, 6, 7, 8 or 9.
At present, research reports for identifying diagnostic biomarkers of expression of blood exosome miRNA for distinguishing benign and malignant ovarian tumors based on second-generation sequencing are not yet seen. The inventors for the first time adopted small RNA sequecning technology to confirm that the discovered serum exosome miRNA combinations have better effect in distinguishing benign and malignant ovarian tumors in three independent groups (training group, test group and verification group), and the AUC can reach 0.913 (training group), 0.973 (test group) and 0.924 (verification group). In particular embodiments, to distinguish between benign and malignant ovarian tumors, it is preferred to utilize one or more mirnas selected from the group consisting of: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429. In preferred embodiments, the "plurality" is 2, 3, 4, 5, 6, or 7.
In a further embodiment, the blood extracellular vesicle miRNA can be used to prepare a chip, detection reagent or detection kit for diagnosing ovarian cancer, in particular early diagnosis of ovarian cancer, preferably for distinguishing benign tumors from malignant tumors of the ovary. For example, the miRNA chip may comprise a solid support; and an oligonucleotide probe orderly immobilized on the solid support, the oligonucleotide probe specifically binding to the miRNA. In a preferred embodiment, the oligonucleotide probe comprises a complementary binding region; and/or a linker region attached to the solid support.
In a specific embodiment, the invention further provides a detection kit, wherein the detection kit is provided with a detection reagent for detecting the miRNA, or the detection kit is provided with the miRNA chip.
The invention selects one of the content miRNA in the serum exosomes for identifying diagnosis biomarkers and constructing an ovarian cancer diagnosis model. However, there are also many other exosomes, such as proteins, mRNA, lncRNA, etc., which can also serve as biomarkers and achieve similar effects as mirnas. In addition, the serum of patients contains various components, such as ctDNA (Circulating tumor DNA), CTC (Circulating tumor cell), proteins and the like, which can be found as diagnostic biomarkers of ovarian cancer and developed into independent liquid biopsy diagnosis technologies.
The method of the invention
Based on the blood extracellular vesicle miRNA, the invention also provides various methods for utilizing the miRNA.
For example, using the miRNA, the present invention provides a method for diagnosing ovarian cancer, comprising the steps of:
(a) Constructing a benign/malignant classification model according to miRNA expression data of the training queue and combining a pathology detection result;
(b) And taking the miRNA expression quantity of the object to be detected as a variable, combining a reference value, and judging that the object is benign when the risk value is smaller than or equal to the reference value, otherwise, judging that the object is malignant ovarian cancer.
In a preferred embodiment, the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; it is preferable to distinguish between benign and malignant ovarian tumors. In differentiating between benign and malignant ovarian tumors, the control sample is a benign ovarian lesion sample.
In a preferred embodiment, the reference value is 0.2.
The invention has the advantages that:
(1) The invention provides a method for establishing a high-accuracy diagnosis model by taking serum exosome miRNA combination as a biomarker to distinguish benign and malignant ovarian tumors, thereby improving the accuracy of ovarian cancer diagnosis.
(2) The invention adopts various samples of benign ovarian diseases in the real world as control samples, the benign control has strong heterogeneity, and the result obtained by the design adopted by the invention can reflect the real world situation more than the samples of healthy population or the mixture of healthy population and benign diseases adopted by the published screening research are used as controls.
(3) The invention applies exosome (exosome) extraction reagent L3525 independently developed by Shanghai thought Di biomedical science and technology limited company to extract serum exosome, and carries out subsequent exosome miRNA detection.
(4) The invention adopts a multivariate logistic regression model (multivariate logistic regression model) statistical analysis method to find 7 serum exosome miRNAs, comprising: the high-accuracy risk prediction model for the benign and malignant ovarian tumor is constructed by hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.
(5) The present invention confirms the high accuracy of the risk prediction model for 7 serum exosome miRNAs using two additional independent cohort (test and validation) data.
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. The experimental procedure, which does not address the specific conditions in the examples below, is generally followed by routine conditions, such as, for example, sambrook et al, molecular cloning: conditions described in the laboratory manual (Cold Spring Harbor Laboratory Press, 2001) or as recommended by the manufacturer. Percentages and parts are by weight unless otherwise indicated.
Examples
Material
The materials utilized in the examples below are all commercially available.
Method
1. Study cohort and clinical information
The study is incorporated into a total of 79 cases of patients with suspected ovarian cancer through imaging (such as ultrasonic examination, abdominal cavity CT scanning and the like), tumor markers (such as CA125, HE4 and the like), cytology, histopathological examination and the like, and blood samples of the patients are collected before operation. Each patient in the group gave an accurate diagnosis after surgery based on the pathological examination results.
2. Extraction and characterization of serum exosomes
1) Blood collection and extraction of serum exosomes
All the blood samples of the patients with benign and malignant ovarian tumor in the study are collected into a vacuum blood collection tube (REF 367814, BD, USA) with a red cover of 5ml before operation or drug treatment, slowly and gently overturned up and down for several times, vertically standing at room temperature for 1-2 hours, and centrifuging at normal temperature and low speed for 2000g,10min after the blood clot is coagulated and contracted; and taking the sample with the hemolysis grade smaller than 4 levels for the next treatment after centrifugation. Transfer supernatant to a 1.5ml centrifuge tube and re-centrifuge, 8000g,4 degrees, 10min. The supernatant was dispensed in 1ml per tube and stored in a minus 80 degree refrigerator. The serum samples were removed and incubated in a 37℃metal bath until completely thawed, 12000g, centrifuged at 4℃for 10min, 500. Mu.l of supernatant was transferred to a 0.45 μm tube filter (Costar, CLS8163-100EA, corning, USA), 12000g, centrifuged at 4℃for 5min, and the filtrate was transferred to a 0.22 μm tube filter (Costar, CLS8161-100EA, USA) for 12000g, and centrifuged at 4℃for 5min. Transfer the filtrate to a 1.5ml centrifuge tube and add 1/4 volume of L-exosome precipitant (L3525, 3D med, shanghai, china), incubate 30min at 4℃after mixing, centrifuge for 30min at 4700g at 4℃and discard the supernatant and add 200. Mu.l PBS (phosphate buffer saline) to suspend exosome precipitation.
2) Characterization of serum exosomes
In order to detect the characteristics of serum exosomes of patients with benign and malignant ovarian tumors, the invention adopts a transmission electron microscope to detect the exosome forms, and further utilizes an immunohybridization experiment to detect the expression level of exosome characteristic proteins. Exosome form feature identification: the isolated extracellular vesicles were resuspended in PBS, then 4% paraformaldehyde was added to fix the extracellular vesicles, and the exosomes were transferred onto a carbon-coated 200 mesh electron microscope copper mesh. Secondly, washing the copper mesh with PBS for 2 times, freshly preparing PBS containing glycine (50 mM) and washing the copper mesh for 3min, freshly preparing PBS containing 0.5% BSA and washing the copper mesh again for 10min, finally, dyeing the copper mesh with 2% uranyl acetate, and carrying out characteristic analysis on the external secretion form by adopting a transmission electron microscope (H-7650,Hitachi High-Technologies, japan) after dyeing is finished.
Exosome characteristic protein detection: exosomes were precipitated with an N-type exosome precipitant (N3525, 3d, shanghai, china) and then lysed on ice with RIPA lysate (P0013B, beyotide, shanghai, china) for 30min. Constant-pressure electrophoresis was performed for about 1h with 4% -20% SDS-PAGE gel (# 4561095, bio-Rad, USA), PVDF membrane (Millipore) was electroblotted for 45min with constant flow, and 5% skim milk powder was blocked overnight. Resistance information: TSG101 (1:1000 dimuted, ab125011, abcam, england), CD63 (1:1000 dimuted, ab216130, abcam, england), CD9 (1:1000 dimuted, ab92726, abcam, england), alix (1:1000diluted,2171,Cell Signaling Technology,Danvers,MA,USA), syntenin (1:1000 dimuted, ab19903, abcam, england) and Calnexin (1:1000diluted,2679,Cell Signaling Technology,Danvers,MA,USA) antibodies were incubated at room temperature for 2h, TBST was washed four times for 10min each time, rabbit-derived secondary antibody (A0208, biyun) or murine-derived secondary antibody (A0216, biyun) was incubated at room temperature for 1h, TBST was washed four times for 10min each time, developed with a chemiluminescent system (Tanon-5200 Multi, shangai, china).
The serum exosomes are extracted by adopting an exosome extraction reagent L3525 independently researched and developed by Shanghai thought Di biomedical science and technology limited company. However, other alternatives are known to those skilled in the art, including but not limited to: a. ultracentrifugation; b. density gradient centrifugation; c. ultrafiltration centrifugation; d. immunomagnetic bead method; e. other commercial exosome extraction reagents.
3. Exosome miRNA extraction and expression level detection
1) Extraction of miRNA of serum exosome
The isolation of Serum exosome miRNA was performed using the miRNeasy Serum/Plasma Kit (21789, QIAGEN, shanghai, china), and the specific procedure was as described in the specification. miRNA quantification and fragment distribution detection were performed using Agilent 2100 analyzers fitted with corresponding chips (5067-1548, agilent, USA).
2) Expression detection of serum exosomes
The invention adopts small RNA sequencing to detect the expression level of miRNA of serum exosomes. The library was constructed using NEBNExt, multiplex Small RNA Library Prep Set for Illumina (E7300L, NEB, USA) kit, and the specific procedure was according to the product instructions. The total volume of each serum sample miRNA is 100ng and is not more than 6 mu l, and the PCR amplification is carried out for 18 cycles by connecting a 3 'joint, hybridizing a reverse transcription primer, connecting a 5' joint, reverse transcription and adding a Illumina index primers label. The PCR product was purified using a NucleoSpin Gel and PCR Clean-up (740609.50, MACHEREY-NAGEL, germany) kit and the DNA eluted with 30. Mu.l NE buffer. By usingGX Touch TM Detection of DNA quantification and fragment distribution by HT nucleic acid analyzer and its associated chip (CLS 138948, perkinElmer, USA) and reagent (CLS 760672, perkinElmer, USA). Typically 20-25 libraries were sequenced in equimolar mix package lane, using a Illumina HiSeq PE150 analyzer.
The invention utilizes the second generation sequencing technology-small RNA sequencing to detect the expression of the miRNA of the serum exosome. However, other alternatives are known to those skilled in the art, including but not limited to: a.Q-PCR detection; b. detecting a chip; c. other second generation sequencing methods; d. three generation sequencing methods.
4. Sequencing data analysis procedure
Based on small RNA sequencing detection technology, the expression level of miRNA in the serum exosomes of the patient is obtained. The analytical flow of the sequencing data is as follows:
1) Sequencing data alignment. After removal of small RNA sequencing data sequencing adaptors, sequencing data was aligned to human reference genome hg19 (genome download link: http:// hgdownload. Soe. Ucsc. Edu/goldenPath/hg 19/bigmaps /) using BWA software (version: 0.7.12-r 1039) and the number of reads aligned to miRNAs was counted.
2) miRNA annotation. Mirnas were annotated using the Gencode v25 and miRBase v21 databases, leaving the annotated as known mature mirnas for subsequent analysis.
3) miRNA filtration. For a training queue, reserving mature miRNAs with the length less than or equal to 30nt and at least covering 2 reads in each sample in training queue data for subsequent analysis; for the test and validation queues, the mirnas screened by the training queue were retained and each sample in the test and validation queue data was covered with at least 2 reads of mature mirnas for subsequent analysis.
4) miRNA expression level is standardized. And respectively carrying out miRNA expression quantity standardization processing on the training queue sample, the test queue and the verification queue sample by using an M value weighted tail-end average value method (TMM, trimmed mean of M-values) in a limma analysis packet in the R language.
5. Biomarker discovery
Based on the expression level of miRNAs in the training queue, grouping samples according to pathological detection results, and adopting a statistical method to discover miRNAs which can be used for distinguishing benign ovarian tumors from malignant ovarian tumors as biomarkers. The process is as follows:
1) Training the queue packets. Based on the pathology detection results, the patients in the training cohort were divided into two groups, benign tumor patient group and malignant tumor patient group.
2) Waiting for weatherSelecting molecular markers. In a training queue, miRNAs with different expression amounts of two groups of benign and malignant patients are analyzed by using a linear model fitting (limma-voom) method in an R language limma analysis package, and the miRNAs with the expression amount of more than or equal to 4, the variation amount between the two groups of miRNAs being more than or equal to 2 times and the P value of the test result being less than or equal to 0.05 are used as candidate molecular markers. Subsequently, GSE53829 in the NCBI GEO public database is searched and downloaded (download link:https://www.ncbi.nlm.nih.gov/ geo/query/acc.cgiacc＝GSE53829) Whole genome real-time quantitative polymerase chain reaction (qRT-PCR, quantitative real-time polymerase chain reaction) miRNA expression profile data. The data in GSE53829 were divided into two groups, 15 normal ovarian tissues and 48 malignant ovarian cancer tissue data groups, depending on tissue type. And analyzing miRNAs with different data expression amounts in two groups by using a t test (Student's t test) method in R language, wherein the miRNAs with the variable amount between the two groups being more than 2 times and the P value of the test result being less than or equal to 0.05 are used as candidate molecular markers. The total number of candidate molecular markers is 296, and the candidate molecular markers are used for subsequent analysis.
3) A molecular marker. And selecting an intersection of the candidate molecular marker in the training queue and the candidate molecular marker in the GSE53829 data set as a molecular marker. Finally 7 miRNAs are selected as molecular markers for subsequent construction of benign and malignant ovarian tumor risk scoring models.
The analysis method adopted by the invention-least absolute shrinkage and selection operator (LASSO, the Least Absolute Shrinkage and Selectionator operator) model. However, other alternatives are known to those skilled in the art, including but not limited to: a. linear regression; b. a support vector machine; c. a bayesian classifier; d. a neural network.
6. Ovarian tumor benign and malignant risk scoring model
And constructing a benign and malignant ovarian tumor risk scoring model based on a multi-element logistic regression model (multivariate logistic regression model) by using the miRNA expression quantity data of the training queue and using 7 molecular markers as variables and combining pathological detection results. The model consists of three parts, namely parameters, a model formula and a reference value. The process is as follows:
1) Model parameters. In the training queue, 7 molecular markers are used as variables, and 100 times of repeated 10-fold cross validation method is used to obtain model correction parameters and model coefficients of the molecular markers.
2) Risk scoring model. The risk score model formula is as follows:
Risk-Score＝∑Gi*βi+α
wherein, risk-Score is a benign and malignant Risk prediction value, gi represents an expression value of the ith miRNA, βi represents a Risk Score model coefficient of the ith miRNA, i=1, 2 … n, n is a total number of predicted benign and malignant molecular markers, and α represents a model correction value. The risk value of each sample can be obtained by using a risk scoring model and the expression quantity of 7 molecular markers in each sample, and the height of the risk value can reflect the benign and malignant properties of the sample.
3) Reference value. When the risk value is less than or equal to the reference value, the sample is predicted to be benign; otherwise, the disease is predicted to be malignant. And drawing an ROC curve of the training queue according to the risk value and the pathology detection result of each patient in the training queue. And determining a reference value according to ROC curve results under the condition that the specific value is greater than 0.5 and the sensitivity value is greater than 0.5.
4) Model efficacy evaluation. The training queue samples were divided into a low risk group (i.e., predicted as benign lesions) and a high risk group (i.e., predicted as malignant lesions) based on a reference value of 0.2. And evaluating the prediction efficiency of the model by taking the pathological detection result as a true value. The model prediction efficiency evaluation method comprises AUC (value range 0-1), specificity (value range 0-1) and sensitivity (value range 0-1), and the higher the value is, the better the effect is.
7. Testing and validation of predictive efficacy of risk scoring models
And in the test queue and the verification queue, verifying the efficacy of model prediction benign and malignant according to the risk score model and the reference value determined in the training queue. The process is as follows:
1) Risk value. In the test queue and validation queue, a risk value for each sample is calculated.
2) And (5) verifying model efficiency. Patients in the test and validation cohorts were divided into low risk groups (co-training cohorts) and high risk groups, respectively, based on a reference value of 0.2. And drawing ROC curves of a test queue and a validation queue by taking a pathological detection result as a true value, and evaluating the prediction efficiency of the model, wherein the higher the value is, the better the effect is, including AUC, specificity and sensitivity.
8. Application of ovarian tumor benign and malignant risk scoring model
1) Collecting peripheral blood of a patient suspected to be ovarian cancer as a clinical diagnosis result, obtaining peripheral blood exosomes, and obtaining expression of biomarkers by using small RNA sequencing;
2) Obtaining a risk value of each patient by using a risk scoring model;
3) And comparing the risk value with a model reference value to give a prediction result of the risk of ovarian cancer of each patient.
Example 1.
This example recruits three study queues altogether. Specific study cohorts and clinical information are as follows:
training cohorts 29 patients, including 13 benign tumor patients and 16 malignant tumor patients (table 1). The cohort patients were tested for 20, including 5 benign tumor patients and 15 malignant tumor patients (table 1). The cohort patients were validated for 30, including 15 benign tumor patients and 15 malignant tumor patients (table 1). The types of malignant tumor specimens include low-grade, high-grade serous carcinoma, mucinous carcinoma, and the like. The benign tumor samples are selected from ovarian serous cystadenoma, ovarian mucinous cystadenoma, ovarian oviduct abscess, endometrial atypical hyperplasia and the like. Table 1 shows clinical information of patient age and pathological diagnosis. The analysis results showed that there was no significant difference in age and proportion of patients with benign and malignant disease between the two groups of patients.
Table 1. Clinical information of ovarian cancer patients.
Example 2 extraction and characterization of serum exosomes
In this example, exosome extraction reagents L3525 and N3525, independently developed by Shanghai Di biomedical technology, inc., were used to extract exosomes in serum from ovarian cancer patients. In order to detect the characteristics of the serum exosomes of patients with benign and malignant ovarian tumors, a transmission electron microscope is used for detecting the morphology of the exosomes, and an immunohybridization experiment is further used for detecting the expression level of exosome characteristic proteins. The typical "horseshoe-like" morphology of exosomes was seen by transmission electron microscopy (see fig. 1). The detection result shows that the exosome characteristic proteins TSG101, CD63, CD9, alix and Syntenin are all expressed in the representative sample extracted by the patent, and the exosome negative protein Calnexin is not expressed (see figure 2).
EXAMPLE 3 biomarker discovery
In this example small RNA sequencing was used to detect the expression levels of serum exosomes mirnas in patients with benign and malignant ovarian tumors. Based on the expression quantity of miRNAs in a training queue, grouping samples according to a pathological detection result, and using a statistical method to discover miRNAs which can be used for distinguishing benign and malignant ovarian tumors as biomarkers, wherein the candidate molecular markers comprise 9 in total, including hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p and hsa-miR-429, and are used for subsequent analysis. Subsequently, the miRNA expression profile data was generated according to GSE53829 qRT-PCR in the NCBI GEO public database. Data in GSE53829 were divided into two groups according to tissue type, and mirnas that could be used to differentiate normal ovarian tissue from malignant ovarian cancer tissue were explored using statistical methods as candidate molecular markers. And selecting an intersection of the candidate molecular marker in the training queue and the candidate molecular marker in the GSE53829 data set as a molecular marker. Finally 7 miRNAs are selected as molecular markers, including hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429, and are used for subsequently constructing a benign and malignant ovarian tumor risk scoring model.
EXAMPLE 4 ovarian tumor benign and malignant Risk scoring model
In the embodiment, the training queue miRNA expression level data is utilized, 7 molecular markers are used as variables, and a benign and malignant ovarian tumor risk scoring model is constructed based on a multiple logistic regression model (multivariate logistic regression model) and combined with a pathological detection result. The model consists of three parts, namely parameters, a model formula and a reference value. Model correction parameters and model coefficients (table 2) of the molecular markers and a risk score model formula were obtained using a 10-fold cross validation method repeated 100 times with 7 molecular markers as variables. The risk value of each sample can be obtained by using a risk scoring model and the expression quantity of 7 molecular markers in each sample, and the height of the risk value can reflect the benign and malignant properties of the sample.
The receiver operating characteristics (ROC curve, receiver operating characteristic curve) of the training queue are plotted according to the risk value and pathology detection result of each patient in the training queue (fig. 3). According to the ROC curve result, under the condition that the specific value is larger than 0.5 and the sensitivity value is larger than 0.5, the reference value is determined to be 0.2. When the risk value is less than or equal to the reference value, the sample is predicted to be benign; otherwise, the disease is predicted to be malignant. And evaluating the prediction efficiency of the model by taking the pathological detection result as a true value. The model predictive efficacy assessment method included AUC (range 0-1), specificity (range 0-1) and sensitivity (range 0-1), 0.913 (fig. 3), 92.3% and 87.5% (table 3), respectively. The results show that: in a training queue, the risk prediction model has higher AUC, specificity and sensitivity, and better model prediction efficiency.
Table 2. Parameters of risk scoring model constructed with 7 mirnas as markers.
Example 5 Risk scoring model predictive efficacy testing and validation
In this embodiment, in the test queue and the verification queue, the efficacy of model prediction of benign and malignant performance is verified according to the risk score model and the reference value determined in the training queue. Patients in the test and validation cohorts were divided into low risk groups (co-training cohorts) and high risk groups, respectively, based on a reference value of 0.2. In the test cohort, ROC curves (fig. 4) were plotted with pathology test results as true values, and model predictive efficacy was assessed, including AUC (fig. 4), specificity and sensitivity, at 0.973, 100% and 86.7%, respectively (table 3). In the validation cohort, ROC curves (fig. 5) were plotted with pathology test results as true values, and model predictive efficacy was assessed, including AUC (fig. 5), specificity and sensitivity, at 0.924, 93.3% and 86.7%, respectively (table 3). The results show that: in the test queue and the verification queue, the risk prediction model has higher AUC, specificity and sensitivity, namely, the model prediction efficiency is better.
TABLE 3 seven molecular marker model efficacy assessment
All documents mentioned in this application are incorporated by reference as if each were individually incorporated by reference. Further, it will be appreciated that various changes and modifications may be made by those skilled in the art after reading the above teachings, and such equivalents are intended to fall within the scope of the claims appended hereto.
Claims (10)
1. Use of blood extracellular vesicle miRNA in preparation of chip, detection reagent or detection kit for diagnosing ovarian cancer, wherein the miRNA is
(i) One or more mirnas selected from the group consisting of: hsa-miR-1-3p, hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p, hsa-miR-3679-5p and hsa-miR-429; or alternatively
(ii) A miRNA complementary to the miRNA sequence of (i).
2. The use of claim 1, wherein said diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is to distinguish between benign and malignant ovarian tumors.
3. The use of claim 1 or 2, wherein the miRNA is one or more miRNA selected from the group consisting of: hsa-miR-1246, hsa-miR-141-3p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-203a-3p and hsa-miR-429.
4. A miRNA chip, said miRNA chip comprising:
a solid phase carrier; and
an oligonucleotide probe orderly immobilized on the solid support, the oligonucleotide probe specifically binding to miRNA;
wherein the miRNA is the miRNA of any one of claims 1-3.
5. The miRNA chip of claim 4, wherein the oligonucleotide probe comprises:
a complementary binding region; and/or
A junction region attached to the solid support.
6. The miRNA chip of claim 5 for early diagnosis of ovarian cancer; preferably, the ovarian tumor is distinguished.
7. Use of the miRNA chip of any one of claims 4-6 for preparing a detection kit for diagnosing ovarian cancer.
8. The use of claim 7, wherein the diagnosis of ovarian cancer is an early diagnosis of ovarian cancer; preferably, the early diagnosis of ovarian cancer is to distinguish between benign and malignant ovarian tumors.
9. A detection kit, which is provided with a detection reagent for detecting miRNA;
wherein the miRNA is the miRNA of any one of claims 1-3;
alternatively, the detection kit contains the miRNA chip of any one of claims 4-6.
10. The test kit of claim 9, wherein the test kit is used for early diagnosis of ovarian cancer; preferably for differentiating between benign and malignant ovarian tumors.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211231900.8A CN117802235A (en) | 2022-09-30 | 2022-09-30 | Application of blood extracellular vesicle miRNA in diagnosis of ovarian cancer |
| PCT/CN2023/122823 WO2024067848A1 (en) | 2022-09-30 | 2023-09-28 | Use of blood extracellular vesicle mirna in ovarian cancer diagnosis |
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| CN202211231900.8A CN117802235A (en) | 2022-09-30 | 2022-09-30 | Application of blood extracellular vesicle miRNA in diagnosis of ovarian cancer |
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| EP2806273B1 (en) * | 2007-07-25 | 2017-09-06 | University of Louisville Research Foundation, Inc. | Exosome-associated microRNA as a diagnostic marker |
| KR102492149B1 (en) * | 2020-10-21 | 2023-01-26 | 순천향대학교 산학협력단 | MicroRNA-1246 for diagnosing of ovarian cancer and use thereof |
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