Ovarian cancer marker combination and application thereof

Abstract

The invention discloses an ovarian cancer marker combination and application of the ovarian cancer marker combination, a marker is miRNA, and the miRNA combination comprises miR-27a-3p and miR-142-3p. The invention also discloses an ovarian cancer diagnosis system constructed by the miRNA marker combination. The marker combination and the ovarian cancer diagnosis system provided by the invention can be used for detecting and judging the possibility of suffering from ovarian cancer by using a noninvasive blood sample which is convenient to draw materials, and have relatively high sensitivity and specificity.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical detection and relates to an ovarian cancer marker combination and application thereof. Background Art

[0002] According to 2017 cancer statistics, ovarian cancer (OC) is the fifth leading cause of cancer death among women worldwide. OC incidence varies significantly across regions, with rates in developed regions of the world nearly twice as high as in less developed regions. Ovarian cancer is difficult to diagnose early because symptoms often don't appear until the cancer is advanced, and surgical resection is not feasible in the early stages. Furthermore, by the time the cancer is diagnosed, patients may already have extensive ascites and peritoneal metastasis. The five-year survival rate for ovarian cancer patients is only 30% despite treatment with taxane chemotherapy, radiotherapy, and aggressive cytoreductive surgery. This is partly due to the difficulty in diagnosing OC early and the lack of effective treatments for patients with recurrent or advanced ovarian cancer. However, the five-year survival rate for patients diagnosed with stage I OC is as high as 90%, while for those with stage II disease, it is as high as 70%. Early diagnosis of OC could significantly improve clinical outcomes and patient survival.

[0003] Currently, the main screening methods for ovarian cancer include ultrasound, CT scans, and serum tumor marker tests. Common OC serum markers include CA125, HCG, and alpha-fetoprotein. However, these screening methods have low sensitivity and cannot diagnose the early stages of tumors. The search for more sensitive and specific ovarian cancer markers is becoming increasingly important. Therefore, a non-invasive molecular diagnostic technology based on liquid biopsy that can detect early, tiny ovarian cancer lesions and predict treatment efficacy has important medical value. It can not only help patients improve their survival rates, but also assist doctors in determining the best treatment plan. There is a huge market space and customer demand.

[0004] MicroRNAs (miRNAs) are newly discovered small, non-coding RNA molecules of approximately 18-25 nucleotides in length. They are highly evolutionarily conserved and comprise approximately 1% of the genome. They negatively regulate the expression of protein-coding genes at the transcriptional or post-transcriptional levels by binding to their target mRNAs through non-complete or near-complete complementarity, leading to mRNA degradation or inhibition of translation. Approximately 30% of the genes in the human genome are regulated by miRNAs. Most of these miRNA targets are involved in biological processes such as transcription, signal transduction, and tumorigenesis, playing important roles in cell growth, proliferation, differentiation, and apoptosis. Aberrant miRNA regulation has been shown to be closely linked to tumor formation and progression. In-depth research on the relationship between miRNAs and cancer has revealed that miRNAs are not limited to the initial stages of cancer development but also implicated in disease progression, drug sensitivity, and patient prognosis.

[0005] Currently, there is no specific microRNA biomarker combination or detection kit that can be used to detect and judge the association with ovarian epithelial cancer using non-invasive and easily obtained blood samples. Summary of the Invention

[0006] One of the purposes of the present invention is to provide an ovarian cancer marker and a combination thereof, which can assist in assessing the risk of ovarian cancer and monitoring the therapeutic efficacy of ovarian cancer patients through changes in miRNA in the plasma of ovarian cancer patients.

[0007] The specific technical solution is as follows: a miRNA combination, wherein the miRNA combination includes miR-27a-3p and miR-142-3p.

[0008] Plasma microRNA detection data. The diagnostic kit based on plasma microRNA combines the unique properties of plasma microRNA with conventional molecular biology detection techniques to rapidly analyze the composition of microRNA in ovarian cancer, with strong clinical applicability. Because changes in the physiological state of organ tissues can cause changes in the composition of plasma microRNA, plasma microRNA can serve as a "fingerprint of the disease" to enable early diagnosis of ovarian cancer.

[0009] A second object of the present invention is to provide a kit comprising the miRNA combination as described in one of the objects of the present invention.

[0010] In some embodiments, the kit further comprises at least one of the following:

[0011] (1) Reference substances. Preferably, the reference substances include internal reference substances and / or external reference substances. More preferably, the internal reference substances include one or more of miR-1228-3p, miR-103a-3p, miR-191-5p, and miR-93-5p. Further preferably, the internal reference substance is miR-103a-3p.

[0012] (2) A reagent for detecting a reference or a miRNA combination as described in claim 1 or 2, preferably, the reagent comprises a primer pair and / or a probe combination.

[0013] In some specific embodiments, the kit further comprises instructions, which preferably have the following regression model:

[0014] Logit(P)=0.5+1.1×(miR-27a-3p)-3.2×(miR-142-3p)

[0015] Among them, miR-27a-3p and miR-142-3p represent the expression levels of the corresponding miRNAs.

[0016] Preferably, the instructions are in a printed medium or a readable medium, such as paper, a CD or a USB flash drive.

[0017] A third object of the present invention is to provide the use of the miRNA combination, kit or reagent composition in the above-mentioned invention scheme in the preparation of a diagnostic agent for diagnosing ovarian cancer.

[0018] In some embodiments, the ovarian cancer is selected from any one or more of epithelial ovarian cancer, specific sex cord-stromal tumors, germ cell tumors, and metastatic ovarian cancer.

[0019] A fourth object of the present invention is to provide an ovarian cancer diagnosis system, wherein the ovarian cancer diagnosis system comprises the following modules:

[0020] An input module, which is used to input the sample data to be tested, wherein the sample data to be tested includes miRNA detection values, and the miRNA is selected from the miRNA combination described in the first object;

[0021] An analysis module, wherein the analysis module obtains analysis results through the sample data to be tested;

[0022] and a judgment module, wherein the judgment module compares the analysis result with a threshold value to obtain a judgment result.

[0023] The ovarian cancer diagnostic system has one or more of the following features:

[0024] (1) The sample data to be tested is derived from a blood sample; preferably, a plasma sample;

[0025] (2) The miRNA detection value is the miRNA expression level or the miRNA expression level after normalization, preferably, the miRNA expression level after qPCR normalization, more preferably, the internal reference used in qPCR is miR-103a-3p;

[0026] (3) The analysis module uses a logistic regression model to perform modeling and obtain analysis results; preferably, the logistic regression model uses the following formula:

[0027] Logit(P)=0.5+1.1×(miR-27a-3p)-3.2×(miR-142-3p)

[0028] Among them, miR-27a-3p and miR-142-3p represent the expression levels of the corresponding miRNAs.

[0029] (4) When judging, when the analysis result value is greater than or equal to the threshold, it is judged as having a high risk of ovarian cancer; when the analysis result value is less than the threshold, it is judged as having a low risk of ovarian cancer. Preferably, the threshold is 0.66.

[0030] The fifth object of the present invention is to provide a readable medium, wherein the readable medium stores a program, and when the program is executed by a processor, it can realize the function of the ovarian cancer diagnosis system of the fourth object of the present invention.

[0031] A sixth object of the present invention is to provide an ovarian cancer diagnostic device, comprising:

[0032] (1) The readable medium according to the fifth object of the present invention;

[0033] (2) a processor, configured to execute a program to implement the functions of the ovarian cancer diagnosis system;

[0034] Preferably, the system further includes an output device for outputting the diagnosis result.

[0035] The present invention has the following advantages:

[0036] 1. The specific plasma miRNA screened out is used as a new ovarian cancer marker, which has the advantages of convenient sample collection, easy sample storage, low detection cost, high detection sensitivity, and dynamic monitoring. This method can be widely used in disease surveys and other work, and can be used as an effective way to diagnose the disease early and evaluate the therapeutic effect.

[0037] 2. Plasma miRNA, as a new disease marker, can improve the low sensitivity and low specificity of existing marker detection to a certain extent, significantly improve the clinical detection rate of the disease and achieve early detection and early treatment of the disease.

[0038] 3. By establishing an evaluation model, a comprehensive judgment of multiple indicators can be made to improve the analytical performance of a single marker indicator.

[0039] 4. Based on the above-mentioned research on the correlation between plasma microRNA and ovarian epithelial cancer, the applicant can use specific microRNAs that are stably present in plasma as detection markers for ovarian epithelial cancer and establish an in vitro method for detecting specific microRNAs that are stably present in plasma. By detecting changes in specific microRNAs, the applicant can conduct research on early diagnosis of ovarian epithelial cancer, disease course monitoring, recurrence detection, and prognosis and efficacy evaluation.

[0040] Figures in the specification

[0041] Figure 1 : ROC curve of the test set DETAILED DESCRIPTION

[0042] The present invention is further illustrated by way of examples below, but the present invention is not limited to the scope of the examples. Experimental methods in the following examples where specific conditions are not specified were performed according to conventional methods and conditions, or selected according to the product specifications.

[0043] One of the purposes of the present invention is to provide an ovarian cancer marker and a combination thereof, which can assist in assessing the risk of ovarian cancer and monitoring the therapeutic efficacy of ovarian cancer patients through changes in miRNA in the plasma of ovarian cancer patients.

[0044] The miRNA combination comprises miR-27a-3p and miR-142-3p.

[0045] In the technical solution of the present invention, the source of miRNA is a blood sample, such as plasma, serum or blood. Preferably, mature microRNA in human plasma is used.

[0046] A second object of the present invention is to provide a kit comprising the miRNA combination as described in one of the objects of the present invention.

[0047] In some embodiments, the kit further comprises at least one of the following:

[0048] (1) Reference object;

[0049] (2) A reagent for detecting the reference or the miRNA combination.

[0050] In some embodiments, the detection method is RT-PCR, comprising the following steps:

[0051] (1) Separate and obtain plasma samples from subjects.

[0052] (2) Use a miRNA-specific nucleic acid extraction kit to extract small molecule miRNAs, and use a Bioanalyzer nucleic acid fragment analyzer and Qubit for quantification and fragment detection. In addition, select an internal reference gene and positive reference miRNA that are suitable for the experimental sample as a reference for relative quantification in the next step.

[0053] (3) First, the mature miRNA is tailed with polyA polymerase, and then the RNA is reverse transcribed into cDNA using a designed universal reverse transcription primer with a polyT adapter and reverse transcriptase + reaction buffer.

[0054] (4) Perform qPCR quantification using the designed upstream and downstream primers. SYBR Green or Taqman probe methods can be used to create fluorescent product labels. The expression level of the target miRNA can be calculated by relative quantification with the internal reference target for quantitative standardization.

[0055] (5) Detect and compare the changes in the amount of miRNA in patient plasma samples relative to normal plasma.

[0056] (6) Establish a miRNA molecular diagnostic model for ovarian epithelial cancer through bioinformatics data analysis.

[0057] In some embodiments, the reference comprises an internal reference and / or an external reference.

[0058] It should be noted that when detecting miRNA expression, either external or internal references can be used. External references are miRNAs not present in the human body that are artificially added to the sample to be tested (e.g., a plasma sample) and can be used as quality control to verify that the entire experimental process is normal and the results are reliable. In the technical solution described in the present invention, the external references can be, for example, ath-miR-159 and / or cel-miR-39.

[0059] When performing PCR relative quantitative analysis, internal references are required to correct the data of the target feature in order to obtain accurate results. In some preferred embodiments, the internal references include one or more of miR-1228-3p, miR-103a-3p, miR-191-5p, and miR-93-5p.

[0060] More preferably, the internal reference is miR-103a-3p.

[0061] Detection of the miRNA combination includes direct detection of miRNA content or indirect detection of miRNA reverse transcription cDNA or miRNA binding molecules. Technicians in this field are aware of methods such as using fluorescent quantitative PCR to convert the target miRNA into cDNA by reverse transcription, and then performing PCR to achieve real-time fluorescent detection of miRNA.

[0062] Therefore, the reagents for detecting miRNA can be not only primer pairs and probe combinations, but also conventional reagents involved in other detection methods.

[0063] It should be noted that the primer pairs described are used to amplify a portion of a target gene in a sample using PCR, with two primers, a forward primer and a reverse primer, being used to amplify a specific region. Those skilled in the art can design primer pairs based on the target gene sequence using commercially available products or their own. Similarly, those skilled in the art can purchase or prepare the required probes based on the detection method.

[0064] In some embodiments, the kit further comprises a reference substance such as miR-103a-3p, and preferably further comprises a reagent for detecting the reference substance, such as a primer pair and a probe combination.

[0065] In some specific embodiments, the kit further comprises instructions, which preferably have the following regression model:

[0066] Logit(P)=0.5+1.1×(miR-27a-3p)-3.2×(miR-142-3p)

[0067] Among them, miR-27a-3p and miR-142-3p represent the expression levels of the corresponding miRNAs.

[0068] In some embodiments, the expression level of the miRNA may be a normalized qPCR quantitative result.

[0069] In some embodiments, the p-value obtained according to the regression model is used to assess whether the sample to be tested has a risk of ovarian cancer.

[0070] In other preferred embodiments, the sample to be tested is derived from a blood sample, such as plasma, serum, or blood, preferably a plasma sample.

[0071] In some embodiments, the instructions are present on a printed or readable medium, such as paper, a compact disc, or a USB drive.

[0072] A third object of the present invention is to provide the use of the miRNA combination, kit or reagent composition in the above-mentioned invention scheme in the preparation of a diagnostic agent for diagnosing ovarian cancer.

[0073] In some embodiments, the ovarian cancer is selected from any one or more of epithelial ovarian cancer, specific sex cord-stromal tumors, germ cell tumors, and metastatic ovarian cancer.

[0074] A fourth object of the present invention is to provide an ovarian cancer diagnosis system, wherein the ovarian cancer diagnosis system comprises the following modules:

[0075] An input module, which is used to input the sample data to be tested, wherein the sample data to be tested includes miRNA detection values, and the miRNA is selected from the miRNA combination;

[0076] An analysis module, wherein the analysis module obtains analysis results through the sample data to be tested;

[0077] and a judgment module, wherein the judgment module compares the analysis result with a threshold value to obtain a judgment result.

[0078] The ovarian cancer diagnostic system has one or more of the following features:

[0079] (1) The sample data to be tested is derived from a blood sample;

[0080] (2) The miRNA detection value is the miRNA expression level or the miRNA expression level after standardization;

[0081] (3) The analysis module uses a logistic regression model to perform modeling and obtain analysis results;

[0082] (4) When judging, when the analysis result value is greater than or equal to the threshold, it is judged as having a high risk of ovarian cancer; when the analysis result value is less than the threshold, it is judged as having a low risk of ovarian cancer.

[0083] It should be noted that the low risk of ovarian cancer refers to that the sample to be tested is derived from a healthy individual, and the high risk of ovarian cancer refers to that the sample to be tested is derived from an ovarian cancer patient.

[0084] In some specific embodiments, the miRNA detection value is the miRNA expression level after qPCR normalization.

[0085] In some further preferred embodiments, the internal reference used in qPCR is miR-103a-3p; for example, the expression levels of miR-27a-3p and miR-142-3p are detected using miR-103a-3p as the internal reference.

[0086] In some preferred embodiments, the logistic regression model uses the following formula:

[0087] Logit(P)=0.5+1.1×(miR-27a-3p)-3.2×(miR-142-3p)

[0088] Among them, miR-27a-3p and miR-142-3p represent the expression levels of the corresponding miRNAs.

[0089] It should be noted that in the technical solution described herein, by comparing the analysis results (e.g., the p-value obtained by logit(p)) with the threshold, it is possible to accurately determine whether a sample has an ovarian cancer risk. The threshold setting affects the sensitivity and specificity of the ovarian cancer diagnosis system. For higher sensitivity and specificity, a threshold of 0.66 is preferably used.

[0090] The fifth object of the present invention is to provide a readable medium, wherein the readable medium stores a program, and when the program is executed by a processor, it can realize the function of the ovarian cancer diagnosis system of the fourth object of the present invention.

[0091] The readable medium is, for example, an optical disk, a USB flash drive, or any other medium that can be recognized and read by an electronic device.

[0092] A sixth object of the present invention is to provide an ovarian cancer diagnostic device, comprising:

[0093] (1) The readable medium according to the fifth object of the present invention;

[0094] (2) a processor, configured to execute a program to implement the functions of the ovarian cancer diagnosis system;

[0095] Preferably, the system further includes an output device for outputting the diagnosis result.

[0096] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0097] The reagents and raw materials used in the present invention are commercially available.

[0098] Example

[0099] Example 1: Establishment and optimization of the reaction system of the ovarian epithelial cancer miRNA diagnostic kit

[0100] 1. Extract miRNA using miRNeasy Micro Kit (Qiagen, 217084)

[0101] 1.1 Prepare plasma samples. For each sample, aspirate 200 μL of plasma.

[0102] 1.2 Add 1.0 mL of QIAzol Lysis Buffer (lysate) to the sample, vortex to mix, and incubate at room temperature in the dark for 5 minutes. Then, add 1.5 μL of the external reference solution (ath-miR-159a + cel-miR-39). External references are miRNAs not found in humans that are artificially added to the plasma sample to serve as quality control to verify the correctness of the entire experimental process and the reliability of the results.

[0103] 1.3 Add 200 μL of chloroform to the tube containing the lysate in the dark. Incubate at room temperature in the dark for 3 minutes.

[0104] 1.4 Centrifuge at 12,000 x g for 15 minutes in a low-temperature high-speed centrifuge at 4°C. Slowly aspirate 600 μL of the upper colorless aqueous phase into a new 2.0 mL centrifuge tube. Add 900 μL of anhydrous ethanol (1.5 times the volume of the colorless aqueous phase, which can be calculated based on the volume of the aspirated colorless aqueous phase) to the colorless aqueous phase, vortex to mix, and briefly centrifuge.

[0105] 1.5 Transfer 750 μL of the mixed sample to an RNeasy MinElute spin column in a 2.0 mL collection tube. Close the cap and centrifuge at 10,000 x g for 15 seconds. Discard the waste liquid. Repeat this step until all the liquid has passed through the column.

[0106] 1.6 Add 700 μL of Buffer RWT to the RNeasy MinElute spin column, centrifuge at 10,000 x g for 15 seconds, and discard the waste liquid.

[0107] 1.7 Add 500 μL of Buffer RPE to the RNeasy MinElute spin column, centrifuge at 10,000 x g for 15 seconds, and discard the waste liquid.

[0108] 1.8 Add 500 μL of 80% ethanol to the RNeasy MinElute spin column, centrifuge at 10,000 x g for 15 seconds, and discard the waste liquid.

[0109] 1.9 Transfer the RNeasy MinElute spin column into a new 2.0 mL collection tube, open the tube cap, and centrifuge at full speed for 5 minutes.

[0110] 1.10 Transfer the RNeasy MinElute spin column to a new 1.5 mL centrifuge tube. Add 14 μL of nuclease-free water to the RNeasy MinElute spin column and centrifuge at full speed for 3 minutes.

[0111] 2. Tailing Reaction

[0112] Using ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0113] 2.1 According to Table 2 below, prepare enough Poly(A) Reaction Mix in the EP tube to meet the reaction requirements.

[0114] Table 2.

[0115]

[0116] 2.2 Take 2 μL of the thawed mixed sample and add it to the PCR reaction tube, add 3 μL of the prepared Poly(A) Reaction Mix, mix thoroughly, centrifuge briefly, collect the liquid, eliminate bubbles, and place on ice.

[0117] 2.3 Wipe the outer wall of the PCR reaction tube containing the sample and Poly(A) Reaction Mix with toilet paper and place it in the PCR instrument. Select the operating system as 10 μL and run the PCR instrument according to the program in Table 3 below.

[0118] Table 3.

[0119] step temperature time Polyadenylation 37℃ 45 minutes Termination reaction 65℃ 10 minutes Keep 4℃ Keep

[0120] 3. Ligation reaction

[0121] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0122] 3.1 Thaw 50% PEG 8000 at room temperature in advance. According to Table 4 below, prepare enough Ligation Reaction Mix in the EP tube to meet the required amount of reaction.

[0123] Table 4.

[0124]

[0125]

[0126] 3.2 Take 10 μL of the mixed Ligation Reaction Mix and add it to the PCR reaction tube containing the tailed sample, mix thoroughly, centrifuge briefly to eliminate bubbles, and place on ice.

[0127] 3.3 Wipe the outer wall of the PCR reaction tube containing the tailed sample and Ligation Reaction Mix with toilet paper and place it in the PCR instrument. Select the running volume as 15 μL and run according to the program in Table 5 below.

[0128] Table 5.

[0129] step temperature time Polyadenylation 37℃ 45 minutes Termination reaction 65℃ 10 minutes Keep 4℃ Keep

[0130] 4. Reverse transcription reaction (hereinafter referred to as RT)

[0131] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0132] 4.1 According to Table 6 below, prepare enough RT Reaction Mix in the EP tube to meet the reaction requirements.

[0133] Table 6.

[0134]

[0135] 4.2 Take 15 μL of the mixed RT Reaction Mix and add it to the PCR reaction tube containing the sample after ligation.

[0136] 4.3 Wipe the outer wall of the reaction tube containing the sample after ligation with the adapter and the RT Reaction Mix with toilet paper and place it into the PCR instrument. Select the running volume as 30 μL and run according to the program in Table 7 below.

[0137] Table 7.

[0138] step temperature time Reverse transcription 42℃ 15 minutes Termination reaction 85℃ 5 minutes Keep 4℃ Keep

[0139] 5. Pre-amplification reaction

[0140] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0141] 5.1 According to Table 8 below, prepare enough miR-Amp Reaction Mix in the EP tube to meet the reaction needs.

[0142] Table 8.

[0143] Components 1Rxn 4Rxns 10Rxns 2X miR-Amp Master Mix 25 μL 110 μL 275μL 20X miR-Amp Primer Mix 2.5 μL 11μL 27.5μL RNase-free water 17.5μL 77μL 192.5μL Pre-amplification reaction mixture system 45 μL 198μL 495μL

[0144] 5.2 Prepare a corresponding number of new PCR reaction tubes according to the number of samples, and add 45 μL of miR-Amp Reaction Mix to the new PCR reaction tubes.

[0145] 5.3 Vortex the RT reaction product, centrifuge briefly to remove bubbles, and then add 5 μL to the PCR reaction tube containing 45 μL miR-Amp Reaction Mix.

[0146] 5.4 Wipe the outer wall of the PCR reaction tube containing the RT reaction product and miR-Amp Reaction Mix with clean toilet paper and place it in the PCR instrument. Select the running volume as 50 μL and run according to the program in Table 9 below.

[0147] Table 9.

[0148]

[0149] 6. qPCR reaction

[0150] This experiment used TaqMan TM Fast Advanced Master Mix.

[0151] 6.1 Prepare 0.1X TE buffer by diluting TE pH 8.0 1:10. Vortex the prepared 0.1X TE buffer and centrifuge briefly to remove air bubbles.

[0152] 6.2 Dilute the vortexed cDNA template 1:10 with 0.1X TE buffer. Vortex the diluted cDNA template and centrifuge briefly to remove bubbles.

[0153] 6.3 According to Table 10 below, prepare enough PCR Reaction Mix in the EP tube to meet the reaction requirements.

[0154] Table 10.

[0155]

[0156] 6.4 Transfer 15 μL of PCR Reaction Mix to a new PCR reaction tube. Add 5 μL of diluted cDNA template to the PCR reaction tube containing PCR Reaction Mix, vortex thoroughly to mix, and centrifuge briefly to remove bubbles.

[0157] 6.5 Wipe the outer wall of the PCR reaction tube containing the diluted cDNA template and PCR Reaction Mix with clean toilet paper and place it into the qPCR instrument QuantStudio5. Select a running volume of 20 μL and run according to the program in Table 11 below.

[0158] Table 11.

[0159]

[0160] Example 2: Plasma miRNAs detection for ovarian cancer auxiliary diagnosis model establishment

[0161] 1. Sample situation

[0162] A total of 144 samples were collected, including plasma samples from 45 healthy people and 99 patients with ovarian epithelial cancer, including 23 patients with stage I ovarian cancer, 27 patients with stage II ovarian cancer, 25 patients with stage III ovarian cancer, and 24 patients with stage IV ovarian cancer.

[0163] A total of 27 miRNAs were detected, and ath-miR-159 and cel-miR-39 were used as external references.

[0164] 2. Data Quality Control of qPCR-Based miRNA Detection

[0165] Sample quality control: Samples were considered qualified if the negative control NTC showed no amplification or the CT was greater than 34. After sample quality control, 12 cases (1 case of stage III ovarian cancer, 6 cases of stage II ovarian cancer, and 5 cases of stage I ovarian cancer) did not meet the NTC quality control requirements and were deleted.

[0166] miRNA quality control: All samples in the screening phase were tested using qRT-PCR to detect the expression levels of miRNAs in plasma samples. Each miRNA was tested in parallel three times, with a maximum of one failure allowed in three replicate wells. Amplification was considered qualified if the confidence value (Cq Conf) of the amplification reaction fluorescence signal cycle was greater than 0.8; the maximum number of unqualified samples allowed did not exceed 10% of the total samples. The results showed that the miR-429 qPCR results of 17 samples (more than 10%) did not meet the quality control requirements, while the number of unqualified samples for the remaining miRNAs was less than 10% of the total samples. Therefore, miR-429 did not meet the quality control standards, and the remaining 26 miRNAs entered the next analysis stage.

[0167] After the quality control step, 26 miRNAs remained, and 132 samples remained, including 45 healthy controls and 87 cases of ovarian epithelial cancer, including 18 cases of stage I ovarian cancer, 21 cases of stage II ovarian cancer, 24 cases of stage III ovarian cancer, and 24 cases of stage IV ovarian cancer.

[0168] Table 11. 27 miRNAs that passed quality control conditions and the corresponding number of unqualified samples

[0169] Serial number miRNA Number of samples that did not meet quality control conditions (total 144 cases) 1 miR-429 17 2 miR-141-3p 10 3 miR-183-5p 9 4 miR-203a-3p 3 5 miR-200b-3p 1 6 miR-23a-3p 0 7 miR-15b-5p 0 8 miR-200a-3p 0 9 miR-182-5p 0 10 miR-96-5p 0 11 miR-21-5p 0 12 miR-103a-3p 0 13 miR-200c-3p 0 14 miR-92a-3p 0 15 miR-20a-5p 0 16 miR-484 0 17 miR-205-5p 0 18 miR-93-5p 0 19 miR-125b-5p 0 20 miR-29a-3p 0 21 miR-30a-5p 0 22 miR-191-5p 0 23 miR-27a-3p 0 24 miR-126-3p 0 25 miR-16-5p 0 26 miR-483-5p 0 27 miR-142-3p 0

[0170] 3. Data Analysis of qPCR-Based miRNA Detection

[0171] 3.1 Selection of internal reference

[0172] Tools used: GeNorm, Normfinder

[0173] Real-time fluorescence quantitative PCR (RT-PCR) has become a common method for gene expression analysis due to its advantages such as high sensitivity, good reproducibility, strong specificity, and high throughput. When performing PCR relative quantitative analysis, an internal reference gene is required to correct the data for the target feature to obtain accurate results. Selecting a stable internal reference gene is particularly important for improving experimental results. GeNorm and Normfinder are software specifically designed for screening internal reference gene stability.

[0174] The data of the selected five miRNA internal references were analyzed using GeNorm and Normfinder software.

[0175] Table 12 introduces the ranking of the stability results of individual internal references by GeNorm, among which 103a-3p and 93-5p are the most stable.

[0176] Table 13 shows the stability results of the Normfinder single internal reference, among which 103a-3p and 93-5p are the most stable, and 103a-3p is better than 93-5p.

[0177] Based on the results of GeNorm and Normfinder (Tables 12 and 13), 103a-3p was selected as the internal reference.

[0178] Table 12. Ranking of GeNorm single internal reference stability results

[0179] rank gId_0 1 miR-103a-3p 1 miR-93-5p 3 miR-484 4 miR-191-5p 5 miR-16-5p

[0180] Table 13. Normfinder single internal reference stability results

[0181] rank gId_0 1 miR-103a-3p 2 miR-93-5p 3 miR-191-5p 4 miR-484 5 miR-16-5p

[0182] 3.2 Difference Analysis

[0183] 2 in qPCR (fluorescence quantitative PCR) -ΔCT The relative quantitative analysis method is a commonly used method, which is mainly used to compare the expression levels of target genes between different samples. -ΔCT The miRNA expression was analyzed by the method, the fold change value of the ovarian cancer patient group compared with the control group was calculated, and the Wilcoxon rank sum test was used for difference analysis. The results are as follows:

[0184] Table 14

[0185] miRNA fold_change Mann-Whitney P value miR-141-3p 2.460 3.07E-06 miR-183-5p 0.619 3.00E-06 miR-203a-3p 1.010 3.87E-08 miR-200b-3p 2.067 2.31E-06 miR-23a-3p 4.814 2.36E-15 miR-15b-5p 0.565 9.38E-08 miR-200a-3p 0.669 1.60E-09 miR-182-5p 0.539 1.47E-06 miR-96-5p 0.634 7.16E-18 miR-21-5p 0.795 0.0605 miR-200c-3p 0.370 1.14E-11 miR-92a-3p 2.128 6.12E-08 miR-20a-5p 0.729 5.85E-06 miR-484 2.703 9.03E-10 miR-205-5p 0.740 3.03E-08 miR-93-5p 1.431 0.1088 miR-125b-5p 0.587 3.14E-06 miR-29a-3p 0.343 2.38E-14 miR-30a-5p 4.106 2.57E-08 miR-191-5p 2.228 1.10E-05 miR-27a-3p 1.599 0.00368 miR-126-3p 0.870 0.02859 miR-16-5p 0.359 3.78E-13 miR-483-5p 0.671 2.06E-06 miR-142-3p 0.346 4.88E-15

[0186] By comparing the expression of 25 miRNAs in ovarian epithelial cancer and healthy controls, miRNAs with fold change values ​​greater than 1.5 or less than 0.5 and Mann-Whitney P value < 0.05 were selected. A total of 12 miRNAs passed the filtering conditions: miR-141-3p, miR-200b-3p, miR-23a-3p, miR-200c-3p, miR-92a-3p, miR-484, miR-29a-3p, miR-30a-5p, miR-191-5p, miR-27a-3p, miR-16-5p, and miR-142-3p.

[0187] Among them, the expression of eight miRNAs, including miR-141-3p, miR-200b-3p, miR-23a-3p, miR-92a-3p, miR-484, miR-30a-5p, miR-191-5p, and miR-27a-3p, was upregulated in the patient group, while the expression of the remaining four miRNAs was downregulated.

[0188] 3.3 Model establishment

[0189] 3.3.1 Feature Screening

[0190] From the 12 miRNAs screened, we sequentially selected one, two, three, and so on, until we had 12. These combinations were then permuted and combined, yielding a total of 4095 possible combinations. A logistic regression model was constructed for each of these combinations. The feature combination that yielded the highest AUC value was selected as the optimal model combination. Ultimately, these combinations were identified as miR-27a-3p and miR-142-3p.

[0191] 3.3.2 Model establishment

[0192] Logistic regression and 5-fold cross-validation strategies were used to construct and verify the model, respectively. The 132-case dataset was randomly split into five equal parts, each with 26 or 27 cases (because 132 cases was not divisible evenly). Four of these parts (104 to 106 cases) served as training sets for model building, and the remaining part served as a test set to evaluate model performance. Model evaluation was performed using sensitivity, specificity, and AUC values.

[0193] The logistic regression function is as follows, using the training set data for modeling:

[0194]

[0195] In the above formula: x represents the eigenvalue, z is the logit(p) value in logistic regression, and T represents the transposed sign.

[0196] The p values ​​of the logistic regression model of the two features are shown in Table 15 , both of which meet the requirements, namely: miR-27a-3p and miR-142-3p.

[0197] Table 15. Estimated coefficients, standard errors, z-values, and P-values ​​for the logistic regression model

[0198] Estimate Std,Error z value Pr(>|z|) (Intercept) 0.5115 0.2945 1.7366 8.25E-02 miR-27a-3p 1.0960 0.3319 3.3025 9.58E-04 miR-142-3p -3.1528 0.6395 -4.9301 8.22E-07

[0199] Note: “***” indicates P < 0.001, “**” indicates P < 0.01, “*” indicates P < 0.05, “·” indicates P < 0.1, and “” indicates P < 1.

[0200] Table 16. miRNA nucleotide sequence information

[0201] Target Name miRBase Accession Number Mature miRNA Sequence miR-27a-3p MIMAT0000084 UUCACAGUGGCUAAGUUCCGC miR-142-3p MIMAT0000434 UGUAGUGUUUCCUACUUUAUGGA

[0202] Estimate: The estimated value of the coefficient of the predictor variable.

[0203] Std.Error: The standard error of the coefficient estimate, indicating the uncertainty of the estimate.

[0204] z value: The coefficient estimate divided by its standard error, used to test whether the coefficient is significantly different from zero.

[0205] Pr(>|z|): P value, which represents the probability of rejecting the null hypothesis that the coefficient is zero. If the P value is less than 0.05, the coefficient is considered statistically significant.

[0206] Based on the comprehensive results, the model is established as follows:

[0207] Logit(P)=0.5+1.1×(miR-27a-3p)-3.2×(miR-142-3p)

[0208] Among them, miR-27a-3p and miR-142-3p respectively represent the expression levels of the corresponding miRNAs, and the expression levels are the standardized qPCR quantitative results.

[0209] 3.4 Model Evaluation

[0210] The performance of the logistic regression model was evaluated by 5-fold cross validation, the performance indicators were compared, the model stability was analyzed, and the performance evaluation results of the best model were selected. Figure 1 As shown, the logit function in the model is transformed and solved by inverse function to obtain the P value, which is between 0 and 1. This model selects a threshold of 0.66. Samples with a P value less than the threshold are considered to be negative samples (i.e., the control group, which refers to healthy people without ovarian cancer). Samples with a P value greater than or equal to the threshold are considered to be positive samples (i.e., the patient group, which refers to the population of patients with ovarian cancer).

[0211] The formula for calculating the P value is as follows: We already have a logit(P) value. To convert it back to a P value, we need to use the inverse transformation of the sigmoid function.

[0212]

[0213] Taking the logarithm of both sides and solving gives:

[0214]

[0215] Test set ROC curve ( Figure 1 ) can determine the diagnostic efficacy of the above model for ovarian cancer. When the threshold is set at 0.66, the area under the ROC curve (AUC) of the miRNA combination marker for diagnosing ovarian cancer is 0.99, and the sensitivity and specificity of the prediction model for distinguishing liver cancer from the control group are 94% and 100%, respectively.

[0216] Table 17

[0217]

[0218] Sensitivity refers to the percentage of patients who test positive, reflecting the ability of a diagnostic test to identify those who are truly ill.

[0219] Specificity refers to the percentage of negative test results in people who do not have the disease, reflecting the ability of a diagnostic test to identify those who do not have the disease.

[0220] PPV (positive predictive value) refers to the proportion of subjects who have the disease if the test result is positive.

[0221] NPV (negative predictive value) refers to the proportion of subjects who do not have the disease when the test result is negative.

[0222] AUC: The area under the ROC (Receiver Operating Characteristic) curve and the coordinate axis. It reflects the ability of a diagnostic test to distinguish between patients with and without the disease.

[0223] Threshold: The optimal threshold for distinguishing patients from healthy people determined by Youden index.

[0224] Youden Index: This indicates the overall ability of a screening method to detect true patients and non-patients. Youden Index = Sensitivity + Specificity - 1. Values ​​range from 0 to 1, with higher values ​​indicating greater accuracy and higher model discrimination. In this application, the Youden Index was calculated by calculating sensitivity and specificity at different thresholds. The threshold at which the Youden Index is maximized was selected as the optimal diagnostic threshold for our model.

Claims

1. A miRNA combination, characterized in that The miRNA combination includes miR-27a-3p and miR-142-3p.

2. A kit, characterized in that It comprises the miRNA combination according to claim 1.

3. The kit according to claim 2, wherein The kit further comprises at least one of the following: (1) Reference substances. Preferably, the reference substances include internal reference substances and / or external reference substances. More preferably, the internal reference substances include one or more of miR-1228-3p, miR-103a-3p, miR-191-5p, and miR-93-5p. Further preferably, the internal reference substance is miR-103a-3p. (2) A reagent for detecting a reference or a miRNA combination as claimed in claim 1, preferably, the reagent comprises a primer pair and / or a probe combination.

4. The kit according to claim 3, wherein The kit further comprises instructions, which preferably have the following regression model: Logit(P)=0.5+1.1×(miR-27a-3p)-3.2×(miR-142-3p) Among them, miR-27a-3p and miR-142-3p represent the expression levels of the corresponding miRNAs; Preferably, the instructions are in a printed medium or a readable medium, such as paper, a CD or a USB flash drive.

5. Use of the miRNA combination according to claim 1 or the kit according to any one of claims 2 to 4 in the preparation of a diagnostic agent for diagnosing ovarian cancer.

6. The use according to claim 5, characterized in that The ovarian cancer is selected from any one or more of epithelial ovarian cancer, specific sex cord-stromal tumors, germ cell tumors, and metastatic ovarian cancer.

7. An ovarian cancer diagnosis system, characterized in that: It includes the following modules: An input module, which is used to input the sample data to be tested, wherein the sample data to be tested includes miRNA detection values, and the miRNA is selected from one or more of the miRNA combination according to claim 1; An analysis module, wherein the analysis module obtains analysis results through the sample data to be tested; and a judgment module, wherein the judgment module compares the analysis result with a threshold value to obtain a judgment result.

8. The ovarian cancer diagnosis system according to claim 7, wherein: The ovarian cancer diagnostic system has one or more of the following features: (1) The sample data to be tested is derived from a blood sample; preferably, a plasma sample; (2) The miRNA detection value is the miRNA expression level or the miRNA expression level after normalization, preferably, the miRNA expression level after qPCR normalization, more preferably, the internal reference used in qPCR is miR-103a-3p; (3) The analysis module uses a logistic regression model to perform modeling and obtain analysis results; preferably, the logistic regression model uses the following formula: Logit(P)=0.5+1.1×(miR-27a-3p)-3.2×(miR-142-3p) Among them, miR-27a-3p and miR-142-3p represent the expression levels of the corresponding miRNAs; (4) When judging, when the analysis result value is greater than or equal to the threshold, it is judged as having a high risk of ovarian cancer; when the analysis result value is less than the threshold, it is judged as having a low risk of ovarian cancer. Preferably, the threshold is 0.

66.

9. A readable medium, characterized in that The readable medium stores a program, and when the program is executed by a processor, the function of the ovarian cancer diagnosis system according to claim 7 or 8 can be realized.

10. An ovarian cancer diagnostic device, characterized in that: include: (1) The readable medium according to claim 9; (2) a processor, configured to execute a program to implement the functions of the ovarian cancer diagnosis system; Preferably, the system further includes an output device for outputting the diagnosis result.

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