Hepatocellular carcinoma marker as well as detection method and application thereof

Abstract

The invention discloses a marker combination and an application of the marker combination in preparation of a reagent for diagnosing hepatocellular carcinoma, the marker is miRNA, and the miRNA combination comprises let-7g-5p, miR-151a-3p, miR-451a and miR-652-3p. The invention also discloses a hepatocellular carcinoma diagnosis system constructed by the combination of the miRNA markers. The marker combination and the hepatocellular carcinoma diagnosis system provided by the invention can be used for detecting and judging the possibility of suffering from hepatocellular carcinoma by using a noninvasive blood sample which is convenient to draw materials, and have relatively high sensitivity and specificity.

Description

Technical Field

[0001] The invention belongs to the field of biomedical detection and relates to a hepatocellular carcinoma marker and a detection method and application thereof. Background Art

[0002] Liver cancer is mainly divided into three different pathological types: hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and HCC-ICC mixed type. HCC accounts for 93.0% of all PHC. [1] .

[0003] The most widely used method for screening hepatocellular carcinoma in clinical practice is abdominal ultrasound combined with serum alpha-fetoprotein (AFP) testing. However, the sensitivity of this method in detecting early hepatocellular carcinoma is only 63%, which is very likely to cause missed detection. In addition, the specificity of abdominal ultrasound combined with AFP detection is lower than that of abdominal ultrasound alone. For early HCC, the specificity of abdominal ultrasound alone and ultrasound combined with AFP detection is 92% and 84%, respectively. [5] , which can easily lead to misdiagnosis. Therefore, it is necessary to develop a hepatocellular carcinoma screening method with high sensitivity and specificity in all stages of detection.

[0004] Studies have shown that micro RNA (miRNA) plays a key role in the occurrence and development of hepatocellular carcinoma. Moreover, miRNA can be released by cells into body fluids such as peripheral blood and exists stably. Therefore, circulating miRNA is an ideal marker for clinical evaluation such as hepatocellular carcinoma screening, diagnosis, efficacy evaluation, and recurrence monitoring. [6] .

[0005] References:

[0006] [1]Sung, Hyuna et al. "Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36Cancers in 185Countries." CA: cancer journal for clinicians vol.71,3(2021):209-249.

[0007] [2]Han, Bingfeng et al. "Cancer incidence and mortality in China, 2022." Journal of the National Cancer Center vol.4,1 47-53.2Feb.2024

[0008] [3] Zeng, Hongmei et al. "Cancer survival statistics in China 2019-2021: a multicenter, population-based study." Journal of the National Cancer Center vol.4, 3203-213.22Jun.2024

[0009] [4] de Martel, Catherine et al. "Global burden of cancer attributable toinfections in 2018: a worldwide incidence analysis." The Lancet.Global healthvol.8,2(2020):e180-e190.

[0010] [5] He Jie, Chen Wanqing, Shen Hongbing, et al. Guidelines for liver cancer screening in the Chinese population (2022, Beijing)[J]. Journal of Clinical Hepatobiliary Diseases, 2022, 38(8): 1739-1758.

[0011] [6] Ma Shaohui, Cui Wei, Gao Hongjun. Clinical application value and prospects of circulating microRNA in peripheral blood of liver cancer[J]. Oncology Research and Clinic, 2023, 35(3): 223-228. Summary of the invention

[0012] One of the purposes of the present invention is to provide a hepatocellular carcinoma marker and a combination thereof, which can assist in assessing the risk of hepatocellular carcinoma and monitoring the therapeutic efficacy of hepatocellular carcinoma patients through changes in miRNA in the plasma of hepatocellular carcinoma patients.

[0013] Specifically provided is a miRNA combination including let-7g-5p, miR-151a-3p, miR-451a and miR-652-3p.

[0014] Plasma microRNA detection data, the diagnostic kit based on plasma microRNA combines the unique properties of plasma microRNA with conventional molecular biology detection technology, which can quickly analyze the composition of microRNA in hepatocellular carcinoma, and has strong clinical applicability. Since changes in the physiological state of organ tissues can cause changes in the composition of plasma microRNA, plasma microRNA can be used as a "fingerprint structure of the disease" to achieve early diagnosis of hepatocellular carcinoma.

[0015] 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.

[0016] In some embodiments, the kit further comprises at least one of the following:

[0017] (1) References. Preferably, the references include internal references and / or external references. More preferably, the internal references include one or more of miR-1228-3p, miR-103a-3p, miR-191-5p, and miR-93-5p. Further preferably, the internal reference is miR-93-5p.

[0018] (2) A reagent for detecting a reference or a miRNA combination as described in purpose one, preferably, the reagent comprises a primer pair and / or a probe combination.

[0019] In some specific embodiments, the kit further comprises instructions, which preferably have the following regression model:

[0020] logit(P)=-1.42+0.15×(let-7g-5p)-1.83×(miR-151a-3p)-0.58×(miR-451a)+1.78×(miR-652-3p)

[0021] Among them, let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p indicate the expression levels of the corresponding miRNAs;

[0022] Preferably, the instructions are present in a printed medium or a readable medium, such as paper, a CD or a USB flash drive.

[0023] The third object of the present invention is to provide the use of the miRNA combination described in the first object and the kit or reagent composition described in the second object in the preparation of a diagnostic agent for diagnosing hepatocellular carcinoma.

[0024] In some embodiments, the hepatocellular carcinoma is selected from any one or more of diffuse, mass-like, nodular, or small cancer types.

[0025] A fourth object of the present invention is to provide a hepatocellular carcinoma diagnosis system, wherein the hepatocellular carcinoma diagnosis system comprises the following modules:

[0026] 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 purpose one;

[0027] An analysis module, wherein the analysis module obtains analysis results through the sample data to be tested;

[0028] and a judgment module, wherein the judgment module compares the analysis result with the threshold value to obtain a judgment result.

[0029] The hepatocellular carcinoma diagnostic system has one or more of the following features:

[0030] (1) The sample data to be tested is derived from a blood sample; preferably, a plasma sample;

[0031] (2) The miRNA detection value is the miRNA expression level or the miRNA expression level after standardization, preferably, the miRNA expression level after qPCR standardization, and more preferably, the internal reference used in qPCR is miR-93-5p;

[0032] (3) The analysis module uses a logistic regression model to perform modeling and obtain analysis results;

[0033] (4) When judging, when the value of the analysis result is greater than or equal to the threshold, it is judged that there is a high risk of hepatocellular carcinoma; when the value of the analysis result is less than the threshold, it is judged that there is a low risk of hepatocellular carcinoma.

[0034] In some specific embodiments, the threshold is preferably 0.4.

[0035] In some more specific embodiments, the logistic regression model uses the following formula:

[0036] logit(P)=-1.42+0.15×(let-7g-5p)-1.83×(miR-151a-3p)-0.58×(miR-451a)+1.78×(miR-652-3p)

[0037] Among them, let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs.

[0038] The fifth object of the present invention is to provide a readable medium, which stores a program, and when the program is executed by a processor, it can realize the function of the hepatocellular carcinoma diagnosis system described in the fourth object of the present invention.

[0039] A sixth object of the present invention is to provide a hepatocellular carcinoma diagnostic device, the hepatocellular carcinoma diagnostic device comprising:

[0040] (1) The readable medium according to the fifth aspect of the present invention;

[0041] (2) a processor, configured to execute a program to implement the functions of the hepatocellular carcinoma diagnosis system;

[0042] In some embodiments, the hepatocellular carcinoma diagnostic device further comprises an output device for outputting a diagnosis result.

[0043] The present invention has the following advantages:

[0044] In summary, the present invention has the following advantages:

[0045] (1) The screened specific plasma microRNA is used as a new type of hepatocellular carcinoma 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 efficacy.

[0046] (2) Plasma microRNA, as a new disease marker, can improve the shortcomings of low sensitivity and low specificity of existing detection methods to a certain extent, significantly improve the clinical detection rate of the disease, and achieve early detection and early treatment of the disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 : Test set ROC curve. DETAILED DESCRIPTION

[0048] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples. The experimental methods in the following examples without specifying specific conditions are carried out according to conventional methods and conditions, or selected according to the product specifications.

[0049] One of the purposes of the present invention is to provide a hepatocellular carcinoma marker and a combination thereof, which can assist in assessing the risk of hepatocellular carcinoma and monitoring the therapeutic efficacy of hepatocellular carcinoma patients through changes in miRNA in the plasma of hepatocellular carcinoma patients.

[0050] The miRNA combination includes let-7g-5p, miR-151a-3p, miR-451a and miR-652-3p.

[0051] 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.

[0052] 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.

[0053] In some embodiments, the kit further comprises at least one of the following:

[0054] (1) Reference object;

[0055] (2) A reagent for detecting the reference or the miRNA combination.

[0056] In some embodiments, the detection method is RT-PCR, comprising the following steps:

[0057] (1) Separate and obtain plasma samples from subjects.

[0058] (2) Use a miRNA-specific nucleic acid extraction kit to extract small molecule miRNA, and use the Bioanalyzer nucleic acid fragment analyzer and Qubit for quantification and fragment detection. In addition, it is necessary to select the internal reference gene and positive reference miRNA suitable for the experimental sample as a reference for relative quantification in the next step.

[0059] (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.

[0060] (4) Use the designed upstream and downstream primers for qPCR quantification. The SYBR Green method or Taqman probe method can be used to produce fluorescent product labels. The expression level of the target miRNA can be calculated by relative quantification with the internal reference target for quantitative standardization.

[0061] (5) Detect and compare the changes in the amount of miRNA in patient plasma samples relative to normal plasma.

[0062] (6) Establish a miRNA molecular diagnostic model for hepatocellular carcinoma through bioinformatics data analysis.

[0063] In some embodiments, the reference includes an internal reference and / or an external reference.

[0064] It should be noted that when detecting the expression of miRNA, external reference or internal reference can be used. External reference is a miRNA that does not exist in the human body and is artificially added to the sample to be tested (such as a plasma sample). It can be used as a quality control product to detect whether the entire experimental process is normal and whether the results are credible. In the technical solution described in the present invention, the external reference can be, for example, ath-miR-159 and / or cel-miR-39.

[0065] When performing PCR relative quantitative analysis, an internal reference is required to perform data correction on the target feature in order to obtain accurate results. In some preferred embodiments, the internal reference includes one or more of miR-1228-3p, miR-103a-3p, miR-191-5p, and miR-93-5p.

[0066] More preferably, the internal reference is miR-93-5p.

[0067] Detection of the miRNA combination includes direct detection of miRNA content or indirect detection of miRNA reverse transcription cDNA or miRNA binding molecules, which are known to technicians in the field, for example, 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.

[0068] Therefore, the reagents for detecting miRNA may be not only primer pairs and probe combinations, but also conventional reagents involved in other detection methods.

[0069] It should be noted that the primer pair is used to amplify a portion of the target gene in the sample using the PCR method, and a specific region is amplified using two primers, a forward primer and a reverse primer. A person skilled in the art can design a primer pair based on the target gene sequence using commercially available products or by themselves. Similarly, a person skilled in the art can purchase or prepare the required probes based on the detection method.

[0070] In some embodiments, the kit further comprises a reference substance such as miR-93-5p. Preferably, the kit further comprises a reagent for detecting the reference substance, such as a primer pair and a probe combination.

[0071] In some specific embodiments, the kit further comprises instructions, which preferably have the following regression model:

[0072] logit(P)=-1.42+0.15×(let-7g-5p)-1.83×(miR-151a-3p)-0.58×(miR-451a)+1.78×(miR-652-3p)

[0073] Among them, let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs.

[0074] In some embodiments, the expression level of the miRNA may be a standardized qPCR quantitative result.

[0075] 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 hepatocellular carcinoma.

[0076] In other preferred embodiments, the sample to be tested is derived from a blood sample, such as plasma, serum, blood, preferably a plasma sample.

[0077] In some embodiments, the instructions are present on a printed or readable medium, such as paper, a compact disc, or a USB drive.

[0078] The third object of the present invention is to provide the use of the miRNA combination described in the first object and the kit or reagent composition described in the second object in the preparation of a diagnostic agent for diagnosing hepatocellular carcinoma.

[0079] In some embodiments, the hepatocellular carcinoma is selected from any one or more of diffuse, mass-like, nodular, or small cancer types.

[0080] A fourth object of the present invention is to provide a hepatocellular carcinoma diagnosis system, wherein the hepatocellular carcinoma diagnosis system comprises the following modules:

[0081] 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 purpose one;

[0082] An analysis module, wherein the analysis module obtains analysis results through the sample data to be tested;

[0083] and a judgment module, wherein the judgment module compares the analysis result with the threshold value to obtain a judgment result.

[0084] The hepatocellular carcinoma diagnostic system has one or more of the following features:

[0085] (1) The sample data to be tested is derived from a blood sample; preferably, a plasma sample;

[0086] (2) The miRNA detection value is the miRNA expression level or the miRNA expression level after standardization;

[0087] (3) The analysis module uses a logistic regression model to perform modeling and obtain analysis results;

[0088] (4) When judging, when the value of the analysis result is greater than or equal to the threshold, it is judged that there is a high risk of hepatocellular carcinoma; when the value of the analysis result is less than the threshold, it is judged that there is a low risk of hepatocellular carcinoma.

[0089] It should be noted that the low risk of hepatocellular carcinoma means that the sample to be tested is derived from a healthy individual, and the high risk of hepatocellular carcinoma means that the sample to be tested is derived from a hepatocellular carcinoma patient.

[0090] In some specific embodiments, the miRNA detection value is the miRNA expression level after qPCR standardization.

[0091] In some further preferred embodiments, the internal reference used in qPCR is miR-93-5p, for example, the expression levels of let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p are detected using miR-93-5p as an internal reference.

[0092] In some preferred embodiments, the logistic regression model uses the following formula:

[0093] logit(P)=-1.42+0.15×(let-7g-5p)-1.83×(miR-151a-3p)-0.58×(miR-451a)+1.78×(miR-652-3p)

[0094] Among them, let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs.

[0095] It should be noted that in the technical solution of the present invention, by comparing the analysis result (e.g., the p value obtained by logit(p)) with the threshold value, it is possible to accurately determine whether the sample to be tested has a risk of hepatocellular carcinoma. The setting of the threshold value will affect the sensitivity and specificity of the hepatocellular carcinoma diagnosis system. For higher sensitivity and specificity, a threshold value of 0.4 is preferably used.

[0096] The fifth object of the present invention is to provide a readable medium, which stores a program, and when the program is executed by a processor, it can realize the function of the hepatocellular carcinoma diagnosis system described in the fourth object of the present invention.

[0097] The readable medium is, for example, any medium that can be recognized and read by an electronic device, such as an optical disk or a USB flash drive.

[0098] A sixth object of the present invention is to provide a hepatocellular carcinoma diagnostic device, the hepatocellular carcinoma diagnostic device comprising:

[0099] (1) The readable medium according to the fifth aspect of the present invention;

[0100] (2) a processor, configured to execute a program to implement the functions of the hepatocellular carcinoma diagnosis system;

[0101] Preferably, the system further includes an output device for outputting the diagnosis result.

[0102] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0103] The reagents and raw materials used in the present invention are commercially available.

[0104] Example 1: Establishment and optimization of the reaction system of the hepatocellular carcinoma miRNA diagnostic kit

[0105] 1. Extract miRNA using miRNeasy Micro Kit (Qiagen, 217084)

[0106] 1.1 Prepare plasma samples. For each sample, 200 μL of plasma is sufficient.

[0107] 1.2 Add 1.0mL QIAzol Lysis Buffer (referred to as lysate) to the sample, vortex mix, and stand at room temperature in the dark for 5 minutes, then add 1.5μL of external reference mixture (ath-miR-159a+cel-miR-39). External reference is miRNA that does not exist in the human body. It is artificially added to the plasma sample and can be used as a quality control product to detect whether the entire experimental process is normal and whether the results are reliable.

[0108] 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.

[0109] 1.4 Use a low-temperature high-speed centrifuge to centrifuge for 15 minutes at 4°C and 12000xg. Slowly aspirate 600μL of the upper colorless aqueous phase and add it to a new 2.0mL centrifuge tube. Add 900μL of anhydrous ethanol (1.5 times the volume, the volume of anhydrous ethanol added can be calculated based on the volume of the aspirated colorless aqueous phase) to the colorless aqueous phase, vortex to mix, and centrifuge briefly.

[0110] 1.5 Transfer 750 μL of the mixed sample to the RNeasy MinElute spincolumn in a 2.0 mL collection tube, close the lid, centrifuge at 10,000 x g for 15 seconds, and discard the waste liquid. Repeat this step until all the liquid has passed through the column.

[0111] 1.6 Add 700 μL Buffer RWT to the RNeasy MinElute spin column, centrifuge at 10,000 x g for 15 seconds, and discard the waste liquid.

[0112] 1.7 Add 500 μL Buffer RPE into the RNeasy MinElute spin column, centrifuge at 10,000 x g for 15 seconds, and discard the waste liquid.

[0113] 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.

[0114] 1.9 Transfer the RNeasy MinElute spin column into a new 2.0 mL collection tube, open the lid, and centrifuge at full speed for 5 minutes.

[0115] 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.

[0116] 2. Tailing Reaction

[0117] Using ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0118] 2.1 According to Table 1 below, prepare enough Poly(A) Reaction Mix in the EP tube to meet the reaction requirements.

[0119] Table 1.

[0120]

[0121] 2.2 Take 2 μL of the thawed and 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.

[0122] 2.3 Wipe the outer wall of the PCR reaction tube containing the sample and Poly (A) Reaction Mix with toilet paper and put it into the PCR instrument. Select the operating system as 10 μL and run the PCR instrument according to the program in Table 2 below.

[0123] Table 2.

[0124] step temperature time Polyadenylation 37℃ 45 minutes Stop the reaction 65℃ 10 minutes Keep 4℃ Keep

[0125] 3. Ligation reaction

[0126] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0127] 3.1 Thaw 50% PEG 8000 at room temperature in advance. According to Table 3 below, prepare enough Ligation Reaction Mix in the EP tube to meet the required amount of reaction.

[0128] Table 3.

[0129] Components 1R 4Rxns 10Rxns 5X DNA Ligase Buffer 3μL 13.2μL 33μL 50% PEG 8000 4.5μL 19.8μL 49.5μL 25x Ligation Adaptor 0.6μL 2.6μL 6.6μL RNA ligase 1.5μL 6.6μL 16.5μL RNase-free water 0.4μL 1.8μL 4.4μL Ligation reaction mixture 10μL 44μL 110μL

[0130] 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.

[0131] 3.3 Wipe the outer wall of the PCR reaction tube containing the tailed sample and Ligation Reaction Mix with toilet paper and put it into the PCR instrument. Select the running volume as 15μL and run according to the program in Table 4 below.

[0132] Table 4.

[0133] step temperature time Polyadenylation 37℃ 45 minutes Stop the reaction 65℃ 10 minutes Keep 4℃ Keep

[0134] 4. Reverse transcription reaction (hereinafter referred to as RT)

[0135] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0136] 4.1 According to Table 5 below, prepare enough RT Reaction Mix in the EP tube to meet the reaction requirements.

[0137] Table 5.

[0138]

[0139]

[0140] 4.2 Take 15 μL of the mixed RT Reaction Mix and add it to the PCR reaction tube containing the sample after connecting the adapter.

[0141] 4.3 Wipe the outer wall of the reaction tube containing the sample and RT Reaction Mix after connecting the connector with toilet paper and put it into the PCR instrument. Select the running volume as 30μL and run according to the program in Table 6 below.

[0142] Table 6.

[0143] step temperature time Reverse transcription 42℃ 15 minutes Stop the reaction 85℃ 5 minutes Keep 4℃ Keep

[0144] 5. Pre-amplification reaction

[0145] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0146] 5.1 According to Table 7 below, prepare enough miR-Amp Reaction Mix in the EP tube to meet the reaction requirements.

[0147] Table 7.

[0148] Components 1R 4Rxns 10Rxns 2X miR-Amp Master Mix 25μL 110μL 275μL 20X miR-Amp Primer Mix 2.5μL 11μL 27.5μL RNase-free water 17.5μL 77μL 192.5μL Pre-amplification reaction mix 45μL 198μL 495μL

[0149] 5.2 Prepare a corresponding number of new PCR reaction tubes according to the number of samples, and take 45 μL from the miR-Amp Reaction Mix and add it to the new PCR reaction tubes.

[0150] 5.3 Vortex the RT reaction product, centrifuge briefly to eliminate bubbles, and then take 5 μL and add it to the PCR reaction tube containing 45 μL miR-AmpReaction Mix.

[0151] 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 put it into the PCR instrument. Select the running volume as 50 μL and run according to the program in Table 8 below.

[0152] Table 8.

[0153]

[0154] 6. qPCR reaction

[0155] In this experiment, TaqMan TM Fast Advanced Master Mix.

[0156] 6.1 Dilute TE pH 8.0 1:10 to prepare 0.1X TE buffer. Vortex the prepared 0.1X TE buffer and centrifuge briefly to remove bubbles.

[0157] 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.

[0158] 6.3 According to Table 9 below, prepare enough PCR Reaction Mix in the EP tube to meet the reaction requirements.

[0159] Table 9.

[0160] Components 1R 4Rxns TaqMan Fast Advanced Master Mix (2X) 10μL 44.0μL TaqMan miRNA Assay Reagents (Advanced miRNA Assay) (20X) 1μL 4.4μL RNase-free water 4μL 17.6μL PCR reaction mixture 15μL 66μL

[0161] 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, and centrifuge briefly to eliminate bubbles.

[0162] 6.5 Use clean toilet paper to wipe the outer wall of the PCR reaction tube containing the diluted cDNA template and PCR Reaction Mix and put it into the qPCR instrument QuantStudio5. Select the running volume as 20μL and run according to the program in Table 10 below.

[0163] Table 10.

[0164]

[0165] Example 2: A diagnostic model for hepatocellular carcinoma

[0166] 1. Sample situation

[0167] A total of 102 samples were collected, including 24 plasma samples from hepatocellular carcinoma patients and 78 control plasma samples. The control group samples included 44 cases of cirrhosis (28 cases of hepatitis B cirrhosis + 8 cases of hepatitis C cirrhosis + 2 cases of alcoholic cirrhosis + 1 case of cryptogenic cirrhosis + 5 cases of other cirrhosis), 5 cases of hepatitis (4 cases of hepatitis B virus + 1 case of hepatitis C virus), and 29 healthy people.

[0168] A total of 50 miRNAs were detected, and ath-159 and cel-39 were used as external references.

[0169] 2. Data quality control of qPCR-based miRNA detection

[0170] Sample quality control: negative control NTC (No Template Control) was considered qualified if there was no amplification or CT was greater than 34. After sample quality control, 2 hepatocellular carcinoma samples and 2 hepatitis B cirrhosis samples did not meet the NTC quality control requirements, and 2 cirrhosis samples and 1 hepatocellular carcinoma sample failed the external reference quality control. These 7 samples were deleted; the remaining 95 samples included 74 controls (28 healthy subjects, 5 hepatitis samples, and 41 cirrhosis samples) and 21 hepatocellular carcinoma samples.

[0171] miRNA quality control: All samples in the screening stage were tested by qRT-PCR to detect the expression level of miRNA in plasma samples. Each miRNA was tested in parallel 3 times, and a maximum of 1 failure was allowed in 3 replicate wells. If the confidence Cq Conf value of the amplification reaction fluorescence signal cycle number was greater than 0.8, the amplification was qualified; the maximum number of unqualified samples allowed should not exceed 10% of the total samples. After the quality control stage, the remaining 26 miRNAs met the quality control requirements, and the other 2 external references ath-miR-159 and cel-miR-39 also met the quality control requirements.

[0172] Table 11. 26 miRNAs that passed the quality control conditions and the corresponding number of unqualified samples

[0173]

[0174]

[0175] 3. Data Analysis of qPCR-based miRNA Detection

[0176] 3.1 Selection of internal reference

[0177] NormFinder and geNorm software were used to evaluate the expression stability of four candidate internal reference miRNAs (miR-1228-3p, miR-103a-3p, miR-191-5p, miR-93-5p) in qRT-PCR, so as to select stable internal reference controls for quantifying miRNA expression levels in plasma of patients and healthy subjects. Since miR-1228-3p and miR-103a-3p did not pass the quality control setting conditions, only the results of miR-191-5p and miR-93-5p, two candidate internal reference miRNAs, were compared. Based on the geometric mean of the number of reference genes, geNorm calculated the gene expression normalization factor for each sample and sorted them by M value. The smaller the M value, the more stable the miRNA expression. Normfinder sorted the stability by calculating the Stability value. The smaller the Stability value, the more stable the miRNA expression. The geNorm function in the ctrlGene (Version 1.0.1) package of the R language was used to calculate the M value. The NormFinder for R script (https: / / www.moma.dk / software / normfinder) was downloaded from the official website of the Institute of Molecular Medicine at Aarhus University, Denmark, the developer of the NormFinder software, to calculate the S value. Combining the results of the two software, miR-93-5p was selected as the internal reference.

[0178] Table 12. Internal reference selection - Normfinder software results

[0179]

[0180] Table 13. Internal reference selection - GeNorm software results

[0181]

[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 expression of miRNA was analyzed by the method, the foldchange value was calculated, and the Wilcoxon rank sum test was used for difference analysis. The results are as follows:

[0184] Table 14

[0185]

[0186]

[0187] (1) By comparing the expression of 25 miRNAs in hepatocellular carcinoma and healthy controls (the internal reference miR-93-5p was removed), miRNAs with fold change values ​​greater than 1.5 or less than 0.5 and Mann-Whitney Pvalue < 0.05 were selected. A total of 7 miRNAs met the filtering criteria: miR-126-5p, miR-151a-3p, miR-199a-3p, miR-26b-5p, miR-451a, miR-652-3p, and let-7g-5p. Among them, miR-451a was downregulated in patients, while the others were upregulated.

[0188] (2) By comparing the expression of 25 miRNAs in HCC and cirrhosis samples, miRNAs with foldchange values ​​greater than 1.5 or fold change values ​​less than 0.5 and Mann-Whitney Pvalue < 0.05 were selected. A total of 1 miRNA met the filtering criteria: let-7g-5p, which was upregulated in the patient group.

[0189] The above method screened out 7 miRNAs.

[0190] 3.3 Model building

[0191] 3.3.1 LASSO regression for feature screening:

[0192] LASSO (Least Absolute Shrinkage and Selection Operator) is a LASSO that adds an L1 regularization term to the loss function, making the coefficients of some features become zero, thereby achieving feature selection and dimensionality reduction. During the optimization process, LASSO regression will make the coefficients of some features become 0, thereby achieving feature sparsity and eliminating features with little impact on the target variable. During the fitting process, the sparsity of the features is controlled by adjusting the regularization parameter λ (lambda). The cross-validation method is used to obtain the range of λ values, and λ_min is selected as the optimal parameter. The selected λ value is used to rebuild the LASSO model, and feature selection is performed based on the model, that is, features with coefficients that are not 0 are retained as the final feature set.

[0193] By performing LASSO regression on 95 sample data, the optimal feature combination was obtained as let-7g-5p, miR-151a-3p, miR-451a and miR-652-3p.

[0194] 3.3.2 Using Logistic Regression Model

[0195] Logistic regression and 5-fold cross validation strategies were used to construct the model. In the cross validation, the 95 data sets were randomly divided into 5 equal parts, each with 19 cases, of which 4 parts (76 cases) were used as training sets for modeling, and the remaining 1 part (19 cases) was used as a test set for model evaluation.

[0196] The logistic regression function is as follows, using the training set data for modeling:

[0197]

[0198] In the above formula: x represents the eigenvalue, z is the logit(p) value in logistic regression, and T represents the transposed sign.

[0199] 3.3.2 Logistic regression model The p values ​​of the four features are shown in Table 15, all of which meet the requirements, namely: let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p.

[0200] Table 15. Estimated coefficients, standard errors, z-values, and P-values ​​for the logistic regression model

[0201] Estimate Std,Error z value Pr(>|z|) (Intercept) -1.4203 0.3584 -3.963 7.4e-05*** let-7g-5p 0.1540 0.4610 0.334 0.7383 miR-151a-3p -1.8254 0.8518 -2.143 0.0321* miR-451a -0.5832 0.4785 -1.219 0.2229 miR-652-3p 1.7812 0.8165 2.181 0.0292*

[0202] Note: “***” indicates P < 0.001, “**” indicates P < 0.01, “*” indicates P < 0.05, “·” indicates P < 0.1, and “” indicates P < 1.

[0203] Table 16. miRNA nucleotide sequence information

[0204] Target Name miRBase Accession Number Mature miRNA Sequence hsa-let-7g-5p MIMAT0000414 UGAGGUAGUAGUUUGUACAGUU hsa-miR-151a-3p MIMAT0000757 CUAGACUGAAGCUCCUUGAGG hsa-miR-451a MIMAT0001631 AAACCGUUACCAUUACUGAGUU hsa-miR-652-3p MIMAT0003322 AAUGGCGCCACUAGGGUUGUG

[0205] Estimate: The estimated values ​​of the coefficients of the predictor variables.

[0206] Std.Error: The standard error of the coefficient estimate, indicating the uncertainty of the estimate.

[0207] z value: The coefficient estimate divided by its standard error, used to test whether the coefficient is significantly different from zero.

[0208] Pr(>|z|): P value, which indicates 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.

[0209] Based on the comprehensive results, the model is established as follows:

[0210] logit(P)=-1.42+0.15×(let-7g-5p)-1.83×(miR-151a-3p)-0.58×(miR-451a)+1.78×(miR-652-3p)

[0211] Among them, let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs, and the expression levels are standardized qPCR quantitative results.

[0212] 3.4 Model Evaluation

[0213] The performance of the logistic regression model was evaluated through 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 0.4. 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, hepatitis and cirrhosis people), and 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 hepatocellular carcinoma patient population).

[0214] The formula for calculating the P value is as follows: We already have a logit(P) value, and to convert it back to a P value, we need to use the inverse transformation of the sigmoid function.

[0215]

[0216] Taking the logarithm of both sides and solving gives:

[0217]

[0218] Test set ROC curve ( Figure 1 ) can determine the diagnostic efficacy of the above model for hepatocellular carcinoma. When the threshold is set to 0.4, the area under the ROC curve of the miRNA combination marker for diagnosing hepatocellular carcinoma is 0.88, and the sensitivity and specificity of the prediction model for distinguishing liver cancer and the control group are 100% and 82.4%, respectively.

Claims

1. A miRNA combination, characterized in that: The miRNA combination includes let-7g-5p, miR-151a-3p, miR-451a and miR-652-3p.

2. A kit, characterized in that: It comprises the miRNA combination as claimed in claim 1.

3. The kit according to claim 2, characterized in that The kit also includes at least one of the following: (1) References. Preferably, the references include internal references and / or external references. More preferably, the internal references include one or more of miR-1228-3p, miR-103a-3p, miR-191-5p, and miR-93-5p. Further preferably, the internal reference is miR-93-5p. (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, characterized in that The kit also includes instructions, which preferably have the following regression model: logit(P)=-1.42+0.15×(let-7g-5p)-1.83×(miR-151a-3p)-0.58×(miR-451a)+1.78×(miR-652-3p) Among them, let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs; Preferably, the instructions are present 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 hepatocellular carcinoma; preferably, the hepatocellular carcinoma is selected from any one or more of diffuse type, massive type, nodular type or small cancer type.

6. A hepatocellular carcinoma diagnostic 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 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 the threshold value to obtain a judgment result.

7. The hepatocellular carcinoma diagnostic system according to claim 6, characterized in that: The hepatocellular carcinoma 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 standardization, preferably, the miRNA expression level after qPCR standardization, and more preferably, the internal reference used in qPCR is miR-93-5p; (3) The analysis module uses a logistic regression model to perform modeling and obtain analysis results; (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 hepatocellular carcinoma; When the analysis result value is less than the threshold, it is determined to be a low risk of hepatocellular carcinoma. Preferably, the threshold is 0.

4.

8. The hepatocellular carcinoma diagnostic system according to claim 7, characterized in that: The logistic regression model uses the following formula: logit(P)=-1.42+0.15×(let-7g-5p)-1.83×(miR-151a-3p)-0.58×(miR-451a)+1.78×(miR-652-3p) Among them, let-7g-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs.

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 hepatocellular carcinoma diagnosis system according to any one of claims 6 to 8 can be realized.

10. A hepatocellular carcinoma 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 hepatocellular carcinoma diagnosis system; Preferably, the system further includes an output device for outputting the diagnosis result.

Images