Intrahepatic cholangiocellular carcinoma marker as well as detection method and application thereof

Abstract

The invention discloses a marker combination and application of the marker combination in preparation of a reagent for diagnosing intrahepatic cholangiocellular carcinoma, the marker is miRNA, and the miRNA combination comprises any one or more of miR-26b-5p, miR-652-3p or miR-451a. The invention also discloses an intrahepatic cholangiocellular carcinoma diagnosis system constructed by the miRNA marker combination. The marker combination and the intrahepatic cholangiocellular carcinoma diagnosis system provided by the invention can be used for detecting and judging the possibility of suffering from the intrahepatic cholangiocellular carcinoma by utilizing a noninvasive blood sample which is convenient to obtain materials, and have relatively high sensitivity and specificity.

Description

Technical Field

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

[0002] Intrahepatic Cholangiocarcinoma (ICC, hereinafter referred to as intrahepatic cholangiocarcinoma) is the second most common primary liver cancer after hepatocellular carcinoma, with an incidence of about 10-15% of primary liver cancer. Intrahepatic cholangiocarcinoma is a liver cancer that occurs in the bile duct and usually originates from the bile duct epithelium. The symptoms of intrahepatic cholangiocarcinoma mainly include right upper abdominal pain, jaundice, and poor bile flow. Risk factors for intrahepatic cholangiocarcinoma include chronic cholangitis, cirrhosis, and post-cholecystectomy. Intrahepatic cholangiocarcinoma is mainly treated with surgical resection, supplemented by radiotherapy, chemotherapy and other treatment methods.

[0003] At present, there are some difficulties in the diagnosis of ICC in clinical practice. First, the symptoms of ICC are atypical, and it grows slowly in the early stage and is difficult to detect. In addition, due to the complex anatomy and different morphologies of intrahepatic cholangiocarcinoma, it is sometimes difficult to accurately identify hepatocellular carcinoma and intrahepatic cholangiocarcinoma in imaging examinations such as CT and MRI, resulting in uncertain imaging examination results. There are no clear specific biomarkers in clinical practice, and the more commonly used biomarkers such as CA199 and carcinoembryonic antigen (CEA) are not specific enough and can only be used as auxiliary indicators for the diagnosis of tumor diseases.

[0004] Therefore, screening for intrahepatic cholangiocarcinoma requires comprehensive screening and treatment combining clinical symptoms, imaging examinations, histological examinations, and other methods. However, there are some problems and difficulties in early screening, imaging examinations, and histological examinations of intrahepatic cholangiocarcinoma, which makes early screening of intrahepatic cholangiocarcinoma more difficult.

[0005] MicroRNA (miRNA) is a newly discovered non-coding small molecule RNA of about 18-25 nucleotides in length. It is highly conserved in evolution and accounts for about 1% of the genome. It negatively regulates the expression of protein-coding genes at the transcriptional or post-transcriptional level, and binds to its target gene mRNA through non-complete complementarity or nearly complete complementarity, resulting in mRNA degradation or inhibition of its translation. About 30% of the genes in the human genome are regulated by miRNA. Most of the target genes of miRNA are genes involved in biological effects such as transcription, signal transduction, and tumorigenesis, and play an important biological regulatory role in cell growth, proliferation, differentiation, and apoptosis. It has been confirmed that abnormal regulation of miRNA is closely related to tumor formation and progression. With the continuous in-depth study of the relationship between miRNA and cancer, it has been found that miRNA is not only involved in the early stages of cancer formation, but also involves changes in disease conditions, sensitivity to drugs, patient prognosis, and many other aspects.

[0006] Currently, there is no specific microRNA biomarker combination and detection kit that can detect and judge the association with intrahepatic cholangiocarcinoma using non-invasive and easily obtained blood samples. Summary of the invention

[0007] One of the purposes of the present invention is to provide an intrahepatic cholangiocarcinoma marker and a combination thereof, which can assist in assessing the risk of intrahepatic cholangiocarcinoma and evaluating the efficacy monitoring of intrahepatic cholangiocarcinoma patients through the changes in miRNA in the plasma of intrahepatic cholangiocarcinoma patients.

[0008] The specific technical scheme is as follows: a miRNA combination, wherein the miRNA combination includes any one or more of miR-26b-5p, miR-652-3p or miR-451a.

[0009] In some embodiments, the miRNA combination further includes miR-151a-3p and / or miR-143-3p.

[0010] In some specific embodiments, the miRNA combination includes miR-26b-5p, miR-652-3p, miR-451a, miR-151a-3p and miR-143-3p.

[0011] Plasma microRNA detection data: The diagnostic kit based on plasma microRNA combines the unique properties of plasma microRNA with conventional molecular biological detection technology, which can quickly analyze the composition of microRNA in intrahepatic cholangiocarcinoma 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 intrahepatic cholangiocarcinoma.

[0012] The 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.

[0013] In some embodiments, the kit further comprises at least one of the following:

[0014] (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.

[0015] (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.

[0016] In some specific embodiments, the kit further comprises instructions, which preferably have the following regression model:

[0017] logit(P)=-1.21+0.84×(miR-143-3p)-0.78×(miR-26b-5p)-1.38×(miR-151a-3p)-2.08×(miR-451a)+1.44×(miR-652-3p)

[0018] Among them, miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs.

[0019] Preferably, the instructions are present in a printed medium or a readable medium, such as paper, a CD or a USB flash drive.

[0020] The 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 intrahepatic cholangiocarcinoma.

[0021] In some embodiments, the intrahepatic cholangiocarcinoma is selected from any one or more of mass-type intrahepatic cholangiocarcinoma, invasive intrahepatic cholangiocarcinoma, peripheral intrahepatic cholangiocarcinoma, or intrahepatic peripheral cholangiocarcinoma growing in the bile duct.

[0022] A fourth object of the present invention is to provide a system for diagnosing intrahepatic cholangiocarcinoma, wherein the system for diagnosing intrahepatic cholangiocarcinoma comprises the following modules:

[0023] 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;

[0024] An analysis module, wherein the analysis module obtains analysis results through the sample data to be tested;

[0025] and a judgment module, wherein the judgment module compares the analysis result with the threshold value to obtain a judgment result.

[0026] The intrahepatic cholangiocarcinoma diagnostic system has one or more of the following features:

[0027] (1) The sample data to be tested is derived from a blood sample; preferably, a plasma sample;

[0028] (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;

[0029] (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:

[0030] logit(P)=-1.21+0.84×(miR-143-3p)-0.78×(miR-26b-5p)-1.38×(miR-151a-3p)-2.08×(miR-451a)+1.44×(miR-652-3p)

[0031] Among them, miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs.

[0032] (4) When judging, when the value of the analysis result is greater than or equal to the threshold, it is judged as having a high risk of intrahepatic cholangiocarcinoma; when the value of the analysis result is less than the threshold, it is judged as having a low risk of intrahepatic cholangiocarcinoma. Preferably, the threshold is 0.38.

[0033] 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 intrahepatic cholangiocarcinoma diagnosis system of the fourth object of the present invention.

[0034] The sixth object of the present invention is to provide a diagnostic device for intrahepatic cholangiocarcinoma, the diagnostic device for intrahepatic cholangiocarcinoma comprising:

[0035] (1) The readable medium according to the fifth object of the present invention;

[0036] (2) a processor, configured to execute a program to implement the functions of the intrahepatic cholangiocarcinoma diagnosis system;

[0037] Preferably, the system further includes an output device for outputting the diagnosis result.

[0038] The present invention has the following advantages:

[0039] 1. The screened specific plasma miRNA is used as a new type of intrahepatic cholangiocarcinoma 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 for early diagnosis of the disease and evaluation of therapeutic efficacy.

[0040] 2. Plasma miRNA, as a new disease marker, can significantly improve the clinical detection rate of the disease, achieve early detection and early treatment of the disease, and make up for the shortcomings of existing detection methods in specific diagnosis.

[0041] 3. By establishing an evaluation model, a comprehensive judgment can be made on multiple indicators to improve the analytical performance of a single marker indicator. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 : ROC curve of the test set DETAILED DESCRIPTION

[0043] 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.

[0044] One of the purposes of the present invention is to provide an intrahepatic cholangiocarcinoma marker and a combination thereof, which can assist in assessing the risk of intrahepatic cholangiocarcinoma and evaluating the efficacy monitoring of intrahepatic cholangiocarcinoma patients through the changes in miRNA in the plasma of intrahepatic cholangiocarcinoma patients.

[0045] The miRNA combination comprises any one or more of miR-26b-5p, miR-652-3p or miR-451a.

[0046] In some embodiments, the miRNA combination further includes miR-151a-3p and / or miR-143-3p.

[0047] In some specific embodiments, the miRNA combination includes miR-26b-5p, miR-652-3p, miR-451a, miR-151a-3p and miR-143-3p.

[0048] 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.

[0049] The 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.

[0050] In some embodiments, the kit further comprises at least one of the following:

[0051] (1) Reference object;

[0052] (2) A reagent for detecting the reference or the miRNA combination.

[0053] In some embodiments, the detection method is RT-PCR, comprising the following steps:

[0054] (1) Separate and obtain plasma samples from subjects.

[0055] (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.

[0056] (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.

[0057] (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.

[0058] (5) Detect and compare the changes in the amount of miRNA in patient plasma samples relative to normal plasma.

[0059] (6) Establish a miRNA molecular diagnostic model for intrahepatic cholangiocarcinoma through bioinformatics data analysis.

[0060] In some embodiments, the reference includes an internal reference and / or an external reference.

[0061] 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.

[0062] 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.

[0063] More preferably, the internal reference is miR-93-5p.

[0064] 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.

[0065] Therefore, the reagents for detecting miRNA may be not only primer pairs and probe combinations, but also conventional reagents involved in other detection methods.

[0066] 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.

[0067] 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.

[0068] In some specific embodiments, the kit further comprises instructions, which preferably have the following regression model:

[0069] logit(P)=-1.21+0.84×(miR-143-3p)-0.78×(miR-26b-5p)-1.38×(miR-151a-3p)-2.08×(miR-451a)+1.44×(miR-652-3p)

[0070] Among them, miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs.

[0071] In some embodiments, the expression level of the miRNA may be a standardized qPCR quantitative result.

[0072] 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 intrahepatic cholangiocarcinoma.

[0073] In other preferred embodiments, the sample to be tested is derived from a blood sample, such as plasma, serum, blood, preferably a plasma sample.

[0074] In some embodiments, the instructions are present on a printed or readable medium, such as paper, a compact disc, or a USB drive.

[0075] The 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 intrahepatic cholangiocarcinoma.

[0076] In some embodiments, the intrahepatic cholangiocarcinoma is selected from any one or more of mass-type intrahepatic cholangiocarcinoma, invasive intrahepatic cholangiocarcinoma, peripheral intrahepatic cholangiocarcinoma, or intrahepatic peripheral cholangiocarcinoma growing in the bile duct.

[0077] A fourth object of the present invention is to provide a system for diagnosing intrahepatic cholangiocarcinoma, wherein the system for diagnosing intrahepatic cholangiocarcinoma comprises the following modules:

[0078] 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;

[0079] An analysis module, wherein the analysis module obtains analysis results through the sample data to be tested;

[0080] and a judgment module, wherein the judgment module compares the analysis result with the threshold value to obtain a judgment result.

[0081] The intrahepatic cholangiocarcinoma diagnostic system has one or more of the following features:

[0082] (1) The sample data to be tested is derived from a blood sample;

[0083] (2) The miRNA detection value is the miRNA expression level or the miRNA expression level after standardization;

[0084] (3) The analysis module uses a logistic regression model to perform modeling and obtain analysis results;

[0085] (4) When judging, when the value of the analysis result is greater than or equal to the threshold, it is judged that the patient has a high risk of intrahepatic cholangiocarcinoma; when the value of the analysis result is less than the threshold, it is judged that the patient has a low risk of intrahepatic cholangiocarcinoma.

[0086] It should be noted that the low risk of intrahepatic cholangiocarcinoma means that the sample to be tested is derived from a healthy individual, and the high risk of intrahepatic cholangiocarcinoma means that the sample to be tested is derived from a patient with intrahepatic cholangiocarcinoma.

[0087] In some specific embodiments, the miRNA detection value is the miRNA expression level after qPCR standardization.

[0088] In some further preferred embodiments, the internal reference used in qPCR is miR-93-5p, for example, the expression levels of miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p are detected using miR-93-5p as an internal reference.

[0089] In some preferred embodiments, the logistic regression model uses the following formula:

[0090] logit(P)=-1.21+0.84×(miR-143-3p)-0.78×(miR-26b-5p)-1.38×(miR-151a-3p)-2.08×(miR-451a)+1.44×(miR-652-3p)

[0091] Among them, miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p represent the expression levels of the corresponding miRNAs.

[0092] It should be pointed out that in the technical solution of the present invention, by comparing the analysis results (such as 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 intrahepatic cholangiocarcinoma. The setting of the threshold value will affect the sensitivity and specificity of the intrahepatic cholangiocarcinoma diagnosis system. For higher sensitivity and specificity, a threshold value of 0.38 is preferably used.

[0093] 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 intrahepatic cholangiocarcinoma diagnosis system of the fourth object of the present invention.

[0094] 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.

[0095] The sixth object of the present invention is to provide a diagnostic device for intrahepatic cholangiocarcinoma, the diagnostic device for intrahepatic cholangiocarcinoma comprising:

[0096] (1) The readable medium according to the fifth object of the present invention;

[0097] (2) a processor, configured to execute a program to implement the functions of the intrahepatic cholangiocarcinoma diagnosis system;

[0098] Preferably, the system further includes an output device for outputting the diagnosis result.

[0099] 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.

[0100] The reagents and raw materials used in the present invention are commercially available.

[0101] Example 1: Establishment and optimization of the reaction system of the intrahepatic cholangiocarcinoma miRNA diagnostic kit

[0102] 1. Extract miRNA using miRNeasy Micro Kit (Qiagen, 217084)

[0103] 1.1 Prepare plasma samples. For each sample, 200 μL of plasma is sufficient.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 2. Tailing Reaction

[0114] Using ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0115] 2.1 According to Table 1 below, prepare enough Poly(A) Reaction Mix in the EP tube to meet the reaction requirements.

[0116] Table 1.

[0117] Components 1R 4Rxns 10Rxns 10X Poly(A) Buffer 0.5μL 2.2μL 5.5μL ATP 0.5μL 2.2μL 5.5μL Poly(A) enzyme 0.3μL 1.3μL 3.3μL RNase-free water 1.7μL 7.5μL 18.7μL Poly(A) reaction mixture system 3.0μL 13.2μL 33μL

[0118] 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.

[0119] 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.

[0120] Table 2.

[0121] step temperature time Polyadenylation 37℃ 45 minutes Stop the reaction 65℃ 10 minutes Keep 4℃ Keep

[0122] 3. Ligation reaction

[0123] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0124] 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.

[0125] Table 3.

[0126] 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

[0127] 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.

[0128] 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.

[0129] Table 4.

[0130]

[0131]

[0132] 4. Reverse transcription reaction (hereinafter referred to as RT)

[0133] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0134] 4.1 According to Table 5 below, prepare enough RT Reaction Mix in the EP tube to meet the reaction requirements.

[0135] Table 5.

[0136] Components 1R 4Rxns 10Rxns 5X RT Buffer 6μL 26.4μL 66μL dNTP Mix 1.2μL 5.3μL 13.2μL 20X Universal RT Primer 1.5μL 6.6μL 16.5μL 10X RT Enzyme Mix 3μL 13.2μL 33μL RNase-free water 3.3μL 14.5μL 36.4μL RT reaction mixture system 15μL 66μL 165μL

[0137] 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.

[0138] 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.

[0139] Table 6.

[0140] step temperature time Reverse transcription 42℃ 15 minutes Stop the reaction 85℃ 5 minutes Keep 4℃ Keep

[0141] 5. Pre-amplification reaction

[0142] This experiment used ThermoFisher TaqMan TM Advanced miRNA cDNA Synthesis Kit.

[0143] 5.1 According to Table 7 below, prepare enough miR-Amp Reaction Mix in the EP tube to meet the reaction requirements.

[0144] Table 7.

[0145] 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

[0146] 5.2 Prepare a corresponding number of new PCR reaction tubes according to the number of samples, and take 45 μL from miR-Amp Reaction Mix and add it to the new PCR reaction tubes.

[0147] 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.

[0148] 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.

[0149] Table 8.

[0150]

[0151] 6. qPCR reaction

[0152] In this experiment, TaqMan TM Fast Advanced Master Mix.

[0153] 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.

[0154] 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.

[0155] 6.3 According to Table 9 below, prepare enough PCR Reaction Mix in the EP tube to meet the reaction requirements.

[0156] Table 9.

[0157]

[0158] 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.

[0159] 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.

[0160] Table 10.

[0161]

[0162] Example 2: An auxiliary diagnosis model for intrahepatic cholangiocarcinoma

[0163] 1. Sample situation

[0164] A total of 126 samples were collected, including 48 plasma samples from patients with intrahepatic cholangiocarcinoma 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.

[0165] A total of 50 miRNAs were detected, and ath-159 and cel-39 were used as external references.

[0166] 2. Data quality control of qPCR-based miRNA detection

[0167] Sample quality control: negative control NTC (No Template Control) was not amplified or CT was greater than 34, which was considered qualified. After sample quality control, 1 intrahepatic cholangiocarcinoma and 2 hepatitis B cirrhosis samples did not meet the NTC quality control requirements. In addition, only 16 miRNAs were detected in 50 miRNAs from 1 intrahepatic cholangiocarcinoma patient sample, and 2 samples failed the external reference quality control. These 6 samples were deleted; the remaining 120 samples included 74 controls (28 healthy subjects, 5 hepatitis, and 41 cirrhosis samples) and 46 intrahepatic cholangiocarcinoma samples.

[0168] miRNA quality control: All samples in the screening stage were tested for the expression level of miRNA in plasma samples using qRT-PCR. Each miRNA was tested in parallel 3 times, with a maximum of 1 failure allowed in 3 replicates. 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 28 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.

[0169] Table 11. 28 miRNAs that passed the quality control conditions and the corresponding number of unqualified samples

[0170]

[0171]

[0172] 3. Data Analysis of qPCR-based miRNA Detection

[0173] 3.1 Selection of internal reference

[0174] Using NormFinder [1] and geNorm [1]The software evaluates the expression stability of four candidate internal reference miRNAs (miR-1228-3p, miR-103a-3p, miR-191-5p, miR-93-5p) in qRT-PCR, thereby selecting 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.

[0175] Table 12. Internal reference selection - Normfinder software results

[0176]

[0177] Table 13. Internal reference selection - GeNorm software results

[0178]

[0179]

[0180] 3.2 Difference Analysis

[0181] 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 and the foldchange was calculated. [3] Wilcoxon rank sum test [3] The difference analysis results are as follows:

[0182] Table 14.

[0183]

[0184]

[0185] (1) By comparing the expression of 27 miRNAs (excluding the internal reference miR-93-5p) in intrahepatic cholangiocarcinoma and healthy controls, we selected miRNAs with fold change values ​​greater than 1.5 or less than 0.5 and Mann-Whitney Pvalue < 0.05. A total of 10 miRNAs met the filtering criteria: miR-126-5p, miR-143-3p, miR-151a-3p, miR-199a-3p, miR-24-3p, miR-26b-5p, miR-423-3p, miR-451a, miR-652-3p, and miR-664a-3p. Among them, miR-451a was downregulated in patients, while the others were upregulated.

[0186] (2) By comparing the expression of 27 miRNAs in intrahepatic cholangiocarcinoma 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 2 miRNAs met the filtering criteria: miR-192-5p and miR-423-3p, both of which were upregulated in the patient group.

[0187] The above method screened out 11 miRNAs.

[0188] 3.3 Model establishment and verification

[0189] 3.3.1 Model feature screening:

[0190] Stepwise Regression [5] It is a feature screening and optimization method in the construction of multiple linear regression models. It aims to find the combination of independent variables that has the most significant impact on the response variable by gradually adding or deleting variables, so as to build a more concise regression model with better prediction effect. The AIC criterion (Akaike Information Criterion) is a statistic used for model selection. It not only considers the fit of the model, but also penalizes the complexity of the model to avoid overfitting problems. In the stepwise regression method, the AIC criterion is often used as a basis for selecting the optimal feature combination. At each step, the change in the AIC value will determine whether to continue adding or deleting features. Specifically, if the AIC value drops significantly after adding a new feature, the feature is retained; if the AIC value does not change much or drops slightly after deleting a feature, consider deleting the feature.

[0191] By performing stepwise regression on 120 samples, the feature combination with the minimum AIC was selected, which was miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p.

[0192] 3.3.2 Model building using logistic regression

[0193] Using logistic regression [6] and 5-fold crossvalidation [7] The strategy was used to build the model. In the cross-validation, the 120-case data set was randomly divided into 5 equal parts, with 24 cases in each part. 4 parts (96 cases) were used as training sets for modeling, and the remaining 1 part (24 cases) was used as a test set for model evaluation.

[0194] The logistic regression function is as follows, using the training set data for modeling:

[0195]

[0196] In the above formula: x represents the eigenvalue, z is the logit(p) value in logistic regression, and T represents the transposed sign.

[0197] 3.3.3 Logistic regression model The p values ​​of the five features are shown in Table 15, all of which meet the requirements, namely: miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p.

[0198] Table 15. Estimated coefficients, standard errors, z-values, and P-values ​​for the logistic regression model

[0199] Estimate Std,Error z value Pr(>|z|) (Intercept) -1.2069 0.3926 -3.074 0.00211** miR-143-3p 0.8397 0.4195 2.001 0.04535* miR-26b-5p -0.7773 0.5407 -1.438 0.15056 miR-151a-3p -1.3770 0.5124 -2.687 0.00720** miR-451a -2.0831 0.6948 -2.998 0.00272** miR-652-3p 1.4383 0.6068 2.370 0.01778*

[0200] Note: “***” indicates P < 0.001, “**” indicates P < 0.01, “*” indicates P < 0.05, “·” indicates P < 0.1, and “” indicates P < 1.

[0201] Table 16. miRNA nucleotide sequence information

[0202] Target Name miRBase Accession Number Mature miRNA Sequence miR-143-3p MIMAT0000435 UGAGAUGAAGCACUGUAGCUC hsa-miR-26b-5p MIMAT0000083 UUCAAGUAAUUCAGGAUAGGU miR-151a-3p MIMAT0000757 CUAGACUGAAGCUCCUUGAGG miR-451a MIMAT0001631 AAACCGUUACCAUUACUGAGUU miR-652-3p MIMAT0003322 AAUGGCGCCACUAGGGUUGUG

[0203] Estimate: The estimated values ​​of the coefficients of the predictor variables.

[0204] Std.Error: The standard error of the coefficient estimate, indicating the uncertainty of the estimate.

[0205] z value: The coefficient estimate divided by its standard error, used to test whether the coefficient is significantly different from zero.

[0206] 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.

[0207] Based on the comprehensive results, the model is established as follows:

[0208] logit(P)=-1.21+0.84×(miR-143-3p)-0.78×(miR-26b-5p)-1.38×(miR-151a-3p)-2.08×(miR-451a)+1.44×(miR-652-3p)

[0209] Among them, miR-143-3p, miR-26b-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.

[0210] 3.4 Model Evaluation

[0211] 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 0.38. 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). 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 intrahepatic cholangiocarcinoma).

[0212] 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.

[0213]

[0214] Taking the logarithm of both sides and solving gives:

[0215]

[0216] Test set ROC curve ( Figure 1 ) can determine the diagnostic efficacy of the above model for intrahepatic cholangiocarcinoma. When the threshold is set to 0.38, the area under the ROC curve of the miRNA combination marker for diagnosing intrahepatic cholangiocarcinoma is 0.84, and the sensitivity and specificity of the prediction model for distinguishing liver cancer and the control group are 81.8% and 84.6%, respectively.

[0217] References:

[0218] [1] Jensen, J., and T. "Normalization of real-time quantitative RT-PCR data: a model based variance estimation approach to identify genes suited for normalization - applied to bladder - and colon - cancer data - sets." Cancer Res 64.5245(2004):50.

[0219] [2] Vandesompele, Jo, et al. "Accurate normalization of real - time quantitative RT - PCR data by geometric averaging of multiple internal control genes." Genome biology 3(2002):1 - 12.

[0220] [3] Livak, Kenneth J., and Thomas D. Schmittgen. "Analysis of relative gene expression data using real - time quantitative PCR and the 2 - ΔΔCT method." methods 25.4(2001):402 - 408.

[0221] [4] Bauer, David F. "Constructing confidence sets using rank statistics." Journal of the American Statistical Association 67.339(1972):687 - 690.

[0222] [5] Efroymson, Michael Alin. "Multiple regression analysis." Mathematical methods for digital computers(1960):191 - 203.

[0223] [6]Berkson,Joseph."Application of the logistic function to bio-assay."Journal of the American statistical association 39.227(1944):357-365.

[0224] [7]Stone,Mervyn."Cross-validatory choice and assessment ofstatistical predictions."Journal of the royal statistical society:Series B(Methodological)36.2(1974):111-133.

Claims

1. A miRNA combination, characterized in that: The miRNA combination includes any one or more of miR-26b-5p, miR-652-3p or miR-451a.

2. The miRNA combination according to claim 1, characterized in that The miRNA combination also includes miR-151a-3p and / or miR-143-3p.

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

4. The kit according to claim 3, 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 described in claim 1 or 2, preferably, the reagent comprises a primer pair and / or a probe combination.

5. The kit according to claim 3 or 4, characterized in that The kit also includes instructions, which preferably have the following regression model: logit(P)=-1.21+0.84×(miR-143-3p)-0.78×(miR-26b-5p)-1.38×(miR-151a-3p)-2.08×(miR-451a)+1.44×(miR-652-3p) Among them, miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p indicate 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.

6. Use of the miRNA combination according to claim 1 or 2 or the kit according to any one of claims 3-5 in the preparation of a diagnostic agent for diagnosing intrahepatic cholangiocarcinoma; preferably, the intrahepatic cholangiocarcinoma is selected from any one or more of mass-type intrahepatic cholangiocarcinoma, invasive intrahepatic cholangiocarcinoma, peripheral intrahepatic cholangiocarcinoma or intrahepatic peripheral cholangiocarcinoma growing in the bile duct.

7. A diagnostic system for intrahepatic cholangiocarcinoma, 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 or 2; 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.

8. The intrahepatic cholangiocarcinoma diagnostic system according to claim 7, characterized in that: The intrahepatic cholangiocarcinoma 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; preferably, the logistic regression model uses the following formula: logit(P)=-1.21+0.84×(miR-143-3p)-0.78×(miR-26b-5p)-1.38×(miR-151a-3p)-2.08×(miR-451a)+1.44×(miR-652-3p) Among them, miR-143-3p, miR-26b-5p, miR-151a-3p, miR-451a, and miR-652-3p indicate the expression levels of the corresponding miRNAs; (4) When judging, when the value of the analysis result is greater than or equal to the threshold, it is judged as having a high risk of intrahepatic cholangiocarcinoma; when the value of the analysis result is less than the threshold, it is judged as having a low risk of intrahepatic cholangiocarcinoma. Preferably, the threshold is 0.

38.

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 intrahepatic cholangiocarcinoma diagnosis system as claimed in claim 7 or 8 can be realized.

10. A diagnostic device for intrahepatic cholangiocarcinoma, 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 intrahepatic cholangiocarcinoma diagnosis system; Preferably, the system further includes an output device for outputting the diagnosis result.

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