KR20150043790A - Extracting method for biomarker for diagnosis of biliary tract cancer, computing device therefor, biomarker for diagnosis of biliary tract cancer, and biliary tract cancer diagnosis device comprising same - Google Patents

Abstract

The present invention discloses a method for extracting a biomarker for biliary cancer diagnosis, a computing device for the same, and a biomarker for biliary cancer diagnosis and a biliary cancer diagnosis apparatus comprising the same. More specifically, the present invention discloses a method for extracting a biomarker for diagnosis of biliary cancer using microRNA obtained from blood or tissue, a computing device for the same, and a biomarker for diagnosis of biliary cancer and a biliary cancer diagnostic apparatus comprising the same.

Description

TECHNICAL FIELD [0001] The present invention relates to a method for extracting a biomarker for biliary cancer diagnosis, a computing device for the same, a biomarker for diagnosis of biliary cancer, and a biliary cancer diagnostic apparatus including the same. BACKGROUND ART , AND BILIARY TRACT CANCER DIAGNOSIS DEVICE COMPRISING SAME}

The present invention relates to a method for extracting a biomarker for biliary cancer diagnosis, a computing apparatus for the same, and a biomarker for diagnosis of biliary cancer and a biliary cancer diagnostic apparatus comprising the same. More particularly, the present invention relates to a method for extracting biomarkers for diagnosis of biliary cancer using microRNAs obtained from blood or tissues, a computing device for the method, and a biomarker for diagnosis of biliary cancer and a device for diagnosing biliary cancer.

The bile duct is a tube that sends the bile made from the liver to the duodenum. When the branches come out from the liver, most of the left and right bile ducts join together as the branches gather together toward one branch. The bile ducts are divided into intrahepatic bile ducts passing through the liver and extrahepatic bile ducts extending to the duodenum. The gallbladder is called the gallbladder, and the bile ducts inside and outside the bile duct are called bile ducts.

Cholangiocarcinoma, also called cholangiocarcinoma, is a malignant tumor arising in the epithelium of the bile duct, and can be divided into two types according to the site of onset: intrahepatic bile duct cancer and extrahepatic bile duct cancer. Generally, biliary cancer refers to cancer that occurs mainly in extrahepatic bile ducts. Refer to both intrahepatic bile duct cancer and extrahepatic bile duct cancer unless otherwise indicated herein.

It is not easy to accurately identify and diagnose cholangiocarcinoma because it spreads as it penetrates into surrounding tissues and does not form a clear tumor mass. Recently, with the development of diagnostic imaging technology, abdominal ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), percutaneous transluminal cholangiography (PTC), percutaneous spinal biliary drainage (PTBD), endoscopic retrograde cholangiopancreatography ERCP), or angiography, to diagnose bile duct cancer. However, since these diagnostic techniques are expensive and complicated to diagnose and virtually no useful for early diagnosis, development of a biomarker for diagnosis of biliary cancer for early diagnosis is urgent.

In this regard, over the past two decades, there have been dozens of biomarkers for other carcinomas, but biomarkers for bile duct cancer have not yet been commercialized.

On the other hand, microRNA (miRNA) refers to a short single-stranded non-coding RNA molecule composed of about 17 to 25 nucleotides. MicroRNAs are known to regulate the expression of protein-producing genes by interfering with the transcription of target mRNA (gene) or degrading mRNA. MicroRNAs are known to exist not only in tissues but also in blood.

In addition, development of biomarkers using tissue or blood samples is required for ease of handling and diagnosis. Blood samples are particularly advantageous.

In order to solve the above problems, the present invention provides a method for extracting biomarkers for diagnosis of biliary cancer using microRNAs obtained from blood or tissues, and a computing device therefor. In addition, the present invention provides a biomarker for biliary cancer diagnosis and a device for diagnosing bile cancer comprising the biomarker.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, unless further departing from the spirit and scope of the invention as defined by the appended claims. It will be possible.

According to an aspect of the present invention, there is provided a method for extracting biomarkers for diagnosis of biliary cancer, the method comprising: calculating an interaction score obtained by quantifying the degree of complementarity between a microRNA and a gene; Determining n microRNAs and gene pairs having a high interaction score; And extracting microRNAs that are a pair of genes common to the genes specifically expressed in the biliary cancer patients among the n microRNAs and gene pairs.

Hi-miR-93-5p, hsa-miR-106b-5p, hsa-miR-155-p, and hsa-miR- 5p, hsa-miR-181a-5p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-222-3p and hsa-miR-331-3p.

Hi-miR-93-5p, hsa-miR-106b-5p, hsa-miR-155-p, and hsa-miR-15-5p using blood as a biological sample, 5p, hsa-miR-181a-5p, and hsa-miR-222-3p.

An apparatus for diagnosing a bile duct cancer according to an embodiment of the present invention includes the above-described biomarker.

The solutions proposed by the present invention are not limited to the above-mentioned solutions, and other solutions not mentioned can be clearly understood by those skilled in the art from the following description. It will be possible.

The present invention can provide a diagnostic biomarker for bile duct cancer having high specificity and sensitivity. In addition, the present invention can provide a method for discovering a biomarker for diagnosis of bile duct cancer.

The technical effects to be achieved by the present invention are not limited to the technical effects mentioned above, and other technical effects which are not mentioned will be clearly understood by those skilled in the art from the following description It will be possible.

1 is a block diagram of a computing device in accordance with the present invention.
Fig. 2 is a conceptual diagram for explaining an example of calculating an interaction score between miRNA and gene.
3 is a flowchart of a method of calculating an interaction score.
4 is a conceptual diagram for explaining a method of calculating a correlation value between a similar miRNA and a specific gene using a similarity database.
5 is a flowchart of a method of calculating correlation values between similar miRNAs and genes using a similarity database.
6 is a conceptual diagram for explaining a method of calculating a correlation value between a neighboring miRNA and a specific gene using the miRNA cluster database.
7 is a flowchart of a method of calculating a weight value between adjacent miRNAs and specific genes using the miRNA cluster database.
8 is a conceptual diagram for explaining a method of calculating a correlation value between a specific miRNA and a transcription regulatory gene using a transcription factor database.
FIG. 9 is a flow chart of a method for calculating a weight value between a specific miRNA and a transcription regulatory gene using a transcription factor database.
10 is a flowchart of a method for discovering biomarkers for biliary cancer patients based on an integrated analysis algorithm for digging biomarkers.
11 shows a hierarchical cluster analysis result using the data GSE 32957 as a heat map.
FIG. 12 is a conceptual diagram for analysis of small RNA sequencing data, which is one of specific examples of use of next generation dielectric sequencing.
13 is a diagram illustrating hierarchical cluster analysis results using next generation genome sequencing data showing the expression pattern of nine microRNAs expressed in a tissue sample according to the present invention.
FIG. 14 is a diagram illustrating hierarchical cluster analysis results using next generation genome sequencing data showing the expression patterns of six microRNAs expressed in a blood sample according to the present invention. FIG.

Hereinafter, a computing apparatus related to the present invention will be described in detail with reference to the drawings.

The suffix "module" and " part "for the components used in the following description are given or mixed in consideration of ease of specification, and do not have their own meaning or role.

The present invention discloses a computing device (100) using an integrated analysis algorithm for biomarker digging and a biomarker discovered through the computing device (100). Here, the computing device 100 described herein may include a high-speed computing device using electronic circuits such as a personal computer, a workstation, and a supercomputer. Portable devices such as smart phones, PDAs, laptops, and the like, which include a central processing unit and can perform computational processing, may be included in the computing device, even if they are not stationary devices such as computers, workstations, have.

1 is a block diagram of a computing device in accordance with the present invention. 1, a computing device 100 according to the present invention may include a storage unit 110, a user input unit 120, a communication unit 130, and a control unit 140.

The storage unit 110 may store a program for the operation of the control unit 140 and temporarily store input / output data (e.g., a database). Furthermore, the storage unit 110 may store data transmitted and received while the communication unit 130 performs communication.

The storage unit 110 may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD storage unit 110 ), A random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read- , A magnetic storage unit, a magnetic disk, and an optical disk.

The user input unit 120 receives a user input from a user. The user input unit 120 may include a keyboard, a mouse, and the like.

The communication unit 130 receives data from outside or transmits data to the outside through communication. The communication unit 130 according to the present invention can perform a role of receiving various databases from a remote server.

The control unit 140 controls the overall operation of the computing device 100 and performs various operations. The control unit 140 according to the present invention can calculate an interaction score and a correlation value to be described later, and perform an operation for extracting a biomarker for biliary cancer diagnosis.

The computing device 100 according to the present invention may further include a display unit 150 for outputting information. The display unit 150 may serve as an output device for displaying the input of the user and outputting the operation result of the control unit 140. [ The display unit 150 may be a device such as a monitor that assists the computing device 100.

The computing device 100 as described above can be applied to a case where all or some of the embodiments are selectively combined so that various modifications of the following embodiments can be made, .

The biomarker detection method for biliary cancer diagnosis using the above-described computing device 100 will be described in detail.

The integrated analysis algorithm for exploring the biomarkers described in the present invention may be a combination of a differential expression gene analysis algorithm and a microRNA target gene analysis algorithm.

First, the differential expression genetic algorithm will be described. Differential expression genetic algorithms are algorithms for statistically finding genes that are overexpressed or under-expressed in biliary cancer patients differently from normal individuals. One linear model is used to find genes that can distinguish between normal and patient groups (References Statistical Applications in Genetics and Molecular Biology , Vol. 3, No. 1, Article 3).

Differential gene analysis algorithms can be broadly divided into data normalization and statistical analysis. The data normalization step is a step of integrating and correcting microarray data on the entire human gene obtained from normal and patient groups. For data normalization, a Robust Multichip Average (RMA) algorithm can be used (see Biostatistics , Vol. 4, No. 2, 249-264).

The statistical analysis step is a step of selecting a gene having a statistically significant difference in the expression level between two groups (that is, a normal group and a patient group) using the standardized data using a linear model. Statistical probability method (False Discovery Rate) FDR (Reference Journal of the Royal Statistical Society , Series B ( Methodological ) , Vol. 57, No. 1, 289-300) can be used to select genes with a corrected q-value of 0.01 or less.

The inventive computing device 100 may utilize a list of genes that are specifically expressed (over- or under-expressed) in patients with biliary cancer using a differential expression gene analysis algorithm to discover biomarkers for bile duct cancer diagnosis. Since it is a known technology to discover a gene list specifically expressed in a patient with biliary cancer using a differential expression gene analysis algorithm, a detailed description thereof will be omitted.

Next, the microRNA target gene analysis algorithm will be described. The microRNA target gene analysis algorithm described in the present invention is based on the microRNA target gene prediction calculated value obtained from the existing microRNA database, the calculated correlation value between the microRNA and the expression pattern of the gene obtained through the microarray experiment, and the biological mechanism And a weight calculation value according to the weight of the microRNA.

Hereinafter, the calculation method of the microRNA target gene prediction calculation value (or interaction score), the correlation calculation value, and the calculation value of the weight calculation value will be described in detail. For convenience of explanation, it is assumed that miRNAs and genes described in the present invention are equivalent to microRNAs and genes, respectively.

Micro RNA  Target gene Predicted value  calculate

The computing device 100 according to the present invention can calculate an interaction score that quantifies the degree of complementary binding between the microRNA and its target gene. Interaction scores can be used to determine the likelihood that complementarity between the microRNA and its target gene will occur. A method of calculating an interaction score will be described in detail with reference to the drawings described later.

FIG. 2 is a conceptual diagram for explaining an example of calculating an interaction score between a miRNA and a gene, and FIG. 3 is a flowchart of a method of calculating an interaction score.

Referring to FIGS. 2 and 3, first, the computing device 100 generates a database in which a prediction score between a miRNA and a gene is statistically calculated using at least one miRNA Target Prediction tool (S310).

The miRNA target prediction tool may be a software tool that quantifies the degree of binding of a target gene and an miRNA pair capable of inhibiting the process of making a target gene complementary to the target gene, have. Examples of miRNA target prediction tools for obtaining prediction scores of gene-miRNA pairs include Targetscan, miRDB, DIANA-microT, PITA, miRanda MicroCosm, RNAhybrid, PicTar, RNA22 and the like. A brief description of each miRNA target prediction tool is provided in Table 1.

Tool name Tool description (usage information) Reference site Targetscan Using sequence similarity information and conservation information http://www.ncbi.nlm.nih.gov/ pubmed / 18955434 miRDB Sequence similarity information, thermodynamic stability information, and conservation information http://www.ncbi.nlm.nih.gov/ pubmed / 18426918 DIANA-microT Use sequence similarity information and thermodynamic stability information http://www.ncbi.nlm.nih.gov/ pubmed / 15131085 PITA Use sequence similarity information and thermodynamic stability information http://www.ncbi.nlm.nih.gov/ pubmed / 17893677 miRanda Use of thermodynamic stability and conservation information http://www.ncbi.nlm.nih.gov/ pubmed / 14709173 MicroCosm Use of thermodynamic stability information and conservation information http://www.ebi.ac.uk/enright- srv / microcosm / htdocs / targets / v5 / info . html RNAhybrid Use of thermodynamic stability information http://www.ncbi.nlm.nih.gov/ pubmed / 15383676 PicTar Using sequence similarity information and conservation information http://www.ncbi.nlm.nih.gov/ pubmed / 15806104 RNA22 Using sequence pattern information http://www.ncbi.nlm.nih.gov/ pubmed / 16990141

With a target prediction tool, predictive scores between various genes that can complement complementary miRNAs can be scored. The smaller the predictive score, the lower the likelihood of complementary binding between miRNA and gene.

The target prediction tool may be driven by the computing device 100 according to the present invention and a database obtained by statistically calculating the predicted score of the miRNA-gene pair by the calculation processing of the control unit 140 may be obtained, no. The computing device 100 according to the present invention may acquire a database that statistically maps predicted scores of miRNA-gene pairs from a remote server using a target prediction tool.

In order to increase the reliability of the predictive score between miRNA-gene pairs, it is desirable to acquire a plurality of databases using a plurality of target prediction tools rather than using one target prediction tool. In FIG. 2, PITA, DIANA-microT, TargetScan, MicroCosm, miRDB, and miRanda are used as target prediction tools.

When a plurality of databases obtained by statistically predicting the predicted scores of the miRNA-gene pairs are obtained by using a plurality of target prediction tools, the controller 140 calculates the predicted score of the miRNA-gene pairs in order to normalize the database The normalized score can be calculated based on the ranking of the score (S320).

As in the example shown in Table 1, since information used by the miRNA target prediction tool is different and different units can be applied for assigning a prediction score to each database, if a plurality of databases are to be used, normalization thereof is indispensable . In order to normalize the predictive score of the miRNA-gene pair, the control unit 140 ranks the miRNA-gene pairs based on the predicted scores of the miRNA-gene pairs for each database, converts them into standard scores, The standardized scores of the two can be summed to obtain a normalized score. Equation 1 illustrates the mathematical expression used to obtain the normalized score.

In Equation (1), i is an i-th database and n is a number of databases (for example, n = 6, since six databases are acquired through six prediction tools in Fig. 2) , T _i is the total number of miRNA-gene pairs in the i-th database _, and R _{i, j} is the rank of the j-th miRNA-gene pair in the i-th database.

For example, in a first database in which there are 100 miRNA-gene pairs, if the predicted score of the miRNA1-gene1 pair is 20 out of 100 pairs, then the standard score of the miRNA1-gene1 pair in the first database is (100 + 1- 20) /100=0.81. The control unit 140 may calculate the normalization score of the miRNA1-gene1 pair by adding the standard scores of the miRNA1-geng1 pair in the second to the n-th databases.

Thereafter, the control unit 140 may rank the miRNAs for the specific gene and the genes for the specific miRNA based on the normalization score (S330).

For example, when the miRNAs capable of complementary binding with gene 1 are miRNA1, miRNA3, and miRNA4, the control unit 140 determines that the complementary binding with gene 1 is strong (i.e., , And the normalization score is high). In Fig. 2, the normalization score between miRNA1-gene1 is 0.4 and the normalization score between miRNA3-gene1 is set to 0.6, indicating that miRNA1 has a second rank for gene1 and miRNA3 has a first rank.

In this way, the ranking of genes for a particular miRNA can be determined. For example, when the genes capable of complementary binding with miRNA1 are gene1 and gene3, the control unit 140 determines that the complementarity binding with the miRNA1 is strong (i.e., the normalization score is high) based on the normalization scores of miRNA1-gene1 and miRNA1- ) You can determine the order of genes in order. In Fig. 2, the normalization score between miRNA1-gene1 is 0.4 and the normalization score between miRNA1-gene3 is set to 0.5. For miRNA1, gene1 has the second rank and gene3 has the first rank.

Thereafter, the controller 140 may calculate an interaction score between the gene-miRNAs based on the ranking of genes and miRNAs (S340). Equation (2) illustrates the equation used to calculate the interaction score.

In Equation (2), t _mi is the number of pairs of the i-th miRNA with the gene (number of miRNA _i -gene), t _gj is the number of gene _j- miRNA pair with the miRNA of the _jth gene, r _mi is a ranking score of the i-th miRNA for the j-th gene, and _rgj is the ranking score of the j-th gene for the i-th miRNA.

Correlation operation

The above-mentioned target miRNA prediction tool does not have a database related to all miRNAs and all genes in the human body. In the present invention, it is possible to obtain interaction points of various miRNAs and genes that can not be predicted from a target miRNA prediction tool, using similarity among miRNAs, surrounding influence between miRNAs, and transcription factors of genes.

Example 1 - Weight calculation due to correlation

The computing device 100 according to the present invention obtains a correlation value between the specific miRNA obtained through the microarray experiment and the expression pattern of the specific gene and predicts the correlation value between the specific miRNA and the similar miRNA similar to the specific miRNA have. The calculation of the correlation value between the pseudo miRNA and the specific gene will be described in detail with reference to the following figures.

FIG. 4 is a conceptual diagram for explaining a method of calculating a correlation value between a similar miRNA and a specific gene using a similarity database, FIG. 5 is a diagram illustrating a method of calculating a correlation value between a miRNA and a gene using a similarity database Fig.

First, when experimental data including gene expression profiles (Genes Expression Profiles) and miRNA expression profiles (miRNAs Expression Profiles) obtained through microarray experiments are inputted (S510), the controller 140 determines, based on the inputted experiment data, A correlation between a specific miRNA and a specific gene can be calculated (S520).

Explaining the microarray experiment first, a gene microarray is also called a DNA microarray as a tool for measuring the amount of gene expression in whole or part of a living organism. With the introduction of gene microarrays, the ability to observe genes can be extended to whole organisms at the level of individual genes, enabling life to be studied as a system. In addition, since gene microarrays are basically performed on a large scale by parallelizing existing gene detection techniques, there has also been a dramatic change in the way data is processed and analyzed. The gene microarray is generally performed by first immobilizing several thousands to tens of thousands of gene sequences on a slide surface of about 1 cm 2, extracting RNA from cells collected under various experimental conditions, reverse-transcribing the DNA with a fluorescent material . Next, the labeled DNA is hybridized to a microarray, scanned to form an image, and an image analysis program is used to measure the intensity of the color development depending on the fluorescent material for each gene position, And computer science to compare with quantified gene expression data.

Through such microarray experiments, the degree of expression between a specific miRNA and a specific gene can be quantified. The correlation between a specific miRNA and a specific gene may be Pearson's correlation, which represents the relative amount of increase or decrease in the expression level of a specific miRNA as the expression level of a specific gene increases.

Then, the computing device 100 may acquire a similarity value of a similar miRNA similar to a specific miRNA using the miRNA Similarity DataBase (S530). The miRNA similarity database may contain similarity values that quantify the functional similarity between miRNAs. The miRNA similarity database may have been obtained through a BLAST or BLAT tool already known.

Then, the computing device 100 may calculate the correlation between the similar miRNA and the specific gene using the similarity value (S540). A linear regression model using similarity values can be used to calculate the weights between similar miRNAs and genes.

Example 2 - Correlation operation considering miRNA surrounding influence

The computing device 100 according to the present invention may calculate a correlation value for a neighboring miRNA that forms a cluster with a specific miRNA. The calculation of the correlation value in consideration of the influence between the miRNAs will be described later with reference to the drawings.

FIG. 6 is a conceptual diagram for explaining a method of calculating a correlation value between an adjacent miRNA and a specific gene using the miRNA cluster database, and FIG. 7 is a conceptual diagram illustrating a method of calculating a weight value between adjacent miRNAs and specific genes using the miRNA cluster database And the like.

First, when experimental data including gene expression profiles (Genes Expression Profiles) and miRNA expression profiles (miRNAs Expression Profiles) obtained through microarray experiment are input (S710), the controller 140 determines, based on the inputted experiment data, Correlation between a specific miRNA and a specific gene can be calculated (S720).

Thereafter, the computing device 100 may extract an adjacent miRNA located within an effective effective distance from the specific miRNA input as experimental data using the miRNA Cluster DataBase (S730). The miRNA cluster database includes distance data between miRNAs, which allows computing device 100 to determine that miRNAs located within 10 kb (kilobase) of a particular miRNA are at an effective effective distance. However, the effective distance does not necessarily have to be set to 10kb, which may vary depending on the selection.

After that, the computing device 100 can calculate a correlation value between a neighboring miRNA and a gene located within an effective effective distance with a specific miRNA (S740). For example, in the example shown in FIG. 6, when miRNA ₁ is a contiguous miRNA of miRNA _i , the computing device 100 may calculate a correlation value for miRNA ₁ -gene _m .

Example 3 - Correlation Operation Considering Transcription Factor

The computing device 100 according to the present invention can calculate a correlation value in consideration of a transcription factor between genes. The calculation of the correlation value in consideration of the transcription factor between genes will be described later with reference to the drawings.

FIG. 8 is a conceptual diagram for explaining a method of calculating a correlation value between a specific miRNA and a transcription regulatory gene using a transcription factor database, and FIG. 9 is a conceptual diagram illustrating the relationship between a specific miRNA and a transcription regulatory gene Fig.

First, when experimental data including gene expression profiles (Genes Expression Profiles) and miRNA expression profiles (miRNAs Expression Profiles) obtained through microarray experiment are inputted (S910), the controller 140 determines, based on the inputted experiment data, A correlation between a specific miRNA and a specific gene can be calculated (S920).

Thereafter, the computing device 100 can identify the presence of a transcriptional regulatory gene that specifically binds to the transcriptional regulatory region DNA of a specific gene and activates or inhibits the transcription of the specific gene from the transcription factor database (Transcription Factor DataBase) S930).

If a transcriptional regulatory gene of a particular gene is present, the computing device 100 may calculate the correlation value between the transcriptional regulatory gene and the miRNA (S940). As an example, in the example shown in Figure 8, in the case of a gene transcriptional regulatory genes is a gene _{m n,} computing device 100 is based on a correlation value between _a miRNA -gene _n, between _a miRNA -gene _m The correlation value can be calculated.

Based on the correlation values computed in Examples 1-3, the computing device 100 calculates the interaction score for the gene of the pseudo miRNA, the interaction score for the gene of the adjacent miRNA, and the interaction score for the miRNA of the transcription control gene .

Once the miRNA-gene interaction score is derived through the microRNA target gene analysis algorithm, the computing device 100 can detect the biomarker using the differential expression gene list of the biliary cancer patient using the differential expression gene analysis algorithm .

A method for analyzing a biomarker for biliary cancer patients based on the above-described integrated analysis algorithm for digging biomarkers will be described in detail.

10 is a flowchart of a method for discovering biomarkers for biliary cancer patients based on an integrated analysis algorithm for digging biomarkers. For convenience of explanation, the computing device 100 is assumed to be storing a list of genes that are differentially expressed (e.g., overexpressed or underexpressed) in a biliary cancer patient using a differential expression gene analysis algorithm.

Referring to FIG. 10, the computing device 100 may calculate an interaction score between miRNA-genes using a microRNA target gene analysis algorithm (S1010). The steps of calculating the interaction score are the same as those described above with reference to FIGS. 4 to 9, so a detailed description thereof will be omitted.

Thereafter, the computing device 100 selects an miRNA-gene pair having an interaction score within the n-th uppermost (S1020), and selects a normal person and a normal person in the biliary cancer patient based on the gene and differential expression gene analysis algorithm in the selected miRNA- A set of miRNAs that are pairs of intersections of differently expressed gene lists can be determined as a biomarker for biliary cancer diagnosis (S1030). In other words, a set of miRNAs, a pair of genes specifically expressed differently from normal individuals, can be determined as a biomarker for biliary cancer diagnosis in a differential expression gene analysis algorithm, while having a high interaction score.

In another example, the computing device 100 determines m genes in descending order of the interaction score of the miRNA-gene pairs, and calculates the intersection of the gene lists that are specifically expressed differently from the normal individuals in the biliary cancer patients based on the differential expression gene analysis algorithm Can be determined as biomarkers for the diagnosis of biliary cancer.

Using the six samples of miRNA prediction tools, Targetscan, miRDB, DIANA-microT, PITA, miRanda and MicroCosm, and using tissue as a biological sample, the top n genes of the miRNA- Hs-miR-93-5p, hsa-miR-106b-5p, hsa-miR-155-5p, and hsa-miR- hsa-miR-181a-5p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-222-3p and hsa-miR-331-3p can be determined as biomarkers for bile duct cancer diagnosis .

When blood is used as a biological sample, hsa-miR-21-5p, hsa-miR-93-5p, hsa-miR-106b-5p, hsa-miR- And hsa-miR-222-3p are determined as biomarkers for diagnosis of bile duct cancer.

The base sequence of each miRNA belonging to the above-mentioned biomarker is shown in Table 2 below.

Mature_id miRNA_id Sequence hsa-miR-21-5p hsa-mir-21 UAGCUUAUCAGACUGAUGUUGA hsa-miR-93-5p hsa-mir-93 CAAAGUGCUGUUCGUGCAGGUAG hsa-miR-106b-5p hsa-mir-106b UAAAGUGCUGACAGUGCAGAU hsa-miR-155-5p hsa-mir-155 UUAAUGCUAAUCGUGAUAGGGGU hsa-miR-181a-5p hsa-mir-181a-1, hsa-mir-181a-2 AACAUUCAACGCUGUCGGUGAGU hsa-miR-200a-3p hsa-mir-200a UAACACUGUCUGGUAACGAUGU hsa-miR-200b-3p hsa-mir-200b UAAUACUGCCUGGUAAUGAUGA hsa-miR-222-3p hsa-mir-222 AGCUACAUCUGGCUACUGGGU hsa-miR-331-3p hsa-mir-331 GCCCCUGGGCCUAUCCUAGAA

The experimental procedure and results of the biomarker for diagnosis of bile duct cancer obtained by the above-mentioned results will be described in detail.

Biliary cancer patient samples and microarray experiments

He has undergone a healing abstinence treatment at the Liver Cancer Institute and Zhongshan Hospital, Fudan University, China, from 2002 to 2003, and at the Kanazawa University Hospital, Ishikawa, Japan, Intrahepatic cholangiocellular carcinoma (ICC) and combined hepatocellular cholangiocarcinoma (CHC) tissues were obtained from patients with prior consent.

Samples were approved by the Institutional Review Board of the corresponding laboratory and were recorded by the Secretariat of the National Institutes of Health (NIH) of Human Subjects Research. A total of 23 ICC and CHC cases were used to generate mRNA and microRNA signatures. Early diagnosis was based on serum test and imaging, and pathologists confirmed it histopathologically. Characterization of 68 white ICC patients from an independent group has recently been described (Hepatology, Vol. 56, No. 5, 1792-803).

Verification of the biomarker set of the present invention

In the biomarker set for the tissue sample of the present invention, a total of 35 patients, including 25 patients with biliary cancer and 10 normal subjects, Using the blood collected from the above-mentioned subjects, GSE Expression Omnibus (GEA) data GSE32957 was used to determine by hierarchical clustering (euclidean distance, complete method). As a result, sensitivity of 96% (24/25) and specificity of 100% (10/10) were excellent in bile duct cancer. 11 is a diagram showing a hierarchical cluster analysis result when the data GSE 32957 is used as a heat map. In Fig. 11, a red bar located at the top of the heat map indicates a cancer patient, and a blue bar indicates a normal person.

In addition, verification of the biochemical marker for a tissue sample and blood sample of the present invention was separately performed. First, biomarkers for tissue samples were composed of 2 patients with biliary cancer and 2 patients with normal biomarkers. The biomarkers for blood samples were 8 patients with biliary cancer and 2 normal patients. Using hierarchical clustering (euclidean distance) method using small RNA sequencing data, which is Next Generation Sequencing (NGS) method, using tissues and blood collected from the subject, complete method). A general description of such small RNA sequencing data analysis is shown in FIG. As a result, the sensitivity of biomarker to tissue samples was 100% (2/2) and specificity was 100% (2/2) for bile duct cancer. In this case, small RNA sequencing data Fig. 13 shows hierarchical cluster analysis results. In addition, the sensitivity of the biomarker to blood samples was 75% (6/8) and the specificity was 50% (1/2) for bile duct cancer. In this case, small RNA sequencing data was used Fig. 14 shows hierarchical cluster analysis results. In FIGS. 13 and 14, a red bar located at the top of the heat map indicates a cancer patient, and a blue bar indicates a normal person.

On the other hand, the above-mentioned biomarkers are used as devices for diagnosing bile duct cancer including them. Examples of the devices for diagnosing biliary cancer include a diagnostic chip, a diagnostic kit, a quantitative PCR (qPCR) device, a field test (POCT) device, and a sequencer. In the diagnostic chip, the diagnostic kit, the quantitative PCR (qPCR) equipment, the field test (POCT) equipment, and the sequencer, portions other than the biomarker set may be known ones.

According to an embodiment of the present invention, the above-described methods can be implemented as code readable by a processor on a medium on which the program is recorded. Examples of the medium that can be read by the processor include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage, etc., and may be implemented in the form of a carrier wave (e.g., transmission over the Internet) .

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Claims (13)

A step of computing an interaction score quantifying the degree of complementarity between the microRNA and the gene;
Determining n microRNAs and gene pairs having a high interaction score; And
A step of extracting microRNAs that are a pair of genes common to the genes specifically expressed in the biliary cancer patients among the n microRNAs and gene pairs
A method for extracting a biomarker for diagnosis of biliary cancer. The method according to claim 1,
The step of calculating the interaction score includes:
Obtaining at least one database of statistical predicted scores between the microRNA and the gene;
Calculating a normalized score from the predicted score between the microRNA and the gene;
Calculating the binding order of the microRNAs and the binding order of the microRNA-specific genes on the basis of the normalized scores; And
Calculating the interaction score based on the binding order of the microRNAs and the binding order of the genes
A method for extracting a biomarker for diagnosis of biliary cancer. 3. The method of claim 2,
Wherein the at least one database is generated using a microRNA target prediction tool. The method of claim 3,
Wherein the microRNA target predicting tool comprises at least one of Targetscan, miRDB, DIANA-microT, PITA, miRanda MicroCosm, RNAhybrid, PicTar and RNA22. 3. The method of claim 2,
Wherein the normalized score is calculated based on a predicted score ranking of microRNA and gene pairs in the one or more databases. 6. The method of claim 5,
Wherein the normalized score is calculated according to the following equation (1): " (1) "
[Equation 1]

(Where i is the i-th database, n is the number of databases, T _i is the total number of microRNA-gene pairs in the i-th database, R _{i ,} MicroRNA-gene pairs) 6. The method of claim 5,
Wherein the interaction score is calculated on the basis of the rank of microRNAs by gene and the rank of genes by microRNA on the basis of the normalized scores. 8. The method of claim 7,
Wherein the interaction score is calculated according to the following equation (2).
&Quot; (2) "

(In the above equation (2)
t _mi is a number of the i-th pair of micro-RNA genes (number of miRNA -gene _i), t is the number of pairs with _gj of the j-th micro RNA gene (number of gene -miRNA _j), r _mi is the j-th The normalized score of the i-th microRNA to the gene, and r _gj denotes the rank of the j-th normalized score of the i-th microRNA) A storage unit for storing data; And
And a control unit for operation,
Wherein the controller calculates an interaction score obtained by quantifying the degree of complementarity between the microRNA and the gene, determines n microRNAs and gene pairs having the highest interaction score, and determines the number of microRNAs and gene pairs among the n microRNAs and gene pairs And extracting microRNAs that are a pair of genes that are expressed in common with the specifically expressed genes. Hs-miR-181a-5p, hsa-miR-159p, hsa-miR- miR-200a-3p, hsa-miR-200b-3p, hsa-miR-222-3p, and hsa-miR-331-3p. Hsa-miR-181a-5p, and hsa-miR-159p, hsa-miR- A biomarker for the diagnosis of bile duct cancer, including -mR-222-3p. An apparatus for diagnosing a bile duct cancer, comprising the biomarker of claim 10 or claim 11. 13. The method of claim 12,
Wherein the device is one of a diagnostic chip, a diagnostic kit, a quantitative PCR (qPCR) instrument, a field test (POCT) instrument and a sequencer.

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