KR102161511B1 - 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 diagnosis of biliary tract cancer, a computing device therefor, and a biomarker for diagnosis of biliary tract cancer, and a biliary cancer diagnosis apparatus including the same. More specifically, the present invention discloses a method of extracting a biomarker for diagnosis of biliary tract cancer using microRNA obtained from blood or tissue, a computing device therefor, and a biomarker for diagnosis of biliary tract cancer, and a biliary cancer diagnosis apparatus including the same.

Description

Extraction method of biomarker for diagnosis of biliary tract cancer, computing device therefor, biomarker for diagnosis of biliary tract cancer, and biliary cancer diagnosis device including the same , AND BILIARY TRACT CANCER DIAGNOSIS DEVICE COMPRISING SAME}

The present invention relates to a method for extracting a biomarker for diagnosis of biliary tract cancer, a computing device therefor, and a biomarker for diagnosis of biliary tract cancer, and a biliary cancer diagnosis apparatus including the same. More specifically, the present invention relates to a method for extracting a biomarker for diagnosis of biliary tract cancer using microRNA obtained from blood or tissue, a computing device therefor, and a biomarker for biliary cancer diagnosis, and a biliary tract cancer diagnosis apparatus including the same.

The bile ducts are the tubes that send bile produced by the liver to the duodenum, and they become thicker while gradually joining as branches gather toward one branch in the liver, and when they come out of the liver, most of the bile ducts on the left and right join together. The bile duct is divided into an intrahepatic bile duct that passes through the liver and an extrahepatic bile duct that extends from the liver to the duodenum. A pouch that temporarily stores and concentrates bile among the extrahepatic bile ducts is called the gallbladder, and these internal and external bile ducts and gallbladder are collectively called the biliary tract.

Biliary cancer, also called bile duct cancer, is a malignant tumor that occurs in the epithelium of the bile duct, and is divided into two types: intrahepatic biliary cancer and extrahepatic biliary cancer. Generally speaking, biliary cancer refers to cancer that occurs mainly in the extrahepatic bile duct. In the present specification, unless otherwise indicated, both intrahepatic biliary cancer and extrahepatic biliary cancer are referred to.

Biliary duct cancer often spreads as if seeping into surrounding tissues and does not form a clear tumor mass, so it is not easy to accurately identify and diagnose the mass. In recent years, with the development of image diagnosis technology, abdominal ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), percutaneous transverse biliary tract (PTC), percutaneous transverse biliary tract drainage (PTBD), endoscopic retrograde biliary tract angiography ( ERCP) or angiography is used to diagnose biliary tract cancer. However, since such image diagnosis technology is expensive and complicated to diagnose, and is practically useless for early diagnosis, it is urgent to develop biomarkers for diagnosis of biliary tract cancer for early diagnosis.

In this regard, there have been dozens of biomarkers for other carcinomas over the past 20 years, but there is no commercialized biomarker for biliary tract cancer yet.

Meanwhile, micro RNA (miRNA) refers to a short single-stranded non-coding RNA molecule composed of about 17 to 25 nucleotides. Micro RNA is known to regulate protein-producing gene expression by interfering with the transcription process of a target mRNA (gene) or by causing the mRNA to be degraded. Micro RNA is known to exist not only in tissues but also in blood.

In addition, it is necessary to develop a biomarker using a tissue or blood sample for ease of handling and diagnosis. Blood samples are particularly advantageous.

In order to solve the above problem, the present invention is to provide a method of extracting a biomarker for diagnosis of biliary tract cancer using microRNA obtained from blood or tissue, and a computing device therefor. In addition, an object of the present invention is to provide a biomarker for diagnosis of biliary tract cancer and an apparatus for diagnosis of biliary tract cancer including the same.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned will be clearly understood by those of ordinary skill in the technical field to which the present invention belongs from the following description. I will be able to.

A method for extracting a biomarker for diagnosis of biliary tract cancer according to an embodiment of the present invention includes: calculating an interaction score obtained by digitizing the degree of complementary binding between micro RNA and genes; Determining n microRNAs and gene pairs having a high interaction score; And extracting microRNAs, which are pairs of genes common to genes specifically expressed in patients with biliary tract cancer, among the n microRNAs and gene pairs.

The biomarker for diagnosis of biliary tract cancer according to an embodiment of the present invention uses tissue as a biological sample, and hsa-miR-21-5p, hsa-miR-93-5p, hsa-miR-106b-5p, hsa-miR-155- 5p, hsa-miR-181a-5p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-222-3p, and hsa-miR-331-3p.

The biomarker for diagnosis of biliary tract cancer according to an embodiment of the present invention uses blood as a biological sample, and hsa-miR-21-5p, hsa-miR-93-5p, hsa-miR-106b-5p, hsa-miR-155- 5p, hsa-miR-181a-5p, and hsa-miR-222-3p.

The apparatus for diagnosing biliary tract cancer according to an embodiment of the present invention includes the above-described biomarker.

The solutions proposed in the present invention are not limited to the above-mentioned solutions, and other solutions not mentioned will be clearly understood by those of ordinary skill in the art from the following description. I will be able to.

In the present invention, a biomarker for diagnosis of biliary tract cancer with high specificity and sensitivity can be provided. In addition, the present invention can provide a method of discovering a biomarker for diagnosis of biliary tract cancer.

The technical effects to be achieved in the present invention are not limited to the technical effects mentioned above, and other technical effects that are not mentioned will be clearly understood by those of ordinary skill in the technical field to which the present invention belongs from the following description. I will be able to.

1 is a block diagram of a computing device according to the present invention.
2 is a conceptual diagram illustrating an example of calculating an interaction score between a miRNA and a gene.
3 is a flowchart of a method of calculating an interaction score.
4 is a conceptual diagram illustrating a method of calculating a correlation value between a miRNA similar and a specific gene using a similarity database.
5 is a flowchart of a method of calculating a correlation value between similar miRNAs and genes using a similarity database.
6 is a conceptual diagram illustrating a method of calculating a correlation value between adjacent miRNAs and a specific gene using a miRNA cluster database.
7 is a flowchart of a method of calculating weight values between adjacent miRNAs and specific genes using a miRNA cluster database.
8 is a conceptual diagram illustrating a method of calculating a correlation value between a specific miRNA and a transcription control gene using a transcription factor database.
9 is a flowchart of a method of calculating a weight value between a specific miRNA and a transcription control gene using a transcription factor database.
10 is a flowchart of a method of discovering a biomarker for a biliary tract cancer patient based on an integrated analysis algorithm for discovering a biomarker.
11 shows a hierarchical cluster analysis result using data GSE32957 as a heat map.
12 is a conceptual diagram of analysis of small RNA sequencing data, which is one of specific application examples of next-generation genome sequencing.
13 is a diagram showing the results of hierarchical cluster analysis using next-generation genome sequencing data showing expression patterns of nine microRNAs expressed in tissue samples according to the present invention.
14 is a diagram showing the results of hierarchical cluster analysis using next-generation genome sequencing data showing expression patterns of six microRNAs expressed in blood samples according to the present invention.

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

The suffixes "module" and "unit" for components used in the following description are given or used interchangeably in consideration of only the ease of preparation of the specification, and do not have meanings or roles that are distinguished from each other.

In the present invention, a computing device 100 to which an integrated analysis algorithm for discovering biomarkers is applied and a biomarker discovered through the computing device 100 are disclosed. Here, the described computing device 100 may include a high-speed operation processing device using an electronic circuit such as a personal computer, a work station, and a super computer. However, even if it is not a fixed device such as a computer, a workstation, or a super computer, a portable device such as a smart phone, PDA, and laptop that includes a central processing unit and can perform arithmetic processing may also be included in the computing device. have.

1 is a block diagram of a computing device according to the present invention. Referring to FIG. 1, the 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 controller 140 and may temporarily store input/output data (eg, a database). Furthermore, the storage unit 110 may store data transmitted and received while the communication unit 130 performs communication.

The storage unit 110 includes a flash memory type, a hard disk type, a multimedia card micro type, and a card type memory (for example, SD or XD storage unit 110 ), etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory) , A magnetic storage unit, a magnetic disk, and an optical disk.

The user input unit 120 serves to receive a user input from a user. The user input unit 120 may include a keyboard and a mouse.

The communication unit 130 serves to receive data from the outside or transmit data to the outside through communication. The communication unit 130 according to the present invention may serve to receive various databases from a remote server.

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

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

The computing device 100 described above is not limited and applicable to the configuration and method of the embodiments to be described below, but all or part of each of the embodiments is selectively combined so that various modifications of the following embodiments can be made. It can also be configured.

A method of discovering a biomarker for diagnosis of biliary tract cancer will be described in detail using the above-described computing device 100.

The integrated analysis algorithm for biomarker discovery described in the present invention may be configured by combining a differentially expressed gene analysis algorithm and a micro RNA target gene analysis algorithm.

First, the differential expression genetic algorithm will be described. The differential expression gene algorithm is an algorithm for statistically meaningful finding of genes that are over-expressed or under-expressed differently from normal subjects in patients with biliary tract cancer.It is one of the advanced statistical methods that can consider various factors. The purpose of this study is to find genes that can distinguish between normal and patient groups using a linear model (Reference Statistical Applications in Genetics and Molecular Biology , Vol. 3, No. 1, Article 3).

The differential expression gene analysis algorithm can be largely divided into a data normalization stage and a statistical analysis stage. The data standardization step is the step of integrating and correcting microarray data for all human genes obtained from the normal and patient groups. For data standardization, a robust multichip average (RMA) algorithm can be used (Reference Biostatistics , Vol. 4, No. 2, 249-264).

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

The computing device 100 of the present invention may use a list of genes that are specifically expressed (overexpressed or underexpressed) in a patient with biliary tract cancer using a differentially expressed gene analysis algorithm in order to discover a biomarker for diagnosis of biliary tract cancer. Since it is already known technology to discover a list of genes that are specifically expressed in patients with biliary tract cancer using a differentially expressed gene analysis algorithm, detailed descriptions thereof will be omitted.

Next, an algorithm for analyzing microRNA target genes will be described. The microRNA target gene analysis algorithm described in the present invention is based on the calculated values for predicting microRNA target genes obtained from the existing microRNA database, the calculated correlation values for the expression patterns of microRNAs and genes obtained through microarray experiments, and biological mechanisms. It is to provide a statistical equation for accurately finding a target gene of a micro RNA by using at least one of the calculated weight values.

Hereinafter, a method of calculating a microRNA target gene prediction calculated value (or interaction score), a correlation calculated value, and a weight calculated value will be described in detail. For convenience of explanation, the descriptions of miRNA and gene in the present invention are assumed to have the same meaning as micro RNA and gene, respectively.

Micro RNA Target gene Predicted value calculate

The computing device 100 according to the present invention may calculate an interaction score obtained by quantifying the degree of complementary binding between the micro RNA and its target gene. The interaction score can be used to determine the likelihood of complementary binding between the microRNA and its target gene. A method of calculating an interaction score will be described in detail with reference to the drawings to be described later.

2 is a conceptual diagram illustrating 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.

2 and 3, first, the computing device 100 uses at least one miRNA Target Prediction tool to generate a database in which prediction scores between miRNAs and genes are statistics It can be obtained (S310).

The miRNA target prediction tool may refer to a software tool that quantifies the degree of binding of a target gene and a miRNA pair capable of complementarily binding to the target gene to inhibit the process of making the target gene into a protein. have. As a miRNA target prediction tool for obtaining prediction scores of gene-miRNA pairs, Targetscan, miRDB, DIANA-microT, PITA, miRanda MicroCosm, RNAhybrid, PicTar, RNA22, etc. may be included. A brief description of each miRNA target prediction tool is listed in Table 1.

Tool name Tool description (usage information) Reference site Targetscan Use of sequence similarity and conservation information http://www.ncbi.nlm.nih.gov/ pubmed /18955434 miRDB Use of sequence similarity information, thermodynamic stability information, and conservation information http://www.ncbi.nlm.nih.gov/ pubmed /18426918 DIANA-microT Use of sequence similarity information and thermodynamic stability information http://www.ncbi.nlm.nih.gov/ pubmed /15131085 PITA Use of 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 Use of sequence similarity information and conservation information http://www.ncbi.nlm.nih.gov/ pubmed /15806104 RNA22 Use of sequence pattern information http://www.ncbi.nlm.nih.gov/ pubmed /16990141

Using a target prediction tool, prediction scores between various genes capable of complementary binding to miRNAs can be scored. The smaller the predicted score, the lower the likelihood of complementary binding between the miRNA and the gene.

The target prediction tool is 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 may be obtained by the computational processing of the control unit 140, but is limited thereto. no. The computing device 100 according to the present invention may obtain a database obtained by statisticalizing prediction scores of miRNA-gene pairs from a remote server using a target prediction tool.

In order to increase the reliability of prediction scores between miRNA-gene pairs, it is preferable to obtain a plurality of databases using a plurality of target prediction tools rather than using a single target prediction tool. In FIG. 2, it is illustrated that PITA, DIANA-microT, TargetScan, MicroCosm, miRDB, and miRanda are used as target prediction tools.

In the case of obtaining a plurality of databases in which the predicted scores of miRNA-gene pairs are statistically obtained using a plurality of target prediction tools, in order to normalize the database, the control unit 140 determines the predicted scores of the miRNA-gene pairs. Based on the ranking of, it is possible to calculate a normalized score (S320).

As in the example shown in Table 1, since the information used by the miRNA target prediction tool is different, and different units may be applied to calculate the prediction score for each database, it is necessary to normalize the information when using multiple databases. I would say this. In order to normalize the predicted score of the miRNA-gene pair, the control unit 140 ranks each database based on the predicted score of the miRNA-gene pair, converts it into a standard score, and converts the miRNA-gene pair in each database. Normalized scores can be obtained by summing the standard scores. Equation 1 illustrates an equation used to obtain a normalized score.

In Equation 1, i is the i-th database, and n is the number of databases (for example, since 6 databases were obtained through 6 prediction tools in FIG. 2, n may be set to 6) , T _i may mean the total number of miRNA-gene pairs in the i-th database _, and R _i,j denote the rank of the j-th miRNA-gene pair in the i-th database.

For example, in the first database in which 100 miRNA-gene pairs exist, if the predicted score of the miRNA1-gene1 pair corresponds to the 20th place out of 100 pairs, 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 a normalization score of the miRNA1-gene1 pair by summing the standard scores of the miRNA1-geng1 pair in the second to nth databases.

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

For example, when miRNAs capable of complementary binding to gene1 are miRNA1, miRNA3, and miRNA4, the control unit 140 has strong complementary binding power with gene1 based on the normalization scores of each of gene1-miRNA1, gene1-miRNA3 and gene1-miRNA4 (i.e. , 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, and it is illustrated that miRNA1 has the second rank and miRNA3 has the first rank with respect to gene1.

In this way, you can determine the ranking of genes for a specific miRNA. For example, when genes capable of complementary binding to miRNA1 are gene1 and gene3, the control unit 140 has strong complementary binding power with miRNA1 based on the normalization scores of each of miRNA1-gene1 and miRNA1-gene3 (that is, a high normalization score). ) You can determine the ranking of the 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, so that gene1 has the second rank and gene3 has the first rank for miRNA1.

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

In Equation 2, t _mi is the number of pairs with the i-th miRNA gene (number of miRNA _i -gene), t _gj is the number of pairs with the j-th gene miRNA (number of gene _j -miRNA), r _mi may mean the rank of the normalization score of the i th miRNA for the j th gene, and r _gj may mean the rank of the normalization score of the j th gene for the i th miRNA.

Correlation operation

The above-described target miRNA prediction tool does not have a database related to all miRNAs and all genes in the human body. In the present invention, using the similarity between miRNAs, peripheral influences between miRNAs, and transcription factors of genes, it is also possible to obtain an interaction score of various miRNAs and genes that cannot be predicted from a target miRNA prediction tool.

Example 1-Weight calculation due to correlation

The computing device 100 according to the present invention can obtain a correlation value for the expression pattern of a specific miRNA and a specific gene obtained through a microarray experiment, and predict a correlation value between a similar miRNA similar to a specific miRNA and a specific gene. have. With reference to the drawings to be described later, the calculation of the correlation value between the miRNA and a specific gene will be described in detail.

FIG. 4 is a conceptual diagram illustrating a method of calculating a correlation value between a similar miRNA and a specific gene using a similarity database, and FIG. 5 is a method of calculating a correlation value between a similar miRNA and a gene using a similarity database Is the flow chart.

First, when experimental data including gene expression profiles and miRNAs expression profiles obtained through microarray experiments are input (S510), the controller 140 is based on the input experimental data, Correlation between a specific miRNA and a specific gene can be calculated (S520).

First of all, when explaining the microarray experiment, a gene microarray is a tool that can measure the amount of gene expression for all or part of a gene in an organism, and is also referred to as a DNA microarray. With the introduction of gene microarrays, the ability to observe genes is extended from the level of individual genes to the entire living organism, allowing the study of living organisms as a single system. In addition, since gene microarrays are basically parallelized and performed on a large scale with existing gene detection techniques, it has brought a drastic change in the way data is processed and analyzed. In general, the method of performing a gene microarray is to first fix thousands to tens of thousands of gene sequences on the surface of a slide of about 1 cm2, then extract RNA from cells collected under various experimental conditions, reverse transcription with DNA, and label with a fluorescent substance. . Subsequently, the labeled DNA is hybridized to a microarray, scanned and shaped into an image, and then the intensity of color development according to the fluorescent substance is measured for each gene location using an image analysis program, thereby determining whether or not the gene is expressed and the degree of expression. And it performs contrast analysis with quantified gene expression data using informatics such as computer engineering.

Through the above microarray experiment, 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 is Pearson's Correlation, which may indicate a relative amount of increase or decrease in the expression level of a specific miRNA according to an increase in the expression level of a specific gene.

Thereafter, the computing device 100 may obtain a similarity value of a miRNA similar to a specific miRNA using a miRNA Similarity Database (S530). The miRNA similarity database may contain similarity values obtained by quantifying functional similarity between miRNAs. The miRNA similarity database may be obtained through a known BLAST or BLAT tool.

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

Example 2-Calculation of correlation considering influence around miRNA

The computing device 100 according to the present invention may calculate a correlation value between a specific miRNA and an adjacent miRNA forming a cluster. For calculating the correlation value in consideration of the influence between miRNAs, reference will be made to the drawings to be described later.

6 is a conceptual diagram for explaining a method of calculating a correlation value between adjacent miRNAs and a specific gene using a miRNA cluster database, and FIG. 7 is a weight value between adjacent miRNAs and a specific gene using a miRNA cluster database This is a flow chart of how to do it.

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

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

Thereafter, the computing device 100 may calculate a correlation value between a gene and an adjacent miRNA located adjacent within an effective distance between the specific miRNA and the effective distance (S740). As an example, in the example shown in FIG. 6, when miRNA _l is an adjacent miRNA of miRNA _i , the computing device 100 may calculate a correlation value for miRNA _l -gene _m .

Example 3-Calculation of correlation considering transcription factor

The computing device 100 according to the present invention may calculate a correlation value in consideration of transcription factors between genes. For calculation of the correlation value in consideration of transcription factors between genes, reference will be made to the drawings to be described later.

8 is a conceptual diagram for explaining a method of calculating a correlation value between a specific miRNA and a transcription control gene using a transcription factor database, and FIG. 9 is a weight value between a specific miRNA and a transcription control gene using a transcription factor database Is a flow chart of how to calculate.

First, when experimental data including gene expression profiles and miRNAs expression profiles obtained through microarray experiments are input (S910), the control unit 140 based on the input experimental data, Correlation between a specific miRNA and a specific gene can be calculated (S920).

Thereafter, the computing device 100 may check the presence or absence of a transcription control gene that activates or inhibits the transcription of a specific gene by specifically binding to the DNA of the transcription control region of a specific gene from the transcription factor database ( S930).

When the transcriptional control gene of a specific gene exists, the computing device 100 may calculate a correlation value between the transcriptional control gene and miRNA (S940). As an example, in the example shown in FIG. 8, when the transcriptional control gene of gene _m is gene _n , the computing device 100 is based on the correlation value between miRNA _a -gene _n, between miRNA _a -gene _m Correlation values can be calculated.

Based on the correlation values calculated through Examples 1 to 3, the computing device 100 calculates an interaction score for a gene of a similar miRNA, an interaction score for a gene of an adjacent miRNA, and an interaction score for a miRNA of a transcription control gene. Can be calculated.

When the interaction score between miRNA-genes is derived through the microRNA target gene analysis algorithm, the computing device 100 may detect the biomarker using the list of specific expressed genes of biliary tract cancer patients using the differentially expressed gene analysis algorithm. .

A method of analyzing a biomarker for a patient with biliary tract cancer based on the above-described biomarker discovery integrated analysis algorithm will be described in detail.

10 is a flowchart of a method of discovering a biomarker for a biliary tract cancer patient based on an integrated analysis algorithm for discovering a biomarker. For convenience of explanation, it is assumed that the computing device 100 stores a list of genes that are specifically expressed (eg, over-expressed or under-expressed) in a biliary tract cancer patient using a differential expression gene analysis algorithm differently from a normal person.

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

Thereafter, the computing device 100 selects a miRNA-gene pair whose interaction score is within the top n-th (S1020), and based on an algorithm for analyzing genes and differentially expressed genes in the selected miRNA-gene pair, the biliary tract cancer patient A set of miRNAs, which are pairs of intersections of differently specifically expressed genes list, may be determined as a biomarker for diagnosis of biliary tract cancer (S1030). That is, even in the differential expression gene analysis algorithm with a high interaction score, a set of miRNAs, which are pairs of genes that are specifically expressed differently from normal people in biliary tract cancer patients, can be determined as biomarkers for biliary tract cancer diagnosis.

As another example, the computing device 100 determines m genes in the order of the highest interaction score of the miRNA-gene pair, and based on the differentially expressed gene analysis algorithm, the intersection of the list of genes specifically expressed differently from the normal person in the biliary tract cancer patient The pair of miRNAs can also be determined as a biomarker for diagnosis of biliary tract cancer.

As a miRNA prediction tool, 6 of Targetscan, miRDB, DIANA-microT, PITA, miRanda, and MicroCosm are used, and when tissue is used as a biological sample, the top n genes (q-value is 0.05) among the interaction scores of the miRNA-gene pair. A set of miRNAs that are pairs of less than or equal to -0.5), hsa-miR-21-5p, hsa-miR-93-5p, hsa-miR-106b-5p, hsa-miR-155-5p, hsa-miR-181a-5p, hsa-miR-200a-3p, hsa-miR-200b-3p, hsa-miR-222-3p, and hsa-miR-331-3p may be determined as biomarkers for diagnosis of biliary tract cancer .

In addition, when using blood as a biological sample, hsa-miR-21-5p, hsa-miR-93-5p, hsa-miR-106b-5p, hsa-miR-155-5p, hsa-miR-181a-5p, And hsa-miR-222-3p is determined as a biomarker for diagnosis of biliary tract cancer.

The base sequence of each miRNA belonging to the above-described 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 process and results of the biomarker for diagnosis of biliary tract cancer obtained by the above results will be described in detail.

Biliary Cancer Patient Sample and Microarray Experiment

Asian-Americans who underwent healing resections in 2002-2003 at Liver Cancer Institute and Zhongshan Hospital (Fudan University in Shanghai, China) and 2008-2010 at Kanazawa University Hospital (Ishikawa, Japan) Intrahepatic cholangiocellular carcinoma (ICC) and combined hepatocellular cholangiocarcinoma (CHC) tissues were obtained from patients with informed consent.

Samples were approved by the corresponding institute's Institutional Review Board and recorded by the U.S. National Institutes of Health (NIH) Secretariat of Human Subjects Research. A total of 23 ICC and CHC cases were used to create mRNA and microRNA signatures. Early diagnosis was made based on serologic examination and imaging, and pathologists confirmed it histologically. Characterization of 68 Caucasian ICC patients from an independent population has been recently described (Hepatology, Vol. 56, No. 5, 1792-803).

Verification of the biomarker set of the present invention

The verification of the determination of biliary tract cancer of the biomarker set for tissue samples of the present invention was performed in a total of 35 patients with biliary tract cancer and 10 normal subjects. Using blood collected from the subject, it was determined by a hierarchical clustering method (hierarchical clustering, euclidean distance, complete method) using GEO (Gene Expression Omnibus) data GSE32957. As a result, the sensitivity to biliary tract cancer was 96% (24/25) and the specificity was 100% (10/10). 11 is a diagram showing a hierarchical cluster analysis result when data GSE32957 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.

Further, verification of the determination of biliary tract cancer of the biomarkers for tissue samples and blood samples of the present invention was performed separately. First, biomarkers for tissue samples were 2 patients with biliary tract cancer and 2 normal people, a total of 4 patients, and biomarkers for blood samples were 8 patients with biliary tract cancer and 2 normal people. Hierarchical clustering, euclidean distance, using Small RNA sequencing data, which is a Next Generation Sequencing (NGS) method, using tissues and blood collected from the target, respectively. complete method). A general description of this small RNA sequencing data analysis is disclosed in FIG. 12. As a result, the sensitivity of the biomarker to the tissue sample for biliary tract cancer was 100% (2/2) and the specificity was 100% (2/2). In this case, Small RNA sequencing data Fig. 13 shows the results of hierarchical cluster analysis when using. In addition, the sensitivity of the biomarker to the blood sample for biliary tract cancer was 75% (6/8) and the specificity was 50% (1/2). In this case, small RNA sequencing data were used. 14 shows the results of hierarchical cluster analysis. 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.

Meanwhile, the above-described biomarker is used as an apparatus for diagnosing biliary tract cancer including the same. The apparatus for diagnosing biliary tract cancer includes a diagnostic chip, a diagnostic kit, a quantitative PCR (qPCR) equipment, a point-of-care (POCT) equipment, and a sequencer. In the diagnostic chip, diagnostic kit, quantitative PCR (qPCR) equipment, field test (POCT) equipment, and sequencer, known parts excluding the biomarker set may be used.

According to an embodiment of the present invention, the above-described methods can be implemented as code readable by a processor in a medium on which a program is recorded. Examples of media 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 (for example, transmission over the Internet). Include.

100: computing device
110: storage unit
120: user input unit
130: communication department
140: control unit
150: display unit

Claims (13)

As a biomarker extraction method for diagnosis of biliary tract cancer,
Calculating an interaction score obtained by quantifying the degree of complementary binding between the micro RNA and the gene;
Determining n microRNAs and gene pairs having a high interaction score; And
Extracting microRNAs, which are pairs of genes common to genes specifically expressed in patients with biliary tract cancer, among the n microRNAs and gene pairs
Including,
The step of calculating the interaction score,
Obtaining at least one or more databases in which prediction scores between microRNAs and genes are statistically calculated;
Calculating a normalized score from the prediction score between the micro RNA and the gene;
Calculating a binding order of micro RNAs for each gene and a binding order of genes for each micro RNAs based on the normalized score; And
Computing an interaction score based on the binding rank of the micro RNA and the binding rank of the gene
Including,
The normalized score is calculated based on the predicted score ranking of the microRNA and gene pairs in the one or more databases, and the normalized score is calculated based on Equation 1 below,
[Equation 1]

(In Equation 1, 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, j} is the j-th database in the i-th database, Means the predicted score ranking of the micro RNA-gene pair),
The interaction score is calculated based on the ranking of the microRNAs for each gene and the rankings of the genes for each microRNA based on the normalized score, and the interaction score is calculated based on Equation 2 below. Methods of extracting diagnostic biomarkers:
[Equation 2]

(In Equation 2,
t _mi is the number of pairs with the i th microRNA gene (number of miRNA _i -gene), t _gj is the number of pairs with the j th gene micro RNA (number of gene _j -miRNA), r _mi is the j th The normalization score rank of the i th microRNA for the gene, r _gj means the normalization score rank of the j th gene for the i th microRNA). The method of claim 1,
The one or more databases are biomarker extraction method for diagnosis of biliary tract cancer, characterized in that generated using a micro RNA target prediction tool. The method of claim 2,
The micro RNA target prediction tool comprises at least one of Targetscan, miRDB, DIANA-microT, PITA, miRanda MicroCosm, RNAhybrid, PicTar, and RNA22. Biomarker extraction method for diagnosing biliary tract cancer. A storage unit for storing data; And
Includes a control unit for calculation,
The control unit calculates an interaction score obtained by quantifying the degree of complementary binding between the microRNA and the gene, determines n microRNAs and gene pairs having a high interaction score, and among the n microRNAs and gene pairs, in patients with biliary tract cancer A computing device for executing the method of extracting a biomarker for diagnosis of biliary tract cancer according to any one of claims 1 to 3, characterized in that it is configured to extract microRNAs, which are pairs of genes common to genes that are specifically expressed. delete delete delete delete delete delete delete delete delete

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