JP7209334B2 - CANCER-SPECIFIC GENE REGULATION NETWORK GENERATION METHOD, GENERATION PROGRAM AND GENERATION DEVICE - Google Patents

Description

TECHNICAL FIELD The present invention relates to a method for generating a cancer-specific gene regulatory network, a program for generating it, and an apparatus for generating it.

Advances in next-generation sequencing technology have enabled various types of analyzes using genomic data. For example, RNA-seq analysis using next-generation sequencers can be used to analyze gene expression differences between normal and cancer cells. Many analyzes can be performed based on the results of differential gene expression analysis. One of the most important analyzes among them is gene expression network analysis. The network analysis makes it possible to understand interactions between genes involved in cancer.

Transcription factors are involved in the regulation of gene expression. Ability to explore networks of transcription factor genes by pairing transcription factor binding matrices, represented in the form of position weight matrices (hereinafter also referred to as “PWM”), with annotation data of enhancer and promoter regions. is known (Reference 1).

Marback,D.,et.al, Nature Methods, 13 (4), pp. 366-370 (2016)

The network obtained by the method described in Cited Document 1 directly links genes encoding transcription factors to target genes. That is, only gene-gene interactions are evaluated. Therefore, the network does not allow us to see the gene-to-protein relationships in the translation process.

Therefore, the present invention aims to generate a transcription factor network that reflects gene transcription and translation processes, and to generate a cancer-specific gene regulatory network that reflects the expression information of cancer-related genes in the transcription factor network. aim. Another object of the present invention is to use the cancer-specific gene regulatory network to support the search for genes and proteins that are targets of novel anticancer agents, and as a result, to provide novel anticancer agents.

In view of the above problems, the present inventors have made intensive studies and found an excellent method for generating a transcription factor gene regulatory network that exhibits interactions between transcription factor genes and transcription factors. Then, by reflecting the results of gene expression difference analysis between normal cells and cancer cells in the network, it was found that a hitherto unknown cancer-specific gene regulatory network can be generated, leading to the present invention. rice field.

One or more embodiments of the invention include the following.
<1>
a plurality of protein nodes representing a transcription factor or a transcription factor complex comprising a plurality of transcription factors;
a plurality of gene nodes representing transcription factor genes encoding said transcription factors;
a translation edge that connects a gene node that is a source node and a protein node that is a target node and represents translation into the transcription factor;
a transcription control edge that connects a protein node that is a source node and a gene node that is a target node and represents the control of expression of the transcription factor gene by the transcription factor or the transcription factor complex; generating a transcription factor gene regulatory network; and transcription factor genes that are differentially expressed between normal cells and cancer cells from the nodes and edges in the transcription factor gene regulatory network generated by said step, and A method of generating a cancer-specific gene regulatory network, comprising: selecting nodes and edges for encoding transcription factors to generate a cancer-specific gene regulatory network.
<2>
The generation process of the transcription factor gene regulatory network is
(1) For each of a plurality of transcription factor candidates and transcription factor complex candidates that may constitute the transcription factor gene regulatory network,
data of transcription factor candidate names, transcription factor complex candidate names, and gene names encoding the transcription factor candidates and transcription factor complex candidates;
position weight matrix (PWM) data of nucleotide sequences to which the transcription factor candidates and transcription factor complex candidates bind;
preparing the sequence data of the first transcription control region of the gene encoding the transcription factor candidate and the transcription factor complex candidate;
(2) obtaining data of protein nodes, gene nodes, and translation edges based on the data of transcription factor candidate names, transcription factor complex candidate names, and gene names encoding them;
(3) performing sequence matching between the PWM data of each of a plurality of transcription factor candidates and transcription factor complex candidates and the sequence data of the first transcription control regions of the genes encoding the transcription factor candidates and transcription factor complex candidates; Obtain the data of the transfer control edge by performing
(4) The method according to <1>, comprising generating a transcription factor gene regulatory network by integrating the data obtained in (2) and (3) above.
<3>
The step of selecting nodes and edges related to transcription factor genes and their encoded transcription factors that are differentially expressed between normal cells and cancer cells is the expression level between genes in normal cells and genes in cancer cells. The method according to <1> or <2>, which comprises performing variation analysis and selecting nodes and edges related to the gene whose expression level varies and the protein encoded by the gene.
<4>
The method according to any one of <1> to <3>, wherein a human cancer-specific gene regulatory network is generated.
<5>
The method according to any one of <1> to <4>, wherein the cancer is selected from the group consisting of cholangiocarcinoma, lung adenocarcinoma, colon cancer and hepatocellular carcinoma.
<6>
A protein node representing a transcription factor or a transcription factor complex that binds to a second transcription control region of a gene corresponding to one gene node selected from the generated cancer-specific gene control network is added to the cancer-specific gene control network. the process of identifying from
The method according to any one of <1> to <5>, further comprising
<7>
The method according to <6>, wherein the first transcriptional control region is a promoter region, and the second transcriptional control region is a region containing an enhancer region, a promoter region and a silencer region.
<8>
The method according to <6> or <7>, wherein the selected gene node is a gene node representing the HDAC2 gene.
<9>
1. A method of generating a cancer-specific gene regulatory sub-network that is common in at least two cancers, comprising:
Comparing at least two cancer-specific gene regulatory networks generated according to the method according to any one of <1> to <8> to generate a cancer-specific gene regulatory sub-network common to them A method comprising steps.
<10>
A computer program for generating a cancer-specific gene regulatory network, which causes a computer to execute the method for generating a cancer-specific gene regulatory network according to any one of <1> to <8>.
<11>
A computer program for generating a cancer-specific gene regulatory sub-network, which causes a computer to execute the method for generating a cancer-specific gene regulatory sub-network according to <9>.
<12>
a plurality of protein nodes representing a transcription factor or a transcription factor complex comprising a plurality of transcription factors;
a plurality of gene nodes representing transcription factor genes encoding said transcription factors;
a translation edge that connects a gene node that is a source node and a protein node that is a target node and represents translation into the transcription factor;
a transcription control edge that connects a protein node that is a source node and a gene node that is a target node and represents the control of expression of the transcription factor gene by the transcription factor or the transcription factor complex; a first generator that generates a transcription factor gene regulatory network;
Nodes and edges related to transcription factor genes differentially expressed between normal cells and cancer cells and transcription factors encoded by the same, from the nodes and edges in the transcription factor gene regulatory network generated by the first generation unit and a second generator that selects to generate the cancer-specific gene regulatory network.
<13>
A protein node representing a transcription factor or a transcription factor complex that binds to a second transcriptional control region of a gene corresponding to one gene node selected from the generated cancer-specific gene control network is selected from the cancer-specific gene control network. The device according to <12>, further comprising a first identifying unit that identifies from within the network.
<14>
1. An apparatus for the generation of cancer-specific gene regulatory sub-networks common in at least two cancers, comprising:
an acquisition unit that acquires at least two cancer-specific gene regulatory networks generated according to the method according to any one of <1> to <8>;
and a third generator that compares the at least two cancer-specific gene regulatory networks to generate a cancer-specific gene regulatory sub-network common to them.
<15>
A protein node representing a transcription factor or a transcription factor complex that binds to a second transcriptional control region of a gene corresponding to one gene node selected from the generated cancer-specific gene regulatory sub-network is selected from the cancer-specific gene The apparatus according to <14>, further comprising a second identifying unit that identifies from within the control subnetwork.
<16>
A composition for treating cancer, comprising an expression promoter of at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5.
<17>
The composition according to <16>, wherein the transcription factor expression promoter is an expression vector for the transcription factor.
<18>
A composition for suppressing HDAC2 expression, comprising at least one transcription factor expression promoter selected from the group consisting of FOXO1, RORA, MEF2A and SOX5.
<19>
The composition according to <18>, wherein the transcription factor expression promoter is an expression vector for the transcription factor.
<20>
A method of treating cancer, comprising the step of administering to a subject in need thereof an expression promoter of at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5.
<21>
Use of an expression enhancer of at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5 in the manufacture of a medicament for use in treating cancer.
<22>
An agent for promoting the expression of at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5 for use in treating cancer.
<23>
A method for suppressing the expression of HDAC2, comprising the step of administering to a subject in need thereof an expression-enhancing agent for at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5, Method.
<24>
Use of an expression promoter of at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5 in the manufacture of a medicament for suppressing HDAC2 expression.
<25>
An expression promoter of at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5, for suppressing the expression of HDAC2.

Cancer-specific gene regulatory networks generated by the methods of the present invention allow a better understanding of the interactions of genes and transcription factors involved in cancer. Conventional gene networks (for example, shown in Non-Patent Document 1) can only evaluate gene-gene interactions. On the other hand, the gene network generated by the present invention reflects transcription and translation processes that actually occur in vivo, and gene-protein-gene interactions can be evaluated. Moreover, by using this network, it is possible to support the search for genes and proteins that are targets of novel anticancer drugs, and to provide novel anticancer drugs.

FIG. 1 is a schematic diagram of a transcription factor gene regulatory network. P1 and P2 represented by squares represent protein nodes. G1 to G4 represented by circles represent gene nodes. Solid arrows represent translation edges (also called “translate_to-edges”). A dashed arrow with a circular tip represents a transfer control edge (also called a “bind_to-edge”). A transcription factor obtained by translating G1 forms a complex (heterodimer) together with a transcription factor obtained by translating G2. The complex binds to the transcription control region of G3 and regulates the expression of G3. FIG. 2 is an example of the "transcription control region" in this specification. FIG. 3 shows cancer-specific gene regulatory sub-networks common in cholangiocarcinoma (BDC), lung adenocarcinoma (LUAD), colorectal cancer (CRC) and hepatocellular carcinoma (HCC). FIG. 4 shows a network obtained by adding information on genes differentially expressed between normal cells and cholangiocarcinoma cells to the cancer-specific gene regulatory sub-network of FIG. Gene nodes represented in dark gray represent genes whose expression is upregulated in cholangiocarcinoma cells compared to normal cells. Gene nodes represented in light gray represent genes whose expression is suppressed in cholangiocarcinoma cells compared to normal cells. FIG. 5 shows a network obtained by adding information on genes differentially expressed between normal cells and lung adenocarcinoma cells to the cancer-specific gene regulatory sub-network of FIG. Gene nodes represented in dark gray represent genes with increased expression in lung adenocarcinoma cells compared to normal cells. Gene nodes represented in light gray represent genes whose expression is suppressed in lung adenocarcinoma cells compared to normal cells. FIG. 6 shows a network obtained by adding information on genes differentially expressed between normal cells and colon cancer cells to the cancer-specific gene regulatory sub-network of FIG. Gene nodes represented in dark gray represent genes with increased expression in colon cancer cells compared to normal cells. Gene nodes represented in light gray represent genes whose expression is suppressed in colon cancer cells compared to normal cells. FIG. 7 shows a network obtained by adding information on genes differentially expressed between normal cells and hepatocellular carcinoma cells to the cancer-specific gene regulatory sub-network of FIG. Gene nodes represented in dark gray represent genes whose expression is upregulated in hepatocellular carcinoma cells compared to normal cells. Gene nodes represented in light gray represent genes whose expression is suppressed in hepatocellular carcinoma cells compared to normal cells. FIG. 8 shows a graph identifying genes that bind to the second transcriptional control region of the HDAC2 gene (here, a region containing an enhancer region, a promoter region and a silencer region) in the cancer-specific gene regulatory subnetwork of FIG. . In the figure, the gene nodes framed by bold lines (HDAC2, TCF3, ZNF146, ZFP64, and E2F8) are transcription factors that bind to the second transcriptional control region of the HDAC2 gene, whose gene expression is enhanced in all four types of cancer cells. represents the gene that encodes the In the figure, the gray dotted gene nodes (FOXO1, RORA, MEF2A, SOX5) bind to the second transcriptional control region of the HDAC2 gene, whose gene expression is all suppressed in four types of cancer cells. represents a gene that encodes a transcription factor that The gene nodes (BHLHE40, PBX1) in the open dotted frame encode transcription factors that bind to the second transcriptional control region of the HDAC2 gene, with mixed upregulation and downregulation of gene expression in four types of cancer cells. represents a gene. FIG. 9 is a diagram showing an example of a schematic configuration of an information processing device 100 used in the method of the present invention. FIG. 10 shows a flow chart showing an example of overall processing operations in a method for generating a cancer-specific gene regulatory network. FIG. 11 shows a flow chart showing an example of overall processing operations in a cancer-specific gene regulatory sub-network generation method. FIG. 12 shows a flowchart showing an example of the operation of the overall processing when performing the protein node identification step in the method of the present invention.

Hereinafter, embodiments of the present invention will be described with reference to the drawings as necessary. The configuration of the embodiment is an example, and the configuration of the present invention is not limited to the specific configuration of the embodiment.

<Method for generating cancer-specific gene regulatory network>
The method for generating a cancer-specific gene regulatory network of the present invention comprises:
a plurality of protein nodes representing a transcription factor or a transcription factor complex comprising a plurality of transcription factors;
a plurality of gene nodes representing transcription factor genes encoding said transcription factors;
a translation edge that connects a gene node that is a source node and a protein node that is a target node and represents translation into the transcription factor;
a transcription control edge that connects a protein node that is a source node and a gene node that is a target node and represents the control of expression of the transcription factor gene by the transcription factor or the transcription factor complex; generating a transcription factor gene regulatory network; and transcription factor genes that are differentially expressed between normal cells and cancer cells from the nodes and edges in the transcription factor gene regulatory network generated by said step, and selecting nodes and edges for encoding transcription factors to generate a cancer-specific gene regulatory network.

As used herein, the term “transcription factor gene regulatory network” refers to a network modeled by interacting causal relationships between transcription factors and transcription factor genes. A transcription factor gene regulatory network can be represented by a directed graph that includes multiple protein nodes, multiple gene nodes, translation edges, and transcriptional regulatory edges. Here, "protein node" refers to a transcription factor or a transcription factor complex comprising multiple transcription factors. A "gene node" represents a transcription factor gene that encodes the transcription factor. A “translation edge” connects a gene node, which is a source node, and a protein node, which is a target node, and represents translation into the transcription factor. A “transcription control edge” connects a protein node, which is a source node, and a gene node, which is a target node, and represents control of expression of the transcription factor gene by the transcription factor or the transcription factor complex.

As used herein, the term "cancer-specific gene regulatory network" refers to the selection of nodes and edges related to transcription factors and genes encoding them that are differentially expressed between normal cells and cancer cells derived from the same tissue. The resulting transcription factor gene regulatory network.

As used herein, the term "transcription factor" refers to a factor that binds to a transcription control region and regulates the process of gene transcription. Transcription factors primarily regulate the transcription initiation response. Transcription factors include a group of basic transcription factors required to place RNA polymerase in the promoter region on DNA, and various transcription factors that bind to transcription control regions existing upstream and downstream of the transcription region to regulate the frequency of initiation of RNA synthesis. can be broadly classified into transcriptional regulators of Transcription factors regulate transcription either singly or by forming complexes containing the same or different transcription factors (also called "transcription factor complexes"). As used herein, "a gene encoding a transcription factor complex" means all transcription factor genes that constitute the transcription factor.

As used herein, the term "transcriptional control region" refers to a sequence region that exists upstream or downstream of a transcribed region and is capable of regulating the transcription level of a gene. A transcription control region can be, for example, a promoter region, an enhancer region, a silencer region, a terminator region, and the like. Also, for example, the transcription control region can be a region containing at least one selected from a promoter region, an enhancer region, a silencer region and a terminator region.

As used herein, the term "promoter region" refers to a region on DNA that determines the transcription start site of a gene and directly regulates its frequency. is an array. A promoter region usually exists upstream of a structural gene, but is not limited to this, and may also exist downstream of a structural gene.

As used herein, the term "enhancer region" refers to a sequence that is usually used to increase the expression efficiency of a gene of interest. Such enhancers are well known in the art.

As used herein, the term "silencer region" generally refers to a sequence that has the function of suppressing gene expression and quiescing.

As used herein, the term "terminator region" refers to a sequence that is usually located downstream of the protein-encoding region of a gene and involved in the termination of transcription and the addition of a poly A sequence when DNA is transcribed into mRNA. . It is known that terminators are involved in mRNA stability and affect gene expression levels.

The method for generating a cancer-specific gene regulatory network of the present invention can be applied to any animal. Preferably, the cancer-specific gene regulatory network produced is a mammalian cancer-specific gene regulatory network, more preferably a human cancer-specific gene regulatory network.

The method for generating a cancer-specific gene regulatory network of the present invention can be applied to any cancer. For example, carcinomas include brain tumor, skin cancer, cervical cancer, esophageal cancer, lung cancer (including lung adenocarcinoma), gastric cancer, duodenal cancer, breast cancer, prostate cancer, cervical cancer, endometrial cancer, pancreatic cancer, and liver cancer. , hepatocellular carcinoma, colon cancer, colon cancer, bladder cancer, and ovarian cancer. Examples of sarcoma include osteosarcoma, chondrosarcoma, rhabdomyosarcoma, leiomyosarcoma, liposarcoma, and angiosarcoma. Furthermore, hematopoietic malignancies include malignant lymphomas including Hodgkin's lymphoma and non-Hodgkin's lymphoma; leukemias including acute myelogenous leukemia, chronic myelogenous leukemia, acute lymphocytic leukemia and chronic lymphocytic leukemia; and multiple myeloma. be.

In the method for generating a cancer-specific gene regulatory network of the present invention, the step of generating a transcription factor gene regulatory network is not particularly limited, but is preferably
(1) For each of a plurality of transcription factor candidates and transcription factor complex candidates that may constitute the transcription factor gene regulatory network,
data of transcription factor candidate names, transcription factor complex candidate names, and gene names encoding the transcription factor candidates and transcription factor complex candidates;
position weight matrix (PWM) data of nucleotide sequences to which the transcription factor candidates and transcription factor complex candidates bind;
preparing the sequence data of the first transcription control region of the gene encoding the transcription factor candidate and the transcription factor complex candidate;
(2) obtaining data of protein nodes, gene nodes, and translation edges based on the data of transcription factor candidate names, transcription factor complex candidate names, and gene names encoding them;
(3) performing sequence matching between the PWM data of each of a plurality of transcription factor candidates and transcription factor complex candidates and the sequence data of the first transcription control regions of the genes encoding the transcription factor candidates and transcription factor complex candidates; Obtain the data of the transfer control edge by performing
(4) Generating a transcription factor gene regulatory network by integrating the data obtained in (2) and (3) above.

The above step (1) of preparing data can be performed using data contained in an existing database. Examples of such databases include TRANSFAC (Wingender E., BRIEFINGS IN BIOINFORMATICS. VOL 9. NO 4. 326-332), JASPAR (Khan A. et al., Nucleic Acids Research, VOL 46, D1, D260-D266) , HOCOMOCO (Kulakovskiy I.V. et al., Nucleic Acids Research, VOL46, D1, D252-D259).

As used herein, the term “position weight matrix (PWM) data” refers to matrix data obtained by vertically viewing the results of sequence alignment and calculating and quantifying the frequency of appearance of bases at each position. PWM reflects the frequency of sequences bound by a transcription factor and represents the binding motif of the transcription factor. One or more PWMs can be defined for one transcription factor. This is due to the fact that one transcription factor can form a complex with another transcription factor and that the binding site of the transcription factor can change in a ligand-dependent manner.

The above step (2) of acquiring data of protein nodes, gene nodes and translation edges can be performed, for example, in the CPU of the information processing device described below.

The above step (3) of acquiring data of transcription control edges is performed by inputting, for example, the PWM of a candidate transcription factor and searching for the existence of it in the sequence of the first transcription control region. It can be done using analysis tools. Such analysis tools include, for example, FIMO software (Grant C.E. et al., Bioinformatics 27(7), 2011, pp.1017-1018), and MATCH software provided in the TRANSFAC database (Kel A.E. et al., Nucleic Acids Research 31(13), 2003, pp.3576-3579). The programs stored in these pieces of software may be stored in the storage device of the information processing device described below.

By adjusting the sequence length of the first transcriptional control region, the number of edges and nodes included in the network can be adjusted. The length of the first transcription control region suitable for obtaining a transcription factor gene control network is not particularly limited. For example, when the first transcription control region is a promoter region, its length is, for example, 5,00-5,000 nucleotides, preferably 1,000-3,000 nucleotides.

Combining the data obtained in steps (2) and (3) above may include removing nodes that are not connected to the transfer control edges. In addition, when two nodes are connected by two or more transfer control edges, a step of leaving only one transfer control edge and removing the other transfer control edges may be included. The step of integrating the data acquired in steps (2) and (3) above can be performed, for example, by the CPU of the information processing device described below.

As used herein, "a transcription factor gene differentially-expressed between normal cells and cancer cells" means It means transcription factor genes with differential levels. In the method for generating a cancer-specific gene regulatory network of the present invention, nodes and edges related to transcription factor genes differentially expressed between normal cells and cancer cells and the transcription factors encoded by them are selected to select cancer-specific gene regulatory networks. The step of generating a gene regulatory network includes analyzing expression level fluctuations between genes in normal cells and genes in cancer cells, and selecting nodes and edges related to genes whose expression levels have changed and proteins encoded by them. may include Expression level variation analysis is not particularly limited, but includes, for example, a method of analyzing known cancer gene expression datasets using software such as Bioconductor's DESeq2, limma, and edgeR, and a method of analyzing by a tensor decomposition method. The programs stored in the software may be stored in the storage device of the information processing device described below. Alternatively, the network generation step may be performed using data obtained by previously performing expression level change analysis. The network generation process may be performed, for example, by the CPU of the information processing apparatus described below.

The method for generating a cancer-specific gene regulatory network of the present invention comprises a transcription factor or a transcription factor complex that binds to the second transcriptional control region of a gene corresponding to one gene node selected from the generated cancer-specific gene regulatory network. A step of identifying a protein node representing a body from the cancer-specific gene regulatory network may be further included (hereinafter also referred to as "protein node identification step"). In this step, for example, among gene nodes included in the cancer-specific gene regulatory network, those having a large number of connected transcriptional regulatory edges may be selected. Alternatively, for example, one of the gene nodes forming a sub-network obtained by the sub-network generation method of the present invention described below may be selected. In one embodiment, the selected gene node is the gene node representing the HDAC2 gene.

In the protein node identification step, the second transcriptional control region may be the same as or different from the first transcriptional control region. In one embodiment, the first transcriptional control region is a promoter region and the second transcriptional control region is a region comprising an enhancer region, a promoter region and a silencer region.

The step of identifying a protein node is not particularly limited, but for example, an existing analysis tool ( for example FIMO software). Information on the second transcriptional control region can be obtained, for example, from GeneHancer (Fishilevich S. et al., Database, 2017, pp. 1-17), which is a database of human transcriptional control regions and their putative target genes. The step of identifying protein nodes can be performed, for example, by the CPU of the information processing device described below.

The method for generating a cancer-specific gene regulatory network of the present invention comprises displaying a directed graph representing the network on a display medium (eg paper, computer display, etc.), or storing information of the directed graph on a storage medium (eg CD-ROM, DMV). - ROM, etc.).

<Method for generating cancer-specific gene regulatory sub-networks common in at least two cancers>
The method of the present invention for generating a cancer-specific gene regulatory sub-network common to at least two cancers (hereinafter also referred to as the "method for generating a sub-network of the present invention") comprises the cancer-specific gene regulatory network of the present invention. generating a cancer-specific gene regulatory sub-network common to them by comparing at least two cancer-specific gene regulatory networks generated according to the method of generating.

The cancer-specific gene regulatory networks of the present invention, which can assess gene-protein-gene interactions, more accurately reflect gene expression regulation in vivo than conventional gene-gene networks, but can be complex. The cancer-specific gene regulatory networks obtained by generating the cancer-specific gene regulatory networks of the present invention for multiple cancer types and comparing them have a higher number of nodes and edges than individual cancer-specific gene regulatory networks. is limited, there is an advantage that the analysis becomes easy.

The sub-network generation method of the present invention can be applied to any at least two cancers. For example, carcinomas include brain tumor, skin cancer, cervical cancer, esophageal cancer, lung cancer (including lung adenocarcinoma), gastric cancer, duodenal cancer, breast cancer, prostate cancer, cervical cancer, endometrial cancer, pancreatic cancer, and liver cancer. , hepatocellular carcinoma, colon cancer, colon cancer, bladder cancer, and ovarian cancer. Examples of sarcoma include osteosarcoma, chondrosarcoma, rhabdomyosarcoma, leiomyosarcoma, liposarcoma, and angiosarcoma. Furthermore, hematopoietic malignancies include malignant lymphomas including Hodgkin's lymphoma and non-Hodgkin's lymphoma; leukemias including acute myelogenous leukemia, chronic myelogenous leukemia, acute lymphocytic leukemia and chronic lymphocytic leukemia; and multiple myeloma. be. For example, it is selected from the group consisting of cholangiocarcinoma, lung adenocarcinoma, colon cancer and hepatocellular carcinoma.

The step of comparing at least two cancer-specific gene regulatory networks to generate a cancer-specific gene regulatory sub-network common to them can be performed, for example, in the CPU of the information processing device described below.

The cancer-specific gene regulatory sub-network generated by the cancer-specific gene regulatory sub-network generating method of the present invention may further be subjected to the protein node identification step described above. In one embodiment, the gene node selected in this step is a gene node representing the HDAC2 gene.

The subnetwork generation method of the present invention is to display a directed graph representing the subnetwork on a display medium (eg, paper, computer display, etc.), or to display information on the directed graph on a storage medium (eg, CD-ROM, DMV-ROM, etc.). ).

<Information processing device>
The cancer-specific gene regulatory network generation method of the present invention and the sub-network generation method of the present invention (hereinafter also referred to as "the method of the present invention") can be performed using an information processing apparatus. FIG. 9 is a diagram showing an example of a schematic configuration of an information processing device 100 used in the method of the present invention.

The information processing device 100 is an information processing device such as a personal computer, and is used by a user. The information processing device 100 includes a communication device 101 , an input device 102 , a display device 103 , a storage device 110 and a CPU (Central Processing Unit) 120 . Each unit of the information processing apparatus 100 will be described in detail below.

The communication device 101 has a communication interface circuit for communicating with a network such as LAN. The communication device 101 transmits and receives data to and from an external server device (not shown) via a network. The communication device 101 supplies the data received from the server device via the network to the CPU 120, and transmits the data supplied from the CPU 120 to the server device via the network. Note that the communication device 101 may be of any type as long as it can communicate with an external device. The communication device 101 may receive input data from an external server device and supply it to the CPU 120 . Here, in the method for generating a cancer-specific gene regulatory network, the input data are data of transcription factor candidate names and transcription factor complex candidate names, data of gene names encoding the transcription factor candidates and transcription factor complex candidates, Position weight matrix (PWM) data of nucleotide sequences to which the transcription factor candidates and transcription factor complex candidates bind, and sequence data of transcription control regions of genes encoding the transcription factor candidates and transcription factor complex candidates, data used to generate a transcription factor gene regulatory network; and data on gene expression levels in normal and cancer cells (or data on transcription factor genes that are differentially expressed between normal and cancer cells). can be Also, in the cancer-specific gene regulatory sub-network generation method, the input data can be cancer-specific gene regulatory network data for at least two cancers. Moreover, the communication device 101 may transmit the data of the cancer-specific gene regulatory network and the cancer-specific gene regulatory sub-network output from the CPU 120 to an external device.

The input device 102 is an example of an operation unit, and includes an input device such as a touch panel input device, a keyboard, and a mouse, and an interface circuit that acquires signals from the input device. The input device 102 receives a user's input and outputs a signal corresponding to the user's input to the CPU 120 . Input data used in the method of the present invention may be entered through input device 102 .

The display device 103 is an example of a display unit, and has a display made up of a liquid crystal, an organic EL (Electro-Luminescence), or the like, and an interface circuit that outputs image data or various types of information to the display. The display device 103 is connected to the CPU 120 and displays the cancer-specific gene regulatory network and the cancer-specific gene regulatory sub-network output from the CPU 120 on the display.

Storage device 110 is an example of a storage unit. The storage device 110 includes memory devices such as RAM (Random Access Memory) and ROM (Read Only Memory), fixed disk devices such as hard disks, or portable storage devices such as flexible disks and optical disks. The storage device 110 also stores computer programs, databases, tables, and the like used for various processes of the information processing device 100 . The computer program may be installed from a computer-readable portable recording medium such as a CD-ROM (compact disk read only memory), a DVD-ROM (digital versatile disk read only memory), or the like. The computer program is installed in the storage device 110 using a known setup program or the like. The storage device 110 stores, as data, input data acquired by the communication device 101 and the input device 102, and data of the cancer-specific gene regulatory network and cancer-specific gene regulatory sub-network generated by the CPU.

The CPU 120 operates based on programs stored in advance in the storage device 110 . CPU 120 may be a general-purpose processor. Instead of the CPU 120, a DSP (digital signal processor), LSI (large scale integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or the like may be used. The CPU 120 has a first generation unit 121 , a second generation unit 122 , a third generation unit 123 , an acquisition unit 124 , a first identification unit 125 and a second identification unit 126 .

The CPU 120 is connected to the communication device 101, the input device 102, the display device 103, and the storage device 110, and controls these units.

10 to 12 are flowcharts showing an example of the operation of the overall processing by the information processing apparatus 100. FIG.

An example of the operation of the overall processing by the information processing apparatus 100 will be described below with reference to the flowcharts shown in FIGS. The flow of operations described below is executed mainly by the CPU 120 in cooperation with each element of the information processing apparatus 100 based on a program stored in advance in the storage device 110 .

In the method of generating a cancer-specific gene regulatory network of the present invention (FIG. 10), first, the first generation unit 121 receives input from the user using the input device 102 or the communication device 101 from an external server device. Received transcription factor candidate name, transcription factor complex candidate name, gene name encoding said transcription factor candidate and transcription factor complex candidate, position weight matrix of nucleotide sequence to which said transcription factor candidate and transcription factor complex candidate bind Data for generating a transcription factor gene regulatory network, including (PWM) data and sequence data of the first transcription control regions of genes encoding the transcription factor candidates and transcription factor complex candidates (step S101).

Next, the first generation unit 121 creates a list of data of transcription factor candidate names, transcription factor complex candidate names, and gene names encoding them (step S102). Specifically, a list including a plurality of sets is created, with one transcription factor candidate name or transcription factor complex candidate name and one gene name encoding it as one set.

Next, the first generator 121 generates PWM data of each of a plurality of transcription factor candidates and transcription factor complex candidates, and sequence data of the first transcription control regions of genes encoding the transcription factor candidates and transcription factor complex candidates. to create a list of genes, transcription factors that control the expression of the genes, and transcription factor complexes (step S103). Specifically, as a result of the sequence matching, one gene name and one transcription factor name or transcription factor complex name extracted as binding to the first transcription control region of the gene are set as one set, Create a list with multiple sets.

Next, the first generator 121 integrates the lists created in steps S102 and S103 to generate a transcription factor gene regulatory network (step S104). The integration will be described with reference to FIG. The list created in step S102 includes a set of G1 and P1, a set of G2 and P1, and a set of G3 and P2. The list created in step S103 also includes a set of P1 and G3 and a set of P2 and G4. For example, when focusing on P1, a set of G1 and P1, a set of G2 and P1, and a set of P1 and G3 are extracted as sets including P1. After that, translation edges connect between G1 and P1 and between G2 and P1, and P1 and G3 are connected by transcription control edges. By performing the same operation on P2, the transcription factor gene regulatory network shown in FIG. 1 is finally generated.

Next, the second generation unit 122 accepts data on gene expression levels in normal cells and cancer cells, which is input by the user using the input device 102 or received by the communication device 101 from an external server device, Expression level analysis is performed on these data to extract transcription factor gene names that are differentially expressed between normal cells and cancer cells. After that, the second generation unit 122 generates nodes related to transcription factor genes differentially expressed between normal cells and cancer cells and transcription factors encoded by the same, from the transcription factor gene control network generated in step S104. and edges are selected to generate a cancer-specific gene regulatory network (step S105). In step S105, the second generating unit 122 extracts the data of the gene expression level between the normal cells and the cancer cells, instead of the gene expression level data of the normal cells and the cancer cells. It is also possible to receive data on the names of transcription factor genes expressed in cells and use them to generate a cancer-specific gene regulatory network.

In the method of generating a cancer-specific gene regulatory sub-network of the present invention (FIG. 11), the acquisition unit 124 receives input by the user using the input device 102, received by the communication device 101 from an external server device, or , acquire data of at least two cancer-specific gene regulatory networks generated by the second generator (step S111). That is, the cancer-specific gene regulatory network data used here may be generated using the same information processing device, or may be generated using a different information processing device. may

Next, the third generating unit 123 compares the data of at least two cancer-specific gene regulatory networks acquired by the acquiring unit and generates a common cancer-specific gene regulatory sub-network (step S112). ).

Also, in the method of the present invention, the step of identifying transcription factors or transcription factor complexes that bind to the second transcription control region of one gene selected from the cancer-specific gene regulatory network or sub-network (Fig. 12) is It can be done as follows. First, the first identification unit 125 or the second identification unit 126 is input by the user using the input device 102, or received by the communication device 101 from an external server device, or the second generation unit 122 or the second generation unit 126. 3 data of the cancer-specific gene regulatory network or sub-network generated by the generation unit 123, the gene name of one gene selected from them, the sequence data of the second transcriptional control region of the selected gene, Data for identifying protein nodes is received, including PWM data of nucleotide sequences to which transcription factors in the cancer-specific gene regulatory network or sub-network bind (step S121).

Next, the first identification unit 125 or the second identification unit 126 performs sequence matching between the PWM data of each of the plurality of transcription factor candidates and the sequence data of the second transcription control region of the selected gene. , protein nodes representing transcription factors or transcription factor complexes that bind to the second transcription control region are identified from the cancer-specific gene regulatory network or sub-network (step S122).

<Composition of the present invention>
In one aspect, the present invention is a composition for treating cancer, comprising an expression promoter of at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5. In still another aspect, the present invention is a composition for suppressing HDAC2 expression, comprising an expression promoter for at least one transcription factor selected from the group consisting of FOXO1, RORA, MEF2A and SOX5. Together, they are also referred to below as the "composition of the invention".

HDAC2 is highly expressed in cancer cells (eg, cholangiocarcinoma, lung adenocarcinoma, colon cancer and hepatocellular carcinoma cells). On the other hand, from the cancer-specific gene regulatory network information obtained by the method of the present invention, FOXO1, RORA, MEF2A and SOX5 bind to regions including the promoter region, enhancer region and silencer region of HDAC2, and these genes Expression was found to be reduced in the cancer cells compared to normal cells. Therefore, it is considered that promoting the expression of FOXO1, RORA, MEF2A and SOX5 can suppress the expression of HDAC2 in cancer cells. In addition, it is believed that cancer can be finally treated by suppressing the expression of HDAC2.

As used herein, a “transcription factor expression promoter” is any substance that promotes the expression of the transcription factor. Examples include, but are not limited to, a vector containing a polynucleotide encoding the transcription factor.

Subjects for administration of the compositions of the invention are humans or non-human mammals. Non-human mammals specifically include mice, rats, dogs, monkeys, cats, horses, cows, pigs, goats, sheep and the like. Preferably, the subject of administration is a human.

In the composition for treating cancer of the present invention, the type of cancer to be treated is not particularly limited, and carcinomas include brain tumor, skin cancer, cervical cancer, esophageal cancer, and lung cancer (including lung adenocarcinoma). , gastric cancer, duodenal cancer, breast cancer, prostate cancer, cervical cancer, endometrial cancer, pancreatic cancer, liver cancer, hepatocellular carcinoma, colon cancer, colon cancer, bladder cancer, and ovarian cancer. Examples of sarcoma include osteosarcoma, chondrosarcoma, rhabdomyosarcoma, leiomyosarcoma, liposarcoma, and angiosarcoma. Furthermore, hematopoietic malignancies are exemplified by malignant lymphomas, including Hodgkin's lymphoma and non-Hodgkin's lymphoma; leukemias, including acute myelogenous leukemia, chronic myelogenous leukemia, acute lymphocytic leukemia and chronic lymphocytic leukemia; and multiple myeloma. .

The composition of the present invention can be formulated according to conventional methods (for example, Remington's Pharmaceutical Sciences, latest edition, Mark Publishing Company, Easton, U.S.A.) and contains pharmaceutically acceptable carriers and additives. There may be. For example, surfactants, excipients, coloring agents, flavoring agents, preservatives, stabilizers, buffering agents, suspending agents, tonicity agents, binders, disintegrants, lubricants, fluidity enhancers, flavoring agents etc. Furthermore, it is not limited to these, and other commonly used carriers can be used as appropriate. Specifically, light anhydrous silicic acid, lactose, crystalline cellulose, mannitol, starch, carmellose calcium, carmellose sodium, hydroxypropylcellulose, hydroxypropylmethylcellulose, polyvinyl acetal diethylaminoacetate, polyvinylpyrrolidone, gelatin, medium chain fatty acid triglyceride, Examples of carriers include polyoxyethylene hydrogenated castor oil 60, sucrose, carboxymethylcellulose, corn starch, and inorganic salts.

The dosage of the composition of the present invention is, for example, 0.0001 mg to 1,000 mg of transcription factor expression promoter per 1 kg body weight per administration. Alternatively, for example, 0.001 mg/body to 100,000 mg/body of transcription factor expression promoter is administered per patient. However, the dosage of the composition of the present invention is not limited to these.

All documents mentioned herein are hereby incorporated by reference in their entirety.

The embodiments of the invention described below are for illustrative purposes only and are not intended to limit the scope of the invention.

The technical scope of the present invention is limited only by the description of the claims. Modifications of the present invention, such as additions, deletions and replacements of constituent elements of the present invention, can be made without departing from the gist of the present invention.

Example 1
Generation of Transcription Factor Gene Regulatory Network Version 2017.2 of the transcription factor database Transfac Pro database (hereinafter also simply referred to as “Transfac”) was used for the generation of the transcription factor gene regulatory network. Human transcription factor genes with PWM annotations (1298 in total) were selected from the gene data included in Transfac. A list of translate_to-edges was generated from the information on the gene name and transcription factor name of the gene.

The DNA sequences of the genes selected above were extracted. The transcription control region was defined as the promoter region and the polynucleotide upstream of the first exon of each transcript. The length of the transcription control region was 500nt, 1000nt, 2000nt, 3000nt, 4000nt or 5000nt. A network was created for all of these different length transcription control regions. The DNA sequences of selected genes were extracted from Ensembl HG38, a dataset of human genome reference sequences, and Ensembl gene annotation files, gene annotation information.

Usually, one gene can give rise to multiple gene products. For example, in FIG. 2, transcripts 1-4 arise from the BRCA2 gene. In this case, the extent of the transcription control region differs depending on each transcript. In this example, a region obtained by merging the transcription control regions (promoter region in FIG. 2) of the transcript was used as the transcription control region of the gene (BRCA2 in FIG. 2).

FIMO software was used to examine the presence of motifs denoted as PWM in transcriptional control regions, and based on the results a list of bind_to-edges was generated. Specifically, PWM data of a selected human transcription factor gene was input to search whether it exists in the transcription control region of the target transcription factor gene. A p-value threshold of 0.0005 was set for the cutoff of matching sequences. The false discovery rate (FDR) plus the Bonferroni correction was performed to calculate the q-value. A q-value threshold of 0.0005 was used for the cutoff.

The total number of edges and the total number of nodes in the transcription factor gene regulatory network when the length of the transcription control region is changed are shown below.

From the data of the translate_to-edge and bind_to-edge lists obtained above, a transcription factor gene regulatory network was obtained using a computer programmed to integrate these data. At that time, nodes not connected to transfer control edges were removed. Also, when two nodes were connected by two or more transfer control edges, only one edge was left and the other edges were removed.

Example 2
Generation of Cancer-Specific Gene Regulatory Networks From the NCBI GEO database, normal-cancer datasets of cholangiocarcinoma (BDC), lung adenocarcinoma (LUAD), colorectal cancer (CRC) and hepatocellular carcinoma (HCC) (GSE63420, GSE87340, respectively) , GSE104836 and GSE77509) were obtained, and expression level variation analysis was performed. For BDC, the SRA files were downloaded and the fastq files were extracted using sratoolkit. Read count data were calculated using Salmon software (R. Patro et al., Nature Methods 14(4), 417-419 (2017)) in quasi-mapping mode. For LUAD and CRC, we downloaded the read count data provided by the NCBI GEO database. For HCC, normalized read count data was downloaded.

To obtain differential expression results, the raw read count data was used to process the BDC, LUAD and CRC datasets using DESeq2. For HCC, differential gene expression analysis was performed using the limma/voom workflow because the type of data was normalized read counts. After that, by filtering the genes included in the transcription factor gene regulatory network obtained in Example 1 and inputting the data of the above data set, genes whose expression levels fluctuate between normal cells and cancer cells are selected. It was carried out using a computer with a program that can At that time, the p-value was adjusted to 0.05 or less. Genes differentially expressed between normal and cancer cells in each cancer were mapped to the transcription factor network obtained in Example 1. A cancer-specific gene regulatory network was then obtained by excluding from the transcription factor network the nodes for genes whose expression was unchanged between normal and cancer cells.

Example 3
Generation of common cancer-specific gene regulatory sub-networks in cholangiocarcinoma (BDC), lung adenocarcinoma (LUAD), colorectal cancer (CRC) and hepatocellular carcinoma (HCC) Cancer-specific for the four cancers obtained in Example 2 From the general gene regulatory networks, we searched for sub-networks common to them.

The total number of edges and the total number of nodes in the cancer-specific gene regulatory subnetwork when the length of the transcription control region is changed are shown below.

FIG. 3 shows the common cancer-specific gene regulatory subnetwork in BDC, LUAD, CRC and HCC when the promoter region length was set to 2,000 nt. The sub-network consists of 46 gene nodes and 17 protein nodes. The sub-network includes a HDAC2 protein node and a loop structure containing HDAC2 gene nodes; a loop structure containing HDAC2, AMEF2, RORAPHA and MEF2 as protein nodes and HDAC2 gene, RORA gene and MEF2A gene as gene nodes; , a loop structure containing PLZF and HMGIY as protein nodes and ZBTB16 and HMGA1 as gene nodes was found.

Example 4
Selection of Differentially Expressed Genes Using Tensor Decomposition Method For the extraction of differentially expressed genes, feature extraction based on tensor decomposition was performed (Taguchi, Yh., BMC Medical Genomics 2017, 10 (Suppl 4): 67). The tensor decomposition method is an unsupervised method that can extract genes of interest from cancer-normal paired datasets. This method has been found to be an effective way to generate a list of significant genes in paired datasets.

Gene expression profiles from each cancer dataset were used as input. Gene expression profile data were obtained based on raw data type data and libraries. Gene expression profiles for BDC, LUAD, and CRC were generated using the variance-stabilized transfer function of the DESeq2 library included in R. For the HCC dataset, normalized gene expression data were generated using the counts per million function from the limma library.

To perform tensor decomposition analysis, each gene expression matrix is treated as a tensor. A three-dimensional matrix M was constructed from each matrix and used as a tensor. Index i indicates sample, index j indicates experimental condition, and k indicates gene. Since this is a data set comparing two conditions, normal and cancer, the index j has only two values. So the value with index M _ijk is the gene expression level of gene k from sample i at condition j, where it is either normal or cancerous.

The rTensor library from R was used to perform tensor decomposition feature extraction. A univalued decomposition was calculated using the HOSVD function. P-values were calculated using the Chi-square test and corrected using the Benjamini-Hochberg method (Benjamini, Y. et al., Journal of the Royal Statistical Society. Series B (Methodological) 31, 289-300). The p-value threshold was set to 0.05 or less.

Cancer-specific gene regulatory sub-networks highlighting differentially expressed genes found by tensor decomposition are shown in FIGS. 4-7.

Example 5
Identification of protein nodes representing transcription factors or transcription factor complexes that bind to the second transcriptional control region of HDAC2 In this example, a region containing an enhancer region, a promoter region and a silencer region was defined as a "second transcriptional control region". Among the transcription factors encoded by the genes included in the cancer-specific gene regulatory sub-network obtained in Examples 3 and 4, those that bind to the second transcriptional control region of the HDAC2 gene were identified. Specifically, using FIMO software and the GENEHANCER database, PWM data of genes included in the cancer-specific gene regulatory subnetwork were input to determine whether they exist in the second transcriptional control region of the HDAC2 gene. Searched. A p-value threshold of 0.001 was set for the cutoff of matching sequences. Among the genes discovered in this way, the expression level is changed in all of cholangiocarcinoma (BDC), lung adenocarcinoma (LUAD), colorectal cancer (CRC) and hepatocellular carcinoma (HCC) compared to normal cells. 17 genes were found as genes that are The results are shown below. In the table, positive numbers indicate increased expression compared to normal cells, and negative numbers indicate decreased expression compared to normal cells. As a result, the expression of RORA, MEF2A, FOXO1 and SOX5 was decreased in all of the above four cancers. This suggests that reduction of these transcription factors contributes to increased expression of HDAC2 and causes cancer. Therefore, it is speculated that cancer can be treated by suppressing the expression of HDAC2 by using these transcription factor expression promoters. A graph identifying genes that bind to the second transcriptional control region of the HDAC2 gene in the cancer-specific gene regulatory subnetwork is shown in FIG.

Cancer-specific gene regulatory networks generated by the methods of the present invention allow a better understanding of the interactions of genes and transcription factors involved in cancer. Moreover, by using this network, it is possible to support the search for genes and proteins that are targets of novel anticancer drugs, and to provide novel anticancer drugs.

100 Information Processing Device 101 Communication Device 102 Input Device 103 Display Device 110 Storage Device 120 CPU
121 first generation unit 122 second generation unit 123 third generation unit 124 acquisition unit 125 first identification unit 126 second identification unit

Claims (15)

a plurality of protein nodes representing a transcription factor or a transcription factor complex comprising a plurality of transcription factors;
a plurality of gene nodes representing transcription factor genes encoding said transcription factors;
a translation edge that connects a gene node that is a source node and a protein node that is a target node and represents translation into the transcription factor;
a transcription control edge that connects a protein node that is a source node and a gene node that is a target node and represents the control of expression of the transcription factor gene by the transcription factor or the transcription factor complex; generating a transcription factor gene regulatory network; and transcription factor genes that are differentially expressed between normal cells and cancer cells from the nodes and edges in the transcription factor gene regulatory network generated by said step, and A method of generating a cancer-specific gene regulatory network, comprising: selecting nodes and edges for encoding transcription factors to generate a cancer-specific gene regulatory network. The generation process of the transcription factor gene regulatory network is
(1) For each of a plurality of transcription factor candidates and transcription factor complex candidates that may constitute the transcription factor gene regulatory network,
data of transcription factor candidate names, transcription factor complex candidate names, and gene names encoding the transcription factor candidates and transcription factor complex candidates;
position weight matrix (PWM) data of nucleotide sequences to which the transcription factor candidates and transcription factor complex candidates bind;
preparing the sequence data of the first transcription control region of the gene encoding the transcription factor candidate and the transcription factor complex candidate;
(2) obtaining data of protein nodes, gene nodes, and translation edges based on the data of transcription factor candidate names, transcription factor complex candidate names, and gene names encoding them;
(3) performing sequence matching between the PWM data of each of a plurality of transcription factor candidates and transcription factor complex candidates and the sequence data of the first transcription control regions of the genes encoding the transcription factor candidates and transcription factor complex candidates; Obtain the data of the transfer control edge by performing
(4) generating a transcription factor gene regulatory network by integrating the data obtained in (2) and (3). The step of selecting nodes and edges related to transcription factor genes and their encoded transcription factors that are differentially expressed between normal cells and cancer cells is the expression level between genes in normal cells and genes in cancer cells. 3. The method according to claim 1 or 2, comprising performing a variation analysis and selecting nodes and edges related to the gene whose expression level varies and the protein encoded by the gene. 4. The method of any one of claims 1-3, which generates a human cancer-specific gene regulatory network. The method of any one of claims 1-4, wherein the cancer is selected from the group consisting of cholangiocarcinoma, lung adenocarcinoma, colon cancer and hepatocellular carcinoma. A protein node representing a transcription factor or a transcription factor complex that binds to a second transcription control region of a gene corresponding to one gene node selected from the generated cancer-specific gene control network is added to the cancer-specific gene control network. the process of identifying from
The method of any one of claims 1-5, further comprising 7. The method of claim 6, wherein the first transcriptional control region is a promoter region and the second transcriptional control region is a region containing an enhancer region, a promoter region and a silencer region. 8. A method according to claim 6 or 7, wherein the selected gene node is a gene node representing the HDAC2 gene. 1. A method of generating a cancer-specific gene regulatory sub-network that is common in at least two cancers, comprising:
Comparing at least two cancer-specific gene regulatory networks generated according to the method of any one of claims 1 to 8 to generate a cancer-specific gene regulatory sub-network common to them. including, method. A computer program for generating a cancer-specific gene regulatory network, which causes a computer to execute the method for generating a cancer-specific gene regulatory network according to any one of claims 1 to 8. A computer program for generating a cancer-specific gene regulatory sub-network, which causes a computer to execute the method for generating a cancer-specific gene regulatory sub-network according to claim 9. a plurality of protein nodes representing a transcription factor or a transcription factor complex comprising a plurality of transcription factors;
a plurality of gene nodes representing transcription factor genes encoding said transcription factors;
a translation edge that connects a gene node that is a source node and a protein node that is a target node and represents translation into the transcription factor;
a transcription control edge that connects a protein node that is a source node and a gene node that is a target node and represents the control of expression of the transcription factor gene by the transcription factor or the transcription factor complex; a first generator that generates a transcription factor gene regulatory network;
Nodes and edges related to transcription factor genes differentially expressed between normal cells and cancer cells and transcription factors encoded by the same, from the nodes and edges in the transcription factor gene regulatory network generated by the first generation unit and a second generator that selects to generate the cancer-specific gene regulatory network. A protein node representing a transcription factor or a transcription factor complex that binds to a second transcriptional control region of a gene corresponding to one gene node selected from the generated cancer-specific gene control network is selected from the cancer-specific gene control network. 13. The apparatus of claim 12, further comprising a first identification unit identifying within a network. 1. An apparatus for the generation of cancer-specific gene regulatory sub-networks common in at least two cancers, comprising:
an obtaining unit for obtaining at least two cancer-specific gene regulatory networks generated according to the method of any one of claims 1-8;
and a third generator that compares the at least two cancer-specific gene regulatory networks to generate a cancer-specific gene regulatory sub-network common to them. A protein node representing a transcription factor or a transcription factor complex that binds to a second transcriptional control region of a gene corresponding to one gene node selected from the generated cancer-specific gene regulatory sub-network is selected from the cancer-specific gene 15. The apparatus of claim 14, further comprising a second identifier that identifies among control sub-networks.

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