A method for constructing cancer type-specific gene regulatory networks

Abstract

The present invention provides a method for constructing a cancer type-specific gene regulatory network, relying on the characteristics of high-performance computer computing, and efficiently predicting the interaction between transcription factors and genes through data mining technology. While considering the transcription factor regulation of the linear neighbor genes of the genome, the influence of the three-dimensional structure of chromatin on the regulation is also considered, and the regulatory relationship formed by the spatial neighbors is integrated to obtain a more comprehensive transcription factor and gene regulatory relationship, thereby constructing a more complete cancer type-specific gene regulatory network. The present invention can realize the fusion of chromatin state and structural information with gene expression function information, follow the basic law that structure determines function, and can avoid the problem of insufficient information of a single type of data, and the problem of information redundancy of too many types of data. Compared with traditional biological experimental methods, it greatly saves time costs and economic expenses.

Description

Technical Field

[0001] The present invention belongs to the technical field of gene regulatory network reasoning, and specifically relates to a method for constructing a cancer type-specific gene regulatory network. Background Art

[0002] Cancer is a complex disease. Its occurrence is related to multiple factors and is usually accompanied by abnormal gene regulation. Gene regulatory networks are an effective way to study gene regulatory relationships. The expression of a gene is regulated by other genes, and the product of the gene may regulate the expression of other genes. This intricate interaction can be represented by a gene regulatory network. Studying the risk genes and regulatory relationships in cancer by constructing a gene regulatory network has a positive effect on understanding the pathogenesis of cancer and preventing and treating it in advance.

[0003] Gene regulatory networks are mainly composed of two parts: vertices and edges. Vertices represent participants in the gene regulatory process, such as genes and transcription factors, while edges represent regulatory relationships formed by interactions between vertices, such as the regulation of genes by transcription factors. Currently, there are two main methods for constructing gene regulatory networks: the first is to reversely reconstruct gene regulatory networks based on the application of statistical inference methods based on gene expression data. Gene expression data can reflect the relative changes in the expression levels between genes, and can thus be used to infer regulatory relationships between genes. However, due to the limitations of early sequencing technology, it is difficult to discover some complex biological mechanisms through a single type of data.

[0004] The development of current technology has made it possible to detect the degree of chromatin openness. Chromatin openness data reflects the degree of openness of chromatin that allows regulatory factors to approach. Open chromatin is a prerequisite for the binding of regulatory factors. The introduction of this type of data can screen out some false positive results of reverse inference from the source; the second type is based on the positive inference of linear genome neighbors that regulatory factors regulate their linear neighboring genes. However, chromatin is compressed and folded in the cell nucleus to form a complex three-dimensional structure, which allows regulatory elements to approach target genes that are far away from them in space and produce regulatory relationships. Therefore, the results of analysis using this type of method are not ideal. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method for constructing a cancer type-specific gene regulatory network. The technical problem to be solved by the present invention is achieved by the following technical solutions:

[0006] The present invention provides a method for constructing a cancer type-specific gene regulatory network, comprising:

[0007] Step 1: Obtain publicly available cancer chromatin-related data, gene expression data, and position weight matrices of possible binding locations of multiple transcription factors in the genome from the Internet;

[0008] The chromatin-related data include chromatin open data and chromatin three-dimensional interaction data, wherein the chromatin open data include multiple chromatin open fragment features of multiple cancer types, and the chromatin three-dimensional interaction data include target chromatin fragments with three-dimensional interactions of the desired cancer type; the gene expression data include gene expression profiles of the desired cancer type;

[0009] Step 2: Perform cancer type-specific processing on the chromatin open data to obtain specific open chromosome fragments;

[0010] Step 3: Perform cancer type-specific screening on the chromatin three-dimensional interaction data to obtain target chromatin fragment pairs under the required cancer type;

[0011] Step 4: Input the position weight matrix of transcription factors and all specific open chromosome fragments into the R package motifmatchr, and output the predicted specific open chromatin fragments and transcription factor binding results;

[0012] Step 5: Based on the transcription factor binding results, infer the gene that is linearly adjacent to the transcription factor binding position as the first target gene and the gene that is adjacent in three-dimensional space as the second target gene; and the regulatory relationship between the transcription factor and the first target gene is taken as the first candidate regulatory relationship, and the regulatory relationship between the transcription factor and the second target gene is taken as the second candidate regulatory relationship;

[0013] Step 6: determining a reliable first regulatory relationship among the first candidate regulatory relationships, and determining a reliable second regulatory relationship among the second candidate regulatory relationships;

[0014] Step 7: Merge the first regulatory relationship with the second regulatory relationship to obtain a cancer type-specific gene regulatory network based on the merged regulatory relationship.

[0015] Optionally, the step 2 includes:

[0016] Step 2a: The openness scores of all chromatin open fragment features are represented by a matrix;

[0017] Step 2b: Standardize the openness scores in the matrix to the same scale to obtain a standardized matrix;

[0018] Step 2c: calculating the open-specificity index of chromatin open fragments in different cancer types according to the standardized matrix;

[0019] Step 2d: Screen all chromatin open fragments and retain the chromatin fragments whose open specificity index is greater than the specificity threshold;

[0020] Step 2e: All retained open chromatin fragments are assigned to the cancer type with the largest openness score to obtain specific open chromatin fragments under each cancer type, and the specific open chromatin fragments under the same cancer type are grouped into sets.

[0021] Optionally, the normalization matrix in step 2 is:

[0022]

[0023] Among them, Min and Max represent the minimum and maximum values ​​of the open scores of all chromatin fragments, respectively. j and max j denote the minimum and maximum openness scores in the jth cancer type, respectively;

[0024] The open specificity indicators are:

[0025]

[0026] Where N represents the number of cancer types, B j represents the openness score of the corresponding chromatin fragment in cancer type j, which is the standardized matrix B ij The j-th column element of .

[0027] Optionally, step 3 includes:

[0028] Step 3a: According to the genome sequence, the selected specific open chromatin fragments are attempted to match with the target chromosome fragments, thereby mapping the specific open chromatin fragments to the two ends of the three-dimensional interaction;

[0029] Step 3b: If the open chromatin fragment is matched at at least one end of the three-dimensional interaction, the target chromatin fragment pairs with three-dimensional interaction relationship under the desired cancer type are screened.

[0030] Optionally, in step 5, based on the transcription factor binding result, inferring a gene linearly adjacent to the transcription factor binding position as the first target gene and a gene adjacent in three-dimensional space as the second target gene includes:

[0031] The specific open chromosome fragment was input into the R package ChIPseeker, and the gene with the closest linear distance to the specific open chromosome fragment was output as the first target gene;

[0032] The target chromosome fragment was input into the R package ChIPseeker, and the genes on the identified target chromosome fragment were output as the second target gene.

[0033] Optionally, determining a reliable first regulatory relationship in step 6 includes:

[0034] Step 6a1: according to the correlation between the specific open chromatin fragment and the first target gene, the Pearson correlation coefficient between the openness score of the specific open chromatin fragment and the expression profile of the first target gene is calculated;

[0035] Step 6b1: Screening the associations whose absolute values ​​of the Pearson correlation coefficients exceed the first coefficient threshold;

[0036] Step 6c1: Associating the transcription factor with the first target gene through the correlation relationship between the specific open chromatin fragments with a Pearson correlation coefficient exceeding a coefficient threshold and the target gene;

[0037] Step 6d1: Calculate the Pearson correlation coefficient between the expression profile of the specific open transcription factor and the expression profile of the first target gene according to the regulatory relationship between the transcription factor and the first target gene;

[0038] Step 6e1: Among the first candidate regulatory relationships, the regulatory relationships whose absolute values ​​of the Pearson correlation coefficients exceed the second coefficient threshold are selected as reliable regulatory relationships.

[0039] Optionally, determining a reliable second regulatory relationship in step 6 includes:

[0040] Step 6a2: Calculate the Pearson correlation coefficient between the openness score of the target chromatin fragment and the expression profile of the second target gene based on the correlation relationship between the target chromatin fragment and the adjacent second target gene in three-dimensional space;

[0041] Step 6b2: Screening the associations whose absolute values ​​of the Pearson correlation coefficients exceed the first coefficient threshold;

[0042] Step 6c3: Associating the transcription factor with the second target gene through the correlation relationship between the target chromatin fragment and the second target gene whose Pearson correlation coefficient exceeds the coefficient threshold;

[0043] Step 6d4: Calculate the Pearson correlation coefficient between the transcription factor expression profile and the second target gene expression profile based on the regulatory relationship between the transcription factor and the second target gene;

[0044] Step 6e5: Among the second candidate regulatory relationships, the regulatory relationships whose absolute values ​​of the Pearson correlation coefficients exceed the second coefficient threshold are selected as reliable second regulatory relationships.

[0045] Among them, the Pearson correlation coefficient is:

[0046]

[0047] Among them, cov(X,Y) represents the covariance between chromatin fragment X and target gene Y, σ X and σ Yrepresent the standard deviation of chromatin fragment X and target gene Y, respectively.

[0048] Beneficial effects of the present invention:

[0049] 1) The present invention provides a method for constructing a cancer type-specific gene regulatory network. Relying on the characteristics of high-performance computer computing, the interaction between transcription factors and genes is efficiently predicted through data mining technology, which greatly saves time and economic expenses compared to traditional biological experimental methods.

[0050] 2) The present invention can realize the integration of chromatin state and structural information with gene expression function information, following the basic law that structure determines function, which can avoid both the problem of insufficient information of a single type of data and the problem of redundant information of too many types of data.

[0051] 3) While considering the regulation of linear neighboring genes of the genome by transcription factors, the present invention also takes into account the influence of the three-dimensional structure of chromatin on regulation, integrates the regulatory relationship formed by spatial neighbors, obtains a more comprehensive transcription factor and gene regulatory relationship, and then constructs a more complete cancer type-specific gene regulatory network.

[0052] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of a process of constructing a cancer type-specific gene regulatory network provided by an embodiment of the present invention;

[0054] Figure 2 It is a schematic diagram of a three-dimensional interactive inference and regulation relationship provided by an embodiment of the present invention;

[0055] Figure 3 A schematic diagram of a gene regulatory network specifically constructed for human breast cancer provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0057] like Figure 1 As shown, the present invention provides a method for constructing a cancer type-specific gene regulatory network, comprising:

[0058] Step 1: Obtain publicly available cancer chromatin-related data, gene expression data, and position weight matrices of possible binding locations of multiple transcription factors in the genome from the Internet;

[0059] The chromatin-related data include chromatin open data and chromatin three-dimensional interaction data, wherein the chromatin open data include multiple chromatin open fragment features of multiple cancer types, and the chromatin three-dimensional interaction data include target chromatin fragments with three-dimensional interactions of the desired cancer type; the gene expression data include gene expression profiles of the desired cancer type;

[0060] It is worth mentioning that: chromatin openness ATAC-seq data of 23 human cancer types were downloaded from UCSC Xena Browser, including 410 tissue samples and 562,709 chromatin open fragment features; chromatin three-dimensional interaction data of human breast cancer MCF7 cell line were downloaded from GEO database, specifically RNA polymerase-mediated ChIA-PET data, which included 1,065,146 interactions between target chromatin fragments; gene expression data of human breast cancer were downloaded from TCGA database, including 60,483 genes and 1,222 samples; position weight matrix of transcription factors was downloaded from CIS-BP database, including 870 transcription factors.

[0061] Step 2: Perform cancer type-specific processing on the chromatin open data to obtain specific open chromosome fragments;

[0062] Step 2 of the present invention comprises:

[0063] Step 2a: The openness scores of all chromatin open fragment features are represented by a matrix;

[0064] Step 2b: Standardize the openness scores in the matrix to the same scale to obtain a standardized matrix;

[0065] It is worth noting that the openness scores of 562,709 chromatin fragments of 23 cancer types are represented by a matrix C, where the rows represent the characteristics of chromatin open fragments and the columns represent the cancer types. The openness scores of chromatin open fragments of different cancer types are standardized to the same scale, and the standardized matrix is ​​obtained as follows:

[0066]

[0067] Among them, Min and Max represent the minimum and maximum open scores of all chromatin open fragments in all cancer types, respectively. j and max j They represent the minimum and maximum openness scores of all chromatin open fragments in the jth cancer type, respectively; this processing standardizes the openness scores of the 23 cancer types to the interval [Min,Max], making the openness scores of different cancers directly comparable.

[0068] Step 2c: calculating the open-specificity index of chromatin open fragments in different cancer types according to the standardized matrix;

[0069] Among them, the open specificity index is:

[0070]

[0071] Where N represents the number of cancer types, here N = 23, B j represents the openness score of the corresponding chromatin open fragment in cancer type j, which is the standardized matrix B ij The j-th column element of .

[0072] Step 2d: Screen all chromatin open fragments and retain the chromatin fragments whose open specificity index is greater than the specificity threshold;

[0073] Step 2e: All retained open chromatin fragments are assigned to the cancer type with the largest openness score to obtain specific open chromatin fragments under each cancer type, and the specific open chromatin fragments under the same cancer type are grouped into sets.

[0074] It is worth noting that: according to the specificity score, all open chromatin fragments were screened with a threshold of 0.85, and 287,445 specific open chromatin fragments with a score greater than 0.85 were retained. The 287,445 specific open chromatin fragments were respectively assigned to the cancer with the largest openness score among the corresponding 23 cancer types, thus obtaining a set of specific open chromatin fragments of different cancer types.

[0075] Step 3: Perform cancer type-specific screening on the chromatin three-dimensional interaction data to obtain target chromatin fragment pairs under the required cancer type;

[0076] Step 3 of the present invention comprises:

[0077] Step 3a: According to the genome sequence, the selected specific open chromatin fragments are attempted to match with the target chromosome fragments, thereby mapping the specific open chromatin fragments to the two ends of the three-dimensional interaction;

[0078] Step 3b: If the open chromatin fragment is matched at at least one end of the three-dimensional interaction, the target chromatin fragment pairs with three-dimensional interaction relationship under the desired cancer type are screened.

[0079] Step 4: Input the transcription factor position weight matrix and all specific open chromosome fragments into the R package motifmatchr, and output the predicted specific open chromatin fragments and transcription factor binding results;

[0080] Step 5: Based on the transcription factor binding results, infer the gene that is linearly adjacent to the transcription factor binding position as the first target gene and the gene that is adjacent in three-dimensional space as the second target gene; and the regulatory relationship between the transcription factor and the first target gene is taken as the first candidate regulatory relationship, and the regulatory relationship between the transcription factor and the second target gene is taken as the second candidate regulatory relationship;

[0081] The present invention inputs the specific open chromosome segment into the R package ChIPseeker, outputs the gene with the closest linear distance to the specific open chromosome segment as the first target gene; inputs the target chromosome segment into the R package ChIPseeker, outputs the gene on the identified target chromosome segment, and Figure 2 , the heteroterminal gene in the target chromatin fragment pair is used as the second target gene.

[0082] Step 6: determining a reliable first regulatory relationship among the first candidate regulatory relationships, and determining a reliable second regulatory relationship among the second candidate regulatory relationships;

[0083] In the present invention, step 6 of determining a reliable first regulatory relationship includes:

[0084] Step 6a1: according to the correlation between the specific open chromatin fragment and the first target gene, the Pearson correlation coefficient between the openness score of the specific open chromatin fragment and the expression profile of the first target gene is calculated;

[0085] Step 6b1: Screening the associations whose absolute values ​​of the Pearson correlation coefficients exceed the first coefficient threshold;

[0086] Step 6c1: Associating the transcription factor with the first target gene through the correlation relationship between the specific open chromatin fragments with a Pearson correlation coefficient exceeding a coefficient threshold and the target gene;

[0087] Step 6d1: Calculate the Pearson correlation coefficient between the expression profile of the specific open transcription factor and the expression profile of the first target gene according to the regulatory relationship between the transcription factor and the first target gene;

[0088] Step 6e1: Among the first candidate regulatory relationships, the regulatory relationships whose absolute values ​​of the Pearson correlation coefficients exceed the second coefficient threshold are selected as reliable regulatory relationships.

[0089] It is worth noting that: the specific open chromatin fragments were associated with their nearest target genes, and the Pearson correlation coefficient between the openness score of the open chromatin fragments and the gene expression spectrum was calculated; the association pairs of open chromatin fragments and genes whose absolute values ​​of the correlation coefficients exceeded the first coefficient threshold of 0.5 were screened out; the transcription factors bound to the open chromatin fragments were associated with their target genes, and the Pearson correlation coefficients between the transcription factors and the expression spectrum of the associated genes were calculated; the regulatory relationships between transcription factors and genes with strong expression correlations whose absolute values ​​of the correlation coefficients exceeded the second coefficient threshold of 0.7 were screened out as reliable regulatory relationships.

[0090] The present invention determines that the reliable second regulatory relationship includes:

[0091] Step 6a2: Calculate the Pearson correlation coefficient between the openness score of the target chromatin fragment and the expression profile of the second target gene based on the correlation relationship between the target chromatin fragment and the adjacent second target gene in three-dimensional space;

[0092] Step 6b2: Screening the associations whose absolute values ​​of the Pearson correlation coefficients exceed the first coefficient threshold;

[0093] Step 6c3: Associating the transcription factor with the second target gene through the correlation relationship between the target chromatin fragment and the second target gene whose Pearson correlation coefficient exceeds the coefficient threshold;

[0094] Step 6d4: Calculate the Pearson correlation coefficient between the transcription factor expression profile and the second target gene expression profile based on the regulatory relationship between the transcription factor and the second target gene;

[0095] Step 6e5: Among the second candidate regulatory relationships, the regulatory relationships whose absolute values ​​of the Pearson correlation coefficients exceed the second coefficient threshold are selected as reliable second regulatory relationships.

[0096] The present invention associates the target chromatin fragment at one end of the three-dimensional chromatin interaction with the gene on the target chromatin fragment at the other end, and uses the Pearson correlation coefficient to calculate the Pearson correlation coefficient between the openness score of the target chromatin fragment and the gene expression spectrum. The target chromatin fragment and the gene association pairs whose absolute value of the correlation coefficient exceeds the first coefficient threshold of 0.5 are screened out; the transcription factor on the target chromatin fragment is associated with its target gene, and the expression correlation between the transcription factor and the target gene is calculated using the Pearson correlation coefficient; the regulatory relationship between the transcription factor and the gene with a strong expression correlation whose absolute value of the correlation coefficient exceeds the second coefficient threshold of 0.7 is screened out as a reliable regulatory relationship.

[0097] Step 7: Merge the first regulatory relationship with the second regulatory relationship to obtain a cancer type-specific gene regulatory network based on the merged regulatory relationship.

[0098] Among them, the Pearson correlation coefficient is:

[0099]

[0100] Among them, cov(X,Y) represents the covariance between chromatin fragment X and target gene Y, σ X and σ Y represent the standard deviation of chromatin fragment X and target gene Y, respectively.

[0101] refer to Figure 2 , Figure 2 A schematic diagram of a three-dimensional interaction-inferred regulatory relationship provided for an embodiment of the present invention. The main structure is chromatin formed by DNA wrapped around nucleosomes, and the chromatin will fold in three-dimensional space to form a ring structure (the right part is a ring), thereby making the chromatin fragments with a long linear distance close in space (the middle contact part). As shown in the figure, the formation of a three-dimensional interaction requires a pair of target chromatin fragments, where the transcription factor bound to the target chromatin fragment at one end is associated with the target gene at the other end, thereby forming a second candidate regulatory relationship.

[0102] refer to Figure 3 , Figure 3 A schematic diagram of a gene regulatory network specifically constructed for human breast cancer provided in an embodiment of the present invention. Wherein the square represents a transcription factor, the circle represents a gene, the line represents the regulatory relationship between the transcription factor and the gene, and the arrow represents the directionality of the regulation. As can be seen from the figure, the overall structure of the network is relatively sparse, and a large number of genes are only regulated by a small number of transcription factors, which conforms to the sparsity law of the gene regulatory network; at the same time, there is a module structure with tight internal connections and sparse external connections in the network, and its biological significance is that multiple genes often cooperate to exercise specific functions through mutual regulatory effects. The construction of this network is expected to provide new ideas and references for cancer gene regulation research.

[0103] The present invention provides a method for constructing a cancer type-specific gene regulatory network. Relying on the characteristics of high-performance computer computing, the interaction between transcription factors and genes is efficiently predicted through data mining technology. While considering the transcription factor regulation of the linear neighbor genes of the genome, the influence of the three-dimensional structure of chromatin on the regulation is also considered. The regulatory relationship formed by the spatial neighbors is integrated to obtain a more comprehensive transcription factor and gene regulatory relationship, thereby constructing a more complete cancer type-specific gene regulatory network. The present invention can realize the fusion of chromatin state and structural information with gene expression function information, follow the basic law that structure determines function, and can avoid the problem of insufficient information of a single type of data and the problem of information redundancy of too many types of data. Compared with traditional biological experimental methods, it greatly saves time costs and economic expenses.

[0104] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0105] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A method for constructing a cancer type-specific gene regulatory network, characterized in that: include: Step 1: Obtain publicly available cancer chromatin-related data, gene expression data, and position weight matrices of multiple transcription factors at genomic binding sites from the Internet; The position weight matrix is ​​obtained from the downloaded CIS-BP database; The chromatin-related data include chromatin open data and chromatin three-dimensional interaction data, wherein the chromatin open data include multiple chromatin open fragment features of multiple cancer types, and the chromatin three-dimensional interaction data include target chromatin fragments with three-dimensional interactions of the desired cancer type; the gene expression data include gene expression profiles of the desired cancer type; Step 2: Perform cancer type-specific processing on the chromatin open data to obtain specific open chromosome fragments; Step 3: Perform cancer type-specific screening on the chromatin three-dimensional interaction data to obtain target chromatin fragment pairs under the required cancer type; Step 4: Input the position weight matrix and all specific open chromosome segments into the R package motifmatchr, and output the predicted binding results of specific open chromatin segments and transcription factors; Step 5: Based on the binding results, infer the gene that is linearly adjacent to the transcription factor binding position as the first target gene and the gene that is adjacent in three-dimensional space as the second target gene; and the regulatory relationship between the transcription factor and the first target gene is taken as the first candidate regulatory relationship, and the regulatory relationship between the transcription factor and the second target gene is taken as the second candidate regulatory relationship; Step 6: determining a reliable first regulatory relationship among the first candidate regulatory relationships, and determining a reliable second regulatory relationship among the second candidate regulatory relationships; Step 7: Merge the first regulatory relationship with the second regulatory relationship to obtain a cancer type-specific gene regulatory network based on the merged regulatory relationship.

2. The method for constructing a cancer type-specific gene regulatory network according to claim 1, characterized in that: The step 2 comprises: Step 2a: The openness scores of all chromatin open fragment features are represented by a matrix; Step 2b: Standardize the openness scores in the matrix to the same scale to obtain a standardized matrix; Step 2c: calculating the open-specificity index of chromatin open fragments in different cancer types according to the standardized matrix; Step 2d: Screen all chromatin open fragments and retain the chromatin fragments whose open specificity index is greater than the specificity threshold; Step 2e: All retained open chromatin fragments are assigned to the cancer type with the largest openness score to obtain specific open chromatin fragments under each cancer type, and the specific open chromatin fragments under the same cancer type are grouped into sets.

3. The method for constructing a cancer type-specific gene regulatory network according to claim 2, characterized in that: The normalization matrix described in step 2 is: Among them, Min and Max represent the minimum and maximum values ​​of the open scores of all chromatin fragments, respectively. j and max j denote the minimum and maximum openness scores in the jth cancer type, respectively; The open specificity indicators are: Where N represents the number of cancer types, B j represents the openness score of the corresponding chromatin fragment in cancer type j, which is the standardized matrix B ij The j-th column element of .

4. The method for constructing a cancer type-specific gene regulatory network according to claim 1, characterized in that: The step 3 comprises: Step 3a: According to the genome sequence, the selected specific open chromatin fragments are attempted to match with the target chromosome fragments, thereby mapping the specific open chromatin fragments to the two ends of the three-dimensional interaction; Step 3b: If the open chromatin fragment is matched at at least one end of the three-dimensional interaction, the target chromatin fragment pairs with three-dimensional interaction relationship under the desired cancer type are screened.

5. The method for constructing a cancer type-specific gene regulatory network according to claim 1, characterized in that: In step 5, based on the binding result, inferring a gene linearly adjacent to the transcription factor binding position as the first target gene and a gene adjacent in three-dimensional space as the second target gene includes: The specific open chromosome fragment was input into the R package ChIPseeker, and the gene with the closest linear distance to the specific open chromosome fragment was output as the first target gene; The target chromosome fragment was input into the R package ChIPseeker, and the genes on the identified target chromosome fragment were output as the second target gene.

6. The method for constructing a cancer type-specific gene regulatory network according to claim 1, characterized in that: Determining a reliable first regulatory relationship in step 6 includes: Step 6a1: according to the correlation between the specific open chromatin fragment and the first target gene, the Pearson correlation coefficient between the openness score of the specific open chromatin fragment and the expression profile of the first target gene is calculated; Step 6b1: Screening the associations whose absolute values ​​of the Pearson correlation coefficients exceed the first coefficient threshold; Step 6c1: Associating the transcription factor with the first target gene through the correlation relationship between the specific open chromatin fragments with a Pearson correlation coefficient exceeding a coefficient threshold and the target gene; Step 6d1: Calculate the Pearson correlation coefficient between the expression profile of the specific open transcription factor and the expression profile of the first target gene according to the regulatory relationship between the transcription factor and the first target gene; Step 6e1: Among the first candidate regulatory relationships, the regulatory relationships whose absolute values ​​of the Pearson correlation coefficients exceed the second coefficient threshold are selected as reliable regulatory relationships.

7. The method for constructing a cancer type-specific gene regulatory network according to claim 1, characterized in that: Determining a reliable second regulatory relationship in step 6 includes: Step 6a2: Calculate the Pearson correlation coefficient between the openness score of the target chromatin fragment and the expression profile of the second target gene based on the correlation relationship between the target chromatin fragment and the adjacent second target gene in three-dimensional space; Step 6b2: Screening the associations whose absolute values ​​of the Pearson correlation coefficients exceed the first coefficient threshold; Step 6c3: Associating the transcription factor with the second target gene through the correlation relationship between the target chromatin fragment and the second target gene whose Pearson correlation coefficient exceeds the coefficient threshold; Step 6d4: Calculate the Pearson correlation coefficient between the transcription factor expression profile and the second target gene expression profile based on the regulatory relationship between the transcription factor and the second target gene; Step 6e5: Among the second candidate regulatory relationships, the regulatory relationships whose absolute values ​​of the Pearson correlation coefficients exceed the second coefficient threshold are selected as reliable second regulatory relationships.

8. The method for constructing a cancer type-specific gene regulatory network according to claim 6 or 7, characterized in that: The Pearson correlation coefficient is: Among them, cov(X,Y) represents the covariance between chromatin fragment X and target gene Y, σ X and σ Y represent the standard deviation of chromatin fragment X and target gene Y, respectively.

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