Method, device, computer-readable storage medium and product for molecular typing of medulloblastoma patients

Abstract

The present invention discloses a molecular typing method, device, computer-readable storage medium and product for medulloblastoma patients, relating to the field of medulloblastoma typing, the method comprising obtaining whole-genome sequencing data; the whole-genome sequencing data comprising: whole-genome sequencing data of normal cerebellum tissue and whole-genome sequencing data of tumor tissue of medulloblastoma patients; performing differential methylation region DMR analysis on the whole-genome cytosine report file of the whole-genome sequencing data; screening, principal component analysis and t-SNE analysis of the DMR analysis results; clustering the t-SNE result matrix using the kmeans clustering method; annotating copy number variation CNV according to the clustering results to obtain the molecular typing results of medulloblastoma patients. The present invention can accurately type medulloblastoma patients.

Description

Technical Field

[0001] The present invention relates to the field of medulloblastoma typing, and in particular to a method, device, computer-readable storage medium and product for molecular typing of medulloblastoma patients. Background Art

[0002] Medulloblastoma is a non-single solid tumor. The World Health Organization (WHO) categorizes medulloblastoma into four subtypes based on their molecular characteristics: WNT (Wnt pathway activation), SHH (Sonic hedgehog pathway activation), Group 3, and Group 4. Improving the accuracy of medulloblastoma classification will enable more precise diagnosis and treatment of medulloblastoma patients.

[0003] There is currently no molecular classification method for medulloblastoma based on whole-genome methylation data. Therefore, how to accurately classify medulloblastoma patients based on whole-genome methylation data is an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the present invention is to provide a molecular typing method, device, computer-readable storage medium and product for medulloblastoma patients, which can accurately type medulloblastoma patients.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for molecular typing of medulloblastoma patients, comprising:

[0007] Acquiring whole genome sequencing data; the whole genome sequencing data includes: whole genome sequencing data of normal cerebellum tissue and whole genome sequencing data of tumor tissue of medulloblastoma patients;

[0008] Perform differentially methylated region DMR analysis on the whole genome cytosine report file of whole genome sequencing data;

[0009] The DMR analysis results were screened, subjected to principal component analysis, and t-SNE analysis;

[0010] The t-SNE result matrix is ​​clustered using the kmeans clustering method;

[0011] According to the clustering results, copy number variation CNV was used for annotation to obtain the molecular typing results of medulloblastoma patients.

[0012] Optionally, obtaining whole genome sequencing data, followed by:

[0013] The whole-genome sequencing data was processed by Bismark to obtain a whole-genome cytosine report file; the format of the whole-genome cytosine report file was *CX_report.txt.

[0014] Optionally, performing differentially methylated region DMR analysis on the whole genome cytosine report file of the whole genome sequencing data specifically includes:

[0015] Metilene software was used to perform differentially methylated region (DMR) analysis on the whole-genome cytosine report file of whole-genome sequencing data.

[0016] Optionally, the screening, principal component analysis, and t-SNE analysis of the DMR analysis results specifically include:

[0017] The variance of each DMR among patients according to the DMR analysis results;

[0018] Sort all DMRs by their variance in descending order and retain the top 50% of the sorted sequence;

[0019] The retained parts are formed into matrix M, and matrix M is generated based on matrix M rand ;

[0020] For matrix M and matrix M rand Perform principal component analysis and determine the covariance matrix C and matrix M of the matrix M respectively rand The covariance matrix C rand ;

[0021] Use the eigs function of the RSpectra package to calculate the covariance matrix C and the covariance matrix C rand Perform eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors;

[0022] The covariance matrix C rand Compare the maximum eigenvalue of with the eigenvalue of the covariance matrix C to obtain the number of eigenvectors;

[0023] The obtained eigenvectors were used to perform t-SNE analysis using the Rtsne software package.

[0024] Optionally, based on the clustering results, copy number variation CNVs are used for annotation to obtain molecular typing results for medulloblastoma patients, specifically including:

[0025] If the patients in the subgroup showed a value greater than the set value of 6-, they were all WNT type patients;

[0026] If patients in the subgroup with a value greater than the set value showed 9q- and 10q-, they were all SHH patients;

[0027] If the patients in the subgroup showed i17q, 10q- and 8+, which were greater than the set value, they were all Group 3 patients;

[0028] If the patients in the subgroup have i17q, 8- and 11- expression greater than the set value, they are all Group 4 patients.

[0029] A computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for molecular typing of medulloblastoma patients.

[0030] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for molecular typing of medulloblastoma patients.

[0031] A computer program product comprises a computer program, which implements the method for molecular typing of medulloblastoma patients when the computer program is executed by a processor.

[0032] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0033] The present invention provides a method, device, computer-readable storage medium and product for molecular typing of medulloblastoma patients, which performs differential methylation region DMR analysis on the whole genome cytosine report file of whole genome sequencing data to obtain whole genome methylation sequencing data. Based on the whole genome methylation sequencing data, the molecular typing of medulloblastoma patients is performed by screening important features and integrating tumor CNV data, thereby achieving good separation effects of the four subgroups of WNT, SHH, Group 3 and Group 4. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A schematic flow chart of a method for molecular typing of medulloblastoma patients provided in Example 1 of the present invention;

[0036] Figure 2 Schematic diagram of molecular typing results. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] The purpose of the present invention is to provide a molecular typing method, device, computer-readable storage medium and product for medulloblastoma patients, which can accurately type medulloblastoma patients.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0040] Example 1

[0041] like Figure 1 As shown, the present invention provides a method for molecular typing of medulloblastoma patients, the method comprising:

[0042] S101, obtaining whole genome sequencing data; the whole genome sequencing data includes: whole genome sequencing data of normal cerebellum tissue and whole genome sequencing data of tumor tissue of medulloblastoma patients; the whole genome sequencing data of normal cerebellum tissue comes from EGAS00001000561.

[0043] S101 specifically includes:

[0044] The whole-genome sequencing data was processed by Bismark to obtain a whole-genome cytosine report file; the format of the whole-genome cytosine report file was *CX_report.txt.

[0045] S102: Differential methylation region (DMR) analysis was performed on the genome-wide cytosine report files of the whole-genome sequencing data. DMR analysis was performed using Metilene software, with all Metilene software parameters set to default. DMRs with a q value < 0.05 were screened, and the mean methylation β value for each DMR was calculated for each patient (a two-dimensional matrix with rows as DMRs and columns as patient identifiers, β = M / (UM + M), where UM represents unmethylated sites and M represents methylated sites).

[0046] S103, screening, principal component analysis, and t-SNE analysis of the DMR analysis results;

[0047] S103 specifically includes:

[0048] According to the DMR analysis results, the variance of each DMR in patients; there are N DMRs in total, and the variance of each DMR in a given group of patients is Var i , i=1,2,...,N;Var i =var(X i ), i=1, 2, ..., N;

[0049] Sort the variances of all DMRs in descending order and get sort(Var1, Var2, ..., Var N ), and retain the first 50% of the sorted sequence Top_50% = First_N_DMRs (Var1, Var2, ..., Var N / 2 ); where First_N_DMR(...) indicates selecting the first N DMRs.

[0050] The retained parts are formed into matrix M, and matrix M is generated based on matrix M rand The dimension of the matrix M is (N / 2, n), where N is the number of selected DMRs and n is the number of patients. Each element in M ​​represents a β value. Each column of the matrix M is randomly rearranged to generate a new matrix M rand ; The order of elements in each column is randomly disrupted; the matrix M and the matrix M rand Same dimensions.

[0051] For matrix M and matrix M rand Perform principal component analysis and determine the covariance matrix C and matrix M of the matrix M respectively rand The covariance matrix C rand ; Where, C=cov(M); C rand =cov(M rand ).

[0052] Use the eigs function of the RSpectra package to calculate the covariance matrix C and the covariance matrix C rand Perform eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors; the process of eigenvalue decomposition is:

[0053] Evalues_and_Evectors=RSpectra::eigs(C,k=ncol(M));

[0054] Evalues_and_Evectors rand =RSpectra::eigs(C rand , k=ncol(M rand ).

[0055] The covariance matrix Crand The maximum eigenvalue of is compared with the eigenvalue of the covariance matrix C to obtain the number of eigenvectors; use the formula Evalues_rand max =max(Evalues_rand1,Evalues_rand2,...,Evalues_rand n ) Find C rand The maximum value among the eigenvalues ​​is stored in the variable Evalues_randmax; the eigenvalue of C is expressed as: (Evalues1, Evalues2, ..., Evalues n );

[0056] Select m eigenvectors in C so that Evalues m >Evalues_rand max , and finally get V = [Evalues1, Evalues2, ..., Evalues m ];

[0057] t-SNE analysis was performed using the Rtsne software package based on the obtained eigenvector V. Parameters selected were: theta = 0, pca = F, max_iter = 2500, and perplexity = 10.

[0058] S104. The t-SNE result matrix was clustered using the kmeans clustering method to further separate Group 3 and Group 4 types. This process used the kmeans function in the R package broom with the following parameter settings: centers = 4 (a group of patients can contain a maximum of four subtypes: WNT, SHH, Group 3, and Group 4). Other parameters were set to default. This analysis can obtain the category label (cluster 1, cluster 2, cluster 3, and cluster 4) for each MB patient.

[0059] S105. Based on the clustering results, copy number variation CNV is used for annotation to obtain the molecular typing results of medulloblastoma patients.

[0060] S105 specifically includes:

[0061] If the patients in the subgroup showed a value greater than the set value of 6-, they were all WNT type patients;

[0062] If patients in the subgroup with a value greater than the set value showed 9q- and 10q-, they were all SHH patients;

[0063] If the patients in the subgroup showed i17q, 10q- and 8+, which were greater than the set value, they were all Group 3 patients;

[0064] If the patients in the subgroup have i17q, 8- and 11- expression greater than the set value, they are all Group 4 patients.

[0065] Figure 2 The separation results of 283 medulloblastoma patients based on this method (WNT=85, SHH=126, Group3=49, Group4=23) were compared with normal cerebellar tissues, which made the DMRs used in subsequent typing tend to be tumor-pathogenic DMRs, excluding non-pathogenic DMRs. Further screening of the degree of variation of DMRs in all patients can effectively reduce the screening range of subtype-specific DMRs, and avoid the influence of common DMRs between subtypes on the typing results.

[0066] Example 2

[0067] A computer device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a molecular typing method for medulloblastoma patients in Example 1.

[0068] Example 3

[0069] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the molecular typing method for medulloblastoma patients in Example 1.

[0070] Example 4

[0071] A computer program product includes a computer program, which, when executed by a processor, implements the method for molecular typing of medulloblastoma patients in Example 1.

[0072] It should be noted that the object information (including but not limited to object device information, object personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the object or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0073] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present invention may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in each embodiment provided by the present invention may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc., but are not limited to these.

[0074] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for molecular typing of medulloblastoma patients, characterized in that: The method comprises: Acquiring whole genome sequencing data; the whole genome sequencing data includes: whole genome sequencing data of normal cerebellum tissue and whole genome sequencing data of tumor tissue of medulloblastoma patients; Perform differentially methylated region DMR analysis on the whole genome cytosine report file of whole genome sequencing data; The DMR analysis results were screened, subjected to principal component analysis, and t-SNE analysis; The t-SNE result matrix is ​​clustered using the kmeans clustering method; Based on the clustering results, copy number variation CNV was used for annotation to obtain the molecular typing results of medulloblastoma patients; The screening, principal component analysis and t-SNE analysis of the DMR analysis results specifically include: The variance of each DMR among patients according to the DMR analysis results; Sort all DMRs by their variance in descending order and retain the top 50% of the sorted sequence; The retained parts are formed into matrix M, and matrix M is generated based on matrix M rand The dimension of the matrix M is (N / 2, n), where N is the number of selected DMRs and n is the number of patients. Each element in M ​​represents a β value. Each column of the matrix M is randomly rearranged to generate a new matrix M. rand ; The order of elements in each column is randomly disrupted; the matrix M and the matrix M rand The dimensions are the same; For matrix M and matrix M rand Perform principal component analysis and determine the covariance matrix C and matrix M of the matrix M respectively rand The covariance matrix C rand ; Use the eigs function of the RSpectra package to calculate the covariance matrix C and the covariance matrix C rand Perform eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors; the process of eigenvalue decomposition is: Evalues_and_Evectors=RSpectra::eigs(C,k=ncol(M)); Evalues_and_Evectors rand =RSpectra::eigs(C rand ,k=ncol(M rand ); The covariance matrix C rand The maximum eigenvalue of is compared with the eigenvalue of the covariance matrix C to obtain the number of eigenvectors; use the formula Evalues_rand max =max(Evalues_rand1,Evalues_rand2,...,Evalues_rand n ) Find C rand The maximum value among the eigenvalues ​​is stored in the variable Evalues_randmax; the eigenvalue of C is expressed as: (Evalues1, Evalues2, ..., Evalues n ); Select m eigenvectors in C so that Evalues m >Evalues_rand max , and finally get V = [Evalues1, Evalues2, ..., Evalues m ]; t-SNE analysis was performed using the Rtsne software package based on the obtained eigenvectors; the parameters selected were: theta = 0, pca = F, max_iter = 2500, and perplexity = 10.

2. A method for molecular typing of medulloblastoma patients according to claim 1, characterized in that: Acquisition of whole genome sequencing data, followed by: The whole-genome sequencing data was processed by Bismark to obtain a whole-genome cytosine report file; the format of the whole-genome cytosine report file was *CX_report.txt.

3. A method for molecular typing of medulloblastoma patients according to claim 1, characterized in that: The differentially methylated region DMR analysis of the whole genome cytosine report file of the whole genome sequencing data specifically includes: Metilene software was used to perform differentially methylated region (DMR) analysis on the whole-genome cytosine report file of whole-genome sequencing data.

4. A method for molecular typing of medulloblastoma patients according to claim 1, characterized in that: Based on the clustering results, copy number variation CNV was used for annotation to obtain the molecular typing results of medulloblastoma patients, including: If the patients in the subgroup showed a value greater than the set value of 6-, they were all WNT type patients; If patients in the subgroup with a value greater than the set value showed 9q- and 10q-, they were all SHH patients; If the patients in the subgroup showed i17q, 10q- and 8+, which were greater than the set value, they were all Group 3 patients; If the patients in the subgroup have i17q, 8- and 11- expression greater than the set value, they are all Group 4 patients.

5. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for molecular typing of medulloblastoma patients according to any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program implements the method for molecular typing of medulloblastoma patients according to any one of claims 1 to 4.

7. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the method for molecular typing of medulloblastoma patients according to any one of claims 1 to 4.

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