System, device or medium for diagnosing or predicting breast cancer based on methylation marker combination

Abstract

The invention discloses a system, equipment or medium for diagnosing or predicting breast cancer based on methylation marker combination, and belongs to the technical field of tumor markers. Wherein the methylation marker combination comprises a methylation gene combination, and the methylation gene combination comprises MLLT6, AR1, MED24, KCTD11, AADAT, PTPRN2, ACTR1A, CELSR1, ZC3H7A, TRIM8, IGLL5, CORO2B, THADA, CNNM1, SLC16A3, DEAF1 and LCP2. The invention further discloses a kit for detecting the methylation marker combination. A machine learning model is constructed by using methylation level data of the methylation marker combination, and the machine learning model can be used for diagnosing whether a subject suffers from the breast cancer or predicting whether the subject has the risk of suffering from the breast cancer, and has huge clinical application value.

Description

Technical Field

[0001] The present application relates to the technical field of tumor markers, and in particular to a system, device or medium for diagnosing or predicting breast cancer based on a combination of methylation markers. Background Art

[0002] DNA methylation chips can conduct a comprehensive analysis of gene methylation levels in breast cancer samples, revealing which genes show abnormal DNA methylation levels in breast cancer. These differentially expressed methylation levels may be potential biomarkers for diagnosis, prognosis or treatment.

[0003] With the rapid development of methylomics methods, non-invasive breast cancer detection methods based on DNA methylation chips are increasingly showing great application potential. However, there is still a lack of clinically useful breast cancer detection markers. Summary of the invention

[0004] In order to solve at least one of the above technical problems, the technical solution adopted in this application is as follows.

[0005] In a first aspect, the present application provides an application of a methylation level detection reagent of a methylation marker combination in the preparation of a kit for diagnosing or predicting breast cancer, wherein the methylation marker combination includes a methylation gene combination, and the methylation gene combination includes MLLT6, AR1, MED24, KCTD11, AADAT, PTPRN2, ACTR1A, CELSR1, ZC3H7A, TRIM8, IGLL5, CORO2B, THADA, CNNM1, SLC16A3, DEAF1 and LCP2.

[0006] In this application, the diagnosis is an auxiliary diagnosis and needs to be combined with other clinical indicators for diagnosis. If the subject does not show other clinical features of breast cancer, the subject is considered to be at risk of breast cancer and needs medical intervention.

[0007] In some embodiments of the present application, the detection reagent is used to detect the methylation level of the methylation site of each gene in the methylation gene combination.

[0008] Further, the methylation site of the MLLT6 gene includes cg01485645; the methylation site of the AR1 gene includes cg06982805; the methylation site of the MED24 gene includes cg14700282; the methylation site of the KCTD11 gene includes cg02676175; the methylation site of the AADAT gene includes cg00309402; the methylation site of the PTPRN2 gene includes cg14189678; the methylation site of the ACTR1A gene includes cg09800781; the methylation site of the CELSR1 gene includes cg04332442; the methylation site of the ZC3H7A ... The methylation sites of the TRIM8 gene include cg06388937; the methylation sites of the IGLL5 gene include cg23477395; the methylation sites of the CORO2B gene include cg23597015; the methylation sites of the THADA gene include cg17277833; the methylation sites of the CNNM1 gene include cg25842285; the methylation sites of the SLC16A3 gene include cg07016605; the methylation sites of the DEAF1 gene include cg22132501; and the methylation sites of the LCP2 gene include cg01199321.

[0009] Thus, the methylation marker combination includes the following methylation sites: cg01485645, cg06982805, cg14700282, cg02676175, cg00309402, cg14189678, cg09800781, cg04332442, cg04010868, cg06388937, cg23477395, cg23597015, cg17277833, cg25842285, cg07016605, cg22132501 and cg01199321.

[0010] Furthermore, the methylation marker combination also includes at least one of the following methylation sites: cg07677157, cg06123699, cg27469726, cg12277627, cg12804791 and cg17551400.

[0011] In some embodiments of the present application, the methylation markers include at least one of the following methylation sites: cg01485645, cg07677157, cg06982805, cg14700282, cg06123699, cg27469726, cg02676175, cg00309402, cg14189678, cg0980078 1. cg04332442, cg12277627, cg04010868, cg06388937, cg12804791, cg23477395, cg17551400, cg23597015, cg17277833, cg25842285, cg07016605, cg22132501, and cg01199321.

[0012] In some embodiments of the present application, the methylation markers include the following methylation sites: cg01485645, cg07677157, cg06982805, cg14700282, cg06123699, cg27469726, cg02676175, cg00309402, cg14189678, cg09800781 ... cg04332442, cg12277627, cg04010868, cg06388937, cg12804791, cg23477395, cg17551400, cg23597015, cg17277833, cg25842285, cg07016605, cg22132501, and cg01199321.

[0013] In some embodiments of the present application, the detection reagent includes a methylation detection chip.

[0014] The second aspect of the present application provides a method for constructing a model for diagnosing or predicting breast cancer, comprising the following steps: S1, obtaining methylation level data of any combination of methylation markers described in the first aspect of the present application in biological samples of a population, wherein the population includes breast cancer patients and non-breast cancer subjects; S2, using the methylation level data obtained in step S1, construct a machine learning model and perform multi-fold cross validation.

[0015] In some embodiments of the invention, the method further comprises: S3: Verify the machine learning model obtained in step S2 in the validation set.

[0016] In some embodiments of the present application, the methylation level is obtained using one of methylation-sensitive bisulfite sequencing (Bisulfite Sequencing), high-resolution melting analysis (MS-HRM), methylation chip, reduced genome methylation sequencing (RRBS), whole genome bisulfite methylation sequencing (WGBS) and immunoprecipitation sequencing (MeDIP-seq).

[0017] Bisulfite sequencing is one of the most commonly used methods for DNA methylation analysis. It detects DNA methylation levels by converting unmethylated cytosine (C) into uracil (U), thereby converting cytosine bands into uracil bands, while methylated cytosine (C) remains unchanged; then selectively PCR amplifies the DNA fragments treated with bisulfite, and then performs DNA sequencing, and finally compares the measured sequence with the original sequence, counts the methylation sites and quantity, and analyzes the methylation level.

[0018] MS-HRM detects methylation based on differences in DNA melting curves. DNA treated with sodium bisulfite is amplified by PCR, and the amplified product melts during HRM analysis. Different methylation states lead to different melting curves.

[0019] Methylation chips, also known as methylation arrays, can simultaneously detect the methylation status of thousands of CpG sites in a large number of samples. Fluorescently labeled DNA is hybridized with probes fixed on the chip, and the methylation level is determined by detecting the hybridization signal.

[0020] RRBS uses bisulfite treatment and restriction enzyme digestion to enrich for highly methylated DNA fragments. This method can quickly and efficiently detect highly methylated CpG sites in the genome.

[0021] WGBS is a high-throughput method that can detect the methylation levels of all individual cytosine bases on a genome-wide scale. This method requires deep sequencing coverage to obtain accurate methylation data.

[0022] MeDIP-seq uses anti-5-methylcytosine antibody immunoprecipitation followed by high-throughput sequencing. This method can detect the methylation status of all CpG sites in the genome.

[0023] In some embodiments of the present application, the machine learning model is selected from any one of the following: Logistic regression model, support vector machine model, decision tree model, random forest model, neural network model, XGBoost model, linear discriminant analysis model, GBDT model, ADABoost model, naive Bayes model, CatBoost model, LightGBM model, MLP model and ETC model.

[0024] The third aspect of the present application provides a system for diagnosing or predicting breast cancer, comprising the following modules: A data input module, for inputting the methylation level data of the methylation marker combination in the biological sample of the subject, wherein the methylation marker combination includes a methylation gene combination, and the methylation gene combination includes MLLT6, AR1, MED24, KCTD11, AADAT, PTPRN2, ACTR1A, CELSR1, ZC3H7A, TRIM8, IGLL5, CORO2B, THADA, CNNM1, SLC16A3, DEAF1 and LCP2; A database storage module, for storing methylation level data of the methylation marker combination in biological samples of a population, the population comprising breast cancer patients and non-breast cancer subjects; A disease prediction module is connected to the data input module and the database storage module respectively, and is used to construct a machine learning model using the methylation level data of the methylation marker combination in the biological samples of the group, and diagnose whether the subject has breast cancer or predict whether the subject has a risk of breast cancer based on the methylation level data of the methylation marker combination in the biological samples of the subject obtained from the data input module.

[0025] In some embodiments of the present application, the detection reagent is used to detect the methylation level of the methylation site of each gene in the methylation gene combination.

[0026] Further, the methylation site of the MLLT6 gene includes cg01485645; the methylation site of the AR1 gene includes cg06982805; the methylation site of the MED24 gene includes cg14700282; the methylation site of the KCTD11 gene includes cg02676175; the methylation site of the AADAT gene includes cg00309402; the methylation site of the PTPRN2 gene includes cg14189678; the methylation site of the ACTR1A gene includes cg09800781; the methylation site of the CELSR1 gene includes cg04332442; the methylation site of the ZC3H7A ... The methylation sites of the TRIM8 gene include cg06388937; the methylation sites of the IGLL5 gene include cg23477395; the methylation sites of the CORO2B gene include cg23597015; the methylation sites of the THADA gene include cg17277833; the methylation sites of the CNNM1 gene include cg25842285; the methylation sites of the SLC16A3 gene include cg07016605; the methylation sites of the DEAF1 gene include cg22132501; and the methylation sites of the LCP2 gene include cg01199321.

[0027] Thus, the methylation marker combination includes the following methylation sites: cg01485645, cg06982805, cg14700282, cg02676175, cg00309402, cg14189678, cg09800781, cg04332442, cg04010868, cg06388937, cg23477395, cg23597015, cg17277833, cg25842285, cg07016605, cg22132501 and cg01199321.

[0028] Furthermore, the methylation marker combination also includes at least one of the following methylation sites: cg07677157, cg06123699, cg27469726, cg12277627, cg12804791 and cg17551400.

[0029] In some embodiments of the present application, the methylation markers include at least one of the following methylation sites: cg01485645, cg07677157, cg06982805, cg14700282, cg06123699, cg27469726, cg02676175, cg00309402, cg14189678, cg0980078 1. cg04332442, cg12277627, cg04010868, cg06388937, cg12804791, cg23477395, cg17551400, cg23597015, cg17277833, cg25842285, cg07016605, cg22132501, and cg01199321.

[0030] In some embodiments of the present application, the methylation markers include the following methylation sites: cg01485645, cg07677157, cg06982805, cg14700282, cg06123699, cg27469726, cg02676175, cg00309402, cg14189678, cg09800781 ... cg04332442, cg12277627, cg04010868, cg06388937, cg12804791, cg23477395, cg17551400, cg23597015, cg17277833, cg25842285, cg07016605, cg22132501, and cg01199321.

[0031] The fourth aspect of the present application provides a computer device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any method described in the second aspect of the present application when executing the computer program.

[0032] A fifth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any method described in the second aspect of the present application are implemented.

[0033] Compared with the prior art, the invention of this application has the following beneficial effects: Using the methylation level data of the methylation marker combination of the present application to construct a machine learning model can be used to diagnose whether a subject has breast cancer or predict whether a subject has a risk of breast cancer, which has great clinical application value.

[0034] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, wherein: Figure 1 The accuracy curve of training and cross-validation using the GBDT model in Example 2 of the present application is shown; Figure 2 The cross entropy loss curve of training and cross validation using the GBDT model in Example 2 of the present application is shown; Figure 3 The ROC curve of the GBDT model in the independent validation set in Example 3 of the present application is shown; Figure 4 The confusion matrix of classification performed using the GBDT model in an independent validation set in Example 3 of the present application is shown. DETAILED DESCRIPTION

[0036] Unless otherwise indicated, implied from the context, or customary in the prior art, all parts and percentages in this application are based on weight, and the tests and characterization methods used are all synchronized with the filing date of this application. Where applicable, the contents of any patent, patent application or disclosure involved in this application are fully incorporated herein by reference, and their equivalent patent families are also introduced as references, especially the definitions of relevant terms in the art disclosed in these documents. If the definition of a specific term disclosed in the prior art is inconsistent with any definition provided in this application, the definition of the term provided in this application shall prevail.

[0037] In order to make the technical problems, technical solutions and beneficial effects solved by the present application more clearly understood, the present application is further described in detail below in conjunction with embodiments.

[0038] The following examples are used to demonstrate the preferred embodiments of the present application. It will be understood by those skilled in the art that the techniques disclosed in the following examples represent techniques discovered by the inventors that can be used to implement the present application, and therefore can be considered as preferred embodiments of the present application. However, it should be understood by those skilled in the art based on this specification that many modifications can be made to the specific embodiments disclosed herein, and still the same or similar results can be obtained without departing from the spirit or scope of the present application.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, and the disclosure and materials cited therein are hereby incorporated by reference.

[0040] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many technical equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the claims.

[0041] The experimental methods in the following examples are conventional methods unless otherwise specified. The instruments and equipment used in the following examples are conventional laboratory instruments and equipment unless otherwise specified; the experimental materials used in the following examples are purchased from conventional biochemical reagent stores unless otherwise specified.

[0042] Example 1 Marker Screening 1. Inclusion and exclusion criteria of the study cohort population (1) Inclusion criteria for breast cancer patients: Aged between 19 and 72 years old; (2) Exclusion criteria for breast cancer patients: BRCA test negative.

[0043] (3) Controls were subjects without breast cancer (negative mammogram).

[0044] Using the above criteria, peripheral blood samples were collected from 256 breast cancer patients and 258 non-breast cancer subjects in the Affiliated Cancer Hospital of Harbin Medical University.

[0045] 2. Sample Collection Genomic DNA was extracted from whole blood using the QIAamp DNA Blood Kit (Qiagen). DNA concentration was determined using a quantitative fluorescent dsDNA system (Promega) and fluorescence readings at 504 nmEx / 531 nmEm were measured using a 96-well plate reader (TECAN). DNA quality was assessed using a NanoDrop ND-1000 spectrophotometer (TermoscifiC).

[0046] 3. Methylation Analysis The DNA methylation profiles of peripheral blood samples were analyzed using the Illumina Infnium Methylation EPIC array, a genome-wide methylation screening tool that targets more than 850,000 CpG sites in biologically significant regions of the human methylome.

[0047] At least 600 ng of genomic DNA was obtained from each participant for methylation analysis. Genomic DNA was first bisulfate-converted using the EZ DNA Methylation Kit (Zymo Research), and the resulting bisulfate-converted DNA was amplified, hybridized to a methylation array, and scanned with an Illumina scanner according to standard procedures.

[0048] 4. Data Analysis (1) Remove the control samples and the values ​​in the Pval column in the methylation chip and retain the methylation chip signal values; (2) removing sites where the methylation chip signal value was NA in all samples; (3) Normalize all signal values ​​based on samples; (4) Using the Boruta algorithm to screen methylation site markers: The Boruta algorithm is a feature selection method based on random forest. It is mainly used to identify which features in the data set are important and exclude unimportant or redundant features.

[0049] Using the Boruta algorithm to select features is very useful for improving the performance and interpretability of machine learning models because it can reduce the number of input features, thereby avoiding overfitting and speeding up the training process. Boruta's n_estimators=100 is set, and other parameters are the default parameters of the algorithm. 23 characteristic methylation sites are screened out, as shown in Table 1.

[0050] Table 1 Information of 23 characteristic methylation sites

[0051] The methylation sites in Table 1 can be used as markers for diagnosing or predicting breast cancer. By detecting the methylation level of the methylation sites, it is possible to determine whether a subject has breast cancer or has a risk of developing breast cancer.

[0052] Example 2 Construction of a breast cancer diagnosis or prediction model The inventors used different machine learning models: logistic regression model, support vector machine model, decision tree model, random forest model, XGBoost model, linear discriminant analysis model, GBDT model, ADABoost model, naive Bayes model, CatBoost model, LightGBM model, MLP model and ETC model to establish a breast cancer diagnosis model, and evaluated the model through 5-fold cross-validation, and used the area under the curve (AUC) of the receiver operating characteristic curve (ROC) to evaluate the model classification efficiency. The results are shown in Table 2.

[0053] Table 2 Classification performance of different machine learning models

[0054] As can be seen from Table 2, the GBDT model has the best classification performance, and the LightGBM model, logistic regression model, and CatBoost model also achieved good classification performance.

[0055] The accuracy curve and cross entropy loss curve for training and verification using the GBDT model are as follows: Figure 1 and Figure 2 As shown. Figure 1 and Figure 2 It can be seen that as the number of training set samples increases, the accuracy of the validation set tends to stabilize and the cross entropy loss is reduced to the minimum.

[0056] In summary, based on the characteristic methylation sites obtained by screening, the model constructed using GBDT can accurately distinguish breast cancer patients and thus can be used for auxiliary diagnosis of breast cancer.

[0057] Example 3 Independent Validation of the Model In order to verify the performance of the model constructed using GBDT in Example 2, the inventors further collected 56 independent breast cancer patients and 49 non-breast cancer subjects, obtained the methylation levels of the characteristic methylation sites screened in Example 1, and classified them using the model.

[0058] The ROC curve and the classification result confusion matrix are as follows: Figure 3 and Figure 4 shown.

[0059] comprehensive Figure 3 and Figure 4 The performance data of the model built using GBDT in the independent validation set is shown in Table 3. Figure 4It can be seen that when the model built by GBDT is used for prediction, the number of true positives (TP) is 49 and the number of false positives (FN) is 0; the number of true negatives (TN) is 49 and the number of false negatives (FN) is 7.

[0060] Therefore, the accuracy, precision, and recall of the model built using GBDT in the independent validation set are calculated.

[0061] (1) Accuracy Used to evaluate the correct prediction ratio in the model prediction results.

[0062] Accuracy = (TP+TN) / (TP+FP+TN+FN) = 98 / 106 = 0.93 (2) Precision Used to evaluate the proportion of samples predicted to be breast cancer by the model that are correctly predicted.

[0063] Precision = TP / (TP+FP) = 49 / 49 = 1 (3) Recall It is used to evaluate the proportion of samples predicted by the model to be breast cancer to the number of actual breast cancer cases in the samples.

[0064] Recall = TP / (TP+FN) = 49 / 56 = 0.88 (4) F1 score The F1 score is the harmonic mean of precision and recall.

[0065]

[0066] Based on the above results, the performance of the model built using GBDT in the independent validation set is summarized in Table 3.

[0067] Table 3 Performance of the model built using GBDT in the independent validation set

[0068] In summary, based on the characteristic methylation sites obtained through screening, the model constructed using GBDT in Example 2 still has very good discrimination performance in independent validation data.

[0069] All documents mentioned in this application are cited as references in this application, just as each document is cited as reference individually. In addition, it should be understood that after reading the above teaching content of this application, those skilled in the art can make various changes or modifications to this application, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. Use of a methylation level detection reagent of a methylation marker combination in the preparation of a kit for diagnosing or predicting breast cancer, characterized in that: The methylation marker combination includes a methylation gene combination, and the methylation gene combination includes MLLT6, AR1, MED24, KCTD11, AADAT, PTPRN2, ACTR1A, CELSR1, ZC3H7A, TRIM8, IGLL5, CORO2B, THADA, CNNM1, SLC16A3, DEAF1 and LCP2.

2. The use according to claim 1, characterized in that: The detection reagent is used to detect the methylation level of the methylation site of each gene in the methylation gene combination.

3. The use according to claim 2, characterized in that: The methylation site of the MLLT6 gene includes cg01485645; the methylation site of the AR1 gene includes cg06982805; the methylation site of the MED24 gene includes cg14700282; the methylation site of the KCTD11 gene includes cg02676175; the methylation site of the AADAT gene includes cg00309402; the methylation site of the PTPRN2 gene includes cg14189678; the methylation site of the ACTR1A gene includes cg09800781; the methylation site of the CELSR1 gene includes cg04332442; the methylation site of the ZC3H7A gene The sites include cg04010868; the methylation sites of the TRIM8 gene include cg06388937; the methylation sites of the IGLL5 gene include cg23477395; the methylation sites of the CORO2B gene include cg23597015; the methylation sites of the THADA gene include cg17277833; the methylation sites of the CNNM1 gene include cg25842285; the methylation sites of the SLC16A3 gene include cg07016605; the methylation sites of the DEAF1 gene include cg22132501; and the methylation sites of the LCP2 gene include cg01199321.

4. The use according to claim 2 or 3, characterized in that: The methylation marker combination also includes at least one of the following methylation sites: cg07677157, cg06123699, cg27469726, cg12277627, cg12804791 and cg17551400.

5. A method for constructing a model for diagnosing or predicting breast cancer, characterized in that: The following steps are involved: S1, obtaining methylation level data of the combination of methylation markers according to claim 1 in biological samples of a population, wherein the population includes breast cancer patients and non-breast cancer subjects; S2, using the methylation level data obtained in step S1, construct a machine learning model and perform multi-fold cross validation.

6. The method according to claim 5, characterized in that The machine learning model is selected from any one of the following: Logistic regression model, support vector machine model, decision tree model, random forest model, neural network model, XGBoost model, linear discriminant analysis model, GBDT model, ADABoost model, naive Bayes model, CatBoost model, LightGBM model, MLP model and ETC model.

7. A system for diagnosing or predicting breast cancer, characterized in that: Includes the following modules: A data input module, for inputting the methylation level data of the methylation marker combination in the biological sample of the subject, wherein the methylation marker combination includes a methylation gene combination, and the methylation gene combination includes MLLT6, AR1, MED24, KCTD11, AADAT, PTPRN2, ACTR1A, CELSR1, ZC3H7A, TRIM8, IGLL5, CORO2B, THADA, CNNM1, SLC16A3, DEAF1 and LCP2; A database storage module, for storing methylation level data of the methylation marker combination in biological samples of a population, the population comprising breast cancer patients and non-breast cancer subjects; A disease prediction module is connected to the data input module and the database storage module respectively, and is used to construct a machine learning model using the methylation level data of the methylation marker combination in the biological samples of the group, and diagnose whether the subject has breast cancer or predict whether the subject has a risk of breast cancer based on the methylation level data of the methylation marker combination in the biological samples of the subject obtained from the data input module.

8. The system according to claim 7, characterized in that The methylation marker combination includes methylation sites cg01485645, cg07677157, cg06982805, cg14700282, cg06123699, cg27469726, cg02676175, cg00309402, cg14189678, cg09800781, cg043324 42. cg12277627, cg04010868, cg06388937, cg12804791, cg23477395, cg17551400, cg23597015, cg17277833, cg25842285, cg07016605, cg22132501, and cg01199321.

9. A computer device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the method according to any one of claims 5 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 5 to 6 are implemented.

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