Methylation signal detection method, device and equipment based on free DNA sequencing fragment in plasma and storage medium

Abstract

The invention discloses a methylation signal detection method, device and equipment based on free DNA sequencing fragments in plasma and a storage medium, and relates to the technical field of non-invasive disease detection.The method comprises the steps that for each free DNA methylation sequencing fragment, the average methylation value of all CpG loci contained in the free DNA methylation sequencing fragment is calculated and discretized into the preset methylation level; carrying out normalization processing on methylation levels corresponding to methylation sequencing fragments in the preset genome region to obtain methylation state distribution characteristics; and screening out significant differential methylation state distribution characteristics, inputting the significant differential methylation state distribution characteristics into an XGBoost classifier, and outputting to obtain a plasma free DNA methylation characteristic score. According to the method, fragment hierarchical coding is adopted, the continuous methylation value of a single fragment is discretized to a limited level, and the distribution of the continuous methylation value in a genome region is counted, so that weak but specific disease signals are effectively amplified and can be detected from a large number of normal backgrounds.

Description

TECHNICAL FIELD

[0001] The present application relates to a methylation signal detection method based on sequencing fragments of free DNA in plasma, a device, equipment and storage medium, belonging to the technical field of non-invasive disease detection. BACKGROUND

[0002] Liquid biopsy realizes non-invasive disease detection by analyzing biomarkers in body fluids such as blood. Among them, plasma cell-free DNA (cfDNA) is derived from the apoptosis or necrosis process of cells in the body, carries genetic, fragmentomic and epigenetic information, and is considered one of the most sensitive early cancer detection signals.

[0003] Abnormal changes in DNA methylation patterns are a characteristic of many diseases such as cancer, making cfDNA methylation a highly potential biomarker for high-sensitivity early non-invasive diagnosis. However, in early cancer patients, tumor-derived free DNA only accounts for a very small part of the total free DNA, which makes it a major technical challenge to accurately and sensitively detect cancer-specific methylation signals in the background of a large number of non-tumor free DNA.

[0004] So far, many studies have attempted to use DNA methylation profiles to solve this problem. For example, CancerDetector uses a probabilistic framework to infer the proportion of tumor-derived free DNA by the joint methylation pattern of adjacent CpG sites; DISMIR and MethylBERT introduce deep learning techniques to integrate fragment methylation and sequence features to improve the accuracy of tumor-derived free DNA proportion inference. However, these existing technologies have the following significant shortcomings and deficiencies: 1. High tissue dependence, limited clinical practicability: methods such as CancerDetector, DISMIR and MethylBERT are heavily dependent on matched tumor tissue samples and normal plasma samples to identify cancer-specific methylation markers. This requirement is usually difficult to meet in real clinical diagnosis environments, limiting its large-scale application, and making its model vulnerable to specific dataset bias and not strong in generalization ability.

[0005] 2. Lack of universality, high expansion cost: existing methods are mostly developed for specific cancer types, lacking cross-cancer universality. When applying DISMIR or CancerDetector to cancers other than liver cancer, complex and time-consuming marker re-screening or model parameter re-adjustment and training are required, which seriously limits the scalability and reproducibility of the method.

[0006] 3. High computational resource requirement and high application threshold: Especially for deep learning-based methods such as DISMIR and MethylBERT, the model training process requires a large amount of computational resources and time, which sets a high technical threshold and economic cost for its wide deployment and application in clinical institutions.

[0007] 4. Failure to adequately capture tumor heterogeneity: Existing methods have limitations in effectively capturing and reflecting the complex methylation heterogeneity within tumor cells, which limits the understanding of the complexity of cancer biology and the sensitivity of its detection.

[0008] Therefore, there is an urgent need in the art for a new method of cfDNA detection that is not dependent on tissue samples, has wide applicability to cancer and non-cancer diseases, is computationally efficient, and can sensitively capture disease-specific methylation signals. SUMMARY

[0009] The purpose of the present application is to overcome the shortcomings in the prior art and provide a methylation signal detection method, device, equipment and storage medium based on sequencing fragments of free DNA in plasma, which uses fragment hierarchical coding, discretizes the continuous methylation value of a single fragment to a limited level, and statistically analyzes its distribution in the genomic region, effectively amplifying weak but specific disease signals, so that they can be detected from a large amount of normal background.

[0010] To achieve the above-mentioned purpose, the present application is implemented by using the following technical solutions: In a first aspect, the present application provides a methylation signal detection method based on sequencing fragments of free DNA in plasma, comprising: S1, obtaining methylation sequencing data of plasma free DNA; S2, for each methylation sequencing fragment in the methylation sequencing data of plasma free DNA, calculating the average methylation value of all CpG sites contained therein, and discretizing the average methylation value to a preset methylation level; S3, counting the number of methylation sequencing fragments of each preset methylation level within a preset genomic region, and normalizing to obtain the methylation state distribution characteristics of the genomic region; S4, repeating S3, integrating the methylation state distribution characteristics of all genomic regions, and screening for significant differential methylation state distribution characteristics; S5, taking the significant differential methylation state distribution characteristics as input, and outputting the plasma free DNA methylation feature score based on the XGBoost classifier.

[0011] Further, the average methylation value is discretized to the nearest preset methylation level.

[0012] Further, the expression of the preset methylation level is .

[0013] Further, the methylation level corresponding to the methylation sequencing fragment in the preset genomic region is counted, and normalization processing is performed to obtain the methylation state distribution characteristics of the genomic region, including: Counting the number of methylation sequencing fragments corresponding to the preset methylation level in the preset genomic region; According to the number of methylation sequencing fragments, the methylation state distribution characteristics of the genomic region are obtained by normalization processing.

[0014] Further, the expression of the methylation state distribution characteristics is , wherein, represents the number of methylation level 0 normalization value, represents the number of methylation level 0.25 normalization value, represents the number of methylation level 0.5 normalization value, represents the number of methylation level 0.75 normalization value, represents the number of methylation level 1 normalization value.

[0015] Further, the methylation state distribution characteristics of all genomic regions are integrated, and the significantly different methylation state distribution characteristics are screened, including: D1, obtaining disease group data and healthy control group data, the disease group data including a plurality of disease patients' plasma free DNA methylation sequencing data samples, and the healthy control group data including a plurality of healthy testers' plasma free DNA methylation sequencing data; D2, for each methylation sequencing fragment in the plasma free DNA methylation sequencing data, calculating the average methylation value of all CpG sites contained therein, and discretizing the average methylation value into a preset methylation level; D3, counting the methylation level corresponding to the methylation sequencing fragment in the preset genomic region, and performing normalization processing to obtain the methylation state distribution characteristics of the genomic region; D4, repeating D3, integrating the methylation state distribution characteristics of all genomic regions, obtaining the methylation state distribution characteristics of the disease group and the methylation state distribution characteristics of the healthy control group; D5, comparing the methylation state distribution characteristics of the disease group and the methylation state distribution characteristics of the healthy control group, and using statistical test method to preliminarily screen the different methylation state distribution characteristics; D6, screening the different methylation state distribution characteristics based on Boruta algorithm to obtain the significantly different methylation state distribution characteristics.

[0016] Furthermore, it also includes pre-training the XGBoost classifier, the pre-training method comprising: Multiple data samples were acquired, including significantly different methylation state distribution features and their corresponding category labels. The distribution features of significantly differentially methylated states in the data samples are used as input, and the XGBoost classifier is trained using leave-one-out cross-validation to obtain a pre-trained XGBoost classifier.

[0017] Secondly, the present invention also provides a methylation signal detection device based on cell-free DNA sequencing fragments in plasma, comprising: The methylation sequencing data acquisition module is configured to acquire methylation sequencing data of cell-free DNA in plasma; The methylation level discretization module is configured to calculate the average methylation value of all CpG sites contained in each methylated sequencing fragment in the methylated sequencing data of cell-free DNA in plasma, and discretize the average methylation value to a preset methylation level. The methylation state distribution feature processing module is configured to statistically analyze the methylation level of methylated sequencing fragments within a preset genomic region and normalize them to obtain the methylation state distribution feature of that genomic region. The module for screening significantly differentially methylated state distribution features is configured to integrate the methylated state distribution features of all genomic regions and screen out significantly differentially methylated state features from them. The plasma free DNA methylation feature score acquisition module is configured to take the distribution features of significantly differential methylation states as input, and output the plasma free DNA methylation feature score based on the XGBoost classifier.

[0018] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for detecting multilevel methylation signals based on cell-free DNA sequencing fragments in plasma as described in any of the first aspects.

[0019] Fourthly, the present invention also provides an apparatus comprising: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations implementing the multilevel methylation signal detection method based on cell-free DNA sequencing fragments in plasma as described in any of the first aspects.

[0020] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: This invention employs fragment hierarchical encoding, which discretizes the continuous methylation values ​​of a single fragment into a limited number of levels and statistically analyzes their distribution in genomic regions. This effectively amplifies weak but specific disease signals, enabling them to be detected from a large amount of normal background. This is a fundamental improvement over all methods that rely on average methylation levels. The entire testing process of this invention only requires cell-free DNA methylation sequencing data from the patient's plasma, completely eliminating the dependence on difficult-to-obtain matching tissue samples. It does not pre-set specific disease biomarkers and can be seamlessly applied to different types of cancer as well as non-cancerous diseases such as liver disease and amyotrophic lateral sclerosis. Furthermore, the computational cost has been effectively reduced. Attached Figure Description

[0021] Figure 1 This is a schematic flowchart of a method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma, according to one embodiment of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0023] Example 1

[0024] like Figure 1 As shown, this embodiment of the invention provides a method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma, comprising the following steps: Methylation sequencing data is obtained from cell-free DNA (cfDNA, which refers to extracellular DNA fragments circulating in blood and other body fluids). The methylation sequencing data consists of a large number of methylated sequencing fragments.

[0025] For each methylated sequencing fragment, the average methylation value of all CpG sites it contains is calculated, and the average methylation value is discretized to the nearest preset methylation level. In this embodiment, the methylation level is preset to five levels, and its expression is as follows: .

[0026] Within a pre-defined genomic region (e.g., every 500 bp is considered a window), the number of sequencing fragments belonging to each methylation level is counted and normalized to obtain the methylation status distribution characteristics of that genomic region, expressed as: ,in, This represents the normalized value of the number of values ​​attributed to methylation level 0. This represents the normalized number of values ​​attributed to a methylation level of 0.25. This represents the normalized number of values ​​attributed to a methylation level of 0.5. This represents the normalized number of values ​​attributed to a methylation level of 0.75. This represents the normalized number of values ​​attributed to methylation level 1.

[0027] Based on the methylation status distribution characteristics of all genomic regions, significantly differential methylation status distribution characteristics were screened out, including: D1. Obtain data from the disease group and the healthy control group. The disease group data includes methylation sequencing data of cell-free DNA in the plasma of 24 liver cancer patients, and the healthy control group data includes methylation sequencing data of cell-free DNA in the plasma of 30 healthy test subjects.

[0028] D2. For each methylated sequencing fragment in the methylation sequencing data of cell-free DNA in plasma, calculate the average methylation value of all CpG sites contained therein, and discretize the average methylation value to a preset methylation level. D3. Statistically analyze the methylation levels of methylated sequencing fragments within the preset genomic region and normalize them to obtain the methylation status distribution characteristics of the genomic region.

[0029] D4. Repeat D3 to synthesize the methylation status distribution characteristics of all genomic regions and obtain the methylation status distribution characteristics of the disease group and the healthy control group, as shown in Table 1.

[0030] Table 1: Distribution characteristics of methylation status for each sample

[0031] D5. Compare the methylation status distribution characteristics of the disease group and the healthy control group, and use statistical test methods (such as t test) to preliminarily screen and obtain the differential methylation status distribution characteristics.

[0032] D6. Based on the Boruta algorithm, the distribution features of differential methylation states were screened to obtain the distribution features of significantly differential methylation states. The distribution features of significantly differential methylation states refer to feature signals with high confidence and strong correlation with the disease, as shown in Table 2: Table 2: Significant differences in methylation status distribution characteristics among samples

[0033] Construct a pre-trained XGBoost classifier. The pre-training method includes: acquiring multiple data samples, wherein the data samples include significantly differential methylation state distribution features and their corresponding labels; The significantly differentially methylation state distribution features in the data samples are used as input, and leave-one-out cross-validation is used to train the XGBoost classifier, resulting in a pre-trained XGBoost classifier. In this embodiment, leave-one-out cross-validation is used to verify the stability and generalization ability of the model in order to evaluate the performance of the classifier.

[0034] It should be noted that, not limited to the leave-one-out cross-validation method in this embodiment, any other method that can achieve effective pre-training of the XGBoost classifier should also be acceptable.

[0035] Based on a pre-trained XGBoost classifier, the methylation status distribution features of the validation set samples are input, and the corresponding plasma free DNA methylation feature scores are output.

[0036] In this embodiment, the pre-trained XGBoost classifier demonstrated excellent classification performance in the validation, achieving an area under the receiver operating characteristic curve (AUROC) of 0.943, an accuracy of 0.893, a precision of 0.909, and a sensitivity of 0.958 at 90% specificity.

[0037] Plasma free DNA methylation characterization score can be used to assist in disease detection. The higher the plasma free DNA methylation characterization score, the greater the probability of disease. Among them, the disease refers to cancer and methylation-related diseases such as amyotrophic lateral sclerosis (ALS).

[0038] Example 2

[0039] This embodiment also provides a methylation signal detection device based on cell-free DNA sequencing fragments in plasma, used to implement the methylation signal detection method based on cell-free DNA sequencing fragments in plasma described in Embodiment 1, including: The methylation sequencing data acquisition module is configured to acquire methylation sequencing data of cell-free DNA in plasma.

[0040] The methylation level discretization module is configured to calculate the average methylation value of all CpG sites contained in each methylated sequencing fragment in the methylated sequencing data of cell-free DNA in plasma, and discretize the average methylation value to a preset methylation level.

[0041] The methylation state distribution feature processing module is configured to statistically analyze the methylation level of methylated sequencing fragments within a preset genomic region and normalize them to obtain the methylation state distribution feature of that genomic region.

[0042] The module for screening significantly differentially methylated state distribution features is configured to synthesize the methylated state distribution features of all genomic regions and screen out significantly differentially methylated state distribution features.

[0043] The plasma free DNA methylation feature score acquisition module is configured to take the distribution features of significantly differential methylation states as input, and output the plasma free DNA methylation feature score based on the XGBoost classifier.

[0044] Example 3

[0045] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the methylation signal detection method based on cell-free DNA sequencing fragments in plasma as described in any one of Embodiment 1.

[0046] Example 4

[0047] This embodiment also provides a device, including: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations that implement the methylation signal detection method based on cell-free DNA sequencing fragments in plasma as described in any of Embodiment 1.

[0048] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma, characterized in that, include: S1. Obtain methylation sequencing data of cell-free DNA in plasma; S2. For each methylated sequencing fragment in the methylated sequencing data of cell-free DNA in plasma, calculate the average methylation value of all CpG sites contained therein, and discretize the average methylation value to a preset methylation level. S3. Count the number of methylated sequencing fragments at each preset methylation level within the preset genomic region, and normalize them to obtain the methylation status distribution characteristics of the genomic region; S4, repeating S3, integrate the methylation status distribution characteristics of all genomic regions, and screen out the significantly different methylation status distribution characteristics; S5. Using the distribution characteristics of significantly differential methylation states as input, and based on the XGBoost classifier, the output is the plasma free DNA methylation feature score.

2. The method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma according to claim 1, characterized in that, The average methylation value is discretized to the closest preset methylation level.

3. The method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma according to claim 2, characterized in that, The expression for the preset methylation level is: .

4. The method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma according to claim 1, characterized in that, The methylation levels corresponding to methylated sequencing fragments within a predefined genomic region are statistically analyzed and normalized to obtain the methylation status distribution characteristics of that genomic region, including: The number of methylated sequencing fragments corresponding to preset methylation levels within a preset genomic region is counted. The distribution characteristics of methylation status in this genomic region were obtained by normalizing the number of methylated sequencing fragments.

5. The method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma according to claim 1, characterized in that, The expression for the methylation state distribution characteristics is as follows: ,in, This represents the normalized value of the number of values ​​attributed to methylation level 0. This represents the normalized number of values ​​attributed to a methylation level of 0.

25. This represents the normalized number of values ​​attributed to a methylation level of 0.

5. This represents the normalized number of values ​​attributed to a methylation level of 0.

75. This represents the normalized number of values ​​attributed to methylation level 1.

6. The method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma according to claim 1, characterized in that, The methylation status distribution characteristics of all genomic regions are synthesized, and significantly differential methylation status distribution characteristics are screened from them, including: D1. Obtain disease group data and healthy control group data. The disease group data includes methylation sequencing data samples of cell-free DNA in the plasma of multiple disease patients, and the healthy control group data includes methylation sequencing data of cell-free DNA in the plasma of multiple healthy test subjects. D2. For each methylated sequencing fragment in the methylation sequencing data of cell-free DNA in plasma, calculate the average methylation value of all CpG sites contained therein, and discretize the average methylation value to a preset methylation level. D3. Statistically analyze the methylation levels of methylated sequencing fragments within a predefined genomic region and normalize them to obtain the methylation status distribution characteristics of that genomic region. D4. Repeat D3 to synthesize the methylation status distribution characteristics of all genomic regions and obtain the methylation status distribution characteristics of the disease group and the healthy control group. D5. Compare the methylation status distribution characteristics of the disease group and the healthy control group, and use statistical test methods to preliminarily screen and obtain the differential methylation status distribution characteristics. D6. Based on the Boruta algorithm, the distribution features of differential methylation states are screened to obtain the distribution features of significantly differential methylation states.

7. The method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma according to claim 1, characterized in that, It also includes pre-training the XGBoost classifier, the pre-training method comprising: Multiple data samples were acquired, including significantly different methylation state distribution features and their corresponding category labels. The distribution features of significantly differentially methylated states in the data samples are used as input, and the XGBoost classifier is trained using leave-one-out cross-validation to obtain a pre-trained XGBoost classifier.

8. A device for detecting methylation signals based on cell-free DNA sequencing fragments in plasma, characterized in that, The method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma as described in any one of claims 1 to 7 includes: The methylation sequencing data acquisition module is configured to acquire methylation sequencing data of cell-free DNA in plasma; The methylation level discretization module is configured to calculate the average methylation value of all CpG sites contained in each methylated sequencing fragment in the methylated sequencing data of cell-free DNA in plasma, and discretize the average methylation value to a preset methylation level. The methylation state distribution feature processing module is configured to statistically analyze the methylation level of methylated sequencing fragments within a preset genomic region and normalize them to obtain the methylation state distribution feature of that genomic region. The module for screening significantly differentially methylated state distribution features is configured to integrate the methylated state distribution features of all genomic regions and screen out significantly differentially methylated state features from them. The plasma free DNA methylation feature score acquisition module is configured to take the distribution features of significantly differential methylation states as input, and output the plasma free DNA methylation feature score based on the XGBoost classifier.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma as described in any one of claims 1 to 7.

10. A device, characterized in that, include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations that implement the method for detecting methylation signals based on cell-free DNA sequencing fragments in plasma as described in any one of claims 1 to 7.

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