Method for screening for disease marker on the basis of methylation data

Abstract

Provided are a method for screening for a disease marker on the basis of methylation data, comprising: obtaining methylation data of multiple samples of a methylation detection platforms; preprocessing the methylation data; on the basis of the preprocessed methylation data of the a methylation detection platforms, calculating and screening for a difference interval in each methylation detection platform; on the basis of the number of methylation detection platforms having difference intervals, and classifying the difference intervals into w types; calculating characteristic values of the w types and selecting a disease marker on the basis of the characteristic values, wherein the sequencing depths of the a methylation detection platforms are M1-Ma, respectively, and the sequencing breadths thereof are L1-La, respectively; and the methylation detection platform having the maximum value among M1-Ma is different from the methylation detection platform having the maximum value among L1-La.

Description

A method for screening disease markers based on methylation data Technical Field

[0001] The present invention relates to the field of molecular biology and gene detection, and specifically to a method for screening disease markers based on methylation data. Background Art

[0002] According to the 2020 World Cancer Report released by the World Health Organization's Institute for Research on Cancer (IARC), there were 19.3 million new cases of cancer and nearly 10 million deaths worldwide in 2020; one in five people will develop cancer in his or her lifetime; one in eight men and one in 11 women will die from cancer; the number of people who survive five years after a cancer diagnosis is approximately 50.6 million; it is estimated that by 2040, the number of new cases of cancer worldwide will reach 28.4 million.

[0003] According to data released by IARC, lung cancer (17.9%) accounted for the highest proportion of new cancer cases in 2020, followed by colorectal cancer (12.2%), gastric cancer (10.5%), breast cancer (9.1%), liver cancer (9%), and bladder cancer (7.1%). In terms of mortality, lung cancer still accounts for the highest proportion of deaths (23.8%), followed by liver cancer (13%), gastric cancer (12.4%), bladder cancer (10%), and colorectal cancer (9.5%). Early screening and diagnosis facilitate early intervention and treatment, thereby improving patient survival rates.

[0004] cfDNA (cell-free DNA) is a small fragment of DNA found free in peripheral blood. It originates from normal cells or tumor cells and their metabolism, and contains genetic information such as somatic mutations and DNA methylation. Currently, DNA methylation has been shown to be tissue-specific and can be used for early cancer detection. The methylation signature of circulating tumor DNA (ctDNA) can also be used to track the primary tumor site.

[0005] Conventional sequencing methods for detecting methylation include WGBS (whole genome bisulfite sequencing), 450K / 850K arrays, RRBS (Reduced representation bisulfite sequencing), and targeted panel-based capture. Each method has its own advantages and disadvantages. For example, while WGBS covers a wide genomic range, its sequencing depth is low, and it cannot accurately resolve methylation levels in samples. Customized panels, while capable of high-depth sequencing, cover a narrow genomic range, resulting in a limited range of usable markers. How to effectively leverage the strengths and weaknesses of different platforms and maximize data utilization has become a key focus of research.

[0006] Summary of the Invention

[0007] Based on the existing problems, the purpose of this application is to provide a method for screening cancer markers by utilizing the methylation signals of free DNA in the blood, starting from integrating data from different platforms, maximizing the advantages of data from different platforms, thereby improving the accuracy of the screened markers, and the markers have very high sensitivity.

[0008] The specific technical solutions of this application are as follows:

[0009] 1. A method for screening disease markers based on methylation data, comprising the following steps:

[0010] Obtain methylation data of multiple samples from a methylation detection platform;

[0011] Preprocessing of methylation data;

[0012] Based on the pre-processed methylation data of the a methylation detection platforms, the difference intervals in each methylation detection platform are calculated and screened;

[0013] Classifying the difference intervals into w types based on the number of the methylation detection platforms present in the difference intervals;

[0014] Calculating the characteristic values ​​of the w types and selecting disease markers based on the characteristic values;

[0015] Said a is a positive integer greater than or equal to 2,

[0016] The sequencing depths of the a methylation detection platforms are M1 to M a , the sequencing breadths are L1 to L a ,

[0017] In M1~M a The methylation detection platform where the maximum value is located is the same as L1~L a The methylation detection platform where the maximum value in is located is different;

[0018] The multiple samples include disease group samples and normal group samples.

[0019] 2. The method according to item 1, wherein preprocessing the methylation data comprises the following steps:

[0020] The methylation data were subjected to the following procedures: data quality preprocessing and assessment (fastp software), genome alignment (Bismark software), removal of duplicate data caused by samples or experimental techniques (deduplicate_bismark software), or normalization using the champ.norm function.

[0021] 3. The method according to item 1, wherein, based on the preprocessed methylation data of the a methylation detection platforms, calculating and screening the difference interval in each methylation detection platform means determining the difference interval from the difference interval to be selected by one or two of the following indicators: the disease group sample detection AUC when the difference interval to be selected is used as the detection target, and the difference |delta| between the mean or median of the methylation levels of the disease group samples and the normal group samples in the difference interval to be selected when the difference interval to be selected is used as the detection target.

[0022] 4. The method according to item 3, wherein the selected difference interval in which the delta values ​​are simultaneously positive or simultaneously negative in different methylation detection platforms is selected as the difference interval.

[0023] 5. The method according to item 3, wherein the difference interval of |AUC-0.5|>0.2 in different methylation detection platforms is selected.

[0024] 6. The method according to any one of items 1 to 5, wherein the characteristic value of the w types of difference intervals is X, and X satisfies the following formula:

[0025] n represents the number of difference intervals in each type among the w types of difference intervals, where Δβ i It represents the difference between the methylation level of difference interval i in a certain sample and the average methylation level of the sample group to which the sample belongs.

[0026] 7. The method according to item 6, wherein when there is only one methylation detection platform in the difference interval i, Δβ i Indicates the methylation level of a sample in differential interval i on this methylation detection platform minus the average methylation level of the sample group in which it belongs; or

[0027] When the difference interval i exists in s methylation detection platforms, where s is a positive integer greater than or equal to 2, that is, the difference interval i exists in the first methylation detection platform to the sth methylation detection platform; Δβ i Satisfies the following formula:

[0028] b1 is the methylation level of difference interval i in a certain sample on the first methylation detection platform;

[0029] d1 is the average methylation level of the sample group in which the difference interval i is located on the first methylation detection platform;

[0030] b2 is the methylation level of difference interval i in a certain sample on the second methylation detection platform;

[0031] d2 is the average methylation level of the sample group in which the difference interval i is located on the second methylation detection platform;

[0032] b s is the methylation level of difference interval i in a certain sample on the sth methylation detection platform;

[0033] d s is the average methylation level of the sample group in the difference interval i on the sth methylation detection platform.

[0034] 8. The method according to item 6 or 7, wherein calculating the characteristic values ​​of the w types and selecting disease markers based on the characteristic values ​​means constructing a probability model through generalized linear regression using the methylation level data of all difference intervals in the w types to determine whether the difference intervals can be used as disease markers.

[0035] 9. The method according to item 8, wherein the probability model is as follows: logit(p)=ln(p / 1-p)=α1X1+α2X2+α3X3+......+α w X w +C

[0036] Where p is the probability that the subject suffers from the disease;

[0037] X1, X2, X3, ..., X w The X values ​​representing the w types of difference intervals respectively;

[0038] α1, α2, α3, ..., α w Represent the weight coefficients of w types of difference intervals respectively;

[0039] C represents constant;

[0040] w is 2 a -1.

[0041] 10. The method according to any one of items 1 to 9, wherein the methylation data is selected from two or more methylation-related detection methods such as panel data, RRBS data, WGBS, and 450K.

[0042] This application has the following beneficial effects:

[0043] This application utilizes data from methylation detection platforms with varying detection depths and breadths to select w types of markers and calculate their eigenvalues. Based on these eigenvalues, a model for the probability of a subject developing cancer can be derived. The method described herein maximizes the advantages of data from different platforms, thereby improving the accuracy of the selected markers and achieving very high sensitivity. DETAILED DESCRIPTION

[0044] The present application is described in detail below. Although specific embodiments of the present application are shown, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0045] Unless otherwise indicated, the implementation of this application will adopt conventional molecular biology (including recombinant technology), microbiology, cell biology, biochemistry and genetics techniques, which are all within the scope of conventional technical means in this area. Such techniques are described in detail in the literature such as Molecular Cloning: A Laboratory Manual, Second Edition (Sambrook et al., 1989); Oligonucleotide Synthesis (MJ Gait, 1984 edition); Animal Cell Culture (RI Freshney, 1987 edition); Methods in Enzymology series (Academic Press, Inc., USA); Current Protocols in Molecular Biology (FM Ausubel et al., 1987 edition, regularly updated); PCR: The Polymerase Chain Reaction (Mullis et al., 1994 edition). The primers, probes, blockers and kits used in this application can be prepared using standard techniques well known in the art.

[0046] Unless otherwise defined, 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.

[0047] definition

[0048] The application's "primer" refers to an oligonucleotide that can serve as a starting point for synthesis, whether naturally occurring or synthetically produced in a purified restriction digest, when placed under conditions that induce the synthesis of a primer extension product complementary to a nucleic acid chain, i.e., in the presence of nucleotides and an inducer such as a DNA or RNA polymerase and at a suitable temperature and pH. The primer can be single-stranded or double-stranded and must be long enough to induce the synthesis of the desired extension product in the presence of an inducer. The exact length of the primer depends on various factors, including temperature, primer source, and the method used. For example, for diagnostic and prognostic applications, an oligonucleotide primer typically contains at least or more than about 9, 10, 15, 20, 25, or more nucleotides, depending on the complexity of the target sequence, but can contain fewer nucleotides or more nucleotides. The factors involved in determining the appropriate length of the primer are well known to those skilled in the art.

[0049] A "primer pair" as used herein refers to a primer pair that hybridizes to opposite strands of a target DNA molecule or to a region of the target DNA that flanks the nucleotide sequence to be amplified.

[0050] As used herein, a "primer site" refers to a region of a target DNA or other nucleic acid to which a primer hybridizes.

[0051] As used herein, "probe," when referring to nucleic acid sequences, is used in its ordinary sense to refer to a selected nucleic acid sequence that can hybridize to a target sequence under specified conditions and can be used to detect the presence of the target sequence. It will be understood by those skilled in the art that, in certain circumstances, a probe can also serve as a primer, and a primer can serve as a probe.

[0052] As used herein, "DNA methylation" refers to the addition of a methyl group to the 5-position of cytosine (C), typically (but not necessarily) in the context of a CpG (cytosine followed by a guanine) dinucleotide. As used herein, "increased methylation" or "significant methylation" refers to the presence of at least one methylated cytosine nucleotide in a DNA sequence, wherein the corresponding C is unmethylated in a normal control sample (e.g., a DNA sample extracted from a non-cancerous cell or tissue sample or a DNA sample treated for methylation of DNA residues), and in certain embodiments, at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or more Cs may be methylated, wherein the Cs at these positions in the control DNA sample are unmethylated.

[0053] A "methylation assay" as used herein refers to any assay that determines the methylation status of one or more CpG dinucleotide sequences within a DNA sequence.

[0054] The "detection" of the present application refers to any process of observing a marker or marker change (e.g., a change in marker methylation state or the expression level of a nucleic acid or protein sequence) in a biological sample, regardless of whether the marker or marker change is actually detected. In other words, the act of detecting a marker or marker change in a sample is "detection", even if the marker is determined to be absent or below a sensitivity level. Detection can be quantitative, semi-quantitative, or non-quantitative observation, and can be based on comparison with one or more control samples.

[0055] As used herein, "amplification" refers to the process of obtaining multiple copies of a particular genome of nucleic acid, such as genomic DNA or cDNA. Amplification can be achieved using any of a variety of known means, including but not limited to polymerase chain reaction (PCR), transcription-based amplification, and strand displacement amplification (SDA).

[0056] Specificity

[0057] Specificity refers to the proportion of negative test results in samples from patients without a specific clinical disease.

[0058] Sensitivity

[0059] Sensitivity refers to the proportion of samples from patients with established clinical disease that test positive.

[0060] Generalized linear regression refers to connecting random components through a link function, where the link function is a one-to-one corresponding, differentiable transformation.

[0061] Overview

[0062] The present application provides a method for screening disease markers based on methylation data, which includes the following steps:

[0063] Step 1: Obtain methylation data of multiple samples from a methylation detection platform;

[0064] Step 2: Preprocessing of methylation data;

[0065] Step 3: Based on the pre-processed methylation data of the a methylation detection platforms, the difference intervals in each methylation detection platform are calculated and screened;

[0066] Step 4: Classify the difference intervals into w types based on the number of methylation detection platforms present in the difference intervals;

[0067] Step 5: Calculate the characteristic values ​​of the w types and select disease markers based on the characteristic values;

[0068] In step 1, a is a positive integer greater than or equal to 2,

[0069] The sequencing depths of the a methylation detection platforms are M1 to M a , the sequencing breadths are L1 to L a ,

[0070] In M1~M a The methylation detection platform where the maximum value is located is the same as L1~L a The methylation detection platform where the maximum value in is located is different;

[0071] Specifically, the number of the methylation detection platforms is at least 2, among which there is at least one methylation detection platform with a deeper detection depth and at least one methylation detection platform with a wider detection breadth. For example, when the number of the methylation detection platforms is 2, the detection depth and detection breadth of the two methylation detection platforms are different.

[0072] The multiple samples include disease group samples and normal group samples.

[0073] Specifically, all samples in the disease group samples are from subjects suffering from diseases, and all samples in the normal group samples are from subjects in good health.

[0074] The number of samples in the disease group may be the same as or different from the number of samples in the normal group.

[0075] The number of disease group samples is greater than 2, preferably greater than or equal to 10, more preferably greater than or equal to 20, and even more preferably greater than or equal to 40.

[0076] The number of samples in the normal group is greater than 2, preferably greater than or equal to 10, more preferably greater than or equal to 20, and even more preferably greater than or equal to 40.

[0077] The methylation data is selected from two or more methylation-related detection methods such as panel data, RRBS data, WGBS, and 450K.

[0078] In step 2, preprocessing the methylation data includes the following steps:

[0079] The methylation data were subjected to the following procedures: data quality preprocessing and assessment (fastp software), genome alignment (Bismark software), removal of duplicate data caused by samples or experimental techniques (deduplicate_bismark software), or normalization using the champ.norm function.

[0080] In step 3, based on the pre-processed methylation data of the a methylation detection platforms, calculating and screening the difference intervals in each methylation detection platform means determining the difference intervals from the difference intervals to be selected by using one or two of the following indicators:

[0081] The detection AUC of the disease group samples when the difference interval to be selected is used as the detection target, and the difference |delta| between the mean or median of the methylation levels of the disease group samples and the normal group samples in the difference interval to be selected when the difference interval to be selected is used as the detection target.

[0082] Furthermore, the selected difference intervals in which the delta values ​​are simultaneously positive or simultaneously negative in different methylation detection platforms are selected as the difference intervals.

[0083] Furthermore, the difference intervals of |AUC-0.5|>0.2 in different methylation detection platforms were selected.

[0084] In step 4, based on the difference intervals screened in step 3, the difference intervals are classified:

[0085] w is 2 a -1, wherein a is a positive integer greater than or equal to 2, and a is the number of methylation detection platforms.

[0086] Specifically, a can be 2, 3, 4, 5, 6, ...

[0087] Specifically, w can be 3, 7, 15, 31, 63,...

[0088] For example, when the number of the methylation detection platforms is 2, the two methylation detection platforms are respectively the first methylation detection platform and the second methylation detection platform, the difference intervals can be divided into 3 categories, namely, ① the difference interval only in the first methylation detection platform, ② the difference interval only in the second methylation detection platform, and ③ the difference interval in both the first methylation detection platform and the second methylation detection platform.

[0089] For example, when the number of the methylation detection platforms is 3, the 3 methylation detection platforms are the first methylation detection platform, the second methylation detection platform, and the third methylation detection platform, and the difference intervals can be divided into 7 categories, namely, ① the difference interval only in the first methylation detection platform, ② the difference interval only in the second methylation detection platform, ③ the difference interval only in the third methylation detection platform, ④ the difference interval in both the first methylation detection platform and the second methylation detection platform, ⑤ the difference interval in both the first methylation detection platform and the third methylation detection platform, ⑥ the difference interval in both the second methylation detection platform and the third methylation detection platform, and ⑦ the difference interval in the first methylation detection platform, the second methylation detection platform, and the third methylation detection platform.

[0090] In step 5, the characteristic value of the w types of difference intervals is X, and X satisfies the following formula:

[0091] n represents the number of difference intervals in each type among the w types of difference intervals, where Δβ i It represents the difference between the methylation level of difference interval i in a certain sample and the average methylation level of the sample group to which the sample belongs.

[0092] Specifically, when there is only one methylation detection platform in the difference interval i, Δβ i It means that on this methylation detection platform, the methylation level of difference interval i in a certain sample minus the average methylation level of the sample group in which it belongs.

[0093] For example, when a sample S1 is a disease group sample and the difference interval i has only one methylation detection platform, Δβ i It represents the methylation level of difference interval i in S1 minus the average methylation level of the disease group samples on the methylation detection platform.

[0094] For example, when a sample S2 is a normal group sample, and the difference interval i has only one methylation detection platform, Δβ i It represents the methylation level of the difference interval i in S2 minus the average methylation level of the normal group samples on the methylation detection platform.

[0095] Specifically, when the difference interval i exists in s methylation detection platforms, where s is a positive integer greater than or equal to 2, that is, the difference interval i exists on the first methylation detection platform to the sth methylation detection platform; Δβ i Satisfies the following formula:

[0096] b1 is the methylation level of difference interval i in a certain sample on the first methylation detection platform;

[0097] d1 is the average methylation level of the sample group in which the difference interval i is located on the first methylation detection platform;

[0098] b2 is the methylation level of difference interval i in a certain sample on the second methylation detection platform;

[0099] d2 is the average methylation level of the sample group in which the difference interval i is located on the second methylation detection platform;

[0100] bs is the methylation level of difference interval i in a certain sample on the sth methylation detection platform;

[0101] ds is the average methylation level of the sample group in the difference interval i on the sth methylation detection platform;

[0102] For example, when a sample S3 is a disease group sample, and the difference interval i exists on two methylation detection platforms, that is, the difference interval i exists on the first methylation detection platform and the second methylation detection platform; Δβ i Satisfies the following formula:

[0103] b1 is the methylation level of difference interval i in S3 on the first methylation detection platform;

[0104] d1 is the average methylation level of the sample group in which the difference interval i is located on the first methylation detection platform;

[0105] b2 is the methylation level of differential interval i in S3 on the second methylation detection platform;

[0106] d2 is the average methylation level of the sample group in the difference interval i on the second methylation detection platform.

[0107] For example, when a certain sample S4 is a normal group sample, and the difference interval i has three methylation detection platforms, that is, the difference interval i has the first methylation detection platform, the second methylation detection platform, and the third methylation detection platform; Δβ i Satisfies the following formula:

[0108] b1 is the methylation level of difference interval i in S4 on the first methylation detection platform;

[0109] d1 is the average methylation level of the sample group in which the difference interval i is located on the first methylation detection platform;

[0110] b2 is the methylation level of differential interval i in S4 on the second methylation detection platform;

[0111] d2 is the average methylation level of the sample group in which the difference interval i is located on the second methylation detection platform;

[0112] b3 is the methylation level of differential interval i in S4 on the third methylation detection platform;

[0113] d3 is the average methylation level of the difference interval i in the sample group on the third methylation detection platform.

[0114] Furthermore, calculating the characteristic values ​​of the w types and selecting disease markers based on the characteristic values ​​means constructing a probability model through generalized linear regression using the methylation level data of all difference intervals in the w types to determine whether the difference interval can be used as a disease marker.

[0115] Furthermore, the probability model is as follows: logit(P)=ln(p / 1-P)=α1X1+α2X2+α3X3+......+α w X w +C

[0116] Wherein, P is the probability that the subject suffers from the disease. When P is greater than or equal to 0.5, the subject is judged to be sick, and when P is less than 0.5, the subject is judged to be healthy.

[0117] X1, X2, X3, ..., X w Represents the X value of w types of difference intervals;

[0118] α1, α2, α3, ..., α w Represent the weight coefficients of w types of difference intervals respectively;

[0119] C represents constant;

[0120] w is 2 a -1.

[0121] In the present application, the difference interval and the marker can be used interchangeably. The higher the weight coefficient is, the higher the accuracy and sensitivity of the difference interval corresponding to the weight coefficient as a marker for detecting the disease will be.

[0122] In the present application, the disease can be liver cancer, lung cancer, pancreatic cancer, breast cancer, cervical cancer, endometrial cancer, colorectal cancer, nasopharyngeal cancer, ovarian cancer, prostate cancer, skin cancer, bladder cancer, gastric cancer, prostate cancer, colon cancer, bone cancer, brain cancer, rectal cancer, esophageal cancer, tongue cancer, kidney cancer, uterine body cancer, testicular cancer, urinary cancer, melanoma, astrocytic carcinoma, meningioma, Hodgkin's lymphoma, non-Hodgkin's lymphoma, acute lymphocytic leukemia, chronic lymphocytic leukemia, acute myeloid leukemia, chronic myeloid leukemia, adult T-cell leukemia lymphoma, hepatocellular carcinoma, bronchial cancer, multiple myeloma, basal cell tumor, seminoma, chondrosarcoma, myosoma, fibrosarcoma and the like.

[0123] Example

[0124] This application provides general and / or specific descriptions of the materials and experimental methods used in the experiments. In the following examples, unless otherwise specified, % represents wt%, i.e., percentage by weight. All reagents or instruments used without manufacturer indication are commercially available conventional reagents.

[0125] Example 1

[0126] WGBS experimental process:

[0127] 1.1.cfDNA extraction and purification:

[0128] 1.1.1. Preparation of plasma samples:

[0129] Centrifuge the blood sample at 2000 g for 10 minutes at 4°C and transfer the plasma to a fresh centrifuge tube. Centrifuge the plasma sample at 16000 g for 10 minutes at 4°C and proceed to the next step depending on the type of collection tube used. The collection tube used in this experiment was other.

[0130] 1.1.2. Lysis and binding:

[0131] 1.1.2.1. Pipette 100 μl, 200 μl, 300 μl, 400 μl, and 500 μl of QIAGEN Proteinase K into 50 ml centrifuge tubes.

[0132] 1.1.2.2. Add 1 ml, 2 ml, 3 ml, 4 ml, or 5 ml of plasma or serum to the above 50 ml centrifuge tube.

[0133] 1.1.2.3. Add 0.8 ml, 1.6 ml, 2.4 ml, 3.2 ml, and 4.0 ml of Buffer ACL (containing 1.0 μg carrier RNA), cover, and vortex for 30 seconds. Note: Mix thoroughly to ensure complete lysis; proceed immediately to the next step.

[0134] 1.1.2.4. Incubate at 60°C for 30 min.

[0135] 1.1.2.5. Take out the centrifuge tube, place it on the test bench, and unscrew the tube cap.

[0136] 1.1.2.6. Add 1.8 ml, 3.6 ml, 5.4 ml, 7.2 ml, and 9.0 ml of Buffer ACB to a 50 ml centrifuge tube; cover and mix for 15-30 seconds.

[0137] 1.1.2.7. Incubate the lysis mixture on ice for 5 minutes.

[0138] 1.1.2.8. Insert the QIAamp Mini column into the vacuum pump connector adapter and insert the 20 ml tube extender into the column. Note: Make sure the tube extender is firmly inserted into the QIAamp Mini column to prevent sample leakage.

[0139] 1.1.2.9. Carefully add the lysate-buffer ACB mixture from step 7 to the tube extender of the QIAamp Mini column and turn on the vacuum pump. When all the lysate has been completely extracted from the Mini column, turn off the vacuum pump and release the pressure to 0 mbar. Carefully remove and discard the tube extender.

[0140] 1.1.3. Washing:

[0141] 1.1.3.1. Add 600 μl of Buffer ACW1 to the Mini column. With the cap open, start the vacuum pump. After all the liquid has passed through the column membrane, turn off the vacuum pump and release the pressure to 0 mbar.

[0142] 1.1.3.2. Add 750 μl of Buffer ACW2 to the Mini column. With the cap open, start the vacuum pump. After all the liquid has passed through the column membrane, turn off the vacuum pump and release the pressure to 0 mbar.

[0143] 1.1.3.3. Add 750 μl of ethanol (96-100%) to the Mini column, open the lid, and start the vacuum pump. After all the liquid has passed through the column membrane, turn off the vacuum pump and release the pressure to 0 mbar.

[0144] 1.1.3.4. Cover the QIAamp Mini column cap, remove it from the adapter, and discard the VacConnector. Place the QIAamp Mini column in a clean 2 ml collection tube and centrifuge at high speed (20,000 g; 14,000 rpm) for 3 minutes.

[0145] 1.1.3.5. Place the QIAamp Mini column in a new 2 ml collection tube. Open the tube cap and incubate at room temperature for 5 minutes to allow the membrane to completely dry.

[0146] 1.1.4. Elution of cfDNA:

[0147] Place the QIAamp Mini column in a 1.5ml elution tube and discard the 2ml collection tube from step 14. Add 20-150μl of Buffer AVE to the center of the Mini membrane; cap the tube and incubate at room temperature for 3 minutes.

[0148] 1.1.4.2. Centrifuge at full speed (20,000 g; 14,000 rpm) for 1 min to elute the nucleic acid.

[0149] For cfDNA samples, Agilent2100 performed fragment detection and Qubit was directly used for subsequent experiments.

[0150] 1.2. Bisulfite conversion and purification:

[0151] 1.2.1. Prepare CT Conversion Reagent:

[0152] 1.2.1.1. Add 700 μl of NF water, 300 μl of M-Dilution Buffer, and 50 μl of M-Dissolving Buffer to a tube of CT conversion reagent. Mix thoroughly by vortexing or shaking frequently for 10 minutes at room temperature.

[0153] 1.2.1.2. After mixing, aliquot the solution into 10 reaction volumes.

[0154] 1.2.2. Perform bisulfite conversion on the DNA library and prepare the reaction system according to the table below.

[0155] Table 1

[0156] 1.2.3. Adjust the pipette volume to 100 μl, gently pipette up and down 6 times to mix, then divide the tube into two and place them on the PCR instrument.

[0157] 1.2.4. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 105°C.

[0158] Table 2

[0159] 1.2.5. Take a new 1.5ml centrifuge tube and add 600μl of M-Binding Buffer.

[0160] 1.2.6. After PCR, briefly centrifuge and transfer the two tubes of the same sample into the corresponding 1.5ml centrifuge tubes mentioned above and mix thoroughly.

[0161] 1.2.7. Add the mixed sample to the Zymo-Spin TM Invert the IC Column to mix thoroughly, and centrifuge at 10,000 x g for 30 seconds.

[0162] 1.2.8. Add 100 μl of M-Wash Buffer to the column and centrifuge at 10,000 x g for 30 s.

[0163] 1.2.9. Add 200 μl of M-Desulphonation Buffer to the column, incubate at room temperature for 15-20 min, and centrifuge at 10,000 x g for 30 s.

[0164] 1.2.10. Add 200 μl of M-Wash Buffer to the column and centrifuge at 10,000 x g for 30 s.

[0165] 1.2.11. Repeat the previous step once.

[0166] 1.2.12. Place the column in a new collection tube and centrifuge again at 10,000 x g for 30 seconds.

[0167] 1.2.13. Place the recovery column in a new 1.5 ml EP tube, add 15 μl of low EDTA buffer to the center of the column membrane, and centrifuge at 10,000 x g for 30 seconds.

[0168] 1.3. Transformation:

[0169] Preheat the PCR instrument to 95°C.

[0170] 1.3.2. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 105°C.

[0171] Table 3

[0172] 1.3.3. After incubation, immediately place the tube on ice for 2 minutes.

[0173] 1.4. Adapter ligation and purification:

[0174] 1.4.1. Configure the reaction system according to the following table:

[0175] Table 4

[0176] 1.4.2. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 105°C.

[0177] Table 5

[0178] 1.5. Sample extension and purification:

[0179] 1.5.1. Configure the reaction system according to the following table:

[0180] Table 6

[0181] 1.5.2. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 105°C.

[0182] Table 7

[0183] 1.5.3. When DNA protection buffer is added, the liquid will turn blue. Gently pipette to mix thoroughly, then divide the tubes into two and place them in the PCR instrument.

[0184] 1.5.4. Set the following program and run: Heated lid at 105°C.

[0185] Table 8

[0186] 1.5.5. Prepare the purification system according to the following table:

[0187] Table 9

[0188] 1.5.6. Add the above proportion of magnetic beads to each sample for recovery, shake and mix thoroughly, then centrifuge immediately.

[0189] 1.5.7. Incubate at room temperature for 5 minutes.

[0190] 1.5.8. Vortex and mix thoroughly. Centrifuge briefly and place on a magnetic rack for adsorption until the solution becomes clear (~2 min). After the solution is clear, aspirate the supernatant.

[0191] 1.5.9. Add 200 μl of 80% ethanol to wash the beads for 30 seconds. Discard the supernatant and carefully remove any remaining ethanol from the inner wall of the dropper.

[0192] 1.5.10. Repeat the above steps.

[0193] 1.5.11. Add the optimal volume of low EDTA TE buffer recommended in the table above for elution and vortex to mix.

[0194] Incubate at room temperature for 2 minutes.

[0195] 1.5.13. Place on a magnetic stand for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0196] 1.5.14. Transfer the entire eluate to a new 0.2 mL PCR tube, ensuring that the eluate does not contain the magnetic beads.

[0197] 1.6. Adapter ligation and purification:

[0198] 1.6.1. Prepare the library reaction system according to the following table:

[0199] Table 10

[0200] 1.6.2. Set up the following program and run: Heated lid 0°C:

[0201] Table 11

[0202] Prepare the purification system according to the following table:

[0203] Table 12

[0204] 1.6.4. Add the above proportion of magnetic beads to each sample for recovery, shake and mix thoroughly, then centrifuge immediately.

[0205] 1.6.5. Incubate at room temperature for 5 minutes.

[0206] 1.6.6. Vortex and mix thoroughly. Centrifuge briefly and place on a magnetic rack for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0207] 1.6.7. Add 200 μl of 80% ethanol to wash the beads for 30 seconds. Discard the supernatant and carefully remove any remaining ethanol from the inner wall of the dropper.

[0208] 1.6.8. Repeat the above steps.

[0209] 1.6.9. Add the optimal volume of low EDTA TE buffer recommended in the table above for elution and vortex to mix.

[0210] 1.6.10. Incubate at room temperature for 2 min.

[0211] 1.6.11. Place on a magnetic rack for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0212] 1.6.12. Transfer the entire eluate to a new 0.2 mL PCR tube, ensuring that the eluate does not contain the magnetic beads.

[0213] 1.7. Library amplification and purification:

[0214] 1.7.1. Prepare the library reaction system according to the following table:

[0215] Table 13

[0216] 1.7.2. Set up the following program and run: Heated lid at 105°C:

[0217] Table 14

[0218] 1.7.3. The recommended number of cycles is as follows:

[0219] Table 15

[0220] 1.7.4. Prepare the purification system according to the following table:

[0221] Table 16

[0222] 1.7.5. Transfer the PCR product into a 1.5 ml centrifuge tube.

[0223] 1.7.6. Add the above proportion of magnetic beads to each sample for recovery, shake and mix thoroughly, then centrifuge immediately.

[0224] 1.7.7. Incubate at room temperature for 5 minutes.

[0225] 1.7.8. Vortex and mix thoroughly. Centrifuge briefly and place on a magnetic rack for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0226] 1.7.9. Add 500 μl of 80% ethanol to wash the beads for 30 seconds. Discard the supernatant and carefully remove any remaining ethanol from the inner wall of the dropper.

[0227] 1.7.10. Repeat the above steps.

[0228] 1.7.11. Place on the magnetic stand for 5-10 minutes until the beads are dry (avoid over-drying, as over-drying may result in reduced DNA recovery).

[0229] 1.7.12. Add the optimal volume of low EDTA TE buffer recommended in the table above for elution and vortex to mix.

[0230] Incubate at room temperature for 2 minutes.

[0231] 1.7.14. Place on a magnetic rack for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0232] 1.7.15. Transfer the entire eluate to a new 0.2 mL PCR tube, ensuring that the eluate does not contain the magnetic beads.

[0233] 1.7.16. Aspirate 1 μl for qubit calibration and perform 2100 quality control.

[0234] 1.7.17. Take 1 μl of library and quantify using Qubit and record the library concentration.

[0235] 1.7.18. Take 1 μl of sample and use Agilent 2100 to measure the library fragment length.

[0236] 1.7.19. Sequencing was performed using the Illumina high-throughput sequencing platform.

[0237] Panel experimental process:

[0238] 1.1.cfDNA extraction and purification:

[0239] 1.1.1. Preparation of plasma samples:

[0240] Centrifuge the blood sample at 2000 g for 10 minutes at 4°C and transfer the plasma to a fresh centrifuge tube. Centrifuge the plasma sample at 16000 g for 10 minutes at 4°C and proceed to the next step depending on the type of collection tube used. The collection tube used in this experiment was other.

[0241] 1.1.2. Lysis and binding:

[0242] 1.1.2.1. Pipette 100 μl, 200 μl, 300 μl, 400 μl, and 500 μl of QIAGEN Proteinase K into 50 ml centrifuge tubes.

[0243] 1.1.2.2. Add 1 ml, 2 ml, 3 ml, 4 ml, or 5 ml of plasma or serum to the above 50 ml centrifuge tube.

[0244] 1.1.2.3. Add 0.8 ml, 1.6 ml, 2.4 ml, 3.2 ml, and 4.0 ml of Buffer ACL (containing 1.0 μg carrier RNA), cover, and vortex for 30 seconds. Note: Mix thoroughly to ensure complete lysis; proceed immediately to the next step.

[0245] 1.1.2.4. Incubate at 60°C for 30 min.

[0246] 1.1.2.5. Take out the centrifuge tube, place it on the test bench, and unscrew the tube cap.

[0247] 1.1.2.6. Add 1.8 ml, 3.6 ml, 5.4 ml, 7.2 ml, and 9.0 ml of Buffer ACB to a 50 ml centrifuge tube; cover and mix for 15-30 seconds.

[0248] 1.1.2.7. Incubate the lysis mixture on ice for 5 minutes.

[0249] 1.1.2.8. Insert the QIAamp Mini column into the vacuum pump connector adapter and insert the 20 ml tube extender into the column. Note: Make sure the tube extender is firmly inserted into the QIAamp Mini column to prevent sample leakage.

[0250] 1.1.2.9. Carefully add the lysate-buffer ACB mixture from step 7 to the tube extender of the QIAamp Mini column and turn on the vacuum pump. When all the lysate has been completely extracted from the Mini column, turn off the vacuum pump and release the pressure to 0 mbar. Carefully remove and discard the tube extender.

[0251] 1.1.3. Washing:

[0252] 1.1.3.1. Add 600 μl of Buffer ACW1 to the Mini column. With the cap open, start the vacuum pump. After all the liquid has passed through the column membrane, turn off the vacuum pump and release the pressure to 0 mbar.

[0253] 1.1.3.2. Add 750 μl of Buffer ACW2 to the Mini column. With the cap open, start the vacuum pump. After all the liquid has passed through the column membrane, turn off the vacuum pump and release the pressure to 0 mbar.

[0254] 1.1.3.3. Add 750 μl of ethanol (96-100%) to the Mini column, open the lid, and start the vacuum pump. After all the liquid has passed through the column membrane, turn off the vacuum pump and release the pressure to 0 mbar.

[0255] 1.1.3.4. Cover the QIAamp Mini column cap, remove it from the adapter, and discard the VacConnector. Place the QIAamp Mini column in a clean 2 ml collection tube and centrifuge at high speed (20,000 g; 14,000 rpm) for 3 minutes.

[0256] 1.1.3.5. Place the QIAamp Mini column in a new 2 ml collection tube. Open the tube cap and incubate at room temperature for 5 minutes to allow the membrane to completely dry.

[0257] 1.1.4. Elution of cfDNA:

[0258] Place the QIAamp Mini column in a 1.5ml elution tube and discard the 2ml collection tube from step 14. Add 20-150μl of Buffer AVE to the center of the Mini membrane; cap the tube and incubate at room temperature for 3 minutes.

[0259] 1.1.4.2. Centrifuge at full speed (20,000 g; 14,000 rpm) for 1 min to elute the nucleic acid.

[0260] For cfDNA samples, Agilent2100 performed fragment detection and Qubit was directly used for subsequent experiments.

[0261] 1.2. Bisulfite conversion and purification:

[0262] 1.2.1. Prepare CT Conversion Reagent:

[0263] 1.2.1.1. Add 700 μl of NF water, 300 μl of M-Dilution Buffer, and 50 μl of M-Dissolving Buffer to a tube of CT conversion reagent. Mix thoroughly by vortexing or shaking frequently for 10 minutes at room temperature.

[0264] 1.2.1.2. After mixing, aliquot the solution into 10 reaction volumes.

[0265] 1.2.2. Perform bisulfite conversion on the DNA library and prepare the reaction system according to the table below.

[0266] Table 1

[0267] 1.2.3. Adjust the pipette volume to 100 μl, gently pipette up and down 6 times to mix, then divide the tube into two and place them on the PCR instrument.

[0268] 1.2.4. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 105°C.

[0269] Table 2

[0270] 1.2.5. Take a new 1.5ml centrifuge tube and add 600μl of M-Binding Buffer.

[0271] 1.2.6. After PCR, briefly centrifuge and transfer the two tubes of the same sample into the corresponding 1.5ml centrifuge tubes mentioned above and mix thoroughly.

[0272] 1.2.7. Add the mixed sample to the Zymo-SpinIC Column, invert to mix, and centrifuge at 10,000 x g for 30 seconds.

[0273] 1.2.8. Add 100 μl of M-Wash Buffer to the column and centrifuge at 10,000 x g for 30 s.

[0274] 1.2.9. Add 200 μl of M-Desulphonation Buffer to the column, incubate at room temperature for 15-20 min, and centrifuge at 10,000 x g for 30 s.

[0275] 1.2.10. Add 200 μl of M-Wash Buffer to the column and centrifuge at 10,000 x g for 30 s.

[0276] 1.2.11. Repeat the previous step once.

[0277] 1.2.12. Place the column in a new collection tube and centrifuge again at 10,000 x g for 30 seconds.

[0278] 1.2.13. Place the recovery column in a new 1.5 ml EP tube, add 15 μl of low EDTA buffer to the center of the column membrane, and centrifuge at 10,000 x g for 30 seconds.

[0279] 1.3. Transformation:

[0280] Preheat the PCR instrument to 95°C.

[0281] 1.3.2. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 105°C.

[0282] Table 3

[0283] 1.3.3. After incubation, immediately place the tube on ice for 2 minutes.

[0284] 1.4. Adapter ligation and purification:

[0285] 1.4.1. Configure the reaction system according to the following table:

[0286] Table 4

[0287] 1.4.2. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 105°C.

[0288] Table 5

[0289] 1.5. Sample extension and purification:

[0290] 1.5.1. Configure the reaction system according to the following table:

[0291] Table 6

[0292] 1.5.2. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 105°C.

[0293] Table 7

[0294] 1.5.3. When DNA protection buffer is added, the liquid will turn blue. Gently pipette to mix thoroughly, then divide the tubes into two and place them in the PCR instrument.

[0295] 1.5.4. Set the following program and run: Heated lid at 105°C.

[0296] Table 8

[0297] 1.5.5. Prepare the purification system according to the following table:

[0298] Table 9

[0299] 1.5.6. Add the above proportion of magnetic beads to each sample for recovery, shake and mix thoroughly, then centrifuge immediately.

[0300] 1.5.7. Incubate at room temperature for 5 minutes.

[0301] 1.5.8. Vortex and mix thoroughly. Centrifuge briefly and place on a magnetic rack for adsorption until the solution becomes clear (~2 min). After the solution is clear, aspirate the supernatant.

[0302] 1.5.9. Add 200 μl of 80% ethanol to wash the beads for 30 seconds. Discard the supernatant and carefully remove any remaining ethanol from the inner wall of the dropper.

[0303] 1.5.10. Repeat the above steps.

[0304] 1.5.11. Add the optimal volume of low EDTATE buffer recommended in the table above for elution and vortex to mix.

[0305] Incubate at room temperature for 2 minutes.

[0306] 1.5.13. Place on a magnetic stand for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0307] 1.5.14. Transfer the entire eluate to a new 0.2 mL PCR tube, ensuring that the eluate does not contain the magnetic beads.

[0308] 1.6. Adapter ligation and purification:

[0309] 1.6.1. Prepare the library reaction system according to the following table:

[0310] Table 10

[0311] 1.6.2. Set up the following program and run: Heated lid 0°C:

[0312] Table 11

[0313] 1.6.3. Prepare the purification system according to the following table:

[0314] Table 12

[0315] 1.6.4. Add the above proportion of magnetic beads to each sample for recovery, shake and mix thoroughly, then centrifuge immediately.

[0316] 1.6.5. Incubate at room temperature for 5 minutes.

[0317] 1.6.6. Vortex and mix thoroughly. Centrifuge briefly and place on a magnetic rack for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0318] 1.6.7. Add 200 μl of 80% ethanol to wash the beads for 30 seconds. Discard the supernatant and carefully remove any remaining ethanol from the inner wall of the dropper.

[0319] 1.6.8. Repeat the above steps.

[0320] 1.6.9. Add the optimal volume of low EDTATE buffer recommended in the table above for elution and vortex to mix.

[0321] 1.6.10. Incubate at room temperature for 2 min.

[0322] 1.6.11. Place on a magnetic rack for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0323] 1.6.12. Transfer the entire eluate to a new 0.2 mL PCR tube, ensuring that the eluate does not contain the magnetic beads.

[0324] 1.7. Library amplification and purification:

[0325] 1.7.1. Prepare the library reaction system according to the following table:

[0326] Table 13

[0327] 1.7.2. Set up the following program and run: Heated lid at 105°C:

[0328] Table 14

[0329] 1.7.3. The recommended number of cycles is as follows:

[0330] Table 15

[0331] 1.7.4. Prepare the purification system according to the following table:

[0332] Table 16

[0333] 1.7.5. Transfer the PCR product into a 1.5 ml centrifuge tube.

[0334] 1.7.6. Add the above proportion of magnetic beads to each sample for recovery, shake and mix thoroughly, then centrifuge immediately.

[0335] 1.7.7. Incubate at room temperature for 5 minutes.

[0336] 1.7.8. Vortex and mix thoroughly. Centrifuge briefly and place on a magnetic rack for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0337] 1.7.9. Add 500 μl of 80% ethanol to wash the beads for 30 seconds. Discard the supernatant and carefully remove any remaining ethanol from the inner wall of the dropper.

[0338] 1.7.10. Repeat the above steps.

[0339] 1.7.11. Place on the magnetic stand for 5-10 minutes until the beads are dry (avoid over-drying, as over-drying may result in reduced DNA recovery).

[0340] 1.7.12. Add the optimal volume of low EDTATE buffer recommended in the table above for elution and vortex to mix.

[0341] Incubate at room temperature for 2 minutes.

[0342] 1.7.14. Place on a magnetic rack for adsorption until the solution becomes clear (~2 min). Once the solution is clear, aspirate the supernatant.

[0343] 1.7.15. Transfer the entire eluate to a new 0.2 mL PCR tube, ensuring that the eluate does not contain the magnetic beads.

[0344] 1.7.16. Aspirate 1 μl for qubit calibration and perform 2100 quality control.

[0345] 1.8. Hybridization of sample and probe:

[0346] 1.8.1. Mixed samples:

[0347] 1.8.1.1. DNA library dosage refers to the table below. The total dosage can exceed 1500ng, but not exceed 4ug;

[0348] Table 17

[0349] 1.8.1.2. Calculate the amount of different samples and mix them evenly in a centrifuge tube.

[0350] 1.8.1.3. Add the following prehybridization reagents to the mixed sample and mix thoroughly, avoiding any bubbles.

[0351] Table 18

[0352] 1.8.1.4. Dry the mixed pre-hybridization reagents in a vacuum concentrator at room temperature (if heating is required, please use low temperature).

[0353] 1.8.2. Hybridization:

[0354] Incubate the Fast Hybridization Mix at 65°C for 10 min or until all precipitates have dissolved. Vortex quickly and add 20 μl of the lyophilized sample from the previous step to resuspend the sample (do not allow the hybridization solution to return to room temperature). Mix gently with your fingertips to avoid creating bubbles.

[0355] 1.8.2.2. Centrifuge quickly to remove air bubbles and add 30 μl of Hybridization Enhancer to the surface of the above reagents.

[0356] 1.8.2.3. Place the PCR tube in a preheated PCR instrument for hybridization.

[0357] 1.8.2.4. Set the following program and run: Heated lid 85°C.

[0358] Table 19

[0359] 1.8.3. Combined:

[0360] 1.8.3.1. Vortex the pre-equilibrated streptavidin magnetic beads until completely mixed. Add 100 μl of magnetic beads to a 1.5 ml centrifuge tube.

[0361] 1.8.3.2. Add 200 μl of binding buffer and mix thoroughly by pipetting.

[0362] 1.8.3.3. Place the centrifuge tube on a magnetic rack for 1 minute or until the solution is clear, discard the supernatant, and remove the centrifuge tube.

[0363] 1.8.3.4. Repeat the above washing steps 2 times, for a total of 3 times.

[0364] 1.8.3.5. After the final wash, add 200 μl of binding buffer and resuspend by vortexing to mix thoroughly.

[0365] 1.8.3.6. After hybridization is completed, open the lid of the PCR instrument and quickly transfer all the hybridization solution to the equilibrated magnetic beads.

[0366] 1.8.3.7. Mix the magnetic beads with hybridization solution thoroughly on a shaker, rocker, or rotator at room temperature for 30 minutes.

[0367] 1.8.3.8. Remove the centrifuge tube from the mixer, centrifuge rapidly, and place on a magnetic rack for 1 minute. Remove the supernatant and remove the tube.

[0368] 1.8.3.9. Add 200 μl of preheated Wash Buffer 1 and mix thoroughly.

[0369] 1.8.3.10. Incubate at 63°C or 65°C for 5 min.

[0370] 1.8.3.11. Place the centrifuge tube on the magnetic rack for 1 minute, remove the supernatant, and remove the tube.

[0371] Repeat the above steps, add 200 μl of preheated Wash Buffer 1 again, and mix thoroughly.

[0372] Incubate at 63°C or 65°C for 5 min.

[0373] 1.8.3.14. Transfer the liquid to a new tube; place it on a magnetic rack for 1 minute, discard the supernatant, and remove the tube.

[0374] 1.8.3.15. Add 200 μl of preheated wash buffer 2 and mix thoroughly using a pipette tip.

[0375] 1.8.3.16.Incubate at 48℃ for 5 minutes.

[0376] 1.8.3.17. Place on the magnetic rack for 1 minute, remove the supernatant and take out the tube.

[0377] 1.8.3.18. Repeat (steps 3.15-3.17) and wash twice, for a total of three times.

[0378] 1.8.3.19. For the final time, aspirate the wash solution using a 10μl pipette tip.

[0379] 1.8.3.20. Add 45 μl of water, mix well, and incubate the solution on ice.

[0380] 1.8.4. Post-capture PCR amplification, purification, and quality control:

[0381] 1.8.4.1. Set the following program and run: Heated lid 105°C.

[0382] Table 20

[0383] 1.8.4.2. Mix the magnetic bead mixture in 1.3 and pipette 22.5 μl into a 0.2 ml PCR tube.

[0384] 1.8.4.3. Add 2.5 μl of amplification primers and 25 μl of KAPA HiFi HotStart ReadyMix to a 0.2 ml PCR tube for a total of 50 μl reaction volume.

[0385] 1.8.4.4. Mix gently with a pipette tip, centrifuge quickly, place in a PCR instrument, and start amplification.

[0386] 1.8.4.5. Vortex to thoroughly mix the pre-equilibrated DNA purification magnetic beads.

[0387] 1.8.4.6. Add 90 μl (1.8*) of DNA purification magnetic beads to the amplified PCR product and vortex to mix thoroughly.

[0388] 1.8.4.7. Incubate at room temperature for 5 minutes.

[0389] 1.8.4.8. Place the centrifuge tube on a magnetic rack for 1 minute and discard the supernatant after the solution is clarified.

[0390] 1.8.4.9. Without removing the centrifuge tube from the magnetic rack, add 200 μl of freshly prepared 80% ethanol, incubate for 1 minute, and discard the supernatant; repeat the 80% ethanol wash once (2 times in total), keeping the centrifuge tube on the magnetic rack.

[0391] 1.8.4.10. Carefully remove any remaining ethanol using a 10μl pipette tip and allow to stand at room temperature for 5-10 minutes or until the beads are dry. Be careful not to overdry the beads.

[0392] 1.8.4.11. Remove the tube from the magnetic stand and add 32 μl of water. Mix thoroughly by pipetting with a pipette tip and incubate at room temperature for 2 minutes.

[0393] 1.8.4.12. Place the centrifuge tube on a magnetic rack for 3 minutes or until the solution is clear.

[0394] 1.8.4.13. Transfer 30 μl of supernatant to a clean 0.2 ml centrifuge tube.

[0395] 1.8.4.14. Take 1 μl of library and quantify using Qubit and record the library concentration.

[0396] 1.8.4.15. Take 1 μl of sample and use Agilent 2100 to measure the library fragment length.

[0397] 1.8.4.16. Sequencing was performed using the Illumina high-throughput sequencing platform.

[0398] RRBS experimental process:

[0399] 1.1.cfDNA extraction and purification

[0400] 1.1.1. Preparation of plasma samples:

[0401] Centrifuge the blood sample at 2000 g for 10 minutes at 4°C and transfer the plasma to a fresh centrifuge tube. Centrifuge the plasma sample at 16000 g for 10 minutes at 4°C and proceed to the next step depending on the type of collection tube used. The collection tube used in this experiment was other.

[0402] Table 1

[0403] 1.1.2. Lysis and binding

[0404] 1.1.2.1. Prepare the binding solution / bead mixture according to the table below and mix thoroughly.

[0405] Table 2

[0406] Add an appropriate volume of plasma sample.

[0407] 1.1.2.2. Thoroughly mix the plasma sample and binding solution / bead mixture.

[0408] 1.1.2.3. Incubate on a rotary mixer for 10 minutes to allow the cfDNA to bind to the magnetic beads.

[0409] 1.1.2.4. Place the binding tube on the magnetic rack for 5 minutes until the solution becomes clear and the magnetic beads are completely adsorbed on the magnetic rack.

[0410] 1.1.2.5. Carefully discard the supernatant with a pipette. Keep the tube on the magnetic rack for a few minutes and remove the remaining supernatant with a pipette.

[0411] 1.1.3. Washing

[0412] 1.1.3.1. Resuspend the beads in 1 ml of wash solution.

[0413] 1.1.3.2. Transfer the resuspension to a new 1.5ml centrifuge tube without adsorption. Keep the combined tube.

[0414] 1.1.3.3. Place the centrifuge tube containing the bead resuspension on a magnetic rack for 20 seconds.

[0415] 1.1.3.4. Aspirate the supernatant obtained from the separation into the washing and binding tube, collect the remaining beads after washing into the resuspension solution again, and discard the lysis / binding tube.

[0416] 1.1.3.5. Place the tube on a magnetic rack for 2 min until the solution becomes clear and the beads aggregate on the magnetic rack. Remove the supernatant using a 1 ml pipette.

[0417] 1.1.3.6. While the tube remains on the magnetic stand, use a 200 μL pipette to remove as much residual liquid as possible.

[0418] 1.1.3.7. Remove the tube from the magnetic rack, add 1 ml of wash solution, and vortex for 30 seconds.

[0419] 1.1.3.8. Place on a magnetic rack for 2 min until the solution becomes clear and the beads aggregate on the magnetic rack. Remove the supernatant using a 1 ml pipette.

[0420] 1.1.3.9. While the tube remains on the magnetic stand, use a 200 μL pipette to completely remove any remaining liquid.

[0421] 1.1.3.10. Remove the tube from the magnetic rack, add 1 ml of 80% ethanol, and vortex for 30 seconds.

[0422] 1.1.3.11. Place on the magnetic stand for 2 minutes. When the solution becomes clear, remove the supernatant with a 1 ml pipette.

[0423] 1.1.3.12. Leave the tube on the magnetic stand and remove any remaining liquid using a 200μL pipette.

[0424] 1.1.3.13. Repeat steps 1.1.3.10.-1.1.3.12. above once with 80% ethanol and remove as much supernatant as possible.

[0425] 1.1.3.14. Leave the tube on the magnetic rack and air dry the beads for 3-5 minutes.

[0426] 1.1.4. Elution of cfDNA

[0427] 1.1.4.1. Add eluent according to the table below.

[0428] Table 3

[0429] 1.1.4.2. Vortex for 5 minutes and place on a magnetic stand for 2 minutes. When the solution becomes clear, aspirate the cfDNA in the supernatant.

[0430] 1.1.4.3. Use the purified cfDNA immediately, or transfer the supernatant to a new centrifuge tube and store at -20°C.

[0431] 1.1.4.4. Fragment detection was performed using Agilent 2100, and Qubit was directly used for subsequent experiments.

[0432] 1.2.MspI digestion:

[0433] 1.2.1. Transfer less than 100 ng of DNA to a PCR tube, add the following reagents, and vortex to mix:

[0434] Table 3

[0435] 1.2.2. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 85°C.

[0436] Table 4

[0437] 1.3. Adapter ligation and purification:

[0438] Prepare the following reagents according to the table below, pipette gently to mix, and centrifuge briefly:

[0439] Table 5

[0440] 1.3.2. Set the following program to perform the reaction on a PCR instrument: heated lid temperature 85°C.

[0441] Table 6

[0442] Prepare the following reagents according to the table below, pipette gently to mix, and centrifuge briefly:

[0443] Table 7

[0444] 1.3.4. Set up the following program to perform the reaction on the PCR instrument:

[0445] Table 8

[0446] 1.3.5. In a 1.5 ml centrifuge tube, add DNA Binding Buffer to the above product at a ratio of 7:1 (i.e., add 364 μl of DNA Binding Buffer to 52 μl of product), mix thoroughly, transfer to a Zymo-Spin IC spin column, and centrifuge at ≥10,000 x g for 30 seconds.

[0447] 1.3.6. Add 200 μl DNA Wash Buffer to the spin column and centrifuge at ≥10,000 x g for 30 seconds. Discard the waste solution and repeat this step. (You can perform a dry spin after washing to ensure complete removal of the wash buffer.)

[0448] 1.3.7. Transfer the spin column to a new 1.5 ml centrifuge tube, add 20 μl DNA Elution Buffer, incubate at room temperature for 1 min, and centrifuge at ≥10,000 x g for 30 s.

[0449] 1.4 Bisulfite treatment and purification:

[0450] 1.5.1. Prepare the necessary reagents in advance and dissolve them. Add the reagents according to the following table:

[0451] Table 9

[0452] 1.5.3. Set the following program and run: Heated lid at 105°C.

[0453] Table 10

[0454] 1.5.4. Add 600 μl of M-Binding Buffer to a Zymo-Spin IC Column, add the sulfite-converted sample to the column, cap the column, invert eight times, and centrifuge at ≥10,000 x g for 30 seconds.

[0455] 1.5.5. Discard the waste solution, add 100 μl M-Wash Buffer to the centrifuge column, and centrifuge at ≥10,000 x g for 30 seconds.

[0456] 1.5.6. Add 200 μl of L-Desulphonation Buffer to the centrifuge column and incubate at 20°C-30°C for 15-20 minutes. After incubation, centrifuge at ≥10,000 x g for 30 seconds. (Note: Incubation time should not exceed 20 minutes.)

[0457] 1.5.7. Add 200 μl M-Wash Buffer to the column and centrifuge at ≥10,000 x g for 30 seconds. Discard the waste solution.

[0458] 1.5.8. Repeat the previous step.

[0459] Transfer the spin column to a new 1.5 ml centrifuge tube, add 24 μl DNA Elution Buffer, incubate for 1 min, and centrifuge at ≥10,000 x g for 30 s to obtain the sulfite-converted DNA.

[0460] 1.6. Amplification and purification:

[0461] Prepare the reaction system according to the table below, mix thoroughly by pipetting, and centrifuge briefly:

[0462] Table 11

[0463] 1.6.2. Set the following program and start the PCR program: heated lid 105℃

[0464] Table 12

[0465] 1.6.4. In a 1.5 ml centrifuge tube, add DNA Binding Buffer to the above product at a ratio of 7:1 (i.e., add 350 μl of DNA Binding Buffer to 50 μl of product), mix thoroughly, transfer to a Zymo-Spin IC spin column, and centrifuge at ≥10,000 x g for 30 seconds.

[0466] 1.6.5. Add 200 μl DNA Wash Buffer to the spin column and centrifuge at ≥10,000 x g for 30 seconds. Discard the waste solution and repeat this step. (You can dry-spin after washing to ensure complete removal of the wash buffer.)

[0467] Transfer the spin column to a new 1.5 ml centrifuge tube, add 15 μl DNA Elution Buffer, incubate at room temperature for 1 min, and centrifuge at ≥10,000 x g for 30 s.

[0468] 1.6.7. Use a pipette to transfer 13 μl of supernatant to a 1.5 ml centrifuge tube and label the sample information.

[0469] 1.6.8. Take 1 μl of library and quantify using Qubit and record the library concentration.

[0470] 1.6.9. Take 1 μl of sample and use Agilent 2100 to measure the library fragment length.

[0471] 1.6.10. Sequencing was performed using the Illumina high-throughput sequencing platform.

[0472] Example 2: Constructing a cancer risk assessment classification model

[0473] Panel data: Boercheng collected 10-20 ml of peripheral blood from 60 lung cancer patients and 60 healthy subjects. Plasma samples were separated and cfDNA was extracted and purified using the panel detection method described in Example 1. Data from 40 lung cancer patients and 40 healthy subjects were selected as the training set, and the remaining data were used as the validation set.

[0474] WGBS data: Boercheng collected 10-20 ml of peripheral blood from 30 lung cancer patients and 30 healthy subjects. Plasma samples were separated and cfDNA was extracted and purified using the WGBS method described in Example 1. Data from 20 lung cancer patients and 20 healthy subjects were selected as the training set, and the remaining data were used as the validation set.

[0475] RRBS data: Boercheng collected 10-20 ml of peripheral blood from 20 lung cancer patients and 20 healthy subjects. Plasma samples were separated and cfDNA was extracted and purified using the RRBS detection method described in Example 1. Data from 15 lung cancer patients and 15 healthy subjects were selected as the training set, and the remaining data were used as the validation set.

[0476] Using the training set sample data from these three different platforms, the screening method of this application was used to screen for differential intervals (markers). The top 50 markers were selected based on AUC from the three different platforms, totaling 115 markers, as follows:

[0477] Table 13: Coordinates and gene information of 115 markers.

[0478] Note: The marker types are as follows: ① differential sites only in WGBS data, ② differential sites only in panel data, ③ differential sites only in RRBS, ④ differential sites in WGBS data, panel data, and RRBS, ⑤ differential sites only in WGBS data and panel data, ⑥ differential sites only in panel data and RRBS, ⑦ differential sites only in WGBS and RRBS.

[0479] Then, the methylation level data of these 115 markers were extracted using the data of the test set, and the generalized linear regression model was constructed as follows: logP / (1-P)=0.19X1+0.211X2+0.334X3+0.805X4+0.925X5+0.502X6+0.445X7.

[0480] In the formula, P represents the probability of a subject having cancer. A subject is considered to have cancer when the model's predicted score P value exceeds 0.5. In the test set, the predictive sensitivity was 91.43%, the specificity was 91.43%, and the AUC was 0.975. It is important to note that the weight coefficients for markers in categories 4 and 5 of this formula are much higher than those for other marker types. Therefore, these categories will be the focus of future research.

[0481] According to the above formula, the P value of each test set sample is as follows: (In the following test set, sample.NO.1-sample.NO.35 are lung cancer patients, and sample.NO.3-sample.NO.70 are not cancer patients. If the model calculates a score greater than 0.5, the subject is judged to have lung cancer (i.e., 1), otherwise it is judged to be non-cancer (i.e., 0))

[0482] Table 14 shows the statistical results calculated using the above model.

[0483] Table 14

[0484] Table 15 shows the comparison between the model calculation results and clinical test results.

[0485] Table 15

[0486] Summary: As shown in Tables 14 and 15, when the markers screened by the method described in this application are used to predict diseases, the prediction sensitivity is 91.43% and the specificity is 91.43%.

[0487] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application in any other manner. Any person skilled in the art may utilize the above disclosed technical content to modify or modify the present application into equivalent embodiments with equivalent variations. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present application and are based on the technical essence of the present application shall still fall within the scope of protection of the present application.

Claims

1. A method for screening disease markers based on methylation data, wherein: The steps include: Obtain methylation data of multiple samples of a methylation detection platform; Preprocess the methylation data; Based on the pre-processed methylation data of the a methylation detection platforms, the difference interval in each methylation detection platform is calculated and screened; Based on the number of the methylation detection platforms present in the difference interval, the difference interval is classified into w types; Calculating the characteristic values ​​of the w types and selecting disease markers based on the characteristic values; a is a positive integer greater than or equal to 2, The sequencing depths of the a methylation detection platforms are M1 to M a The sequencing widths are L1 to L a , In M1~M a The methylation detection platform where the maximum value is located is consistent with L1~L a The methylation detection platform where the maximum value in is located is different; The multiple samples include disease group samples and normal group samples.

2. The method according to claim 1, wherein: Preprocessing of methylation data includes the following steps: The methylation data were subjected to: data quality preprocessing and evaluation (fastp software), genome alignment (Bismark software), removal of duplicate data caused by samples or experimental techniques (deduplicate_bismark software) or standardization using the champ.norm function.

3. The method according to claim 1, wherein: Based on the preprocessed methylation data of the a methylation detection platforms, calculating and screening out the difference intervals in each methylation detection platform means determining the difference intervals from the difference intervals to be selected by one or two of the following indicators: the disease group sample detection AUC when the difference interval to be selected is used as the detection target, and the difference |delta| between the mean or median of the methylation levels of the disease group samples and the normal group samples in the difference interval to be selected when the difference interval to be selected is used as the detection target.

4. The method according to claim 3, wherein: The delta values ​​in different methylation detection platforms are simultaneously positive or simultaneously negative in the selected difference interval as the difference interval.

5. The method according to claim 3, wherein: The difference interval of |AUC-0.5|>0.2 in different methylation detection platforms was selected.

6. The method according to any one of claims 1 to 5, wherein: The characteristic value of the w types of difference intervals is X, and X satisfies the following formula: n represents the number of difference intervals in each type among the w types of difference intervals, where Δβ i It represents the difference between the methylation level of difference interval i in a certain sample and the average methylation level of the sample group to which the sample belongs.

7. The method according to claim 6, wherein: When there is only one methylation detection platform in the difference interval i, Δβ i It means that on this methylation detection platform, the methylation level of difference interval i in a certain sample minus the average methylation level of the sample group to which it belongs; or When the difference interval i exists in s methylation detection platforms, where s is a positive integer greater than or equal to 2, that is, the difference interval i exists on the first methylation detection platform to the sth methylation detection platform; Δβ i Satisfies the following formula: b1 is the methylation level of difference interval i in a certain sample on the first methylation detection platform; d1 is the average methylation level of the sample group in which the difference interval i is located on the first methylation detection platform; b2 is the methylation level of difference interval i in a certain sample on the second methylation detection platform; d2 is the average methylation level of the sample group in which the difference interval i is located on the second methylation detection platform; b s is the methylation level of difference interval i in a certain sample on the sth methylation detection platform; d s is the average methylation level of the sample group in which the difference interval i is located on the sth methylation detection platform.

8. The method according to claim 6 or 7, wherein: Calculating the characteristic values ​​of the w types and selecting disease markers based on the characteristic values ​​means constructing a probability model through generalized linear regression using the methylation level data of all difference intervals in the w types to determine whether the difference intervals can be used as disease markers.

9. The method according to claim 8, wherein: The probability model is as follows: logit(p)=ln(p / 1-p)=α1X1+α2X2+α3X3+......+α w X w +C Where p is the probability that the subject suffers from the disease; X1, X2, X3, ..., X w Represents the X values ​​of w types of difference intervals; α1, α2, α3, ..., α w Represent the weight coefficients of w types of difference intervals respectively; C stands for constant; w is 2 a -1.

10. The method according to any one of claims 1 to 9, wherein: The methylation data is selected from two or more methylation-related detection methods such as Panel data, RRBS data, WGBS, and 450K.

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