CN118703626A - A method and kit for multiplex PCR targeted methylation sequencing - Google Patents

Abstract

The embodiments of this specification provide a method and kit for multiplex PCR targeted methylation sequencing, which includes a DNA methylation site combination for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening. The embodiments of this specification also provide a DNA methylation site combination, which has good sensitivity and specificity, can effectively detect or screen colorectal cancer patients, and shows significant differences in methylation levels between known colorectal cancer patients and non-colorectal cancer patients. It can be used as a marker for early screening of colorectal cancer, risk prediction, etc., and can also be used to design diagnostic reagents or kits. The embodiments of this specification also provide a device for colorectal cancer screening or colorectal cancer risk prediction.

Description

A method and kit for multiplex PCR targeted methylation sequencing

Technical Field

The present invention relates to the field of biotechnology, and in particular to a method and a kit for multiplex PCR targeted methylation sequencing.

Background Art

Colorectal cancer is one of the five major cancers in my country. Its incidence rate is second only to gastric cancer among digestive tract tumors. The number of deaths from colorectal cancer in China accounts for 7.8% of the total number of deaths from malignant tumors. Both the incidence and mortality rates of colorectal cancer are in the top five. In addition, colorectal cancer in my country mostly occurs in middle-aged men, with a high incidence age of 40 to 60 years old, and an average age of 48.3 years. At present, there is still a long way to go in the prevention and treatment of colorectal cancer.

DNA methylation is an important mechanism that drives tumor development. In different types of tumors, it can be observed that the DNA methylation level is damaged to varying degrees. At the biological mechanism level, genomic methylation can reveal gene regulation information in different life processes. Biological fluid samples contain tumor-specific DNA methylation signals and can be used as a sample source for identifying colorectal cancer biomarkers. Based on targeted sequencing, bisulfite-treated DNA methylation technology can perform NGS deep sequencing on the target genomic region. In addition to obtaining whether the target methylation site is methylated, it can also obtain statistically significant methylation site sequencing depth and corresponding methylation rate data.

Therefore, it is desirable to provide a method and kit for multiplex PCR targeted methylation sequencing to accurately and rapidly screen for colorectal cancer.

Summary of the invention

One or more embodiments of the present specification provide a use of a DNA methylation site combination as a biomarker or a detection reagent for a DNA methylation site combination in the preparation of a kit for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening, wherein the DNA methylation site combination includes one or more of the following groups: site SOCS-1_24 located on the SOCS-1 gene with chromosome coordinates chr16: 11348913; site cg24403845_103 located on the cg24403845 gene with chromosome coordinates chr10: 108924288.

In some embodiments, the DNA methylation site combination also includes one or more of the following sites: site HOXD3_26 located on the HOXD3 gene with chromosome coordinates chr2: 177027898; site SDC2-2_46 located on the SDC2-2 gene with chromosome coordinates chr8: 97505775; site SFRP1_48 located on the SFRP1 gene with chromosome coordinates chr8: 41167015; site SP9_25 located on the SP9 gene with chromosome coordinates chr2: 175199694; site TAC1-2_135 located on the TAC1-2 gene with chromosome coordinates chr7: 97361597; site TBR1_161 located on the TBR1 gene with chromosome coordinates chr2: 162283730.

In some embodiments, the combination of DNA methylation sites includes a combination consisting of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161.

In some embodiments, the detection reagent includes a primer pair group for amplifying the DNA methylation site combination; wherein the primer pair for amplifying SOCS-1_24 is shown in SEQ ID NO: 1 and SEQ ID NO: 2; the primer pair for amplifying cg24403845_103 is shown in SEQ ID NO: 3 and SEQ ID NO: 4; the primer pair for amplifying HOXD3_26 is shown in SEQ ID NO: 5 and SEQ ID NO: 6; the primer pair for amplifying SDC2-2_46 is shown in SEQ ID NO: 7 and SEQ ID NO: 8; the primer pair for amplifying SFRP1_48 is shown in SEQ ID NO: 9 and SEQ ID NO: 10; the primer pair for amplifying SP9_25 is shown in SEQ ID NO: 11 and SEQ ID NO: 12; the primer pair for amplifying TAC1-2_135 is shown in SEQ ID NO: 13 and SEQ ID NO: 14; the primer pair for amplifying TBR1_161 is shown in SEQ ID NO: 15 and SEQ ID NO: 16. As shown in NO:16.

One or more embodiments of the present specification provide a device for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening, the device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following method when executing the program:

Obtaining the methylation level of a combination of DNA methylation sites in a biological sample of a subject, wherein the combination of DNA methylation sites includes: site SOCS-1_24 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; and/or site cg24403845_103 located at chromosome coordinates chr10:108924288 on the cg24403845 gene;

Based on the methylation level of the combination of DNA methylation sites, a prediction model is used to assess whether the subject has colorectal cancer, predict the risk of the subject developing colorectal cancer, assess the effect of treatment for colorectal cancer in the subject and/or assess the effect of colorectal cancer treatment drugs.

In some embodiments, the DNA methylation site combination also includes one or more of the following sites: site HOXD3_26 located on the HOXD3 gene with chromosome coordinates chr2: 177027898; site SDC2-2_46 located on the SDC2-2 gene with chromosome coordinates chr8: 97505775; site SFRP1_48 located on the SFRP1 gene with chromosome coordinates chr8: 41167015; site SP9_25 located on the SP9 gene with chromosome coordinates chr2: 175199694; site TAC1-2_135 located on the TAC1-2 gene with chromosome coordinates chr7: 97361597; site TBR1_161 located on the TBR1 gene with chromosome coordinates chr2: 162283730.

One or more embodiments of the present specification provide a prediction model for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening using methylation sites, the methylation sites include site SOCS-1_24 located on the SOCS-1 gene with chromosome coordinates of chr16:11348913; and site cg24403845_103 located on the cg24403845 gene with chromosome coordinates of chr10:108924288; the prediction model is represented by the formula Risk score=-4.38+5.16*(cg24403845_103)+1.18*SOCS-1_24, wherein Risk score represents the risk score of a subject having colorectal cancer, developing colorectal cancer, and the risk level of colorectal cancer of a subject after receiving treatment and/or medication, and the gene site represents the methylation level of the gene site.

In some embodiments, the methylation sites further include: site HOXD3_26 located at chromosome coordinates chr2:177027898 on the HOXD3 gene; site SDC2-2_46 located at chromosome coordinates chr8:97505775 on the SDC2-2_ gene; site SFRP1_48 located at chromosome coordinates chr8:41167015 on the SFRP1 gene; site SP9_25 located at chromosome coordinates chr2:175199694 on the SP9 gene; site TAC1-2_135 located at chromosome coordinates chr7:97361597 on the TAC1-2 gene; and site TBR1_161 located at chromosome coordinates chr2:162283730 on the TBR1 gene. The prediction model can also be represented by the formula Risk score＝-7.11+2.89*cg24403845_103+3.61*SOCS-1_24+1.05*TAC1-2_135+0.92*SDC2-2_46+0.77*SFRP1_48+0.72*TBR1_161+0.67*SP9_25+0.19*HOXD3_26, wherein Risk score represents the risk score of the subject suffering from colorectal cancer, developing colorectal cancer, and the risk level of colorectal cancer after the subject receives treatment and/or medication, and each gene locus represents the methylation level of the gene locus.

One or more embodiments of the present specification provide a methylation site combination for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening, the DNA methylation site combination includes: site SOCS-1_24 located on the SOCS-1 gene with chromosome coordinates chr16: 11348913; and/or site cg24403845_103 located on the cg24403845 gene with chromosome coordinates chr10: 108924288.

In some embodiments, the DNA methylation site combination also includes one or more of the following sites: site HOXD3_26 located on the HOXD3 gene with chromosome coordinates chr2: 177027898; site SDC2-2_46 located on the SDC2-2 gene with chromosome coordinates chr8: 97505775; site SFRP1_48 located on the SFRP1 gene with chromosome coordinates chr8: 41167015; site SP9_25 located on the SP9 gene with chromosome coordinates chr2: 175199694; site TAC1-2_135 located on the TAC1-2 gene with chromosome coordinates chr7: 97361597; site TBR1_161 located on the TBR1 gene with chromosome coordinates chr2: 162283730.

One of the embodiments of the present specification provides a detection reagent for the DNA methylation site combination described above, wherein the detection reagent includes a primer pair group for amplifying the DNA methylation site combination; wherein the primer pair for amplifying SOCS-1_24 is shown as SEQ ID NO:1 and SEQ ID NO:2; and the primer pair for amplifying cg24403845_103 is shown as SEQ ID NO:3 and SEQ ID NO:4.

One or more embodiments of the present specification provide a kit for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening, wherein the kit comprises the detection reagent described above.

BRIEF DESCRIPTION OF THE DRAWINGS

This specification will be further described in the form of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure.

FIG1 is an application scenario diagram of a system for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening according to some embodiments of this specification;

FIG2 is a schematic diagram of the architecture of a computing device according to some embodiments of the present specification;

FIG3 is a module diagram of a system for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening according to some embodiments of this specification;

FIG4 is a schematic diagram of a process for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening according to some embodiments of this specification;

FIG5A and FIG5B are the correlations between the methylation rates of different DNA methylation sites in normal samples and colorectal cancer samples according to some embodiments of the present specification;

FIG6 is an analysis flow chart of establishing a prediction model according to some embodiments of this specification;

FIG. 7A and FIG. 7B are methylation rate distribution diagrams of methylation site β values and M values in normal samples and colorectal cancer samples in the prediction model according to some embodiments of the present specification;

8A and 8B are ROC curve diagrams of risk prediction for a training sample set using a prediction model associated with two methylation sites and a waterfall diagram of the risk score predicted for the training sample set according to some embodiments of the present specification;

9A and 9B are ROC curve diagrams and waterfall diagrams of risk scores predicted for a training sample set using a prediction model associated with eight methylation sites according to some embodiments of the present specification;

10A and 10B are waterfall diagrams of risk scores predicted for a validation sample set using a prediction model associated with two methylation sites and ROC curve diagrams of risk scores predicted for a validation sample set according to some embodiments of the present specification;

11A and 11B are waterfall plots and ROC curve graphs of the risk scores predicted for the validation sample set using a prediction model associated with eight methylation sites according to some embodiments of the present specification.

DETAILED DESCRIPTION

In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, without paying creative work, this specification can also be applied to other similar scenarios based on these drawings. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

It should be understood that the "system", "device", "unit" and/or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and/or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprises" and "includes" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may be added to these processes, or one or more operations may be removed from these processes.

DNA methylation is one of the forms of DNA chemical modification. It refers to the process in which a methyl group (CH3-) is covalently bound to the 5th carbon atom of cytosine in the CpG structure under the action of DNA methyltransferase (DNMTs). It often occurs in the CpG island region of the gene promoter and is an important epigenetic marker. Existing studies have shown that abnormal DNA methylation is an important influencing factor leading to the occurrence of various types of cancer. For example, high methylation in the promoter region of some tumor-related genes will inhibit the expression of the corresponding gene, and conversely, low methylation will promote the expression of the corresponding gene. This specification is through high-throughput detection and analysis of 105 specific methylation sites in samples of early colorectal cancer patients and normal healthy people, and based on a specific algorithm, it efficiently finds a combination of methylation sites that can accurately distinguish early lung cancer patients from healthy people, and establishes a colorectal cancer prediction model through 154 training samples composed of colorectal cancer patients and healthy people. Through the analysis of 84 verification samples, accurate, rapid and non-invasive clinical screening of colorectal cancer can be achieved. On the other hand, DNA methylation sites can be used as markers for colorectal cancer screening, disease risk prediction, colorectal cancer treatment effect evaluation, and colorectal cancer treatment drug screening. The test samples of this DNA methylation site combination can be widely derived from the subject's body fluids, cells, tissues and organs, especially the subject's colorectal lavage fluid, and can be used to achieve accurate, rapid, and non-invasive colorectal cancer screening, disease risk prediction, prognosis prediction, and drug evaluation.

The present specification provides a system and device for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening. The system and device evaluate the possibility of a subject suffering from colorectal cancer or the risk of developing colorectal cancer based on the relevant methylation levels of the aforementioned DNA methylation site combination.

The present specification also provides a detection reagent for a DNA methylation site combination, including a primer pair group for amplifying the aforementioned DNA methylation site combination, which can be widely used in multiple aspects including colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening.

The present specification also provides a kit for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening.

This specification also provides the use of DNA methylation site combinations as biomarkers, and the use of detection reagents for DNA methylation site combinations. The uses include but are not limited to the use in preparing a kit for colorectal cancer screening, the use in preparing a kit for predicting the risk of colorectal cancer, the use in preparing a kit for evaluating the therapeutic effect of colorectal cancer, the use in preparing a kit for screening colorectal cancer therapeutic drugs, etc., which can take into account and improve the sensitivity and specificity of screening, prediction, and selection.

According to one aspect of the present specification, a system for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening is provided. FIG1 is an application scenario diagram of a system for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening according to some embodiments of the present specification. As shown in FIG1 , scenario 100 may include a processing device 110 and a storage device 120.

The processing device 110 can process data and/or information. In some embodiments, the processing device 110 can obtain data and/or information from the storage device 120 or other components of the scene 100 (e.g., the user terminal 140, the detection device 160), and execute program instructions based on this information and/or data to perform one or more functions described in this specification. For example, the processing device 110 can obtain a training sample set from the storage device 120 and build a prediction model based on the training sample set. For another example, the processing device 110 can obtain methylation level related information of the DNA methylation site combination of the subject's biological sample 150 measured by the detection device 160, and call the prediction model stored in the storage device 120 to process the methylation level related information to assess the possibility of the subject suffering from colorectal cancer or the risk of developing colorectal cancer. In some embodiments, the processing device 110 can be a server or a central processing unit.

The storage device 120 can be used to store data and/or information. In some embodiments, the storage device 120 can store data and/or information obtained from the processing device 110 or other components of the scene 100 (e.g., the user terminal 140, the detection device 160). For example, the storage device 120 can store a prediction model for use by the processing device 110. For another example, the storage device 120 can obtain and store information related to the methylation level of a combination of DNA methylation sites of a subject's biological sample 150 from the detection device 160. For another example, the storage device 120 can receive and store information uploaded by the user terminal 140, such as the subject's identity information.

In some embodiments, the scene 100 also includes a network 130. The network 130 can be used to provide a channel for information exchange. In some embodiments, the processing device 110 and other components of the scene 100 (e.g., the storage device 120, the user terminal 140, the detection device 160) can exchange information through the network 130. For example, the processing device 110 can receive data in the storage device 120 through the network 130. For another example, the methylation level-related information of the DNA methylation site combination of the subject's biological sample 150 measured by the detection device 160 can be transmitted to the processing device 110 through the network 130. In some embodiments, the network 130 can be any one or more of a wired network or a wireless network. For example, the network 130 can include a cable network, an optical fiber network, etc. In some embodiments, the network 130 can be a point-to-point, shared, centralized, etc., or a combination of multiple topologies. In some embodiments, the network 130 can include one or more network access points. For example, through access points such as base stations and/or one or more network exchange points, one or more components of the scene 100 can be connected to the network 130 to exchange data and/or information.

In some embodiments, the scene 100 also includes a user terminal 140. The user terminal 140 can be used to implement the services provided by the scene 100 to the user. For example, the user can send the methylation level related information of the DNA methylation site combination of the subject's biological sample to the processing device 110 through the user terminal 140. For another example, the user can receive the evaluation result of the subject sent by the processing device 110 through the user terminal 140. For another example, the user can send the clinical test results of the subject to the processing device 110 through the user terminal 140, so that the processing device 110 updates the training sample set based on the clinical test results of the subject and iterates the prediction model. In some embodiments, the user terminal 140 may include one or any combination of a smart phone 140-1, a tablet computer 140-2, a laptop computer 140-3, etc. or other devices with input and/or output functions.

In some embodiments, the scene 100 further includes a detection device 160 for detecting the methylation level of a combination of DNA methylation sites of a subject's biological sample 150. As an example, the detection device may include a device for implementing one or more of the following methods: WGBS, RRBS, oxBS-seq, MethylCap-seq, MBD-seq, MeDIP-seq, HPLC, MSRF, MASP, methylation chip method, pyrophosphate sequencing method, dPCR, and MS-PCR.

According to another aspect of the present specification, a computing device is provided. FIG. 2 is a schematic diagram of the architecture of a computing device according to some embodiments of the present specification. As shown in FIG. 2, a computing device 200 includes a processor 210, a memory 220, an input/output interface 230, and a communication port 240. In some embodiments, the computing device 200 may implement the processing device 110 and/or the storage device 120. For example, the processing device 110 may be implemented on the computing device 200, and the computing device 200 is configured to perform the functions of the processing device 110 described in the present specification. In some embodiments, an apparatus for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation, and/or colorectal cancer treatment drug screening may be implemented in the computing device 200.

The processor 210 can execute computing instructions (program code) and execute the functions of the processing device 110 described in this specification. The computing instructions may include programs, objects, components, data structures, processes, modules and functions (functions refer to specific functions described in this application). For example, the processor 210 can process the instructions of the possibility of colorectal cancer screening or colorectal cancer risk prediction input by the user. Specifically, the processor 210 can obtain the methylation level of the DNA methylation site combination (for example, one or more of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161) in the subject's biological sample, and based on the methylation level of the DNA methylation site combination, the prediction model is used to detect whether the subject suffers from colorectal cancer or predict the risk of the subject suffering from colorectal cancer. In some embodiments, computing device 200 may include one or more processors 210; processor 210 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), and any circuits and processors capable of performing one or more functions, etc., or any combination thereof.

The memory 220 may store data/information obtained from any component of the scene 100. In some embodiments, the memory 220 may include a random access memory (RAM), a read-only memory (ROM), etc., or any combination thereof.

The input/output interface 230 can be used to input or output signals, data or information. In some embodiments, the input/output interface 230 can be used to implement the interaction between the user (e.g., subject, operator, etc.) and the processor 210. In some embodiments, the user can input relevant information of the subject (e.g., information related to the methylation level of the DNA methylation site combination, and basic identity information such as name and age) through the input/output interface 230. In some embodiments, the input/output interface 230 may include an input device and an output device. For example, a keyboard, a mouse, a display device, a microphone, and a speaker, etc.

The communication port 240 can be connected to the network 130 for data communication. The connection can be a wired connection, a wireless connection, or a combination of the two, such as a connection via an electric cable, an optical cable, a mobile network, WIFI, WLAN, or Bluetooth. In some embodiments, the communication port 240 can be a standardized port, such as RS232, RS485, etc. In some embodiments, the communication port 240 can be a specially designed port.

FIG3 is a module diagram of a system for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening according to some embodiments of this specification. As shown in FIG3, a system 300 for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening includes an acquisition module 310 and an analysis module 320. In some embodiments, these modules may be included in the processing device 110 or the processor 210.

The acquisition module 310 can be used to obtain the methylation level of a combination of DNA methylation sites in a biological sample of a subject. For example, the combination of DNA methylation sites may include one or more sites of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161.

In some embodiments, the acquisition module 310 may include a detection unit and an information processing unit. The detection unit may be used to perform DNA methylation detection on a biological sample of a subject. Exemplarily, the detection unit may include a device for implementing one or more of the following methods: WGBS, RRBS, oxBS-seq, MethylCap-seq, MBD-seq, MeDIP-seq, HPLC, MSRF, MASP, methylation chip method, pyrophosphate sequencing, dPCR, and MS-PCR. The information processing unit may be used to process the detection data of the detection unit to obtain information related to the methylation level of the DNA methylation site combination of the subject's biological sample.

The analysis module 320 can be used to evaluate whether the subject may have colorectal cancer or be at risk of developing colorectal cancer based on the methylation level of the combination of DNA methylation sites in the subject's biological sample using a prediction model, evaluate the effect of colorectal cancer treatment and/or screen colorectal cancer treatment drugs. In some embodiments, the analysis module 320 can be used to evaluate using a model based on the methylation threshold of the combination of DNA methylation sites. In some embodiments, the analysis module 320 can be used to evaluate using a model built based on a machine learning algorithm or a deep learning algorithm.

In some embodiments, the system 300 further includes a determination module 330. The determination module 330 can be used to obtain a training sample set, the training sample set including the methylation levels of DNA methylation sites of known colorectal cancer patients and non-colorectal cancer patients (healthy people); and analyze the training sample set using a ROC curve to determine a prediction model for distinguishing colorectal cancer patients from non-colorectal cancer normal people.

The term "ROC curve" (or receiver operating characteristic curve) is a curve drawn with the sensitivity of the experiment (true positive rate) as the ordinate and 1-specificity (false positive rate) as the abscissa. The ROC curve can evaluate the performance of the model.

More information about how each module of the system 300 implements its functions can be found elsewhere in this specification (eg, FIG. 4 and its description).

It should be understood that the system 300 and its modules for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening shown in FIG3 can be implemented in various ways. For example, in some embodiments, the system 300 and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art can understand that the above-mentioned methods and systems can be implemented using computer executable instructions and/or included in a processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided. The system and its modules of this specification can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (for example, firmware).

It should be noted that the above description of the system 300 and its modules is only for the convenience of description, and does not limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the various modules, or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, the acquisition module, analysis module and training module disclosed in Figure 3 can be different modules in a system, or one module can implement the functions of two or more of the above modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are all within the scope of protection of this specification.

According to another aspect of this specification, a method for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening is provided. Figure 4 is a schematic flow chart of colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening according to some embodiments of this specification. As shown in Figure 4, process 400 includes steps 401 and 403. In some embodiments, at least a portion of the steps in process 400 (e.g., step 401, step 403) can be completed by a computing device (computing device 200 as shown in Figure 2, processing device 110 as shown in Figure 1). For example, at least a portion of the steps in process 400 can be implemented as an instruction (e.g., application program) stored in storage device 120, memory 220. The processing device 110 in Figure 1, the processor 210 and/or module in Figure 2 can execute the instruction, and when executing the instruction, the processing device 110, the processor 210 and/or the module can be configured to execute process 400. The operations of the process shown below are for illustration purposes only. In some embodiments, process 400 can be completed using one or more additional operations not described and/or one or more operations not described. In addition, the order of operations of the process shown in Figure 4 and described below is not intended to be limiting.

Step 401, obtaining the methylation level of the DNA methylation site combination in the subject's biological sample. In some embodiments, step 401 may be performed by a computing device (eg, the processing device 110 of FIG. 1 , the acquisition module 310 of FIG. 3 ).

In some embodiments, the methylation level of a combination of DNA methylation sites in a biological sample of a subject with colorectal cancer can be distinguished from the methylation level of a combination of DNA methylation sites in a biological sample of a subject without colorectal cancer (or normal subject).

As used herein, the term "subject" (or "individual") refers to an object that is observed, tested, or experimented on. In some embodiments, the subject can be a mammal. Mammals include, but are not limited to, primates (including humans and non-human primates) and rodents (e.g., mice and rats). In some embodiments, the mammal can be a human.

The term "biological sample" (or "sample", "sample") refers to a composition separated from an organ, tissue, cell and/or body fluid of a subject, which composition contains one or more target analytes (e.g., nucleic acids, metabolites, etc.). In some embodiments, the biological sample can be derived from a subject's body fluid. Body fluids include, but are not limited to, whole blood, plasma, serum, tissue fluid, saliva, urine, lavage fluid (e.g., colorectal lavage fluid), etc., or a combination thereof. In some embodiments, the biological sample is derived from a subject's colorectal cancer lavage fluid, in particular, a formed component of a colorectal cancer lavage fluid, and the formed components of the colorectal lavage fluid may include one or more of circulating free nucleic acids (e.g., circulating free DNA (cfDNA) derived from the colorectum), circulating tumor cells (CTCs) (e.g., tumor cells released by colorectal tumors), and exfoliated cells (e.g., cells shed by colorectal tissue).

The term "methylation level" is an indicator for evaluating the methylation state of a DNA methylation site. In some embodiments, the methylation level can be quantitatively described by the methylation M value of the DNA methylation site.

In some embodiments, the methylation M value of the DNA methylation site can be obtained based on the methylation β value of the DNA methylation site by logarithmic transformation, and the methylation β value and M value of the DNA methylation site can be calculated by formula (1) and formula (2):

Among them, Methylated and Unmethylated are the number of methylated and unmethylated sequences at the site to be detected, respectively, and offset is a small constant used to stabilize the ratio of low-intensity signals to prevent the denominator from being zero.

The β value reflects the ratio of oligonucleotides that can match a given methylated sequence. It is used to represent the value of DNA methylation level. It is the methylation rate of the detection site in the sequence, ranging from 0 (completely unmethylated) to 1 (completely methylated). The β value has a relatively intuitive biological meaning. It is generally believed that a β value ≥ 0.6 is considered to be completely methylated, ≤ 0.2 is considered to be completely unmethylated, and between the two is considered to be partially methylated.

The M value is essentially a logarithmic transformation of the ratio of methylated to unmethylated signal intensities, which facilitates subsequent statistical analysis of the data. Compared with the β value, the distribution of the M value is closer to the normal distribution, which makes the M value particularly useful in differential methylation analysis. This property of the M value makes it particularly suitable for parametric statistical tests (such as t-tests and ANOVA) and linear model analysis. The β value changes with changes in methylation levels, especially at extreme low methylation (close to 0) or high methylation (close to 1) levels, its variance decreases significantly. This heterogeneity of variance may affect the sensitivity and specificity of statistical analysis. In contrast, the variance of the M value is relatively more stable and does not change significantly with changes in methylation levels, which makes it more reliable when comparing methylation differences between different samples or groups.

In some embodiments, the methylation site β value can be used to preliminarily screen DNA methylation differential sites, and the methylation site M value can be used to train a logistic regression model.

In some embodiments, the DNA methylation site combination is suitable for detecting different stages of colorectal cancer, such as early stage (e.g., stage I, stage II) and late stage (e.g., stage III, stage IV). In some preferred embodiments, the DNA methylation site combination is suitable for distinguishing early colorectal cancer populations from normal populations.

The DNA methylation site combination includes one or more DNA methylation sites. As used herein, the term "DNA methylation site" (or "methylation site") refers to a methyl group covalently bound to the 5' carbon position of the cytosine of the CpG dinucleotide of the genomic DNA, becoming 5-methylcytosine (5mC). In some embodiments, the methylation state of each DNA methylation site in the DNA methylation site combination may be related to the occurrence and development of colorectal cancer, and the DNA methylation site of the DNA methylation site combination may be located on a colorectal cancer-related gene (e.g., a known or potentially potential colorectal cancer tumor suppressor gene). Non-limiting examples of colorectal cancer-related genes may include, but are not limited to: SOCS-1, cg24403845, HOXD3, SDC2-2, SFRP1, SP9, TAC1-2 and TBR1.

In some embodiments, the combination of DNA methylation sites may include one or more DNA methylation sites located on SOCS-1, cg24403845, HOXD3, SDC2-2, SFRP1, SP9, TAC1-2 and/or TBR1.

The methylation level of each DNA methylation site in the combination of DNA methylation sites is significantly associated with colorectal cancer. It is understandable that for each DNA methylation site in the combination of DNA methylation sites, there is a significant difference between its methylation level in a known colorectal cancer population and its methylation level in a normal population.

In some embodiments, the combination of DNA methylation sites may include at least 1, 2, 3, 4, 5, 6, 7 or 8 sites in the following group: site SOCS-1_24 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site cg24403845_103 located at chromosome coordinates chr10:108924288 on the cg24403845 gene; site HOXD3_26 located at chromosome coordinates chr2:177027898 on the HOXD3 gene; site SDC2_27 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_28 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_29 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_20 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_21 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_22 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_23 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_24 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_25 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene; site SDC2_26 located at chromosome coordinates chr16:11348913 on the SOCS-1 gene -2_ site SDC2-2_46 with chromosome coordinates chr8:97505775 on the gene; site SFRP1_48 with chromosome coordinates chr8:41167015 on the gene SFRP1; site SP9_25 with chromosome coordinates chr2:175199694 on the gene SP9; site TAC1-2_135 with chromosome coordinates chr7:97361597 on the gene TAC1-2; and site TBR1_161 with chromosome coordinates chr2:162283730 on the gene TBR1.

It should be noted that the chromosome coordinate information used in this article is derived from the human reference genome hg19 (GRCh37). It should be noted that the chromosome coordinate information used in this article can also be converted into the human reference genome hg38 (GRCh38), and this specification does not limit this.

In some preferred embodiments, the DNA methylation site combination may include SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161. Optionally, the DNA methylation site combination may also include DNA methylation sites on one or more other colorectal cancer-related genes. In some embodiments, the DNA methylation site combination may include one or more of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161 and one or more of the other methylation sites in Example Table 1.

In some embodiments, the DNA methylation site combination may include one, two, three, four, five, six, seven or all of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161. In some embodiments, the DNA methylation site combination may include a combination containing at least SOCS-1_24 and cg24403845_103. For example, the DNA methylation site combination may include the two combinations of SOCS-1_24 and cg24403845_103. For another example, the DNA methylation site combination may include the two combinations of SOCS-1_24 and cg24403845_103 and the remaining 1, 2, 3, 4 or 5 of the eight sites.

In some more preferred embodiments, the DNA methylation site combination may consist of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161. In some embodiments, the DNA methylation site combination may consist of one or more of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161 and other methylation sites in Table 1 of the embodiment.

There is a significant correlation between the methylation level of the DNA methylation site combination provided in some embodiments of this specification and colorectal cancer. The methylation state of the DNA methylation site combination can be quantified and used to measure the methylation level of the DNA methylation site combination. Samples containing the DNA methylation site combination can be widely collected from organs, tissues, cells and body fluids of subjects, and in particular can be collected from colorectal lavage fluid of subjects. The application of the DNA methylation site combination as a colorectal cancer marker in colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation, colorectal cancer treatment drug screening, etc. can achieve screening/diagnosis, prediction, and evaluation of sensitivity and specificity.

In some embodiments, the methylation level of the DNA methylation site combination can be obtained by detecting a biological sample of a subject using a detection reagent for the DNA methylation site combination. The detection reagent for the DNA methylation site combination is used to detect the methylation level of the DNA methylation site combination.

More information about detection reagents for DNA methylation site combinations can be found elsewhere in this specification.

The computing device (e.g., the processing device 110 of FIG. 1 , the acquisition module 310 of FIG. 3 ) can implement the execution of step 401 in a variety of ways. In some embodiments, the processing device 110 can call the methylation level related information of the DNA methylation site combination of the subject's biological sample stored in the storage device 120. For example, the methylation level related information of the DNA methylation site combination of the subject's biological sample is uploaded to the storage device 120 by the user terminal 140 via the network 130, and the processing device 110 can call and obtain the methylation level related information for further analysis and evaluation. In some embodiments, the processing device 110 can receive the methylation level related information of the DNA methylation site combination of the subject's biological sample obtained by the detection device 160. For example, the processing device 110 sends a detection instruction to the detection device 160 (e.g., a PCR instrument and/or an NGS sequencer), and the detection device 160 detects and obtains the methylation level related information of the DNA methylation site combination of the subject's biological sample based on the detection instruction, and sends the methylation level related information to the processing device 110. In some embodiments, the processing device 110 may obtain information related to the methylation level of a combination of DNA methylation sites in a biological sample of a subject based on user input.

Step 403, based on the methylation level of the DNA methylation site combination, using a prediction model to evaluate whether the subject has colorectal cancer, predict the risk of the subject developing colorectal cancer, evaluate the effect of colorectal cancer treatment on the subject, and/or evaluate the effect of colorectal cancer treatment drugs. In some embodiments, step 403 can be performed by a computing device (e.g., the processing device 110 of FIG. 1, the analysis module 320 of FIG. 3).

One or more embodiments of the present specification provide a predictive model for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening using methylation sites.

In some embodiments, the prediction model can be a logistic regression model based on a combination of DNA methylation sites. The prediction model can be implemented by calculating the risk score of the subject suffering from colorectal cancer, developing colorectal cancer, and the risk level of colorectal cancer after the subject receives treatment and/or administration, so as to evaluate the possibility of the subject suffering from cancer, the risk of developing cancer, the effect of the subject's treatment of colorectal cancer, and/or the effect of the colorectal cancer treatment drug. In some embodiments, the logistic regression model can obtain the risk of the subject suffering from colorectal cancer and developing colorectal cancer by calculating the risk score. In some embodiments, the prediction model can be represented by the following formula (3):

Risk score＝-4.38+5.16*(cg24403845_103)+1.18*SOCS-1_24 (3)

Among them, Risk score represents the risk score of the subject suffering from colorectal cancer, developing colorectal cancer, and the risk level of colorectal cancer after the subject receives treatment and/or medication, and the gene locus represents the methylation M value of the gene locus.

In some embodiments, the risk score calculation formula may also be represented by the following formula (4):

Risk score＝-7.11+2.89*cg24403845_103+3.61*SOCS-1_24+1.05*TAC1-2_135+0.92*SDC2-2_46+0.77*SFRP1_48+0.72*TBR1_161+0.67*SP9_25+0.19*HOXD3_26 (4)

Among them, Risk score represents the risk score of the subject suffering from colorectal cancer, developing colorectal cancer, and the risk level of colorectal cancer after the subject receives treatment and/or medication, and the gene locus represents the methylation M value of the gene locus.

The risk score can be used to predict whether the subject's biological sample is a colorectal cancer positive sample. In some embodiments, if the risk score of the subject's biological sample calculated by the prediction model is greater than the risk score threshold, then the subject's biological sample can be predicted to be a colorectal cancer positive sample. In some embodiments, the risk score threshold can be 0.

In some embodiments, the input of the prediction model constructed using logistic regression can be the methylation M value of the combination of DNA methylation sites of the subject's biological sample, and the output of the prediction model can be the risk score of the subject suffering from colorectal cancer. The prediction model can be obtained by training the initial model using a training sample set. Among them, the training sample set can include the methylation level of the combination of DNA methylation sites of one or more known colorectal cancer patient samples and the methylation M value of the combination of DNA methylation sites of non-colorectal cancer patient (e.g., healthy person) samples, as well as a label indicating whether the sample object suffers from colorectal cancer. The term "known colorectal cancer patient" refers to an object or individual with clinical symptoms of colorectal cancer and clinical diagnosis verification (e.g., the type and nature of the disease have been confirmed by biopsy). The term "non-colorectal cancer patient" refers to an object or individual who does not suffer from colorectal cancer and has no obstacles in daily life. In some embodiments, the training set can be the methylation level of the colorectal lavage fluid sample group obtained in the embodiment. In some embodiments, the methylation level of the colorectal lavage fluid validation group obtained in the embodiment can be used to verify the prediction model.

Exemplarily, in the training sample set used to train the prediction model, the label of the colorectal cancer patient sample can be 1, and the label of the non-colorectal cancer patient sample can be 0. The methylation level of the DNA methylation site combination of the subject's biological sample is used as the model input, and the corresponding risk score can be obtained through model calculation. The risk score can be converted into a probability value indicating that the subject sample is a colorectal cancer patient sample (label is 1) through the sigmod function, and its value is between 0 and 1. The larger the risk score, the closer the corresponding probability value is to 1, indicating that the possibility of the subject sample being a colorectal cancer patient sample is greater; the smaller the risk score, the closer the corresponding probability value is to 0, indicating that the possibility of the subject sample being a colorectal cancer patient sample is smaller; when the risk score is 0, the corresponding probability value is equal to 50%, indicating that the probability of the subject sample being a colorectal cancer patient sample is 50%.

For more details about the prediction model, see the relevant content and description of Example 2.

The computing device (e.g., the processing device 110 of FIG. 1 , the analysis module 320 of FIG. 3 ) can implement the execution of step 403 in a variety of ways. In some embodiments, the processing device 110 can call the prediction model stored in the storage device 120, and use the prediction model to process the methylation level related information of the DNA methylation site combination of the subject's biological sample to obtain an evaluation result. In other embodiments, the processing device 110 can update the prediction model stored in the storage device 120 based on user instructions, and use the updated prediction model to obtain the evaluation result. Among them, the processing device 110 can collect the methylation level related information of the associated DNA methylation site combination of the colorectal cancer group and the normal group from a public or non-public database through the network 130, which is used to update the training sample set and optimize the prediction model. The processing device 110 can also update the training sample set based on user input or based on the data/information uploaded by the user terminal 140, and optimize the prediction model.

In some embodiments, the AUC of the prediction model provided in this specification may be greater than 0.9, 0.93, 0.95. In some embodiments, the sensitivity of the prediction model provided in this specification may be greater than 90%, 92%, 94%, 95%, 96%, 97%. In some embodiments, the specificity of the prediction model provided in this specification may be greater than 90%, 92%, 94% or 95%.

It should be noted that the above description of process 400 is only for example and illustration, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 400 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

According to another aspect of the present specification, a device for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening is provided. The device may include a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening methods shown in some embodiments of the present specification may be implemented.

More information about methods for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment efficacy assessment and/or colorectal cancer treatment drug screening can be found elsewhere in this specification (e.g., Figure 4 and its description).

According to another aspect of the present specification, a detection reagent for a combination of DNA methylation sites is provided. The combination of DNA methylation sites can be used as a biomarker for detecting colorectal cancer, including one or more sites (e.g., 2, 5 or 8) of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161.

In some embodiments, the detection reagent of the DNA methylation site combination includes a primer set for amplifying the DNA methylation site combination. The primer set for amplifying the DNA methylation site combination is used to obtain a specific amplified fragment containing the DNA methylation site combination and amplify the detection information.

In some embodiments, the primer set for amplifying the combination of DNA methylation sites includes primer pairs for amplifying one or more sites of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, SFRP1_48, SP9_25, TAC1-2_135 and TBR1_161. Optionally, the primer pair for amplifying SOCS-1_24 is as shown in SEQ ID NO:1 and SEQ ID NO:2, or the primer sequence of the primer pair has at least 95%, 96%, 97%, 98% or 99% similarity with the sequences shown in SEQ ID NO:1 and SEQ ID NO:2, respectively. Optionally, the primer pair used to amplify cg24403845_103 is as shown in SEQ ID NO: 3 and SEQ ID NO: 4, or the primer sequence of the primer pair has at least 95%, 96%, 97%, 98% or 99% similarity with the sequence shown in SEQ ID NO: 3 and SEQ ID NO: 4. Optionally, the primer pair used to amplify HOXD3_26 is as shown in SEQ ID NO: 5 and SEQ ID NO: 6, or the primer sequence of the primer pair has at least 95%, 96%, 97%, 98% or 99% similarity with the sequence shown in SEQ ID NO: 5 and SEQ ID NO: 6. Optionally, the primer pair for amplifying SDC2-2_46 is shown in SEQ ID NO:7 and SEQ ID NO:8, or the primer sequence of the primer pair has at least 95%, 96%, 97%, 98% or 99% similarity to the sequence shown in SEQ ID NO:7 and SEQ ID NO:8, respectively. Optionally, the primer pair for amplifying SFRP1_48 is shown in SEQ ID NO:9 and SEQ ID NO:10, or the primer sequence of the primer pair has at least 95%, 96%, 97%, 98% or 99% similarity to the sequence shown in SEQ ID NO:9 and SEQ ID NO:10, respectively. Optionally, the primer pair for amplifying SP9_25 is shown in SEQ ID NO:11 and SEQ ID NO:12, or the primer sequence of the primer pair has at least 95%, 96%, 97%, 98% or 99% similarity to the sequence shown in SEQ ID NO:11 and SEQ ID NO:12, respectively. Optionally, the primer pair for amplifying TAC1-2_135 is as shown in SEQ ID NO: 13 and SEQ ID NO: 14, or the primer sequence of the primer pair has at least 95%, 96%, 97%, 98% or 99% similarity with the sequence shown in SEQ ID NO: 13 and SEQ ID NO: 14. Optionally, the primer pair for amplifying TBR1_161 is as shown in SEQ ID NO: 15 and SEQ ID NO: 16, or the primer sequence of the primer pair has at least 95%, 96%, 97%, 98% or 99% similarity with the sequence shown in SEQ ID NO: 15 and SEQ ID NO: 16.

In some embodiments, the detection reagent of the DNA methylation site combination may also include other reagents for detecting the methylation level, such as methylation conversion reagents and/or sequencing reagents. As an example, the detection method of the methylation level may include but is not limited to WGBS, RRBS, oxBS-seq, MethylCap-seq, MBD-seq, MeDIP-seq, HPLC, MSRF, MASP, methylation chip method, pyrophosphate sequencing, dPCR, MS-PCR, etc., or a combination thereof. In some preferred embodiments, other reagents may include reagents used to implement one or more methods of WGBS, RRBS, oxBS-seq, MethylCap-seq, MBD-seq, MeDIP-seq, HPLC, MSRF, MASP, methylation chip method, pyrophosphate sequencing, dPCR and MS-PCR. In some more preferred embodiments, other reagents may include reagents used to implement WGBS or RRBS.

According to another aspect of the present specification, a kit is provided for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening. The kit comprises a detection reagent for the DNA methylation site combination shown in some embodiments of the present specification.

According to another aspect of the present specification, there is provided a use of a DNA methylation site combination as a biomarker in the preparation of a kit for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect evaluation and/or colorectal cancer treatment drug screening. The DNA methylation site combination is the DNA methylation site combination shown in some embodiments of the present specification.

The experimental methods in the following examples are conventional methods unless otherwise specified. The experimental materials used in the following examples are purchased from conventional biochemical reagent companies unless otherwise specified. The quantitative tests in the following examples are repeated three times, and the results are averaged.

Example

Example 1 Determination of methylation sites and methylation levels

1. Collect colorectal lavage fluid samples

A total of 238 intestinal lavage fluid samples were collected from 208 colorectal cancer patients and 30 healthy normal people as experimental samples. After collection, the samples were stored in 50mL lavage fluid DNA preservation tubes containing 7mL of additives. The tubes were centrifuged at 4000rpm for 10min, the supernatant was discarded, and the precipitate was washed with 1×PBS.

2. Statistics of specific methylation sites

A total of 117 methylation sites were counted, including 105 known and/or potential colorectal cancer-related genes and 12 ATCB gene methylation sites for quality control. The specific information is shown in Table 1.

Table 1 Methylation site information

3. DNA extraction from lavage fluid sample group

Extract DNA from the lavage fluid sample group. Add 180 μL of Buffer GTL to the above precipitate and resuspend the precipitate. Then add 20 μL of proteinase K and vortex to mix. Incubate in a 56°C water bath for 1 hour until the sample is completely dissolved. Continue in a 90°C water bath for 1 hour. Centrifuge briefly to collect the solution on the tube wall to the bottom of the tube. Add 200 μL of Buffer GL and vortex to mix thoroughly. Add 200 μL of anhydrous ethanol and vortex to mix thoroughly. Centrifuge briefly to collect the solution on the tube wall to the bottom of the tube.

Add all the solution in the tube to the silicon-based material membrane in the centrifuge tube, add 500 microliters of Buffer GW1 with anhydrous ethanol to the silicon-based material membrane, centrifuge at 12,000rpm for 1 minute, discard the waste liquid in the collection tube, and put the silicon-based material membrane back into the collection tube. Add 500 microliters of Buffer GW2 with anhydrous ethanol to the silicon-based material membrane, centrifuge at 12,000rpm for 1 minute, discard the waste liquid in the collection tube, and put the silicon-based material membrane back into the collection tube. Centrifuge at 12,000rpm for 2 minutes, discard the waste liquid in the collection tube, and place the silicon-based material membrane at room temperature for several minutes to dry thoroughly.

Place the silicon matrix material membrane in a new centrifuge tube, add 50-200 μL of Buffer GE, place at room temperature for 2-5 minutes, centrifuge at 12000 rpm for 1 minute, collect the DNA solution, and store it at -20°C for further use. Use a micro-spectrophotometer Nano-300 and Qubit to measure the DNA concentration (the concentration must be not less than 1 ng/μL).

4. DNA methylation transformation of lavage fluid sample group

The lavage fluid sample group was treated with sulfite: 50 μL lavage fluid precipitated DNA sample, 150 μL Bisulfite Mix, and 25 μL MBuffer B-protection solution were added to the PCR tube. After a brief centrifugation, the PCR tube was placed on the PCR instrument, incubated at 85°C for 50 minutes, cooled to room temperature, and briefly centrifuged. Among them, the lavage fluid precipitated DNA was taken from the aforementioned DNA solution, and the DNA content in the 50 μL lavage fluid precipitated DNA sample was 50-500 ng. The preparation of Bisulfite Mix includes adding 1.2 mL MBuffer A-conversion solution to a dry powder tube containing sodium bisulfite, and shaking and mixing until the dry powder is completely dissolved.

DNA purification after sulfite treatment: all the solutions in the above PCR tubes were transferred into a 1.5mL centrifuge tube; 285μL MBuffer C-binding solution, 115μL isopropanol, and 10μL magnetic bead suspension were added to the centrifuge tube (please mix thoroughly before use), and the mixture was shaken for 10min; after a brief centrifugation, the mixture was placed on a magnetic rack for adsorption for 2min, and the supernatant was discarded; 1000μL MBuffer D-washing solution was added to the centrifuge tube, and the mixture was kept on the magnetic rack for incubation for 30s, and the supernatant was discarded; 1000μL MBuffer E-incubation solution was added to the centrifuge tube, and the mixture was incubated at room temperature for 15min, and the mixture was placed on a magnetic rack for adsorption for 2min, and the supernatant was discarded; 1000μL MBuffer D-washing solution was added to the centrifuge tube, and the mixture was kept on the magnetic rack for incubation for 30s, and the supernatant was discarded; this step was repeated once. After the excess washing solution in the centrifuge tube was cleaned, it was placed on a clean bench and blown dry for 5min.

Purify and recover DNA from the lavage fluid sample group: add 50 μL MBuffer F-elution buffer to the centrifuge tube, warm at 56°C to help improve the elution efficiency, vortex to mix it thoroughly, and wait for 5 minutes. Centrifuge briefly and place it on the magnetic rack for adsorption for 2 minutes. Pipette the supernatant into a clean new centrifuge tube, collect the DNA solution as the DNA conversion sample, and store it at -20°C for further use.

5. Multiplex PCR-NGS testing

In the first round of PCR, 8 pairs of methylation-specific primers were designed for 8 colorectal cancer-specific genes (primer sequences are shown in Table 2) and PCR reactions were performed on 238 sample nucleic acids. The ACTB internal reference gene control was set to control the NGS process and sulfite conversion process. The reaction system included: 10×ACE buffer, 3μL; dNTP Mix (10mM), 1μL; Primer mixed primer, 5μL; TMAC 600mM, 2.5μL; 50% glycerol, 6μL; 5×Enhancer, 2μL; sterile water, 5μL; Ace DNA Taq enzyme, 0.5μL; DNA conversion sample (i.e., purified DNA after sulfite treatment), 5μL.

Table 2 Sequence table of methylation specific primers

The reaction conditions of the first round of PCR were as follows: 1) Cycle 1: 95°C for 5 min; 2) Cycle 35: 95°C for 30 s, 52°C for 1 min, 72°C for 30 s; 3) Cycle 1: 72°C for 5 min.

The reaction system of the second round of PCR includes: 10×ACE buffer, 3μL; dNTP Mix (10mM), 1μL; Primer AP5 (5μM), 2μL; Primer Index (5μM), 2μL; 50% glycerol, 6μL; Sterile water, 10.5μL; Ace DNA Taq enzyme, 0.5μL; First round PCR reaction product, 5μL. Among them: The sequence of primer AP5 is

AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT (SEQ ID NO: 19); the sequence of primer index is

CAAGCAGAAGACGGCATACGAGATNNNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT (SEQ ID NO: 20). It should be noted that “NNNNNNNN” represents an index used to distinguish different samples.

The reaction conditions of the second round of PCR were as follows: 1) Cycle 1: 95°C for 10 min; 2) Cycle 21: 95°C for 30 s, 57°C for 30 s, 72°C for 30 s; 3) Cycle 1: 72°C for 5 min.

The amplified product was purified by nucleic acid purification reagent to obtain a sequencing library, and then the sequencing reagent used Miniseq ^TM MidOutput Reagent Cartridge (Illumina) and sequenced on a MiniSeq sequencer (Illumina).

6. Calculation of methylation levels (β value and M value) at each site

The NGS results of 117 loci were statistically analyzed, and the sequencing depth of each locus was no less than 500X. The methylation level (β value and M value) of each locus can be calculated by formulas (1) and (2):

Among them, Methylated and Unmethylated are the number of methylated and unmethylated sequences at the site to be detected, respectively, and offset is a small constant used to stabilize the ratio of low-intensity signals to prevent the denominator from being zero. For more information about β and M values, please refer to the above related content.

7. Methylation rate difference test and correlation analysis

The methylation β values of 105 methylation sites (see Table 1) of 154 training samples were tested and analyzed. The β values of methylation data were used to preliminarily screen these differential sites, that is, the mean methylation β value in normal samples was <0.2, and the mean β value in cancer samples was greater than that in normal samples; the difference in β values between normal samples and cancer samples was tested using t-test, and p-value <0.05 indicated that the difference between the two types of samples was statistically significant. The test found that among all 105 sites, most sites showed methylation β values in cancer samples that were significantly higher than methylation β values in normal samples.

The correlation of methylation rates (methylation β values) at different sites in 30 normal samples and 208 colorectal cancer samples was analyzed, and the Pearson correlation coefficient was used for the correlation. The correlation heat map is shown in Figure 5A (30 normal samples) and Figure 5B (208 colorectal cancer samples). The horizontal and vertical axes of the correlation heat map are both methylation sites. The methylation site information in normal samples and colorectal cancer samples is shown in Table 3. The horizontal axis is arranged from left to right and the vertical axis is arranged from top to bottom in the order of Table 3.

Table 3 Methylation site information table of correlation heat map

In normal samples, the methylation rates of the detected sites were relatively low, among which the sites on the SOCS-1 gene showed a high correlation, and the remaining sites also showed a certain correlation; in colorectal cancer samples, the correlation of methylation rates showed a relatively obvious pattern, different sites of the SOCS-1 gene were highly correlated, and the methylation rates of the three gene sites SP9, TAC1-2, and cg24403845 were all highly correlated; on the remaining four genes, the sites within the gene remained highly correlated, and also showed a certain weak correlation between genes.

Example 2 Prediction model acquisition process

The embodiments of this specification collected lavage fluid samples from 208 colorectal cancer patients and lavage fluid samples from 30 normal persons. Through literature retrieval, 105 methylation sites related to 8 colorectal cancer incidence and the ACTB gene as quality control were detected. A total of 117 methylation sites were detected. Through machine learning methods, logistic regression models related to 2 methylation sites and 8 methylation sites were established as prediction models, respectively, and good results were achieved in the training set and the independent validation set.

The embodiment of this specification proposes an analysis process as shown in FIG6 . The establishment of the prediction model is carried out according to the analysis process as shown in FIG6 , which mainly includes the following steps:

1. Data quality control

Usually, the second-generation sequencing methylation analysis process gives the methylation rate (β value) of the tested site of the sample and the sequencing depth of the corresponding site. During the experiment, due to some problems in the collection, transportation, and extraction of samples, the final sample quality may not meet the requirements of subsequent analysis, such as failure to collect enough and suitable samples during the sample collection process; sample decomposition during storage and transportation; failure of the experimental extraction process, etc., it is necessary to control the quality of the sites and samples of the sequencing results as a whole.

In the examples of this specification, for each site, the sequencing depth is required to be no less than 500, otherwise it is set as a missing value; for each sample, the number of missing sites should be less than 5% of the total test sites, otherwise the sample is judged as an unqualified sample and will be eliminated from subsequent analysis.

2. Splitting of training set and validation set data

After quality control, the embodiments of this specification include methylation sequencing data of 238 lavage fluid samples for downstream data analysis, including 208 colorectal cancer positive samples and 30 negative control samples; the data are randomly split into two parts, as training set and test set, respectively, of which the training set contains 139 colorectal cancer positive samples and 15 negative control samples; the validation set contains 69 colorectal cancer positive samples and 15 negative control samples. The samples in the training set are used to extract features and train models; the validation set samples are completely independent of the training set samples, and the prediction model obtained from the training set is used to predict the results and evaluate the obtained prediction model. The methylation conversion level of the internal reference gene ACTB in these samples is above 99%, ensuring that the sulfite treatment process of each site of each gene is normal.

3. Conversion and normalization of β value and M value

The β value has a more intuitive biological meaning. The β value is generally Beta distributed, while the M value is close to normal distribution and is more suitable for general model training and application. The formula for the mutual conversion between the M value and the β value is as follows:

The M value is normalized by normalization formula (6):

Among them, M _normal is the normalized M value, μ _normal and σ _normal are the mean and variance of the M value of the methylation sites of normal samples in the training set, respectively. All samples are normalized using this formula and parameters, including training sets and validation sets, normal samples and cancer samples.

The methylated β value is converted into an M value, and then the M value is standardized, that is, according to the mean and variance of the normal sample in the training set, the converted and normalized M value will conform to the standard normal distribution in the normal sample; in the cancer sample, the same conversion is performed, and the mean and variance still take the values of the normal sample. As shown in Figures 7A and 7B, the methylation rate distribution diagram of the 8 sites in the examples of this specification in normal samples and colorectal cancer samples is shown, and Figures 7A and 7B represent the β values and normalized M values of the 8 methylation sites, respectively.

4. Feature screening and model training

The embodiment of this specification detects the methylation rate of 105 sites in 8 genes (see Table 1), which are sites related to colorectal cancer reported in the literature, plus 12 sites of the ACTB gene for quality control. The methylation rate in the same gene region is generally highly correlated (see Figure 5A and Figure 5B), that is, the methylation information of these sites is highly redundant; the methylation rates in some different gene regions may also be highly correlated.

Using the training set data, the appropriate model is trained and selected in an increasing manner with the number of features. The specific process is as follows: (1) To reduce the amount of calculation required for model establishment, the best five sites are retained for each gene region, i.e., the sites with the most significant p-value based on the t-test, and the remaining sites are not included in the model training process; (2) m sites are selected to start training the logistic regression model (actually starting from a single site, m = 1). Here, the combination of m sites does not include sites from the same region. Each site combination to be selected is trained using the data from the training set. During the training process, the AUC of the leave-one-out method is used to characterize the quality of the model. Assume that there are a total of k samples in the training set, retain one of them, use the remaining k-1 samples to train the logistic regression model, and then use the model to predict the predicted value of the retained sample. Each sample will be retained and predicted once. The predicted value of the sample predicted by these k models is compared with the true value of the sample to calculate the AUC; (3) On the basis of all possible m sites, add one site, that is, list all combinations of m+1 sites. Each combination also does not contain sites from the same gene region. Repeat the above model training, prediction, and AUC to characterize the prediction effect; (4) In the optimal model, a total of 8 methylation sites are included, of which cg24403845_103 and SOCS-1_24 have a contribution value significantly greater than the other sites and are the main sites. These two sites are selected to train the logistic regression model to verify the discrimination of the main sites for sample classification.

(1) Prediction model 1: Logistic regression model based on the two loci cg24403845_103 and SOCS-1_24. The risk score is calculated as follows:

Risk scorep＝-4.38+5.16*(cg24403845_103)+1.18*SOCS-1_24 (3)

Among them, Risk score represents the risk score of the subject suffering from colorectal cancer, developing colorectal cancer, and the risk level of colorectal cancer after the subject receives treatment and/or medication, and the gene locus represents the methylation M value of the gene locus.

Based on the logistic regression model of the two loci cg24403845_103 and SOCS-1_24, the M values of the two methylation sites were used to achieve a good effect of distinguishing colorectal cancer samples from normal samples. The leave-one-out cross-validation result is shown in Figure 8A, and the ROC curve area is 0.980. The prediction model associated with the two methylation sites was used to predict the training set samples, as shown in Figure 8B. For cancer samples, there was only one sample with a risk score of <0, a corresponding logistic regression probability of <0.5, and a negative prediction value. The prediction result of this sample was wrong. Except for this sample, the prediction values of all other cancer samples were consistent with the true values; for normal samples, there were 3 samples with a risk score of >0, a corresponding logistic regression probability of >0.5, and a positive prediction value. The prediction results of these 3 samples were wrong. Except for these 3 samples, the prediction values of all other negative samples were consistent with the true values.

(2) Prediction model 2: A logistic regression model based on the eight loci cg24403845_103, SOCS-1_24, TAC1-2_135, SDC2-2_46, SFRP1_48, TBR1_161, SP9_25, and HOXD3_268. The risk score was calculated as follows:

Risk score＝-7.11+2.89*cg24403845_103+3.61*SOCS-1_24+1.05*TAC1-2_135+0.92*SDC2-2_46+0.77*SFRP1_48+0.72*TBR1_161+0.67*SP9_25+0.19*HOXD3_26 (4)

Among them, Risk score represents the risk score of the subject suffering from colorectal cancer, developing colorectal cancer, and the risk level of colorectal cancer after the subject receives treatment and/or medication, and the gene locus represents the methylation M value of the gene locus.

Logistic regression analysis was performed on the above 8 sites using unit logistic regression analysis and multivariate logistic regression analysis, and the results are shown in Table 4. In the results of the unit logistic regression analysis, the M values of the 8 sites were significantly correlated with the phenotype of colorectal cancer; in the results of the multivariate regression analysis, only cg24403845_103 and SOCS-1_24 were statistically significant, indicating that the 8 sites were not completely independent, and in the regression model, these two sites were the two sites that contributed the most, and the remaining sites still increased the accuracy of the model; the model in this manual used the M values of the 8 methylation sites and achieved a good effect of distinguishing colorectal cancer samples from normal samples. The leave-one-out cross-validation result is shown in Figure 9A, and the ROC curve area is 0.996. The prediction model related to the 8 methylation sites is used to predict the training set samples, as shown in Figure 9B. For cancer samples, there is only one sample with a risk score of <0, a corresponding logistic regression probability of <0.5, and a predicted value of negative. The prediction result of this sample is wrong. Except for this sample, the predicted values of all other cancer samples are consistent with the true values. For all normal samples, their risk scores are <0, the corresponding logistic regression probability is <0.5, and the predicted values are negative, which are exactly the same as the true values.

Table 4 Results of unit and multivariate logistic regression analysis

5. Sample prediction and model evaluation

The optimal logistic regression model is obtained through the training set data. The model is applied to the test set data to calculate the risk score of each sample and the probability of the corresponding classification type in the logistic regression. The model is compared with the true phenotype to calculate the predictive efficiency of the model in the independent test set, which is expressed in the form of AUC. AUC (Area Under Curve) is the area under the ROC curve (receiver operating characteristic curve) and the coordinate axis, and its maximum value is 1; the ROC curve is generally above the straight line y=x, so the value range of AUC is between 0.5 and 1. The closer the AUC is to 1.0, the higher the accuracy of the detection method and the higher the application value.

The test set is a sample independent of the training set, which can be used to evaluate the reliability of the model trained with the training set data. The quality control standards of the test set samples are the same as those of the training set; because the final phenotype of the sample cannot be used in the model when predicting the test set samples, it can only be used when comparing with the prediction results, so the K Mean method is used: the data of the test set samples are clustered according to the normalized M value of the sample, the distance from each sample in the test set to each cluster is calculated, and the measured value of the sample in the test set is filled according to the mean of the cluster sample with the closest distance, and used for the prediction of the sample.

For the test samples, the prediction model related to the two methylation sites obtained by the above training was used for prediction. The results in Figure 10A show that for cancer samples, only one sample has a risk score of <0, a corresponding logistic regression probability of <0.5, and a negative prediction value. The prediction result of this sample is wrong. Except for this sample, the prediction values of all other cancer samples are consistent with the true values; for normal control samples in the test samples, only two samples have a risk score of >0, a corresponding logistic regression probability of >0.5, and a positive prediction value. The prediction results of these two samples are wrong. Except for these two samples, the prediction values of all other negative samples are consistent with the true values. The prediction effect of this prediction model on independent test samples is very effective. In the independent test set, the ROC curve area is 0.990 (as shown in Figure 10B).

For the test samples, the prediction model related to the 8 methylation sites obtained by the above training was used for prediction. The results in Figure 11A show that for all cancer samples, the risk score>0, the corresponding logistic regression probability>0.5, and the predicted value is positive, which is exactly the same as the true value; for the normal control samples in the test samples, there is only one sample with a risk score>0, a corresponding logistic regression probability>0.5, and a positive predicted value. The prediction result of this sample is wrong. Except for this sample, the predicted values of all other negative samples are consistent with the true value. The model in this paper is very effective in predicting independent test samples. In the independent test set, the ROC curve area is 0.994 (as shown in Figure 11B), and the validation results show that the sensitivity is 69/69=100% and the specificity is 14/15=93.33%.

In summary, the present invention includes methylation data of 208 columns of colorectal cancer and 30 columns of normal control samples, which are randomly divided into training sets and validation sets. Logistic regression models based on 2 methylation sites and 8 methylation sites were established using the training sets, respectively, and very good results were achieved on the validation set, accurately predicting colorectal cancer samples and normal human samples.

The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and/or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values is as accurate as possible within the feasible range.

Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification is hereby incorporated by reference in its entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and/or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and/or use of terms in this specification shall prevail.

Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims (12)

1. Use of a DNA methylation site combination as biomarker or detection reagent of a DNA methylation site combination for the preparation of a kit for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment effect assessment and/or colorectal cancer treatment drug screening, characterized in that the DNA methylation site combination comprises one or more of the following group:

   chromosome coordinates located on the SOCS-1 gene are chr16:11348913, position SOCS-1_24;

   chromosome coordinates located on cg24403845 gene are chr10:108924288 position cg24403845_103.
2. 2. The use of claim 1, wherein the combination of DNA methylation sites further comprises one or more of the following:

   Chromosome coordinates located on HOXD genes are chr2:177027898, position HOXD3_26;

   Chromosome coordinates located on the SDC2-2_gene are chr8:97505775 position SDC2-2_46;

   Chromosome coordinates located on the SFRP1 gene are chr8:41167015, position SFRP 1-48;

   Chromosome coordinates located on the SP9 gene are chr2:175199694, position SP9_25;

   chromosome coordinates located on the TAC1-2 gene are chr7:97361597 position TAC1-2_135;

   chromosome coordinates located on the TBR1 gene are chr2:162283730, position TBR1_161.
3. 3. The use of claim 2, wherein the DNA methylation site combination comprises a combination consisting of SOCS-1_24, cg24403845_103, HOXD3_26, SDC2-2_46, sfrp1_48, SP9_25, TAC1-2_135, and tbr1_161.
4. 4. The use of claims 1-2, wherein the detection reagent comprises a primer pair set for amplifying the DNA methylation site combination; wherein,

   The primer pair for amplifying SOCS-1_24 is shown as SEQ ID NO.1 and SEQ ID NO. 2;

   the primer pair for amplifying cg24403845_103 is shown as SEQ ID NO. 3 and SEQ ID NO. 4;

   the primer pair for amplifying HOXD-26 is shown as SEQ ID NO. 5 and SEQ ID NO. 6;

   the primer pair for amplifying SDC2-2_46 is shown as SEQ ID NO. 7 and SEQ ID NO. 8;

   The primer pair for amplifying SFRP 1-48 is shown as SEQ ID NO. 9 and SEQ ID NO. 10;

   The primer pair for amplifying the SP 9-25 is shown as SEQ ID NO. 11 and SEQ ID NO. 12;

   the primer pair for amplifying TAC1-2_135 is shown as SEQ ID NO. 13 and SEQ ID NO. 14;

   The primer pair for amplifying TBR1_161 is shown as SEQ ID NO. 15 and SEQ ID NO. 16.
5. 5. A device for colorectal cancer screening, colorectal cancer risk prediction, colorectal cancer treatment efficacy assessment and/or colorectal cancer treatment drug screening, the device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program implements the following method:

   Obtaining a methylation level of a combination of DNA methylation sites in a biological sample of a subject, wherein the combination of DNA methylation sites comprises:

   Chromosome coordinates located on the SOCS-1 gene are chr16:11348913, position SOCS-1_24; and/or

   Chromosome coordinates located on cg24403845 gene are chr10:108924288, position cg24403845_103;

   Based on the methylation level of the combination of DNA methylation sites, a predictive model is used to assess whether the subject has colorectal cancer, predict the risk of the subject developing colorectal cancer, assess the efficacy of the subject in treating colorectal cancer, and/or assess the efficacy of colorectal cancer treatment drugs.
6. 6. The device of claim 5, wherein the combination of DNA methylation sites further comprises one or more of the following sites:

   Chromosome coordinates located on HOXD genes are chr2:177027898, position HOXD3_26;

   Chromosome coordinates located on the SDC2-2_gene are chr8:97505775 position SDC2-2_46;

   Chromosome coordinates located on the SFRP1 gene are chr8:41167015, position SFRP 1-48;

   Chromosome coordinates located on the SP9 gene are chr2:175199694, position SP9_25;

   chromosome coordinates located on the TAC1-2 gene are chr7:97361597 position TAC1-2_135;

   chromosome coordinates located on the TBR1 gene are chr2:162283730, position TBR1_161.
7. 7. A predictive model for colorectal cancer screening, colorectal cancer disease risk prediction, colorectal cancer treatment efficacy assessment and/or colorectal cancer treatment drug screening using methylation sites, characterized in that the methylation sites comprise chromosomal coordinates chr16 on the SOCS-1 gene: 11348913, position SOCS-1_24; and the chromosomal coordinate located on cg24403845 gene is chr10:108924288, position cg24403845_103; the predictive model is represented by the formula

   Risk score = -4.38+5.16 (cg 24403845 _103) +1.18 x socs-1_24, wherein Risk score represents the subject suffering from colorectal cancer, the Risk score for developing colorectal cancer, and the Risk level of colorectal cancer after the subject receives treatment and/or administration, and the gene locus represents the methylation level of the gene locus.
8. 8. The predictive model of claim 7, wherein the methylation site further comprises:

   Chromosome coordinates located on HOXD genes are chr2:177027898, position HOXD3_26;

   Chromosome coordinates located on the SDC2-2_gene are chr8:97505775 position SDC2-2_46;

   Chromosome coordinates located on the SFRP1 gene are chr8:41167015, position SFRP 1-48;

   Chromosome coordinates located on the SP9 gene are chr2:175199694, position SP9_25;

   chromosome coordinates located on the TAC1-2 gene are chr7:97361597 position TAC1-2_135; and

   Chromosome coordinates located on the TBR1 gene are chr2:162283730 at position TBR1_161, the predictive model may also be represented by the formula

   Risk score＝-7.11+2.89*cg24403845_103+3.61*SOCS-1_24+1.05*TAC1-2_135+0.92*SDC2-2_46+0.77*SFRP1_48+0.72*TBR1_161+0.67*SP9_25+0.19*HOXD3_26 And (c) a Risk score for colorectal cancer, and a Risk level for colorectal cancer after the subject receives the treatment and/or the administration, wherein the Risk score for colorectal cancer and the Risk level for colorectal cancer are expressed by the Risk score, and each gene locus represents the methylation level of the gene locus.
9. 9. A methylation site combination for colorectal cancer screening, colorectal cancer risk prediction, evaluation of colorectal cancer treatment efficacy and/or screening of colorectal cancer treatment drugs, the DNA methylation site combination comprising:

   Chromosome coordinates located on the SOCS-1 gene are chr16:11348913, position SOCS-1_24; and/or

   Chromosome coordinates located on cg24403845 gene are chr10:108924288 position cg24403845_103.
10. 10. The methylation site combination of claim 9, wherein the DNA methylation site combination further comprises one or more of the following sites:

    Chromosome coordinates located on HOXD genes are chr2:177027898, position HOXD3_26;

    Chromosome coordinates located on the SDC2-2_gene are chr8:97505775 position SDC2-2_46;

    Chromosome coordinates located on the SFRP1 gene are chr8:41167015, position SFRP 1-48;

    Chromosome coordinates located on the SP9 gene are chr2:175199694, position SP9_25;

    chromosome coordinates located on the TAC1-2 gene are chr7:97361597 position TAC1-2_135;

    chromosome coordinates located on the TBR1 gene are chr2:162283730, position TBR1_161.
11. 11. A detection reagent for a combination of DNA methylation sites according to claim 9, characterized in that the detection reagent comprises a primer pair set for amplifying the combination of DNA methylation sites; wherein,

    The primer pair for amplifying SOCS-1_24 is shown as SEQ ID NO.1 and SEQ ID NO. 2;

    the primer pair for amplifying cg24403845_103 is shown as SEQ ID NO. 3 and SEQ ID NO. 4.
12. 12. A kit for colorectal cancer screening, colorectal cancer risk prediction, evaluation of colorectal cancer treatment efficacy and/or screening of colorectal cancer treatment drugs, comprising the detection reagent according to claim 11.