JP2023176770A - Diagnostic support method, diagnostic support apparatus, diagnostic support system, and diagnostic evaluation method - Google Patents

Abstract

To provide a diagnostic support method that is more feasible and reproducible than conventional methods and that determines the possibility of developing colorectal cancer derived from human ulcerative colitis patients and provides supplementary support information for doctors to make diagnosis.SOLUTION: A diagnostic support method comprises: obtaining methylation information of all CpG sites present in a specific differentially methylated region (DMR) in a DNA of a biological sample collected from a human ulcerative colitis patient; calculating an average methylation rate of each specific DMR; and analyzing using the calculated average methylation rate of each specific DMR and multivariate discriminant, analysis results therefrom being used as supporting information for diagnosing the onset of colorectal cancer on the subject; and a specific DMR comprises two or more DMRs selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in Table 1.SELECTED DRAWING: Figure 1

Description

The present invention relates to a diagnostic support method, a diagnostic support system, a diagnostic evaluation method, and a diagnostic support device for supporting or evaluating diagnosis regarding the development of colon cancer in human ulcerative colitis patients.

Ulcerative colitis is an inflammatory bowel disease of unknown cause that primarily causes ulcers and erosions in the large intestine mucosa. Complete cure is extremely difficult, and remissions and relapses occur repeatedly. Symptoms include local symptoms in the large intestine such as diarrhea, abdominal pain, and mucus and bloody stool, as well as systemic symptoms such as fever, vomiting, tachycardia, and anemia. Early detection of colorectal cancer is especially important because patients with ulcerative colitis are more likely to develop colorectal cancer.

Colonoscopy is usually performed for early detection of colorectal cancer. However, visual detection of early colorectal cancer is generally difficult because it largely depends on the skill of the doctor who performs the procedure. In patients with ulcerative colitis, in particular, the intestinal mucosa is already inflamed, making detection of colon cancer even more difficult. Another problem is that colonoscopies place a heavy burden on patients, and many patients do not undergo the test themselves.

The labor-intensive nature and cost of colonoscopy and related tests make surveillance of many ulcerative colitis patients with extensive ulcerative colitis difficult over long periods of time. If it becomes possible to distinguish between patients with ulcerative colitis who are at high risk of developing colon cancer and those with low risk of colon cancer by subgrouping them through simple screening using markers for detecting colon cancer, it will be possible to identify patients with ulcerative colitis. It is believed that this may provide benefits for cancer detection surveillance in patients.

On the other hand, Patent Document 1 describes the methylation of five miRNA genes, miR-1, miR-9, miR-124, miR-137, and miR-34b/c, in tumorous tissues in ulcerative colitis. The methylation rate of the five miRNA genes in biological samples collected from colonic mucosa, which is a non-cancerous area, is significantly higher than that in non-tumorous tissues. It has been reported that it can be used as a detection marker.

Furthermore, in Patent Document 2, the present inventors have proposed a method for determining the possibility of developing colorectal cancer in patients with ulcerative colitis, using the DNA methylation rate in a specific DNA region as a colorectal cancer detection marker.

International Publication No. 2014/151551 International Publication No. 2018/008740

However, in order to determine the possibility of developing colorectal cancer in human ulcerative colitis patients, it is necessary to process a large amount of DNA methylation information to measure the methylation rate of a wide variety of methylation variable regions as in Patent Document 2. However, it was not necessarily a realistically possible method. Therefore, a diagnostic support method and a diagnostic support that more easily and reproducibly determine the possibility of developing colorectal cancer in human ulcerative colorectal cancer patients, and more effectively support or evaluate diagnosis regarding the development of colorectal cancer. A system, a diagnostic evaluation method, and a diagnostic support device for implementing the method have been desired.

The present inventors have made extensive studies to solve the above problems, and have identified human ulcerative colitis (Ulcerative Colitis; hereinafter referred to as "UC" hereinafter) patients (hereinafter referred to as "UC patients" hereinafter). When we comprehensively investigated the methylation rate of CpG sites (cytosine-phosphodiester bond-guanine) in the genomic DNA of patients with cancer, we found that UC patients who developed colorectal cancer (carcinogenic ulcerative colitis patients) and The differentially methylated region disclosed in Patent Document 2, which has a remarkable difference in methylation rate in UC patients who have not developed colorectal cancer (non-cancerous ulcerative colitis patients); hereinafter referred to as " The present invention was completed by discovering 11 new types of DMRs (see Table 1 below) that are different from the DMRs (sometimes referred to as ``DMRs''), and conducting an analysis using the methylation rates of multiple DMRs among these. Ta.

According to the present invention, there is provided a diagnostic support method for supporting diagnosis regarding the development of colon cancer in human ulcerative colitis patients, the method comprising: A first step of acquiring methylation information of all CpG sites present in a plurality of specific methylation variable regions (DMRs) in DNA and calculating the average methylation rate of each of the specific DMRs; a second step of analyzing using the average methylation rate of each of the specific DMRs calculated for the subject and a predetermined multivariate discriminant, and the analysis result in the second step is The specific DMR is used as support information for diagnosing the onset of colorectal cancer regarding the subject, and the specific DMR consists of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in Table 1. Provided is a diagnostic support method characterized by including two or more DMRs selected from the group.

According to the present invention, there is provided a diagnostic support device for supporting diagnosis regarding the onset of colorectal cancer in human ulcerative colitis patients, the device comprising: Methylation rate calculation means that acquires methylation information of all CpG sites present in a plurality of specific methylation variable regions (DMRs) in DNA and calculates the average methylation rate of each of the specific DMRs. and an analysis means for analyzing using the average methylation rate of each of the specific DMRs calculated for the subject and a predetermined multivariate discriminant, and based on the analysis result by the analysis means, a diagnosis support means for generating support information used for diagnosing the onset of colorectal cancer in the subject, and the specific DMR includes TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, shown in Table 1. A diagnostic support device is provided that includes two or more DMRs selected from the group consisting of RYR1, FAM222A, and MDGA1.

According to the present invention, there is provided a diagnosis support system comprising a diagnosis support device and a database for supporting diagnosis regarding the development of colorectal cancer in human ulcerative colitis patients, the diagnosis support device comprising: Obtain methylation information of all CpG sites present in multiple specific methylation variable regions (DMRs) in the DNA of rectal mucosal tissue or colonic mucosal tissue collected from ulcerative colitis patients, and a methylation rate calculation means for calculating an average methylation rate of each of the DMRs, an average methylation rate of each of the specific DMRs calculated for the subject, a multivariate discriminant stored in advance in the database; , and a diagnosis support means that generates support information for use in diagnosing the onset of colorectal cancer in the subject based on the analysis result by the analysis means, and the specific DMR includes a table. A diagnostic support system is provided, comprising two or more DMRs selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in 1. be done.

According to the present invention, there is provided a diagnostic evaluation method for supporting diagnosis regarding the development of colon cancer in human ulcerative colitis patients, the method comprising: A first step of acquiring methylation information of all CpG sites present in a plurality of specific methylation variable regions (DMRs) in DNA and calculating the average methylation rate of each of the specific DMRs; a second step of analyzing using the average methylation rate of each of the specific DMRs calculated for the subject and a predetermined multivariate discriminant, the analysis results in the second step; is used as support information for the diagnosis of colon cancer development regarding the subject, and the specific DMR is selected from TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in Table 1. Provided is a diagnostic evaluation method characterized by including two or more DMRs selected from the group consisting of:

According to the above invention, by examining the average methylation rate of a specific DNR in the DNA of rectal mucosa or large intestine mucosal tissue collected from human ulcerative colitis patients, it is possible to achieve higher feasibility and reproducibility than before. A diagnostic support device, a diagnostic support system, a diagnostic evaluation method, and a diagnostic support method that accurately determine the possibility of developing colorectal cancer in human ulcerative colitis patients and provide supplementary support information for doctors to make a diagnosis. can be provided.

3 is a flowchart relating to implementation of the present invention. FIG. 1 is a system configuration diagram related to implementation of the present invention. FIG. 2 is a diagram showing the results for cancer and non-cancer patients based on the methylation rate of TNFSF4 and NFATC1 among the CpG sites and DMR shown in Table 1 in Example 1. FIG. 2 is a diagram showing the results for cancer and non-cancer patients based on the methylation rates of CACNA1A and RYR1 among the CpG sites and DMRs shown in Table 1 in Example 1. 2 is a diagram showing results for cancer and non-cancer patients based on the methylation rate of FAM222A and MDGA1 among the CpG sites and DMR shown in Table 1 in Example 1. FIG. 1 is a boxplot and an ROC curve of a test for the presence or absence of colon cancer in patients with ulcerative colitis when the methylation rate of TNFSF4 and NFATC1 is used as a DMR biomarker in Example 1. In Example 1, in addition to the methylation rate of TNFSF4 and NFATC1, the methylation rate of CACNA1E is used as a DMR biomarker, and the boxplot and ROC of the test for the presence or absence of colorectal cancer development in patients with ulcerative colitis. It is a curve. In Example 1, in addition to the methylation rate of TNFSF4, NFATC1, and CACNA1E, the methylation rate of MDGA1 is used as a DMR biomarker, and the box whisker of the test for the presence or absence of colorectal cancer development in patients with ulcerative colitis Figure and ROC curve.

Hereinafter, typical embodiments of the present invention will be described, but the present invention is not limited thereto, and can be modified within the scope of achieving the object of the present invention.

<DNA methylation and DNA methylation rate>
In the base sequence of genomic DNA, there is a region (CpG site; Cytosine-Phosphodiester-Guanine Site) in which CG sequences consisting of consecutive nucleobases cytosine and guanine are concentrated. The cytosine residue at the site can undergo methylation modification at the 5th carbon position. In the present invention and the specification of this application, the methylation rate of CpG sites refers to methylated cytosine residues (methylated cytosine residues) among CpG sites contained in DNA in a biological sample collected from an individual organism. ) amount and the amount of unmethylated cytosine residues (unmethylated cytosine) are measured, and it means the ratio (%) of the amount of methylated cytosine to the sum of both.

<DMR>
In the present invention, a differentially methylated region (hereinafter referred to as "DMR" hereinafter) is used as a biomarker to support the diagnosis of colorectal cancer development (hereinafter referred to as "DMR" hereinafter). (Also called "DMR biomarker"). The DMR used as such a DMR biomarker and the CpG site contained therein are preferably those whose methylation rate is significantly different between a group of patients with non-cancerous ulcerative colitis and a group of patients with oncogenic ulcerative colitis. The larger the difference between the two groups, the more reliably it is possible to detect the presence or absence of colorectal cancer.
Here, the oncogenic ulcerative colitis patient group (oncogenic UC patient) is a population consisting of patients who were diagnosed with colorectal cancer after suffering from human ulcerative colitis, and is the "first mother group" of the present invention. corresponds to "group". More specifically, the patient group with oncogenic ulcerative colitis includes (1) patients who were diagnosed with colon cancer after developing human ulcerative colitis and have not yet received treatment, and (2) patients who were diagnosed with colon cancer after developing human ulcerative colitis. Patients who have been diagnosed with cancer and are undergoing treatment, etc. (3) Patients who were diagnosed with colorectal cancer after contracting human ulcerative colitis and whose colorectal cancer has been cured or remitted, but whose colorectal cancer has recurred; may be included. The "first population" in the present invention does not need to be implemented by collecting data covering all of the above (1) to (3), but data regarding some of (1) to (3). It may be implemented based on.
In addition, the non-cancer ulcerative colitis patient group (non-cancer UC patient) is a population consisting of patients who have not yet been diagnosed with colon cancer after suffering from human ulcerative colitis. This corresponds to the second population. In more detail, (1) patients who have not yet been diagnosed with colon cancer after contracting human ulcerative colitis, and (2) patients who have been diagnosed with colon cancer after contracting human ulcerative colitis and whose colon cancer is cured or in remission. This may include patients who have reached. The "second population" in the present invention does not need to be implemented by collecting data covering all of the above (1) to (2), but may be implemented by collecting data related to some of (1) to (2). It may be implemented based on
Both the DMR used as a DMR biomarker in the present invention and the CpG sites contained therein have significantly higher methylation rates in cancerous ulcerative colitis patients than in non-cancer ulcerative colitis patients, that is, the onset of colorectal cancer. This increases the methylation rate.

As for the DMR used as a DMR biomarker in the present invention and the CpG site contained therein, it is more preferable that the difference in methylation rate between a non-cancerous site and a cancerous site of the large intestine is small in the same patient with oncogenic UC.
By using the methylation rate of such CpG sites or the average methylation rate of DMR as a DMR biomarker, even when using rectal mucosal tissue or colonic mucosal tissue collected from a non-cancerous site of cancer-causing UC patients, Similarly to the case of using rectal mucosal tissue or colon mucosal tissue collected from a cancer site, DNA methylation information indicating the possibility of developing colon cancer can be obtained with high sensitivity.

DMRs and CpG sites contained therein used as DMR biomarkers in the present invention are the DMRs and CpG sites contained therein specified in Table 1.
In the table, "Gene Symbol" indicates the name of the gene indicated by the DMR. "Ensemble ID" is an ID linked to the gene indicating the DMR, and by referring to this, it is possible to specify the specific base sequence of the DMR. "Chromosome number" indicates the number of the human genome chromosome in which the DMR exists. "DMR Start" and "DMR End" are numerical values indicating the position on the genome sequence of the relevant chromosome, and "Width" is a numerical value representing the number of base pairs of the relevant DMR. Note that these numbers are just an example, and may differ depending on the reference genomic DNA database, but in that case, priority is given to specifying the DMR region by Gene Symbol and/or Ensemble ID, and those skilled in the art will be able to understand this description. It is possible to appropriately identify a specific DMR region based on the .

DMRs used as DMR biomarkers in the present invention include two or more locations selected from the DMRs listed in Table 1, including three or more locations, four or more locations, five or more locations, six or more locations, seven or more locations, and eight or more locations. It may contain any two or more DMRs among the 11 types of DMRs, but it is preferable that at least TNFS4 and NFATC1 are included.
The genes encoded by these two DMRs are genes involved in the T cell activation pathway or in cell adhesion/migration. More specifically, TNFSF4 (TNF Superfamily Member 4) is a gene encoding a single-transmembrane costimulatory molecule belonging to the TNF superfamily, and may also be written as OX40L. The protein translated from this functions in the interaction between T cells and antigen presenting cells and has the function of regulating the adhesion of activated T cells to endothelial cells. NFATC1 (Nuclear Factor of Activated T Cells 1) is a gene that encodes one of the isoforms of NFAT, and the protein translated from this gene is a calcium regulatory protein located at the N-terminal side of the Rel Homology domain (RHD). domain( It has the function of binding to calcium-dependent phosphatase via CRD).

In addition to the two locations described above, the DMR used as a DMR biomarker in the present invention can further include one or more locations selected from the group consisting of CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, and RYR1.
The genes encoded by these seven DMRs encode various proteins constituting voltage-gated calcium channels and ryanodine receptors, and the proteins translated from these genes are involved in the regulation of intracellular calcium ion concentration.

The DMR used as a DMR biomarker in the present invention includes, in addition to the DMR at one or more locations selected from the above two locations and/or the above seven locations, one or more locations selected from the group consisting of FAM222A and MDGA1. can be included.
FAM222A is a gene encoding a protein whose detailed function has not yet been elucidated, and may also be written as C12orf34. MDGA1 (MAM Domain Containing Glycosylphosphatidylinositol Anchor 1) is a gene encoding a GPI-anchored extracellular protein belonging to the immunoglobulin superfamily.

The DNA collected from the human ulcerative colitis patient who is the subject may be either rectal mucosal tissue or large intestine mucosal tissue, but rectal mucosal tissue is more preferable. The mucous membrane from the deep part of the large intestine needs to be collected using an endoscope or the like, which places a heavy burden on the patient. On the other hand, the rectal mucosa near the anus can be collected relatively easily, and the burden on the patient when collecting it is small. By using CpG sites or DMRs, which have a small difference in methylation rate between non-cancerous and cancerous parts of the colon, as markers, we can detect colorectal cancer using the rectal mucosa near the anus, regardless of where the cancerous part is formed. It is possible to detect all patients who are likely to have the disease.

<DNA methylation information>
In the present invention and the specification of this application, DNA methylation information refers to numerical data indicating the methylation rate of CpG sites, numerical data indicating the methylation rate of the entire DMR, and the average methylation rate of all CpG sites included in the DMR. , numerical data of the DMR methylation level (β value), and numerical data of the Δβ value, which is the difference between the β value of cancerous UC patients and the β value of non-cancer UC patients. The DNA methylation information may be data provided in a computer-usable format and stored on a computer-readable storage medium such as a CD, DVD, hard disk, or semiconductor memory.
Note that the computer in this specification will be described below as having the same meaning as the information processing device of the present invention.

<How to process DNA methylation information>
A method for processing DNA methylation information according to the present invention will be explained using FIGS. 1 and 2. FIG. 1 is a flowchart for implementing the invention. FIG. 2 is a system configuration diagram for implementing the present invention.
Note that FIG. 2 illustrates the computer 10 and database 20 as having mutually independent configurations. However, in implementing the present invention, the computer 10 and the database 20 do not necessarily have to be independent of each other, and the computer 10 and the database 20 may be realized as an integrated configuration (the configuration corresponding to the computer 10 is A configuration corresponding to the database 20 may also be incorporated).

(1) DNA Recovery/Sample Preparation First, DNA is recovered from a biological sample (rectal mucosal tissue or large intestine mucosal tissue) collected from a human ulcerative colitis patient as a test subject, and sample preparation is performed (step S101).
Here, the DNA collection/sample preparation in step S101 may be performed manually by a human, or may be performed mechanically by a device (not shown).
The DNA recovery method is not particularly limited as long as it is a known method, and commercially available DNA extraction/purification kits and the like can be used. Furthermore, the DNA may be collected immediately after the biological sample is collected, or the DNA may be collected after performing various pretreatments. Here, various pretreatments refer to freezing a biological sample or preparing a formalin-fixed and paraffin-embedded FFPE sample according to a conventional method. As a commercially available DNA extraction/purification kit, for example, QIAmp DNA FFPE tissue kit (manufactured by Qiagen) can be used as in Reference Example 1 described below.
The sample preparation is not particularly limited as long as it is a method that allows measurement of the methylation rate of CpG sites in DNA. The method for measuring the methylation rate of a CpG site is a method that can distinguish and quantify methylated cytosine residues and unmethylated cytosine residues for a specific CpG site, and is a method known in the art. Methods include bisulfite sequencing method, COBRA (Combined Bisulfite Restriction Analysis) method, qAMP (quantitative analysis of DNA methylation using real-t ime PCR) method, MIAM (Microarray-based Integrated Analysis of Methylation by Isoschizomers) method, etc. It will be done.
Note that the collection of the biological sample in step S101 can be carried out using, for example, a kit for colon mucosa collection, a colon mucosa collection tool, a colon mucosa collection tool, or a colon mucosa collection tool described in Patent Document 2, International Publication Pamphlet No. WO2018/008153, International Publication Pamphlet No. WO2018/062361, etc. It is more preferable to use an auxiliary tool or the like.

(2) Acquisition of DNA methylation information/Calculation of average methylation rate of specific DMR Next, the computer 10 calculates the specific DMR (DMR shown in Table 1) measured from the sample prepared in step S101. The methylation information of CpG sites present in the DMR is acquired, and the average methylation rate of a specific DMR is calculated (step S103).
The average methylation rate of a DMR calculated in step S103 means the arithmetic mean value (arithmetic mean value) or geometric mean value (geometric mean value) of the methylation rates of all CpG sites present in a given DMR. However, other average values may be used.
The DNA methylation information used to calculate the average methylation rate of a specific DMR in step S103 may be manually input into the computer 10 by a person, or may be provided in a state stored in a storage medium (not shown). It may also be acquired by being read into the computer 10 from a storage medium.
The calculation of the average methylation rate of a specific DMR in step S103 may be executed by the computer 10 in response to a manual execution command by a human (an operation different from the manual input of DNA methylation information), or the computer 10 may calculate the average methylation rate of a specific DMR. It may be performed automatically simultaneously or sequentially with the acquisition of DNA methylation information.

(3) Analysis regarding the onset of colorectal cancer in the test subject Next, the computer 10 uses the average methylation rate of each of the plurality of specific DMRs calculated for the test subject in step S103 to determine the onset of colorectal cancer in the UC patient who is the test subject. The possibilities are analyzed and an analysis result is obtained (step S105).
In the analysis in step S105, the computer 10 uses the multivariate discriminant stored in the database 20. The multivariate discriminant is a functional formula that is predetermined in advance of the analysis in step S105. More specifically, the multivariate discriminant is based on the average methylation rate of each specific DMR obtained from the above-mentioned oncogenic ulcerative colitis patient group (corresponding to the first population) and the above-mentioned non-oncological discriminant. This is a functional formula determined by multivariate analysis using the average methylation rate of each specific DMR obtained from a cancerous ulcerative colitis patient group (corresponding to the second population) as an explanatory variable.
In addition, human ulcerative colitis patients who belong to either the first population or the second population, who are the subjects, have been diagnosed with ulcerative colitis for 10 years or more. It is preferable that the person is In patients who have been affected for a long time, extensive and high-level inflammation in the intestinal mucosa persists for a long time, and they acquire genetic and acquired transformations caused by chronic inflammation. The probability of developing colorectal cancer is evaluated based on the average methylation rate of each specific DMR, DNA methylation information such as β value and Δβ value, etc. It is thought that it is easier to obtain important indicators stably. It is known that, unlike general sporadic colon cancer, DNA damage caused by long-term persistence of chronic inflammation is important for the development of colon cancer in UC patients.

Furthermore, in the analysis in step S105, the computer 10 can perform analysis using a predetermined threshold value in addition to the multivariate discriminant described above. The threshold value is a value determined in advance for the analysis in step S105, and the value may be determined as a response variable based on the multivariate discriminant described above. The threshold may vary depending on the number of cases in the patient group to be analyzed, but the lower limit is 5%, the upper limit is 25%, and any value is acceptable as long as it exceeds 5% and falls below 25%. For example, but not limited to, a value greater than 5% and less than 20%, a value greater than 5% and less than 15%, a value greater than 5% and less than 10%, a value greater than 10% and less than 25%, and a value greater than 15%. Values below 25% and values above 20% and below 25% are listed.

The multivariate discriminant stored in the database 20 accumulates DNA methylation information (average methylation rate of specific DMR obtained from the first population and the second population) that is the target of multivariate analysis. However, the accumulated DNA methylation information may be refined according to the accumulation by machine learning.
Any known method can be applied to the machine learning used to create the above multivariate discriminant within the scope of achieving the purpose of the present invention, and the multivariate discriminant created as a result can be applied using a logistic regression equation. , a linear discriminant, a formula created using a naive Bayes classifier, or a formula created using a support vector machine.

In the analysis of step S105, the computer 10 analyzes the average methylation rate in the DNA extracted from the subject for at least two or more DMRs using the above multivariate discriminant and/or the above threshold; Get results.
Here, the analysis result obtained by the computer 10 may be information indicating the result obtained by substituting the average methylation rate of each specific DNR obtained from the subject into the variables of the multivariate discriminant, It may also be information indicating the magnitude relationship between the average methylation rate of each specific DNR obtained from the subject and the above threshold value, and based on this information, patients with a high/low probability of developing colorectal cancer can be identified. It may also be information indicating the determined result.
For example, if the average methylation rate of a specific DMR calculated for a subject exceeds the above threshold, the computer 10 determines that the subject is positive for developing colon cancer. It should be noted that those skilled in the art can fully understand that even if the computer 10 determines that the test result is positive, the support information generated accordingly will not definitively indicate the onset of colorectal cancer. This is because the support information generated by the computer 10 is merely information for assisting a doctor's diagnosis.
If the average methylation rate of a specific DMR calculated for a subject is below the above threshold, the specificity is about 50% and there is a possibility of a false negative, so the computer 10 does not necessarily need to make a negative determination. do not have.

(4) Generation of Support Information Next, the computer 10 generates support information that supports diagnosis of the onset of colorectal cancer in the UC patient, who is the subject, based on the analysis result obtained in step S105 (step S107).
The support information generated in step S107 may be any information that is useful for a doctor to diagnose the onset of colon cancer in a subject, and may be in any form such as a character string, a numerical value, or an image. For example, the support information may be a specific character string indicating a high possibility of developing colorectal cancer, such as "high possibility of developing" or "low possibility of developing", and the support information may be a specific character string indicating a high possibility of developing colorectal cancer, such as "high possibility of developing" or "low possibility of developing". It may be a character string indicating whether or not to recommend a test (for example, endoscopy, etc.) to confirm the diagnosis, or it may be a numerical value such as an arbitrary percentage within the range of 0 to 100%. It may also be an image displaying the above-mentioned character strings or numerical values alone or in combination.
The support information generated in step S107 may be output by the computer 10, or by an output device (not shown) connected to the computer 10, such as a printer, monitor, or display.

Step S103 described above is a process that corresponds to the "first step" of the present invention, and step S105 described above is a process that corresponds to the "second step" of the present invention.
The specific DMRs used for the analysis in step S105 are two locations selected from the group consisting of DMRs (TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1) shown in Table 1. Includes the above DMR.
Further, the analysis result in step S105 is used as support information for diagnosing the onset of colon cancer regarding the subject in step S107.
Therefore, the above-described DNA methylation information processing method includes the diagnostic support method and diagnostic evaluation method according to the present invention.
Although an embodiment has been described in which the computer 10 is the main body responsible for processing steps S103 and S105, which correspond to the "first step" and "second step" of the present invention, implementation of the present invention is not limited to this. do not have. Within the scope of achieving the object of the present invention, part or all of each process may be realized manually by a human or by cooperation between a human and a computer.

Furthermore, the computer 10 that executes steps S103, S105, and S107 described above is equipped with the "methylation rate calculation means", "analysis means", and "diagnosis support means" of the present invention. This corresponds to a diagnostic support device. A computer system that combines the computer 10 and the database 20 corresponds to a diagnostic support system according to the present invention.
Here, the diagnosis support device (computer 10) is a device that includes a program or software that executes the diagnosis support method according to the present invention, and hardware that executes the program or the software. Typical examples of the hardware include a CPU (or processor), RAM, ROM, hard disk, display, keyboard, mouse, communication interface, and the like. The diagnosis support device (computer 10) stores a program having modules corresponding to each function that implements the diagnosis support method according to the present invention in the RAM or ROM, and executes the program by the CPU (or processor). Good too. Further, such programs are also included within the scope of the present invention.

The present invention has been described above based on typical embodiments. The diagnostic support method, diagnostic support system, diagnostic evaluation method, and diagnostic support device according to the present invention can be used to determine the possibility of developing colorectal cancer in a human UC patient, and can be used to determine the possibility of developing colorectal cancer in a human UC patient. Among CpG sites, the marker is DMR, which shows a significant difference in methylation rate between cancerous ulcerative colitis patient group and non-cancer ulcerative colitis patient group. Then, using the methylation rate of CpG sites or the average methylation rate of DMR as markers, support information is generated that indicates whether or not a human UC patient is likely to have developed colorectal cancer. By outputting the data, it is possible to more objectively and sensitively detect the possibility of developing colorectal cancer in human UC patients, which is extremely difficult to visually identify, and allows doctors to use tests such as endoscopy for definitive diagnosis. It is expected to assist in deciding whether or not to proceed with the early detection of colorectal cancer. Furthermore, by using the diagnostic support method and diagnostic support device according to the present invention, it can be used for screening whether or not it is necessary to perform tests that place a large burden on patients, such as endoscopy, on human UC patients. It can be used for more efficient colon cancer screening, etc. Furthermore, by realizing early detection of colorectal cancer and efficient colorectal cancer screening according to the present invention, an appropriate treatment method for colorectal cancer can be appropriately provided to subjects who need it. . As a treatment method for colorectal cancer, therapeutic drugs known as anticancer drugs that can generally be used for the treatment of colorectal cancer are administered to the subject at an effective dose appropriate for each drug. This can be achieved by administering via the administration route.

The present invention includes the following technical ideas.
(1) A diagnostic support method for supporting diagnosis regarding the development of colon cancer in human ulcerative colitis patients, in which DNA of rectal mucosal tissue or colonic mucosal tissue collected from human ulcerative colitis patients as test subjects, A first step of acquiring methylation information of all CpG sites present in a plurality of specific methylation variable regions (DMRs) and calculating the average methylation rate of each of the specific DMRs, and regarding the subject. a second step of analyzing using the calculated average methylation rate of each of the specific DMRs and a predetermined multivariate discriminant; The specific DMR is selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A and MDGA1 shown in Table 1. A diagnostic support method comprising two or more DMRs.
(2) The diagnosis support method according to (1), wherein the specific DMR includes at least TNFSF4 and NFATC1 shown in Table 1.
(3) The specific DMR according to (2) further includes one or more DMRs selected from the group consisting of CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, and RYR1 shown in Table 1. Diagnostic support method.
(4) The diagnosis support method according to (2) or (3), wherein the specific DMR further includes one or more DMRs selected from the group consisting of FAM222A and MDGA1 shown in Table 1.
(5) In the second step, in addition to the average methylation rate of the specific DMR and the multivariate discriminant, the analysis is performed using a predetermined threshold. Diagnostic support method described in one.
(6) The multivariate discriminant is based on the average methylation rate of the specific DMR obtained from a first population consisting of patients who were diagnosed with colon cancer after suffering from human ulcerative colitis, and By multivariate analysis using as an explanatory variable the average methylation rate of the specific DMR obtained from a second population consisting of patients who have not yet been diagnosed with colon cancer after suffering from ulcerative colitis. The diagnosis support method according to (5), wherein the threshold value is a predetermined functional expression, and the threshold value is a value predetermined based on the multivariate discriminant before the second step is performed.
(7) The threshold value is more than 5% and less than 25%, and the analysis in the second step is performed when the average methylation rate of the specific DMR calculated for the subject exceeds the threshold value. The diagnostic support method according to (6), wherein the test subject is determined to be positive for developing colorectal cancer.
(8) According to (6) or (7), the patients constituting the first population and the second population are patients who have been suffering from human ulcerative colitis for 10 years or more. Diagnostic support method described.
(9) Any one of (1) to (8), wherein the multivariate discriminant is a logistic regression equation, a linear discriminant, an equation created with a naive Bayes classifier, or an equation created with a support vector machine. Diagnosis support method described in one.
(10) The diagnostic support method according to any one of (1) to (9), wherein the DNA collected from the human ulcerative colitis patient as the subject is rectal mucosal tissue.
(11) The calculation in the first step and the analysis in the second step are processes realized by an information processing device, and the information processing device calculates information about the subject based on the results of the analysis in the second step. The method described in any one of (1) to (10) further includes a third step of generating information indicating whether or not to recommend a test to confirm the diagnosis of colorectal cancer onset as the support information. diagnosis support method.
(12) A diagnostic support device for supporting diagnosis regarding the development of colon cancer in human ulcerative colitis patients, which uses DNA from rectal mucosal tissue or colonic mucosal tissue collected from human ulcerative colitis patients as test subjects. Methylation rate calculation means for acquiring methylation information of all CpG sites present in a specific methylation variable region (DMR) and calculating an average methylation rate for each of the specific DMRs, and regarding the subject. an analysis means that performs analysis using the calculated average methylation rate of each of the specific DMRs and a predetermined multivariate discriminant; and a diagnostic support means for generating support information used for diagnosing the onset of the disease. A diagnostic support device comprising two or more DMRs selected from the group consisting of:
(13) A diagnostic support system comprising a diagnostic support device and a database for supporting diagnosis regarding the development of colon cancer in human ulcerative colitis patients, the diagnostic support device comprising: Obtain methylation information of all CpG sites present in multiple specific methylation variable regions (DMRs) in DNA of rectal mucosal tissue or large intestine mucosal tissue collected from a patient with inflammation, and obtain methylation information for each of the specific DMRs. using a methylation rate calculation means for calculating an average methylation rate of each of the specific DMRs calculated for the subject, and a multivariate discriminant stored in advance in the database. and a diagnosis support means that generates support information for use in diagnosing the onset of colorectal cancer in the subject based on the analysis result by the analysis means, and the specific DMR is as shown in Table 1. A diagnostic support system comprising two or more DMRs selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1.
(14) A diagnostic evaluation method for supporting diagnosis regarding the development of colorectal cancer in human ulcerative colitis patients, which comprises: A first step of acquiring methylation information of all CpG sites present in a plurality of specific methylation variable regions (DMRs) and calculating the average methylation rate of each of the specific DMRs, and regarding the subject. a second step of analyzing using the calculated average methylation rate of each of the specific DMRs and a predetermined multivariate discriminant; The specific DMR is used as support information for diagnosing the onset of colorectal cancer regarding the subject, and the specific DMR is selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in Table 1. A diagnostic evaluation method comprising two or more selected DMRs.
(a) Methylation information of all CpG sites present in a specific methylation variable region (DMR) obtained from DNA of rectal mucosal tissue or large intestine mucosal tissue collected from a human ulcerative colitis patient as a subject calculating an average methylation rate of each of the specific DMRs based on the methylation information, and using the calculated average methylation rate of each of the specific DMRs and a predetermined multivariate discrimination. Based on the analysis results, support information used for diagnosing the onset of colorectal cancer in the subject is generated, and the specific DMRs are TNFSF4, NFATC1, CACNA1A, and CACNA1C shown in Table 1. , CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1.
(b) Application of a DNA fragment used as a marker for DNA methylation rate analysis to support diagnosis regarding the onset of colorectal cancer in a human ulcerative colitis patient who is a test subject, the DNA fragment being a human ulcerative colitis patient. A DNA fragment obtained from DNA of rectal mucosal tissue or large intestine mucosal tissue collected from to obtain methylation information, calculate the average methylation rate of each of the specific DMRs based on the methylation information, and calculate the average methylation rate of each of the specific DMRs and the predetermined average methylation rate of each of the specific DMRs. Based on the analysis results, support information for use in diagnosing the onset of colorectal cancer in the subject is generated, and the specific DMR is determined using the TNFSF4 shown in Table 1. An application comprising two or more DMRs selected from the group consisting of NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A and MDGA1.

Next, the present invention will be explained in more detail by showing Examples, but the present invention is not limited thereto.

[Reference example 1]
(1) UC patients targeted for analysis Information regarding the 142 UC patients (39 oncogenic UC patients and 103 non-cancerous UC patients) who were analyzed when determining the multivariate discriminant and threshold values described above is shown in Table 2. show. The UC patients targeted for analysis were all patients who had been suffering from human ulcerative colitis for 10 years or more.

(2) Comprehensive analysis of DNA methylation level (2-1) Collection of biological samples and DNA extraction The rectal mucosal tissues of the above cancerous UC patients and non-cancerous UC patients were collected transanally and frozen at -80°C. and used as a sample. A portion of the sample was cut out, and DNA was extracted using QIAmp DNA FFPE tissue kit (manufactured by Qiagen).

(2-2) Quality evaluation of DNA sample The concentration of the obtained DNA was determined as follows. That is, the fluorescence intensity of each sample was measured using Quant-iT PicoGreen dsDNA Assay Kit (manufactured by Life Technologies), and the concentration was calculated using the λ-DNA calibration curve provided with the kit.
Next, each sample was diluted to 1 ng/μL with TE (pH 8.0), and real-time PCR was performed using Illumina FFPE QC Kit (manufactured by Illumina) and Fast SYBR Green Master Mix (manufactured by Life Technologies). The Ct value was determined. The difference in Ct value between the sample and the positive control (hereinafter referred to as ΔCt value) was calculated for each sample to evaluate the quality. Samples with a ΔCt value of less than 5 were judged to be of good quality and proceeded to the subsequent steps.

(2-3) Bisulfite treatment
Bisulfite treatment was performed on the DNA sample using EZ DNA Methylation Kit (manufactured by ZYMO RESEARCH).

(2-4) Repair of degraded DNA and whole genome amplification The degraded DNA was repaired using the Infinium HD FFPE Restore Kit (manufactured by Illumina) on the DNA after bisulfite treatment. After the repaired DNA was denatured with alkaline, it was neutralized, and enzymes and primers for whole genome amplification of the HumanMethylation EPIC DNA Analysis Kit (manufactured by Illumina) were added, and the mixture was incubated in an Incubation Oven (manufactured by Illumina) at 37°C for 20 hours. As described above, the entire genome was amplified by isothermal reaction.

(2-5) Fragmentation and purification of whole genome amplified DNA Add the fragmentation enzyme of the HumanMethylation EPIC DNA Analysis Kit (manufactured by Illumina) to the whole genome amplified DNA, and use a Microsample Incubator (SciGene) at 37°C. , and reacted for 1 hour. A coprecipitant and 2-propanol were added to the fragmented DNA and centrifuged to precipitate the DNA.

(2-6) Hybridization Hybridization buffer was added to the precipitated DNA and reacted for 1 hour in a Hybridization Oven (manufactured by Illumina) at 48°C to dissolve the DNA. The dissolved DNA was incubated for 20 minutes in a Microsample Incubator (manufactured by SciGene) at 95°C to denature it into a single strand, and then dispensed onto the BeadChip of HumanMethylation EPIC DNA Analysis Kit (manufactured by Illumina). The probe on the BeadChip and the single-stranded DNA were hybridized by reacting in a Hybridization Oven at 48°C for 16 hours or more.

(2-7) Labeling Reaction and Scanning After hybridization, the probe on the BeadChip was subjected to an elongation reaction to bind a fluorescent dye. Next, the BeadChip was scanned using an iSCAN system (manufactured by Illumina), and the methylated fluorescence intensity and unmethylated fluorescence intensity were measured. At the end of the experiment, it was confirmed that all the scan data was available and that the scan was performed normally, and a database was created that stored all the scan data.

[Example 1]
(1) Quantitative and comparative analysis of DNA methylation level Using scan data of DNA methylation information of the analysis target population used in Reference Example 1, quantitative statistical analysis software R (Version: 3.6.3, 64bit, Windows (registered trademark) The scan data were analyzed using the ChAMP package (Version: 2.18.3) of )).

Intensity data was loaded from the scan data IDAT file, normalized using the default setting BIMQ method (β mixed quantile normalization), and DNA methylation levels were determined between the conditions of cancer UC patients and non-cancer UC patients. Detection of differential DNA methylation variable region probes (DMPs) was performed. Probes with a corrected p value <0.05 were detected as DMP by the BH method.

Next, DNA methylation variable region (DMR) was detected using the ProbeLASSO method under the condition that the p value corrected from DMP was <0.05.

The conditions for DMR were that three or more adjacent probes existed in the ProbeLASSO region, and the ProbeLASSO region was set to 2 kb. If the methylation level is high, the β value approaches 1, and if the methylation level is low, the β value approaches 0. For the comparative analysis of the DNA methylation level of the rectal sample group of oncogenic UC patients (n = 39) to the rectal sample group of non-cancer UC patients (n = 103), the rectal sample group of oncogenic UC patients (n = 39) calculated by ChAMP was used. The value (Δβ value) obtained by subtracting the average β value of the non-cancer UC patient rectal sample group (n=103) from the average β value of ) was used.

(2) Selection of DMR by multivariate analysis Using the results of quantitative and comparative analysis of DNA methylation levels, we used the statistical analysis software R (Version: 3.6.3, 64bit, Windows (registered trademark)) to and logistic regression analysis were performed. In addition, narrowing down based on the absolute value of Δβ has been reported as a means of selecting CpG biomarker candidates from comprehensive DNA methylation analysis data (BMC Med genomics vol.4, p.50, 2011; Sex Dev vol. .5, p.70, 2011). According to these methods, biomarker candidates were extracted from 719,407 CpG sites mounted on the BeadChip.

Specifically, first, 2,998 DMRs were extracted as DNA methylation variable regions from 719,407 CpG sites using the ProbeLASSO algorithm, and among them, the absolute value of Δβ value was 0.05. More than 854 DMRs were extracted. Next, ElasticNet regularization was performed with the aim of selecting important biomarker candidates that distinguish between cancerous UC patients and non-cancer UC patients.

Table 3 shows the results of the DMRs containing 5 CpG sites ranked at the top by ElasticNet regularization among the 854 DMRs.

[Examples 2 to 4: Multivariate analysis of clinical samples]
Logistic regression was performed on all 142 samples based on the average methylation rate in a specific DMR calculated from the data of the UC patient group shown in Table 2. Specifically, multivariate discrimination based on a logistic regression model was performed using numerical values (β values) of DMR methylation levels of samples collected from rectal mucosal tissues of 39 cancerous UC patients and 103 non-cancer UC patients. A formula was created to distinguish between colorectal cancer patients and non-cancer patients.
In addition, the response variable obtained by substituting the methylation rate of 39 oncogenic UC patients into the multivariate discriminant and the methylation rate of 38 oncogenic UC patients excluding one outlier were substituted. Based on each of the resulting response variables, thresholds were determined for use in implementing the present invention. In addition, when determining the threshold value, the sensitivity was fixed at 100%. In light of the purpose of the present invention, which is to support the diagnosis of the onset of colorectal cancer, false positives are acceptable because they can be determined through detailed examinations such as endoscopy, but false negatives are to be avoided.
The results of the above multivariate analysis are shown in Table 4.

Logistic regression in which TNFS4 and NFATC1 were selected as DMR biomarkers resulted in an ROC-AUC of 0.849 (Example 2; FIG. 6). Furthermore, in logistic regression in which CACNA1E was selected in addition to TNFS4 and NFATC1 as DMR biomarkers, ROC-AUC was 0.886 (Example 3; FIG. 7). Furthermore, in logistic regression in which MDGA1 was selected in addition to TNFS4, NFATC1, and CACNA1E as DMR biomarkers, ROC-AUC was 0.903 (Example 4; FIG. 8). In all of Examples 2-4, it was high at 0.8 or more. These results confirmed that the possibility of developing colorectal cancer can be evaluated with high sensitivity and high specificity based on the average methylation rate of DMRs at multiple selected locations.

In any of Examples 2-4 above, the DMRs used as DMR biomarkers include at least TNFS4 and NFATC1.
In Example 3, in addition to TNFS4 and NFATC1, CACNA1E was selected as a DMR biomarker. Although the description of Examples is omitted, instead of or in addition to CACNA1E, one or more DMRs selected from the group consisting of CACNA1A, CACNA1C, CACNA1G, CACNG3, CACNG8, and RYR1 can be used as a DMR biomarker. It has been found that even if this selection is made, the results are comparable to those of Example 3.
In Example 4, MDGA1 was selected as a DMR biomarker in addition to TNFS4, NFATC1, and CACNA1E. Although the description of the example is omitted, it is known that even if FAM222A is selected as a DMR biomarker instead of or in addition to MDGA1, the results are comparable to those of Example 4.

Further, in the above Example 2-4, the threshold value determined based on the methylation rate of 39 cancerous UC patients was a value falling within a range of more than 7% and less than 10%. On the other hand, the threshold value determined based on the methylation rate of 38 carcinogenic UC patients, excluding one outlier, rose to about 10% in Example 3 and about 20% in Example 4.

10 Computer 20 Database

Claims (14)

A diagnostic support method for supporting diagnosis regarding the development of colorectal cancer in human ulcerative colitis patients, the method comprising:
Obtain methylation information of all CpG sites present in multiple specific methylation variable regions (DMRs) in DNA of rectal mucosal tissue or colon mucosal tissue collected from human ulcerative colitis patients as test subjects. , a first step of calculating the average methylation rate of each of the specific DMRs;
a second step of analyzing using the average methylation rate of each of the specific DMRs calculated for the subject and a predetermined multivariate discriminant;
has
The analysis results in the second step are used as support information for diagnosing the onset of colorectal cancer regarding the subject,
The specific DMR includes two or more DMRs selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in Table 1.
A diagnostic support method characterized by:

The specific DMR includes at least TNFSF4 and NFATC1 shown in Table 1.
The diagnostic support method according to claim 1. The specific DMR further includes one or more DMRs selected from the group consisting of CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, and RYR1 shown in Table 1.
The diagnostic support method according to claim 2. The specific DMR further includes one or more DMRs selected from the group consisting of FAM222A and MDGA1 shown in Table 1.
The diagnostic support method according to claim 2 or 3. In the second step, in addition to the average methylation rate of the specific DMR and the multivariate discriminant, further analysis is performed using a predetermined threshold value.
The diagnostic support method according to claim 1. The multivariate discriminant is based on the average methylation rate of the specific DMR obtained from a first population consisting of patients who were diagnosed with colon cancer after suffering from human ulcerative colitis, and The mean methylation rate of the specific DMR obtained from a second population of patients who have not yet been diagnosed with colon cancer after suffering from inflammation was determined by multivariate analysis using as an explanatory variable. It is a functional expression,
The threshold value is a value predetermined based on the multivariate discriminant before the second step is performed.
The diagnostic support method according to claim 5. The threshold value is a value exceeding 5% and below 25%,
In the analysis in the second step, if the average methylation rate of the specific DMR calculated for the subject exceeds the threshold, the subject is determined to be positive for developing colorectal cancer.
The diagnostic support method according to claim 6. The patients constituting the first population and the second population are patients who have been suffering from human ulcerative colitis for 10 years or more.
The diagnostic support method according to claim 6 or 7. The multivariate discriminant is a logistic regression equation, a linear discriminant, an equation created with a naive Bayes classifier, or an equation created with a support vector machine.
The diagnostic support method according to claim 1. The DNA collected from the human ulcerative colitis patient who is the subject is rectal mucosal tissue.
The diagnostic support method according to claim 1. The calculation in the first step and the analysis in the second step are processes realized by an information processing device,
The information processing device generates, as the support information, information indicating whether or not to recommend a test for determining the diagnosis of colorectal cancer onset for the subject based on the result of the analysis in the second step. further comprising three steps,
The diagnostic support method according to claim 1. A diagnostic support device for supporting diagnosis regarding the development of colorectal cancer in human ulcerative colitis patients, the device comprising:
Obtain methylation information of all CpG sites present in multiple specific methylation variable regions (DMRs) in DNA of rectal mucosal tissue or colon mucosal tissue collected from human ulcerative colitis patients as test subjects. , methylation rate calculation means for calculating the average methylation rate of each of the specific DMRs;
an analysis means that performs analysis using the average methylation rate of each of the specific DMRs calculated for the subject and a predetermined multivariate discriminant;
Diagnosis support means for generating support information for use in diagnosing the onset of colorectal cancer in the subject based on the analysis result by the analysis means;
Equipped with
The specific DMR includes two or more DMRs selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in Table 1.
A diagnostic support device characterized by: A diagnostic support system comprising a diagnostic support device and a database for supporting diagnosis regarding the development of colorectal cancer in human ulcerative colitis patients, the system comprising:
The diagnosis support device includes:
Obtain methylation information of all CpG sites present in multiple specific methylation variable regions (DMRs) in DNA of rectal mucosal tissue or colon mucosal tissue collected from human ulcerative colitis patients as test subjects. , methylation rate calculation means for calculating the average methylation rate of each of the specific DMRs;
an analysis means that performs analysis using the average methylation rate of each of the specific DMRs calculated for the subject and a multivariate discriminant stored in advance in the database;
Diagnosis support means for generating support information for use in diagnosing the onset of colorectal cancer in the subject based on the analysis result by the analysis means;
Equipped with
The specific DMR is characterized in that it includes two or more DMRs selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in Table 1. Diagnostic support system. A diagnostic evaluation method for supporting diagnosis regarding the development of colorectal cancer in human ulcerative colitis patients, the method comprising:
Obtain methylation information of all CpG sites present in multiple specific methylation variable regions (DMRs) in DNA of rectal mucosal tissue or colon mucosal tissue collected from human ulcerative colitis patients as test subjects. , a first step of calculating the average methylation rate of each of the specific DMRs;
a second step of analyzing using the average methylation rate of each of the specific DMRs calculated for the subject and a predetermined multivariate discriminant;
has
The analysis results in the second step are used as support information for diagnosing the onset of colorectal cancer regarding the subject,
The specific DMR is characterized in that it includes two or more DMRs selected from the group consisting of TNFSF4, NFATC1, CACNA1A, CACNA1C, CACNA1E, CACNA1G, CACNG3, CACNG8, RYR1, FAM222A, and MDGA1 shown in Table 1. diagnostic evaluation method.

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