CN115772571A - Methylation molecular marker for identifying benign and malignant thyroid tumors - Google Patents
Methylation molecular marker for identifying benign and malignant thyroid tumors Download PDFInfo
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Abstract
The invention discloses a methylation molecular marker for identifying benign and malignant thyroid tumors. The invention provides a methylation biomarker, wherein the nucleotide sequence of the methylation biomarker is a DNA fragment shown as SEQ ID No.1-4 in a CUX2 gene; the CpG sites are selected from any one or more CpG sites in 4 DNA fragments shown in SEQ ID No.1, SEQ ID No.2, SEQ ID No.3 and SEQ ID No. 4; for differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors, benign thyroid tumors and malignant thyroid tumors of different subtypes/stages; to distinguish or assist in distinguishing between different subtypes/stages of thyroid malignancies. Compared with benign thyroid tumor, the low methylation phenomenon of the CUX2 gene in the tissue of a thyroid cancer patient is disclosed, and the method has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of the thyroid cancer, reducing the death rate of the thyroid cancer and guiding the formulation of a reasonable clinical treatment scheme.
Description
Technical Field
The invention relates to the field of medicine, in particular to a methylated molecular marker for identifying benign and malignant thyroid tumors.
Background
Thyroid cancer (thyoid cancer) is the most common malignancy of the endocrine system and may include papillary Thyroid carcinoma, follicular Thyroid carcinoma, undifferentiated Thyroid carcinoma, and medullary carcinoma. Among them, papillary carcinomas (PTC) are the most common, accounting for more than 90% of all thyroid malignancies [ Xing, mingzhao; haugen, bryan R; schlumberger, martin (2013), progress in molecular-based management of differentiated viscous cancel, the Lancet,381 (9871), 1058-1069. According to statistics, the prevalence rate of thyroid nodules in adults is about 5-10%, with the most severe population above 60 years of age, as high as 50-70% [ Guth S, theune U, aberle J, et al. Very high prediction of thyroid nodules detected by high frequency (13 MHz) ultrasounds evolution. Eur J Clin Invest 2009;39:699-706.]. The imaging examination is a relatively common thyroid diagnosis method, most of the methods rely on doctor experience to judge, certain result errors exist, and imaging causes certain radiation damage to human bodies. Fine needle biopsy is also a clinically common thyroid cancer diagnostic technique, and the method can evaluate the malignancy and the goodness of the nodule according to the cytological morphology of the puncture object. As the cytological features of benign and malignant Thyroid tumours often overlap, there are about 10-30% of fine needle punctures diagnosed with ambiguous cytological results [ Cibas ES, ali sz.the 2017bethesda System for Reporting Thyroid tissue. 27 (11):1341-6.]. Indeterminate puncture results in approximately 60% of patients suffering from over-treatment or missed diagnosis [ Stewart R, leang YJ, bhatt CR, grodski S, serpell J, lee JC.Quantifying the differences in clinical management of patients with defined and absolute refractory gene biology. Eur J Surg Oncol.2020;46 (2):252-7.]. This not only increases the economic and physical and mental burden on the patient, but also occupies a large amount of public health resources, resulting in a huge financial cost for the healthcare system.
Epigenetics is a genetic expression control that does not involve DNA sequence changes but is heritable and can be inherited to the next generation [ Nicoglou a, merlin f. Epigenetics: a way to bridge the gap between biological fields. Study high phillos Biol Biomed sci.2017;66:73-82]. DNA methylation is one of the important ways of epigenetic regulation, which means that a methyl group is covalently bonded to the 5' carbon position of cytosine in genomic CpG dinucleotides under the action of DNA methyltransferase [ Bird a. Perspectives of epigenetics.nature.2007;447:396-398]. Numerous studies have shown that DNA methylation can cause changes in chromatin structure, DNA conformation, DNA stability and DNA-protein interaction pattern, thereby controlling gene expression [ Moore LD, le T, fan g.dna methylation and its basic function.neuropsychopharmacology.2013;38:23-38].
The DNA methylation marker is the best molecular marker for early diagnosis of tumor in vitro at the present stage, and at present, the sensitivity and specificity of the clinical thyroid cancer diagnosis marker are very limited, especially the early diagnosis marker is lacked, so that a more sensitive and specific early molecular marker is urgently needed to be discovered.
Disclosure of Invention
The invention aims to provide a methylated molecular marker for identifying benign and malignant thyroid tumors.
In a first aspect, the present invention claims a methylation biomarker.
The nucleotide sequence of the methylation biomarker claimed in the invention is the methylation level of all or part of CpG sites in the following fragments (A1) to (A4) in the CUX2 gene:
(A1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment with more than 80% of identity with the DNA fragment;
(A2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment with more than 80% of identity with the DNA fragment;
(A3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment with more than 80% of identity with the DNA fragment;
(A4) The DNA fragment shown in SEQ ID No.4 or the DNA fragment with more than 80 percent of identity with the DNA fragment.
The methylation biomarker comprises the following CpG sites (B1) to (B7) on the nucleotide sequence thereof:
(B1) Any one or more CpG sites in 4 DNA fragments shown in SEQ ID No.1, SEQ ID No.2, SEQ ID No.3 and SEQ ID No.4 in the CUX2 gene;
(B2) All CpG sites on the DNA fragment shown by SEQ ID No.2 (table 2) and all CpG sites on the DNA fragment shown by SEQ ID No.1 (table 1) in the CUX2 gene;
(B3) All CpG sites on the DNA fragment shown by SEQ ID No.2 (table 2) and all CpG sites on the DNA fragment shown by SEQ ID No.3 (table 3) in the CUX2 gene;
(B4) All CpG sites on the DNA fragment shown in SEQ ID No.1 (Table 1) and all CpG sites on the DNA fragment shown in SEQ ID No.3 (Table 3) in the CUX2 gene;
(B5) All CpG sites on the DNA fragment shown by SEQ ID No.2 (table 2), all CpG sites on the DNA fragment shown by SEQ ID No.1 (table 1) and all CpG sites on the DNA fragment shown by SEQ ID No.3 (table 3) in the CUX2 gene;
(B6) All CpG sites (Table 2) in the DNA fragment shown as SEQ ID No.2 in the CUX2 gene or any 23 or any 22 or any 21 or any 20 or any 19 or any 18 or any 17 or any 16 or any 15 or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 CpG sites;
(B7) The CUX2 gene has the DNA fragment shown in SEQ ID No.2, wherein the CpG sites are all or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following 9:
item 1: the DNA fragment shown in SEQ ID No.2 is a CpG site shown by 261-262 th site from 5' end;
item 2: the DNA fragment shown in SEQ ID No.2 is a CpG site shown by 330-331 th sites from 5' end;
item 3: the DNA fragment shown in SEQ ID No.2 is a CpG site shown by 355-356 th site from 5' end;
item 4: the DNA fragment shown in SEQ ID No.2 has CpG sites shown in 369-370, 371-372, 374-375, 380-381 and 382-383 from the 5' end;
item 5: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 392 th to 393 th positions of the 5' end;
item 6: the DNA fragment shown in SEQ ID No.2 is provided with CpG sites shown at 453 th-454 th sites from the 5' end;
item 7: the DNA fragment shown in SEQ ID No.2 has CpG sites shown in 475-476 th and 478-479 th positions from the 5' end;
item 8: the CpG site shown by 484-485 bits from the 5' end of the DNA segment shown in SEQ ID No. 2;
item 9: the DNA fragment shown in SEQ ID No.2 has CpG sites at positions 624-625 from the 5' end.
In a specific embodiment of the invention, some adjacent methylation sites are treated as one methylation site when performing DNA methylation analysis using time-of-flight mass spectrometry since several CpG sites are located on one methylation fragment and the peak pattern is indistinguishable (indistinguishable sites are listed in Table 6), and thus when performing methylation level analysis, and constructing and using related mathematical models.
The methylation biomarker is used as at least one of the following:
(1) Differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors;
(2) Distinguishing or assisting in distinguishing thyroid benign tumors and thyroid malignant tumors of different subtypes;
(3) Differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors of different stages;
(4) Distinguishing or assisting in distinguishing different subtypes of thyroid malignant tumors;
(5) Distinguish or assist in distinguishing different stages of thyroid malignancy.
Further, the different subtypes described in (2) and (4) may be pathotyped, such as histologically typed.
Further, the different stages described in (3) and (5) may be clinical stages.
In a specific embodiment of the present invention, the differentiation or auxiliary differentiation described in (2) for thyroid benign tumor and thyroid malignant tumor of different subtypes may be any one of the following: differentiating or assisting in differentiating between benign thyroid tumors and papillary thyroid carcinomas, differentiating between benign thyroid tumors and follicular thyroid carcinomas, differentiating between benign thyroid tumors and medullary thyroid carcinomas, differentiating between benign thyroid tumors and undifferentiated thyroid carcinomas.
In a specific embodiment of the present invention, the differentiation or the auxiliary differentiation between thyroid benign tumor and thyroid malignant tumor of different stages in (3) may be any one of the following: differentiating or assisting to differentiate thyroid benign tumor and stage I thyroid malignant tumor, differentiating or assisting to differentiate thyroid benign tumor and stage II thyroid malignant tumor, differentiating or assisting to differentiate thyroid benign tumor and stage III thyroid malignant tumor, differentiating or assisting to differentiate thyroid benign tumor and stage IV thyroid malignant tumor.
In a specific embodiment of the present invention, the distinguishing or assisting in distinguishing different subtypes of thyroid cancer in (4) may be any of the following: differentiating or assisting in differentiating papillary thyroid carcinoma from follicular thyroid carcinoma, differentiating or assisting in differentiating papillary thyroid carcinoma from medullary thyroid carcinoma, differentiating or assisting in differentiating papillary thyroid carcinoma from undifferentiated thyroid carcinoma, differentiating or assisting in differentiating follicular thyroid carcinoma from medullary thyroid carcinoma, differentiating or assisting in differentiating follicular thyroid carcinoma from undifferentiated thyroid carcinoma, differentiating or assisting in differentiating medullary thyroid carcinoma from undifferentiated thyroid carcinoma.
In a specific embodiment of the present invention, the differentiation or the auxiliary differentiation of different stages of thyroid malignant tumor in (5) may be any one of the following: differentiating or assisting to differentiate the thyroid malignant tumor of the stage I and the thyroid malignant tumor of the stage II, differentiating or assisting to differentiate the thyroid malignant tumor of the stage I and the thyroid malignant tumor of the stage III, differentiating or assisting to differentiate the thyroid malignant tumor of the stage I and the thyroid malignant tumor of the stage IV, differentiating or assisting to differentiate the thyroid malignant tumor of the stage II and the thyroid malignant tumor of the stage III, differentiating or assisting to differentiate the thyroid malignant tumor of the stage II and the thyroid malignant tumor of the stage IV, differentiating or assisting to differentiate the thyroid malignant tumor of the stage III and the thyroid malignant tumor of the stage IV.
In a second aspect, the present invention claims the use of a methylation biomarker as described in the first aspect hereinbefore in the manufacture of a product; the use of the product is as described in any one of the preceding (1) to (5).
In a third aspect, the invention claims the use of an agent for detecting the methylation level of said methylation biomarker for the manufacture of a product; the use of the product is as described in any one of the preceding (1) to (5).
In a fourth aspect, the invention claims the use of a material for detecting the methylation level of a methylation biomarker as described in the first aspect above and a medium storing a mathematical model and/or a method of using a mathematical model for the manufacture of a product; the use of the product is as described in any one of the preceding (1) to (5).
The mathematical model is obtained according to a method comprising the following steps:
(C1) Respectively detecting the gene methylation levels of n 1A type samples and n 2B type samples;
(C2) And (3) taking the gene methylation level data of all samples obtained in the step (C1), establishing a mathematical model by a two-classification logistic regression method according to the classification modes of the type A and the type B, and determining a threshold value for classification judgment.
Wherein n1 and n2 can both be positive integers of more than 10.
The using method of the mathematical model comprises the following steps:
(D1) Detecting the gene methylation level of a sample to be detected;
(D2) Substituting the gene methylation level data of the sample to be detected, which is obtained in the step (D1), into the mathematical model to obtain a detection index; and then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is the type A or the type B according to the comparison result.
In a specific embodiment of the present invention, the threshold is set to 0.5. Greater than 0.5 is classified as one class and less than 0.5 is classified as another class, equal to 0.5 as an indeterminate gray zone. The type A and the type B are two corresponding classifications, and the grouping of the two classifications, which group is the type A and which group is the type B, are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum johning index (specifically, may be a value corresponding to the maximum johning index). The gray areas greater than the threshold are classified into one category, the gray areas less than the threshold are classified into another category, and the gray areas equal to the threshold are regarded as uncertain gray areas. The type A and the type B are two corresponding classifications, grouping of the two classifications, which group is the type A and which group is the type B, and are determined according to a specific mathematical model without convention.
The type A sample and the type B sample are any one of the following:
(E1) Thyroid benign tumor and thyroid malignant tumor;
(E2) Thyroid benign tumors and thyroid malignant tumors of different subtypes;
(E3) Thyroid benign tumors and thyroid malignant tumors of different stages;
(E4) Thyroid malignancies of different subtypes;
(E5) Thyroid malignancies of different stages.
Further, the different subtypes described in (E2) and (E4) may be pathotyped, such as histologically typed.
Further, the different stages described in (E3) and (E5) may be clinical stages.
In a specific embodiment of the present invention, the benign thyroid tumor and the malignant thyroid tumor of different subtypes in (E2) may be any one of the following: benign tumors of the thyroid and papillary carcinoma of the thyroid, benign tumors of the thyroid and follicular carcinoma of the thyroid, benign tumors of the thyroid and medullary carcinoma of the thyroid, benign tumors of the thyroid and undifferentiated carcinoma of the thyroid.
In a specific embodiment of the present invention, the thyroid benign tumor and thyroid malignant tumor of different stages in (E3) may be any one of the following: thyroid benign tumor and stage I thyroid malignant tumor, thyroid benign tumor and stage II thyroid malignant tumor, thyroid benign tumor and stage III thyroid malignant tumor, thyroid benign tumor and stage IV thyroid malignant tumor.
In a specific embodiment of the present invention, the different subtypes of thyroid malignancy in (E4) may be any of the following: papillary and thyroid carcinomas, papillary and medullary thyroid carcinomas, papillary and undifferentiated thyroid carcinomas, follicular and medullary thyroid carcinomas, follicular and undifferentiated thyroid carcinomas, medullary thyroid carcinomas, and undifferentiated thyroid carcinomas.
In a specific embodiment of the present invention, the different stages of thyroid cancer in (E5) may be any of the following: the first stage thyroid malignant tumor and the second stage thyroid malignant tumor, the first stage thyroid malignant tumor and the third stage thyroid malignant tumor, the first stage thyroid malignant tumor and the fourth stage thyroid malignant tumor, the second stage thyroid malignant tumor and the third stage thyroid malignant tumor, the second stage thyroid malignant tumor and the fourth stage thyroid malignant tumor, the third stage thyroid malignant tumor and the fourth stage thyroid malignant tumor.
In a fifth aspect, the invention claims a kit.
The claimed kit of the invention comprises means for detecting the methylation level of the methylation biomarker as described in the first aspect hereinbefore; the use of the kit is as described in any one of (1) to (5) above.
Further, the kit may further comprise "a medium storing the mathematical model and/or the method for using the mathematical model" as described in the fourth aspect.
In a sixth aspect, the invention claims a system.
The system claimed in the present invention may comprise:
(F1) Reagents and/or instruments for detecting methylation levels of the CUX2 gene;
in (F1), the reagent for detecting the methylation level of the CUX2 gene may be a substance (e.g., a primer pair) for detecting the methylation level of the CUX2 gene as described in the first aspect above. The instrument for detecting the methylation level of the CUX2 gene can be a time-of-flight mass spectrometer. Of course, the reagent for detecting the methylation level of the CUX2 gene can also comprise other conventional reagents for performing time-of-flight mass spectrometry.
(F2) A device comprising a unit X and a unit Y;
the unit X is used for establishing a mathematical model and comprises a data acquisition module, a data analysis processing module and a model output module.
The data acquisition module is configured to acquire (F1) CUX2 gene methylation level data for n1 type a samples and n2 type B samples detected.
The data analysis processing module is configured to receive CUX2 gene methylation level data of the n 1A type samples and the n 2B type samples from the data acquisition module, establish a mathematical model through a two-classification logistic regression method according to the classification modes of the A type and the B type, and determine a threshold value of classification judgment.
Wherein n1 and n2 are both positive integers of 10 or more.
The model output module is configured to receive the mathematical model established by the data analysis processing module and output the mathematical model.
The unit Y is used for determining the type of a sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module.
The data input module is configured to input (F1) detected CUX2 gene methylation level data of the subject.
The data operation module is configured to receive the CUX2 gene methylation level data of the person to be tested from the data input module, and substitute the CUX2 gene methylation level data of the person to be tested into the mathematical model established by the data analysis processing module in the unit X, so as to calculate a detection index.
The data comparison module is configured to receive the detection index calculated by the data operation module and compare the detection index with the threshold determined in the data analysis processing module in the unit X.
The conclusion output module is configured to receive the comparison result from the data comparison module and output the conclusion that the type of the sample to be tested is the type A or the type B according to the comparison result.
The type A sample and the type B sample are any one of the following:
(E1) Thyroid benign tumor and thyroid malignant tumor;
(E2) Benign thyroid tumors and thyroid malignancies of different subtypes;
(E3) Thyroid benign tumors and thyroid malignant tumors of different stages;
(E4) Thyroid malignancies of different subtypes;
(E5) Thyroid malignancies of different stages.
In a specific embodiment of the present invention, the threshold is set to 0.5. Greater than 0.5 is classified as one type, less than 0.5 is classified as another type, and equal to 0.5 is considered as an indeterminate gray zone. The type A and the type B are two corresponding classifications, and the grouping of the two classifications, which group is the type A and which group is the type B, are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum jordan index (specifically, may be a value corresponding to the maximum jordan index). The gray areas greater than the threshold are classified into one category, the gray areas less than the threshold are classified into another category, and the gray areas equal to the threshold are regarded as uncertain gray areas. The type A and the type B are two corresponding classifications, grouping of the two classifications, which group is the type A and which group is the type B, and the type A and the type B are determined according to a specific mathematical model without convention.
Further, the different subtypes described in (E2) and (E4) may be pathotyped, such as histologically typed.
Further, the different stages described in (E3) and (E5) may be clinical stages.
In a specific embodiment of the present invention, the benign thyroid tumor and the malignant thyroid tumor of different subtypes in (E2) may be any one of the following: benign tumors of the thyroid and papillary carcinoma of the thyroid, benign tumors of the thyroid and follicular carcinoma of the thyroid, benign tumors of the thyroid and medullary carcinoma of the thyroid, benign tumors of the thyroid and undifferentiated carcinoma of the thyroid.
In a specific embodiment of the present invention, the thyroid benign tumor and thyroid malignant tumor of different stages in (E3) may be any one of the following: thyroid benign tumor and stage I thyroid malignant tumor, thyroid benign tumor and stage II thyroid malignant tumor, thyroid benign tumor and stage III thyroid malignant tumor, thyroid benign tumor and stage IV thyroid malignant tumor.
In a specific embodiment of the present invention, the different subtypes of thyroid malignancy in (E4) may be any of the following: papillary and thyroid carcinomas, papillary and medullary thyroid carcinomas, papillary and undifferentiated thyroid carcinomas, follicular and medullary thyroid carcinomas, follicular and undifferentiated thyroid carcinomas, medullary thyroid carcinomas, and undifferentiated thyroid carcinomas.
In a specific embodiment of the present invention, the different stages of thyroid cancer in (E5) may be any of the following: the first stage thyroid malignant tumor and the second stage thyroid malignant tumor, the first stage thyroid malignant tumor and the third stage thyroid malignant tumor, the first stage thyroid malignant tumor and the fourth stage thyroid malignant tumor, the second stage thyroid malignant tumor and the third stage thyroid malignant tumor, the second stage thyroid malignant tumor and the fourth stage thyroid malignant tumor, the third stage thyroid malignant tumor and the fourth stage thyroid malignant tumor.
In each of the above aspects, the substance or reagent for detecting the methylation level of the CUX2 gene may comprise (or be) a primer combination for amplifying a full-length or partial fragment of the CUX2 gene.
Further, the partial fragment may be at least one of:
(G1) The DNA fragment shown in SEQ ID No.1 or the DNA fragment contained in the DNA fragment;
(G2) A DNA fragment shown as SEQ ID No.2 or a DNA fragment contained therein;
(G3) A DNA fragment shown as SEQ ID No.3 or a DNA fragment contained therein;
(G4) The DNA fragment shown in SEQ ID No.4 or the DNA fragment contained in the DNA fragment;
(G5) A DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.1 or a DNA fragment comprising the same;
(G6) A DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.2 or a DNA fragment contained therein;
(G7) A DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.3 or a DNA fragment contained therein;
(G8) A DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.4 or to a DNA fragment comprising the same.
In each of the above aspects, the primer combination may be primer pair a and/or primer pair B and/or primer pair C and/or primer pair D.
The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is single-stranded DNA shown by 11 th-35 th nucleotides of SEQ ID No.5 or SEQ ID No. 5; the primer A2 is single-stranded DNA shown by 32 th to 56 th nucleotides of SEQ ID No.6 or SEQ ID No. 6.
The primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is single-stranded DNA shown by 11 th-35 th nucleotides of SEQ ID No.7 or SEQ ID No. 7; the primer B2 is single-stranded DNA shown by 32 th to 56 th nucleotides of SEQ ID No.8 or SEQ ID No. 8.
The primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is single-stranded DNA shown by 11 th-35 th nucleotides of SEQ ID No.9 or SEQ ID No. 9; the primer C2 is single-stranded DNA shown by the 32 nd to 56 th nucleotides of SEQ ID No.10 or SEQ ID No. 10.
The primer pair D is a primer pair consisting of a primer D1 and a primer D2; the primer D1 is single-stranded DNA shown by 11 th-35 th nucleotides of SEQ ID No.11 or SEQ ID No. 11; the primer D2 is single-stranded DNA shown by the 32 nd to 56 th nucleotides of SEQ ID No.12 or SEQ ID No. 12.
In addition, the invention also claims a method for distinguishing the sample to be detected as the type A sample or the type B sample. The method may comprise the steps of:
(A) The mathematical model may be established according to a method comprising the steps of:
(A1) Respectively detecting the CUX2 gene methylation levels (training sets) of n 1A type samples and n 2B type samples;
(A2) And (2) taking the methylation level data of the CUX2 genes of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to the classification modes of the type A and the type B, and determining a threshold value for classification judgment.
Wherein n1 and n2 in (A1) are both positive integers of 10 or more.
(B) Whether the sample to be tested is an a-type sample or a B-type sample can be determined according to a method comprising the following steps:
(B1) Detecting the methylation level of the CUX2 gene of the sample to be detected;
(B2) Substituting the CUX2 gene methylation level data of the sample to be detected, which are obtained in the step (B1), into the mathematical model to obtain a detection index; and then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is the type A or the type B according to the comparison result.
In a specific embodiment of the present invention, the threshold is set to 0.5. Greater than 0.5 is classified as one class and less than 0.5 is classified as another class, equal to 0.5 as an indeterminate gray zone. The type A and the type B are two corresponding classifications, and the grouping of the two classifications, which group is the type A and which group is the type B, are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum johning index (specifically, may be a value corresponding to the maximum johning index). The gray areas greater than the threshold are classified into one category, the gray areas less than the threshold are classified into another category, and the gray areas equal to the threshold are regarded as uncertain gray areas. The type A and the type B are two corresponding classifications, and the grouping of the two classifications, which group is the type A and which group is the type B, are determined according to a specific mathematical model without convention.
The type a sample and the type B sample may be any one of the foregoing (E1) - (E5).
Any of the above mathematical models may be changed in practical applications according to the detection method of DNA methylation and the fitting manner, and is determined according to a specific mathematical model without any convention.
In the embodiment of the present invention, the model is specifically ln (y/(1-y)) = b0+ b1x1+ b2x2+ b3x3+ \8230, + bnXn, where y is a detection index obtained after a dependent variable is substituted into the model for methylation values of one or more methylation sites of a sample to be tested, b0 is a constant, x1-xn are independent variables, i.e., methylation values of one or more methylation sites of the test sample (each value is a value between 0 and 1), and b1-bn are weights assigned to the methylation values of each site by the model.
One specific model established in the embodiment of the present invention is a model for distinguishing or assisting in distinguishing a benign thyroid tumor from a malignant thyroid tumor, and the model specifically includes: ln (y/(1-y)) =0.887-1.253 cux2_B _8-3.665 cux2_B _9-0.482 _ cux2_B _10+0.293 cux2_B _11.12.13.14.15+0.657 cux2_B _16 _ -0.726 + CUX2_B _17+1.615 + CUX2_B _18.19+0.258 + CUX2_B _20+1.204 + CUX2_D _21. The CUX2_ B _8 is the methylation level of CpG sites shown in 261-262 th sites of the DNA fragment shown in SEQ ID No.2 from the 5' end; the CUX2_ B _9 is the methylation level of CpG sites shown in 330-331 th sites of the 5' end of the DNA fragment shown in SEQ ID No. 2; the CUX2_ B _10 is the methylation level of the CpG sites shown in 355-356 th site of the 5' end of the DNA fragment shown in SEQ ID No. 2; the CUX2_ B _11.12.13.14.15 is the methylation level of CpG sites shown in positions 369-370, 371-372, 374-375, 380-381 and 382-383 from the 5' end of the DNA fragment shown in SEQ ID No. 2; the CUX2_ B _16 is the methylation level of the CpG sites shown from 392 th to 393 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the CUX2_ B _17 is the methylation level of CpG sites 453-454 from the 5' end of the DNA fragment shown in SEQ ID No. 2; the CUX2_ B _18.19 is the methylation level of the CpG sites 475-476 and 478-479 from the 5' end of the DNA fragment shown in SEQ ID No. 2; the CUX2_ B _20 is the methylation level of the CpG sites shown in 484-485 positions from the 5' end of the DNA fragment shown in SEQ ID No. 2; the CUX2_ B _21 is the methylation level of the CpG sites shown in 624-625 th position from the 5' end of the DNA fragment shown in SEQ ID No. 2. The threshold of the model is 0.5. Patients with a detection index greater than 0.5 calculated by the model are selected as patients with thyroid malignant tumors, and patients with a detection index less than 0.5 are selected as patients with thyroid benign tumors.
In the above aspects, the detecting the methylation level of the CUX2 gene is detecting the methylation level of the CUX2 gene in a tumor tissue sample.
In the invention, the methylation level of the methylation sites on the DNA fragments shown in SEQ ID No.1, 2, 3 and 4 in the CUX2 gene in the malignant thyroid tumor tissue is obviously lower than that of the benign thyroid tumor.
In the present invention, thyroid cancers of different clinical characteristics such as: methylation levels of methylation sites on the DNA fragments shown in SEQ ID Nos. 1, 2, 3 and 4 in the CUX2 gene in papillary carcinoma, follicular carcinoma, medullary carcinoma and undifferentiated carcinoma tumor tissues are getting lower.
In the present invention, the methylation level of the methylation sites on the DNA fragments represented by SEQ ID Nos. 1, 2, 3 and 4 in the CUX2 gene in the tissue becomes lower as the stage of thyroid cancer increases.
Any of the above CUX2 genes can be specifically identified in Genbank accession numbers: NM-015267.4 (GI: 1519242248), transcript variant 1; NM-001370598.1 (GI: 1647818744), transcript variant 2.
The invention proves that the CUX2 methylation of the biopsy sample can be used as a potential marker for differential diagnosis of benign thyroid tumors, malignant thyroid tumors, and malignant thyroid tumors of different subtypes or different stages. The invention has important scientific significance and clinical application value for identifying benign thyroid tumors, malignant thyroid tumors, thyroid tumors of different subtypes or different stages and guiding the formulation of a reasonable clinical treatment scheme.
Drawings
Fig. 1 is a schematic diagram of a mathematical model.
Fig. 2 is an illustration of a mathematical model of benign and malignant thyroid tumors.
Detailed Description
The present invention is described in further detail below with reference to specific embodiments, and the examples are given only for illustrating the present invention and not for limiting the scope of the present invention. The examples provided below serve as a guide for further modifications by a person skilled in the art and do not constitute a limitation of the invention in any way.
The experimental procedures in the following examples, unless otherwise indicated, are conventional and are carried out according to the techniques or conditions described in the literature in the field or according to the instructions of the products. Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.
Example 1 primer design for detecting methylation site of CUX2 Gene
CpG sites on four fragments (CUX 2_ A fragment, CUX2_ B fragment, CUX2_ C fragment and CUX2_ D fragment) of the CUX2 gene are selected for the detection to perform correlation analysis of methylation level and thyroid malignant tumor.
The CUX2_ A fragment (SEQ ID No. 1) is located in the hg19 reference genome chr12:111617741-111618413, the antisense strand.
The CUX2_ B fragment (SEQ ID No. 2) is located in the hg19 reference genome chr12:111618585-111619307, antisense strand.
The CUX2_ C fragment (SEQ ID No. 3) is located in the hg19 reference genome chr12:111619341-111620060, the antisense strand.
The CUX2_ D fragment (SEQ ID No. 4) is located in the hg19 reference genome chr12:111620093-111620790, antisense strand.
The site information in the CUX2_ A fragment is shown in Table 1.
The site information in the CUX2_ B fragment is shown in Table 2.
The site information in the CUX2_ C fragment is shown in Table 3.
The site information in the CUX2_ D fragment is shown in Table 4.
TABLE 1 CpG site information in CUX2_ A fragment
| CpG sites | Position of CpG sites in the sequence |
| CUX2_A_1 | 26-27 from the 5' end of SEQ ID No.1 |
| CUX2_A_2 | 80-81 from the 5' end of SEQ ID No.1 |
| CUX2_A_3 | 102-103 from the 5' end of SEQ ID No.1 |
| CUX2_A_4 | 110-111 from the 5' end of SEQ ID No.1 |
| CUX2_A_5 | 147-148 th bits from 5' end of SEQ ID No.1 |
| CUX2_A_6 | 203-204 of SEQ ID No.1 from the 5' end |
| CUX2_A_7 | 251-252 of SEQ ID No.1 from 5' end |
| CUX2_A_8 | SEQ ID No.1 at positions 297-298 from the 5' end |
| CUX2_A_9 | 420-421 bits from 5' end of SEQ ID No.1 |
| CUX2_A_10 | SEQ ID No.1 at position 598-599 from the 5' end |
| CUX2_A_11 | SEQ ID No.1 at positions 637-638 from the 5' end |
| CUX2_A_12 | 647-648 from 5' end of SEQ ID No.1 |
TABLE 2 CpG site information in CUX2_ B fragment
TABLE 3 CpG site information in CUX2_ C fragment
| CpG sites | Position of CpG sites in the sequence |
| CUX2_C_1 | 26-27 from the 5' end of SEQ ID No.2 |
| CUX2_C_2 | 83-84 from the 5' end of SEQ ID No.3 |
| CUX2_C_3 | 114-115 th from 5' end of SEQ ID No.3 |
| CUX2_C_4 | SEQ ID No.3 from 146 th to 147 th of 5' end |
| CUX2_C_5 | 224-225 from the 5' end of SEQ ID No.3 |
| CUX2_C_6 | 254-255 from the 5' end of SEQ ID No.3 |
| CUX2_C_7 | SEQ ID No.3 from position 257 to 258 of the 5' end |
| CUX2_C_8 | SEQ ID No.3 from position 268 to 269 of the 5' end |
| CUX2_C_9 | 558-559 th position from 5' end of SEQ ID No.3 |
| CUX2_C_10 | SEQ ID No.3 from 565 th to 566 th positions of 5' end |
| CUX2_C_11 | No.3 from 605 to 606 of the 5' end of SEQ ID No.3 |
| CUX2_C_12 | The position 646-647 from the 5' end of SEQ ID No.3 |
| CUX2_C_13 | 649-650 th site from 5' end of SEQ ID No.3 |
| CUX2_C_14 | SEQ ID No.3 from position 654 to 655 of the 5' end |
| CUX2_C_15 | 671-672 th position from 5' end of SEQ ID No.3 |
| CUX2_C_16 | 694-695 th position from 5' end of SEQ ID No.3 |
TABLE 4 CpG site information in CUX2_ D fragment
Specific PCR primers were designed for the four fragments (CUX 2_ a fragment, CUX2_ B fragment, CUX2_ C fragment and CUX2_ D fragment) as shown in table 5. SEQ ID No.5, SEQ ID No.7, SEQ ID No.9 and SEQ ID No.11 are forward primers; SEQ ID No.6, SEQ ID No.8, SEQ ID No.10 and SEQ ID No.12 are reverse primers. In SEQ ID No.5, SEQ ID No.7, SEQ ID No.9 and SEQ ID No.11, the 1 st to 10 th sites from the 5' end are nonspecific labels, and the 11 th to 35 th sites are specific primer sequences; the positions 1 to 31 from the 5' position in SEQ ID No.6, SEQ ID No.8, SEQ ID No.10 and SEQ ID No.12 are non-specific tags, and the positions 32 to 56 are specific primer sequences. The primer sequence does not contain SNP and CpG sites.
TABLE 5 CUX2 methylation primer sequences
Example 2 detection of methylation of CUX2 Gene and analysis of the results
1. Research sample
380 cases of thyroid benign tumor tissues and 598 cases of thyroid malignant tumor tissues were collected with the patients' informed consent. The thyroid cancer stage is determined by the eighth stage system of the United states cancer Association (AJCC). Thyroid malignancies include, depending on the type of pathology, the four major classes of papillary thyroid carcinoma, follicular thyroid carcinoma, medullary thyroid carcinoma and undifferentiated thyroid carcinoma. The 598 thyroid cancer patients collected at this time include 380 thyroid papillary carcinomas, 138 thyroid follicular carcinomas, 44 thyroid medullary carcinomas and 36 thyroid undifferentiated carcinomas. According to the pathological staging, there were 470 stage I patients, 68 stage II patients, 24 stage III patients, and 36 stage IV patients among 598 patients with thyroid malignancies.
2. Methylation detection
1. Total DNA in tumor tissue was extracted.
2. Total DNA from the tissue sample prepared in step 1 was bisulfite treated (see Qiagen for DNA methylation kit instructions). After bisulfite treatment, the unmethylated cytosines (C) in the original CpG sites are converted to uracils (U), while the methylated cytosines remain unchanged.
3. And (3) taking the DNA treated by the bisulfite in the step (2) as a template, adopting 4 pairs of specific primers in the table 5 to perform PCR amplification by DNA polymerase according to a reaction system required by a conventional PCR reaction, wherein all the primers adopt a conventional standard PCR reaction system and are amplified according to the following procedures.
The PCR reaction program is: 95 ℃,4min → (95 ℃,20s → 56 ℃,30s → 72 ℃,2 min) 45 cycles → 72 ℃,5min → 4 ℃,1h.
4. Taking the amplification product in the step 3, and carrying out DNA methylation analysis through a flight time mass spectrum, wherein the specific method comprises the following steps:
(1) Mu.l of a shrimp basic phosphate (SAP) solution (0.3 ml SAP 2 [0.5U ] +1.7ml H2O) was added to 5. Mu.l of the PCR product and then incubated in a PCR apparatus (37 ℃,20min → 85 ℃,5min → 4 ℃,5 min) according to the following procedure;
(2) Taking out 2 mu l of SAP treated product obtained in the step (1), adding the SAP treated product into a 5 mu l T-Cleavage reaction system according to the instruction, and then incubating the mixture at 37 ℃ for 3h;
(3) Adding 19 mu l of deionized water into the product obtained in the step (2), and then performing deionization incubation for 1h by using 6 mu g of Resin in a rotary shaking table;
(4) Centrifuging at 2000rpm for 5min at room temperature, and loading the micro-supernatant with 384SpectroCHIP by a Nanodipen mechanical arm;
(5) Performing time-of-flight mass spectrometry; the data obtained were collected with the SpectroACQUIRE v3.3.1.3 software and visualized with the MassArray EpiTyper v1.2 software.
The reagents used in the flight time mass spectrometry detection are all kits (T-clean Mass clean Reagent Auto Kit, cat # 10129A); the detection instrument used for the time-of-flight mass spectrometry detection is MassARRAY O.R Analyzer Chip Prep Module 384, model: 41243; the data analysis software is self-contained software of the detection instrument.
5. And (5) analyzing the data obtained in the step (4).
Statistical analysis of the data was performed by SPSS Statistics 23.0.
The nonparametric test was used for comparative analysis between the two groups.
The discrimination effect of multiple combinations of CpG sites for different sample groupings was achieved by logistic regression and statistical methods of subject curves.
All statistical tests were two-sided, with p-values <0.05 considered statistically significant.
Through mass spectrometry experiments, a total of 61 distinguishable peak patterns were obtained. The methylation level of each sample at each CpG site can be automatically obtained by calculating the peak area using the spectra acquire v3.3.1.3 software according to the formula "methylation level = peak area of methylated fragment/(peak area of unmethylated fragment + peak area of methylated fragment)".
3. Analysis of results
1. Analysis of methylation level of CUX2 gene of thyroid gland benign tumor, thyroid gland malignant tumor, different subtypes of thyroid gland malignant tumor and different stages of thyroid gland malignant tumor
The methylation level of all CpG sites in the CUX2 gene was analyzed using 380 benign thyroid tumors and 598 malignant thyroid tumor tissue samples as the study material. The results show that the median level of methylation of the CUX2 gene of benign thyroid tumor is 0.69 (IQR = 0.45-0.82), the median level of methylation of the CUX2 gene of malignant thyroid tumor is 0.65 (IQR = 0.40-0.80), the median level of methylation of the CUX2 gene of papillary thyroid carcinoma is 0.65 (IQR = 0.41-0.81), the median level of methylation of the CUX2 gene of follicular thyroid carcinoma is 0.62 (IQR = 0.39-0.77), the median level of methylation of the CUX2 gene of medullary thyroid carcinoma is 0.56 (IQR = 0.30-0.72), and the median level of methylation of the CUX2 gene of undifferentiated thyroid carcinoma is 0.50 (IQR = 0.22-0.69); the median level of methylation of the CUX2 gene at stage I of the thyroid malignancy is 0.64 (IQR = 0.41-0.81), the median level of methylation of the CUX2 gene at stage II of the thyroid malignancy is 0.54 (IQR = 0.35-0.73), the median level of methylation of the CUX2 gene at stage iii of the thyroid malignancy is 0.50 (IQR = 0.26-0.69), and the median level of methylation of the CUX2 gene at stage iv of the thyroid malignancy is 0.45 (IQR = 0.22-0.67). By comparing and analyzing the methylation levels among the several patients, the methylation levels of all CpG sites in the CUX2 gene in the benign thyroid tumor are found to be significantly higher than those in the CUX2 gene in the malignant thyroid tumor (Table 6), and the differences among thyroid cancers with four different clinical characteristics of papillary thyroid cancer, follicular thyroid cancer, medullary thyroid cancer and undifferentiated thyroid cancer are more and more obvious (Table 6). In addition, with the increase of the stage of the thyroid malignant tumor, the methylation level of the methylation sites on the DNA fragments shown in SEQ ID No.1, 2, 3 and 4 in the CUX2 gene in the tissue is lower and lower (Table 6), and the difference between the thyroid malignant tumor and the thyroid benign tumor is more and more obvious.
TABLE 6 CUX2 Gene methylation levels in thyroid benign tumor and thyroid malignant tumor and their subtypes and stages
Note: the CpG sites in the tables all refer to distinguishable CpG sites.
2. The methylation level of the CUX2 gene in the tumor tissue can distinguish thyroid benign tumor from thyroid malignant tumor of different subtypes
By comparatively analyzing the methylation levels of CUX2 in 380 cases of thyroid benign tumor and 598 cases of thyroid malignant tumor, it was found that the methylation levels of CUX2_ A fragment, CUX2_ B fragment, CUX2_ C fragment and CUX2_ D fragment were significantly lower in patients with thyroid malignant tumor, papillary thyroid cancer, follicular thyroid cancer, medullary thyroid cancer and thyroid undifferentiated carcinoma than in patients with thyroid benign tumor. The specific results are shown in Table 7.
TABLE 7 CUX2 Gene methylation level differences between thyroid benign tumors and different subtype thyroid malignant tumors
Note: the CpG sites in the tables all refer to distinguishable CpG sites.
3. The methylation level of the CUX2 gene in the tumor tissue can distinguish thyroid malignant tumors of different subtypes
By comparatively analyzing the CUX2 methylation levels of different subtype thyroid malignant tumor cases (380 cases of papillary thyroid cancer, 138 cases of follicular thyroid cancer, 44 cases of medullary thyroid cancer and 36 cases of undifferentiated thyroid cancer), it was found that there was a significant difference between the CUX2 gene methylation levels of patients with papillary thyroid cancer, follicular thyroid cancer, medullary thyroid cancer and undifferentiated thyroid cancer. The specific results are shown in Table 8.
TABLE 8 differences in the methylation level of the CUX2 gene between subtypes of thyroid malignancies
Note: the CpG sites in the tables all refer to distinguishable CpG sites.
4. The methylation level of the CUX2 gene in the tumor tissue can distinguish thyroid benign tumors from thyroid malignant tumors in different stages
By comparatively analyzing the methylation level of CUX2 in 380 cases of benign thyroid tumors and in different stages of patients with malignant thyroid tumors (470 patients with stage I, 68 patients with stage II, 24 patients with stage III and 36 patients with stage IV), it was found that the methylation levels of the CUX2_ A fragment, the CUX2_ B fragment, the CUX2_ C fragment and the CUX2_ D fragment in patients with stage I, II, III and IV thyroid cancer were significantly lower than the methylation levels of the corresponding fragments in patients with benign thyroid tumors (P < 0.05). The specific results are shown in Table 9.
TABLE 9 differences in the methylation level of the CUX2 gene between thyroid benign tumors and thyroid malignant tumors of different stages
Note: the CpG sites in the table all refer to distinguishable CpG sites.
5. The methylation level of the CUX2 gene in the tumor tissue can distinguish thyroid malignant tumors of different stages
Through comparative analysis of CUX2 methylation levels of thyroid malignant tumor patients of different stages (470 patients of stage I, 68 patients of stage II, 24 patients of stage III and 36 patients of stage IV), the results show that the CUX2 gene methylation levels of thyroid malignant tumor patients of stage I, stage II, stage III and stage IV are different significantly (P is less than 0.05). The specific results are shown in Table 10.
TABLE 10 differences in methylation levels of the CUX2 gene between different stages of thyroid malignancies
Note: the CpG sites in the tables all refer to distinguishable CpG sites.
6. Establishment of mathematical model for assisting cancer diagnosis by CUX2 gene methylation
The mathematical model established by the invention can be used for achieving the following purposes:
(1) Distinguishing thyroid malignant tumor patients from thyroid benign tumors;
(2) Distinguishing thyroid benign tumors from thyroid malignant tumors of different subtypes;
(3) Differentiating benign thyroid tumors from malignant thyroid tumors of different stages;
(4) Distinguishing different subtypes of thyroid malignant tumor;
(5) Differentiating different stages of thyroid malignant tumor.
The mathematical model is established as follows:
(A) The data source is as follows: in step one, the methylation levels of the target CpG sites (one or more combinations of Table 1-Table 4) of the tissue samples of 380 cases of thyroid benign tumors and 598 cases of thyroid malignant tumors (380 cases of papillary thyroid carcinoma, 138 cases of thyroid follicular carcinoma, 44 cases of medullary thyroid carcinoma and 36 cases of thyroid undifferentiated carcinoma) are listed (the detection method is the same as in step two).
(B) Model building
Any two types of patient data (for example, patients with benign thyroid tumor and malignant thyroid tumor, patients with benign thyroid tumor and thyroid papillary carcinoma, patients with benign thyroid tumor and thyroid follicular carcinoma, patients with benign thyroid tumor and thyroid medullary carcinoma, patients with benign thyroid tumor and thyroid undifferentiated carcinoma, patients with papillary thyroid carcinoma and thyroid follicular carcinoma, patients with papillary thyroid carcinoma and thyroid medullary carcinoma, patients with papillary thyroid carcinoma and thyroid undifferentiated carcinoma, patients with thyroid follicular carcinoma and thyroid medullary carcinoma, patients with thyroid follicular thyroid carcinoma and thyroid undifferentiated carcinoma, patients with thyroid medullary carcinoma and thyroid undifferentiated carcinoma, patients with benign thyroid tumor and malignant thyroid tumor I, patients with benign thyroid tumor and malignant thyroid tumor II, patients with benign thyroid tumor and malignant thyroid tumor III, patients with benign thyroid tumor and malignant thyroid tumor IV, patients with malignant thyroid tumor I and malignant tumor II, patients with malignant tumor and malignant tumor I, malignant tumor II, thyroid III, and malignant tumor III) are selected according to the requirements, and used as a mathematical regression model for establishing a logistic regression model for patients with benign thyroid tumor III, and malignant tumor IV. The numerical value corresponding to the maximum Johnson index calculated by the mathematical model formula is a threshold value or 0.5 is directly set as the threshold value, the detection index obtained after the sample to be detected is tested and substituted into the model calculation is classified into one class (B class) when being larger than the threshold value, and classified into the other class (A class) when being smaller than the threshold value, and the detection index is equal to the threshold value and is used as an uncertain gray zone. When a new sample to be detected is predicted to judge which type the sample belongs to, firstly, the methylation level of one or more CpG sites on the CUX2 gene of the sample to be detected is detected by a DNA methylation determination method, then the data of the methylation levels are substituted into the mathematical model, the detection index corresponding to the sample to be detected is obtained by calculation, then the detection index corresponding to the sample to be detected is compared with the threshold value, and the sample to be detected belongs to which type the sample to be detected is determined according to the comparison result.
Examples are: as shown in FIG. 1, the data of methylation level of single CpG sites or combination of multiple CpG sites of CUX2 gene in training set is used to establish mathematical model for distinguishing A class and B class by statistical software such as SAS, R, SPSS, etc. using formula of two-classification logistic regression. The mathematical model is here a two-class logistic regression model, specifically: ln (y/(1-y)) = b0+ b1x1+ b2x2+ b3x3+ \8230, + bnXn, where y is a detection index obtained by substituting a dependent variable, i.e., the methylation level of one or more methylation sites in a test sample, into a model, b0 is a constant, x1-xn are independent variables, i.e., the methylation level of one or more methylation sites in the test sample (each value is a number between 0 and 1), and b1-bn are weights assigned to the methylation level of each site by the model. In specific application, a mathematical model is established according to the methylation level (x 1-xn) of one or more DNA methylation sites of a sample detected in a training set and known classification conditions (class A or class B, and the value of y is respectively assigned with 0 and 1), so that the constant B0 of the mathematical model and the weight B1-bn of each methylation site are determined, and the value corresponding to the maximum york index calculated by the mathematical model is used as a threshold value or 0.5 is directly set as a divided threshold value. And (3) after the sample to be detected is tested and substituted into the model for calculation, the detection index (y value) obtained is classified as B when being larger than the threshold, classified as A when being smaller than the threshold, and is equal to the threshold to be used as an uncertain gray area. The class a and the class B are two corresponding classes (grouping of two classes, which group is the class a, and which group is the class B, which are determined according to a specific mathematical model, and no convention is made here). When a sample from a subject is predicted to determine which class it belongs to, a biopsy sample (i.e., tumor tissue) from the subject is first collected and DNA is then extracted therefrom. After the extracted DNA is converted by bisulfite, the methylation level of a single CpG site or the methylation level of a combination of a plurality of CpG sites of the CUX2 gene of the subject is detected by a DNA methylation detection method, and then the methylation data obtained by detection is substituted into the mathematical model. If the methylation level of one or more CpG sites of the CUX2 gene of the subject is substituted into the mathematical model, and the calculated value is the detection index which is larger than the threshold value, the subject judges that the detection index in the training set is larger than the class B; if the methylation level data of one or more CpG sites of the CUX2 gene of the subject is substituted into the mathematical model, and the calculated value, namely the detection index, is less than the threshold value, the subject belongs to the class (class A) with the detection index less than the threshold value in the training set; if the methylation level data of one or more CpG sites of the CUX2 gene of the subject is substituted into the mathematical model, the calculated value, i.e. the detection index, is equal to the threshold value, then the subject cannot be judged to be in the A class or the B class.
Examples are: as shown in fig. 2, methylation of 9 distinguishable CpG sites of CUX2_ B (CUX 2_ B _8, CUX2_ B _9, CUX2_ B _10, CUX2_ B _11.12.13.14.15, CUX2_ B _16, CUX2_ B _17, CUX2_ B _18.19, CUX2_ B _20, CUX2_ B _ 21) and the use of mathematical modeling for the identification of benign and malignant thyroid tumor tissue: the data of the methylation levels of the above 9 distinguishable CpG sites of CUX2_ B which have been detected in training sets of thyroid benign tumor patients and thyroid malignant tumor patients (in this case, 380 thyroid benign tumor patients and 598 thyroid malignant tumor patients) were used to establish a mathematical model for identifying thyroid malignant tumor patients by SPSS software or R software using a formula of two-class logistic regression. The mathematical model is here a two-class logistic regression model, whereby the constant b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: ln (y/(1-y)) =0.887-1.253 cu 2_B _8-3.665 cu 2_B _9-0.482 cu 2 _B10 +0.293 cu 2_B _11.12.13.14.15+0.657 cu CUX2_B _16-0.726 cu 2 _B17 +1.615 cu 2_B _20+1.204 cu 2_B _B21, wherein y is a factor, and the detection index is obtained by converting methylation level models of the above 9 distinguishable CpG sites of CUX2_ B of the sample to be detected after substituting into the factor. CUX2_ B _11, CUX2_ B _12, CUX2_ B _13, CUX2_ B _14 and CUX2_ B _15 are in the same fragment, and CUX2_ B _18 and CUX2_ B _19 are in the same fragment, so CUX2_ B _11.12.13.14.15 and CUX2_ B _18.19 represent the average of the methylation levels at these two sites, respectively. Under the condition that 0.5 is set as a threshold, the methylation levels of the 9 distinguishable CpG sites of the CUX2_ B of the sample to be detected are substituted into the model for calculation, the obtained detection index, namely the y value is less than the threshold and classified as a thyroid benign tumor patient, the y value is more than the threshold and classified as a thyroid malignant tumor patient, and the y value is equal to the threshold and is not determined as the thyroid benign tumor patient or the thyroid malignant tumor patient. The area under the curve (AUC) of this model was calculated to be 0.77 (table 15). Specific examples of the method for determining the subject include a method in which DNAs are extracted from biopsy samples (i.e., tumor tissues) collected from two subjects (A, B), and the extracted DNAs are converted with bisulfite, and then the methylation levels of 9 CpG sites of CUX2_ B _8, CUX2_ B _9, CUX2_ B _10, CUX2_ B _11.12.13.14.15, CUX2_ B _16, CUX2_ B _17, CUX2_ B _18.19, CUX2_ B _20, and CUX2_ B _21 of the subjects are measured by a DNA methylation measurement method. The detected methylation level data information is then substituted into the mathematical model. The methylation level data of the 9 distinguishable CpG sites of CUX2_ B of the first subject is substituted into the mathematical model, and the calculated value is 0.81 and more than 0.5, so that the first subject is judged as a thyroid malignant tumor patient (which is consistent with clinical diagnosis); and (3) substituting the methylation level data of the 9 distinguishable CpG sites of CUX2_ B of the second subject into the mathematical model to calculate a value of 0.39 less than 0.5, and judging the second subject to be the thyroid benign tumor patient (which is consistent with clinical diagnosis).
(C) Evaluation of model Effect
According to the above method, mathematical models for finding patients with benign thyroid tumor and malignant thyroid tumor, patients with benign thyroid tumor and papillary thyroid carcinoma, patients with benign thyroid tumor and follicular thyroid carcinoma, patients with benign thyroid tumor and medullary thyroid carcinoma, patients with benign thyroid tumor and undifferentiated thyroid carcinoma, patients with papillary thyroid carcinoma and follicular thyroid carcinoma, patients with papillary thyroid carcinoma and medullary thyroid carcinoma, patients with papillary thyroid carcinoma and undifferentiated thyroid carcinoma, patients with follicular thyroid carcinoma and medullary thyroid carcinoma, patients with follicular thyroid carcinoma and undifferentiated thyroid carcinoma, patients with medullary thyroid carcinoma and undifferentiated thyroid carcinoma, patients with benign thyroid tumor and malignant thyroid tumor stage I, patients with benign thyroid tumor and malignant thyroid tumor stage II, patients with benign thyroid tumor and malignant thyroid tumor stage I, patients with malignant thyroid tumor and malignant thyroid tumor stage II, patients with malignant tumor stage III and malignant tumor stage I, patients with malignant tumor stage I and malignant tumor stage II, patients with malignant tumor stage III and malignant tumor stage II, and its effectiveness was evaluated by a receiver curve (ROC curve). The larger the area under the curve (AUC) obtained by the ROC curve, the better the discrimination of the model, and the more effective the molecular marker. The evaluation results after mathematical model construction using different CpG sites are shown in tables 11, 12, 13 and 14. In tables 11, 12, 13 and 14, 1 CpG site represents a site of any CpG site in the CUX2_ B amplified fragment, 2 CpG sites represent a combination of any 2 CpG sites in the CUX2_ B amplified fragment, 3 CpG sites represent a combination of any 3 CpG sites in the CUX2_ B amplified fragment, \ 8230; \8230, and so on. The values in the table are ranges for the results of different site combinations (i.e., the results of any combination of CpG sites are within the range).
The results of the above studies show that the differentiation ability of the CUX2 gene methylation for each group (patients with benign thyroid tumor and malignant thyroid tumor, patients with benign thyroid tumor and papillary thyroid carcinoma, patients with benign thyroid tumor and follicular thyroid carcinoma, patients with benign thyroid tumor and medullary thyroid carcinoma, patients with benign thyroid tumor and undifferentiated thyroid carcinoma, patients with papillary thyroid carcinoma and follicular thyroid carcinoma, patients with papillary thyroid carcinoma and medullary thyroid carcinoma, patients with papillary thyroid carcinoma and undifferentiated thyroid carcinoma, patients with follicular thyroid carcinoma and undifferentiated thyroid carcinoma, patients with medullary thyroid carcinoma and undifferentiated thyroid carcinoma, patients with benign thyroid tumor and malignant thyroid tumor stage I, patients with benign thyroid tumor and malignant thyroid tumor stage II, patients with benign thyroid tumor and malignant thyroid tumor stage III, patients with benign thyroid tumor and malignant thyroid tumor stage IV, patients with malignant thyroid tumor stage I and malignant thyroid tumor stage II, patients with malignant thyroid tumor stage II and malignant tumor stage II, patients with malignant thyroid tumor stage III and malignant tumor stage II, patients with malignant thyroid tumor stage II and malignant tumor stage II, patients with increased number of malignant thyroid tumor stage I and malignant thyroid tumor.
In addition, among the CpG sites shown in tables 1 to 4, there are cases where a combination of a few preferred sites is better in discrimination than a combination of a plurality of non-preferred sites. For example, the combination of 9 distinguishable CpG sites shown in table 15, table 16, table 17 and table 18, CUX2_ B _8, CUX2_ B _9, CUX2_ B _10, CUX2_ B _11.12.13.14.15, CUX2_ B _16, CUX2_ B _17, CUX2_ B _18.19, CUX2_ B _20, and CUX2_ B _21, is a preferred site for any of 9 combinations in CUX2_ D.
In summary, cpG sites and their various combinations in the CUX2 gene, cpG sites and their various combinations in the CUX2_ A fragment, cpG sites and their various combinations in the CUX2_ B fragment, cpG sites and their various combinations in the CUX2_ C fragment, cpG sites and their various combinations in the CUX2_ D fragment, CUX2_ B _8, CUX2_ B _9, CUX2_ B _10, CUX2_ B _11.12.13.14.15, CUX2_ B _16, CUX2_ B _17, CUX2_ B _18.19, CUX2_ B _20, CUX2_ B _21 sites and their various combinations, methylation levels at CUX2_ E sites and various combinations thereof and CpG sites at CUX2_ A, CUX2_ B, CUX2_ C and CUX2_ D and various combinations thereof for thyroid benign and malignant tumors, thyroid benign and papillary carcinoma, thyroid benign and follicular carcinoma, thyroid benign and medullary carcinoma, thyroid benign and undifferentiated, papillary and follicular thyroid carcinoma, thyroid papillary and undifferentiated, thyroid medullary carcinoma, thyroid benign and undifferentiated, thyroid malignant tumors I and malignant tumors, thyroid malignant tumors II and malignant tumors, thyroid benign and malignant tumors I and malignant tumors IV, thyroid malignant tumors I and malignant tumors III and malignant tumors I and malignant tumors IV, thyroid malignant tumors and malignant tumors III and malignant tumors IV, patients with stage II thyroid malignant tumor and stage III thyroid malignant tumor, patients with stage II thyroid malignant tumor and stage IV thyroid malignant tumor, and patients with stage III thyroid malignant tumor and stage IV thyroid malignant tumor have discrimination ability.
TABLE 11 CpG sites of CUX2_ B and combinations thereof for differentiating benign thyroid tumors from malignant thyroid tumors of different subtypes
Note: the CpG sites in the tables all refer to distinguishable CpG sites.
TABLE 12 CpG sites of CUX2_ B and combinations thereof for differentiating thyroid benign tumors from thyroid malignant tumors of different stages
Note: the CpG sites in the tables all refer to distinguishable CpG sites.
TABLE 13 CpG sites of CUX2_ B and their combination for distinguishing different subtype thyroid malignant tumor patients
Note: the CpG sites in the table all refer to distinguishable CpG sites.
TABLE 14 CpG sites of CUX2_ B and combinations thereof for differentiating patients with thyroid malignancies of different stages
Note: the CpG sites in the tables all refer to distinguishable CpG sites.
TABLE 15 optimal CpG sites for CUX2_ B and combinations thereof for differentiating benign thyroid tumors from malignant thyroid tumors of different subtypes
Note: the CpG sites in the table all refer to distinguishable CpG sites.
TABLE 16 optimal CpG sites for CUX2_ B and combinations thereof for differentiating benign thyroid tumors from malignant thyroid tumors of different stages
Note: the CpG sites in the table all refer to distinguishable CpG sites.
TABLE 17 optimal CpG sites of CUX2_ B and combinations thereof for differentiation between different subtypes of thyroid malignancies
Note: the CpG sites in the table all refer to distinguishable CpG sites.
TABLE 18 optimal CpG sites for CUX2_ B and combinations thereof for differentiation between different stages of thyroid malignancies
Note: the CpG sites in the table all refer to distinguishable CpG sites.
The present invention has been described in detail above. It will be apparent to those skilled in the art that the invention can be practiced within a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with reference to specific examples, it will be appreciated that the invention may be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The use of some of the essential features is made possible within the scope of the claims attached below.
Claims (10)
1. A methylation biomarker characterized by: the nucleotide sequence of the methylation biomarker is the methylation level of all or part of CpG sites in the following fragments (A1) to (A4) in the CUX2 gene:
(A1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment with more than 80% of identity with the DNA fragment;
(A2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment with more than 80% of identity with the DNA fragment;
(A3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment with more than 80% of identity with the DNA fragment;
(A4) A DNA fragment shown in SEQ ID No.4 or a DNA fragment with more than 80% of identity with the DNA fragment;
the methylation biomarker comprises CpG sites located on the nucleotide sequence of the methylation biomarker, wherein the CpG sites are represented by any one of (B1) to (B7):
(B1) Any one or more CpG sites in 4 DNA fragments shown in SEQ ID No.1, SEQ ID No.2, SEQ ID No.3 and SEQ ID No.4 in the CUX2 gene;
(B2) All CpG sites on the DNA segment shown in SEQ ID No.2 and all CpG sites on the DNA segment shown in SEQ ID No.1 in the CUX2 gene;
(B3) All CpG sites on the DNA fragment shown in SEQ ID No.2 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the CUX2 gene;
(B4) All CpG sites on the DNA segment shown in SEQ ID No.1 and all CpG sites on the DNA segment shown in SEQ ID No.3 in the CUX2 gene;
(B5) All CpG sites on the DNA fragment shown in SEQ ID No.2, all CpG sites on the DNA fragment shown in SEQ ID No.1 and all CpG sites on the DNA fragment shown in SEQ ID No.3 in the CUX2 gene;
(B6) All CpG sites or any 23 or any 22 or any 21 or any 20 or any 19 or any 18 or any 17 or any 16 or any 15 or any 14 or any 13 or any 12 or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 CpG site in the DNA fragment shown as SEQ ID No.2 in the CUX2 gene;
(B7) The CUX2 gene has the DNA fragment shown in SEQ ID No.2, wherein the CpG sites are all or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following 9:
item 1: the DNA fragment shown in SEQ ID No.2 is a CpG site shown by 261-262 th site from 5' end;
item 2: the DNA fragment shown in SEQ ID No.2 is a CpG site shown by 330-331 th sites from 5' end;
item 3: the DNA fragment shown in SEQ ID No.2 has CpG sites shown in 355-356 bits from the 5' end;
item 4: the DNA fragment shown in SEQ ID No.2 has CpG sites shown in 369-370, 371-372, 374-375, 380-381 and 382-383 from the 5' end;
item 5: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 392 th to 393 th positions at the 5' end;
item 6: the DNA fragment shown in SEQ ID No.2 is provided with CpG sites shown at 453 th-454 th sites from the 5' end;
item 7: the DNA fragment shown in SEQ ID No.2 has CpG sites shown in 475-476 th and 478-479 th positions from the 5' end;
item 8: the CpG site shown by 484-485 bits from the 5' end of the DNA segment shown in SEQ ID No. 2;
item 9: the DNA fragment shown in SEQ ID No.2 shows CpG sites at 624-625 sites from the 5' end;
the methylation biomarker is used as at least one of the following:
(1) Distinguishing or assisting in distinguishing thyroid benign tumors from thyroid malignant tumors;
(2) Differentiating or assisting in differentiating benign thyroid tumors and thyroid malignant tumors of different subtypes;
(3) Differentiating or assisting in differentiating thyroid benign tumors from thyroid malignant tumors of different stages;
(4) Distinguishing or assisting in distinguishing different subtypes of thyroid malignant tumors;
(5) Distinguish or assist in distinguishing different stages of thyroid malignancy.
2. Use of the methylation biomarker of claim 1 in the preparation of a product; the use of the product is at least one of the following:
(1) Distinguishing or assisting in distinguishing thyroid benign tumors from thyroid malignant tumors;
(2) Differentiating or assisting in differentiating benign thyroid tumors and thyroid malignant tumors of different subtypes;
(3) Differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors of different stages;
(4) Distinguishing or assisting in distinguishing different subtypes of thyroid malignant tumor;
(5) Distinguish or assist in distinguishing different stages of thyroid malignancy.
3. Use of a substance for detecting the methylation level of the methylation biomarker of claim 1 in the manufacture of a product; the product has at least one of the following uses:
(1) Differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors;
(2) Differentiating or assisting in differentiating benign thyroid tumors and thyroid malignant tumors of different subtypes;
(3) Differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors of different stages;
(4) Distinguishing or assisting in distinguishing different subtypes of thyroid malignant tumors;
(5) Distinguish or assist in distinguishing different stages of thyroid malignancy.
4. Use of a substance for detecting the methylation level of a methylation biomarker according to claim 1 and a medium having stored thereon a mathematical model and/or a method of using the mathematical model for the manufacture of a product; the product has at least one of the following uses:
(1) Differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors;
(2) Differentiating or assisting in differentiating benign thyroid tumors and thyroid malignant tumors of different subtypes;
(3) Differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors of different stages;
(4) Distinguishing or assisting in distinguishing different subtypes of thyroid malignant tumors;
(5) Differentiating or assisting in differentiating different stages of thyroid malignant tumor;
the mathematical model is obtained according to a method comprising the following steps:
(C1) Respectively detecting the gene methylation levels of n 1A-type samples and n 2B-type samples;
(C2) Taking the gene methylation level data of all samples obtained in the step (C1), establishing a mathematical model by a two-classification logistic regression method according to the classification modes of the type A and the type B, and determining a threshold value for classification judgment;
the using method of the mathematical model comprises the following steps:
(D1) Detecting the gene methylation level of a sample to be detected;
(D2) Substituting the gene methylation level data of the sample to be detected, which is obtained in the step (D1), into the mathematical model to obtain a detection index; then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result;
the type A sample and the type B sample are any one of the following:
(E1) Thyroid benign tumor and thyroid malignant tumor;
(E2) Benign thyroid tumors and thyroid malignancies of different subtypes;
(E3) Benign thyroid tumors and thyroid malignancies of different stages;
(E4) Thyroid malignancies of different subtypes;
(E5) Thyroid malignancies of different stages.
5. A kit comprising a means for detecting the methylation level of the methylation biomarker of claim 1; the kit is used for at least one of the following purposes:
(1) Differentiating or assisting in differentiating benign thyroid tumors from malignant thyroid tumors;
(2) Differentiating or assisting in differentiating benign thyroid tumors and thyroid malignant tumors of different subtypes;
(3) Differentiating or assisting in differentiating thyroid benign tumors from thyroid malignant tumors of different stages;
(4) Distinguishing or assisting in distinguishing different subtypes of thyroid malignant tumor;
(5) Distinguish or assist in distinguishing different stages of thyroid malignancy.
6. The kit of claim 5, wherein: the kit further comprises a medium as described in claim 4 in which the mathematical model and/or the method of using the mathematical model is stored.
7. A system, comprising:
(F1) Reagents and/or instruments for detecting methylation levels of the CUX2 gene;
(F2) A device comprising a unit X and a unit Y;
the unit X is used for establishing a mathematical model and comprises a data acquisition module, a data analysis processing module and a model output module;
the data acquisition module is configured to acquire (F1) CUX2 gene methylation level data for the n1 type a samples and the n2 type B samples detected;
the data analysis processing module is configured to receive CUX2 gene methylation level data of the n 1A type samples and the n 2B type samples from the data acquisition module, establish a mathematical model through a two-classification logistic regression method according to the classification modes of the A type and the B type, and determine a threshold value of classification judgment;
the model output module is configured to receive the mathematical model established by the data analysis processing module and output the mathematical model;
the unit Y is used for determining the type of a sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module;
the data input module is configured to input (F1) CUX2 gene methylation level data of the subject detected;
the data operation module is configured to receive CUX2 gene methylation level data of the person to be tested from the data input module, and substitute the CUX2 gene methylation level data of the person to be tested into the mathematical model established by the data analysis processing module in the unit X, so as to calculate a detection index;
the data comparison module is configured to receive a detection index calculated by the data operation module and compare the detection index with the threshold determined in the data analysis processing module in the unit X;
the conclusion output module is configured to receive the comparison result from the data comparison module and output the conclusion that the type of the sample to be tested is the type A or the type B according to the comparison result;
the type A sample and the type B sample are any one of the following:
(E1) Thyroid benign tumor and thyroid malignant tumor;
(E2) Benign thyroid tumors and thyroid malignancies of different subtypes;
(E3) Benign thyroid tumors and thyroid malignancies of different stages;
(E4) Thyroid malignancies of different subtypes;
(E5) Thyroid malignancies of different stages.
8. The use or kit or system according to any one of claims 3-7, wherein: the substance or reagent for detecting the methylation level of the CUX2 gene comprises a primer combination for amplifying the full-length or partial fragment of the CUX2 gene;
further, the partial fragment is at least one fragment selected from the following fragments:
(G1) The DNA fragment shown in SEQ ID No.1 or the DNA fragment contained in the DNA fragment;
(G2) A DNA fragment shown as SEQ ID No.2 or a DNA fragment contained therein;
(G3) A DNA fragment shown as SEQ ID No.3 or a DNA fragment contained therein;
(G4) The DNA segment shown in SEQ ID No.4 or the DNA segment contained in the DNA segment;
(G5) A DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.1 or a DNA fragment comprising the same;
(G6) A DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.2 or a DNA fragment comprising the same;
(G7) A DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.3 or a DNA fragment comprising the same;
(G8) A DNA fragment having an identity of 80% or more with the DNA fragment represented by SEQ ID No.4 or a DNA fragment contained therein.
9. The use or kit or system according to claim 8, wherein: the primer combination is a primer pair A and/or a primer pair B and/or a primer pair C and/or a primer pair D;
the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is single-stranded DNA shown by 11 th-35 th nucleotides of SEQ ID No.5 or SEQ ID No. 5; the primer A2 is single-stranded DNA shown by 32 th-56 th nucleotides of SEQ ID No.6 or SEQ ID No. 6;
the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is single-stranded DNA shown by 11 th-35 th nucleotides of SEQ ID No.7 or SEQ ID No. 7; the primer B2 is single-stranded DNA shown by 32 th to 56 th nucleotides of SEQ ID No.8 or SEQ ID No. 8;
the primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is single-stranded DNA shown by 11 th-35 th nucleotides of SEQ ID No.9 or SEQ ID No. 9; the primer C2 is single-stranded DNA shown by 32 th-56 th nucleotides of SEQ ID No.10 or SEQ ID No. 10;
the primer pair D is a primer pair consisting of a primer D1 and a primer D2; the primer D1 is single-stranded DNA shown by 11 th-35 th nucleotides of SEQ ID No.11 or SEQ ID No. 11; the primer D2 is single-stranded DNA shown by 32 th to 56 th nucleotides of SEQ ID No.12 or SEQ ID No. 12.
10. The use or kit or system of any one of claims 2 to 9, wherein: detecting the methylation level of the methylation biomarker of claim 1 is detecting the methylation level of the methylation biomarker of claim 1 in a tumor tissue sample.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211580424.0A CN115772571A (en) | 2022-12-09 | 2022-12-09 | Methylation molecular marker for identifying benign and malignant thyroid tumors |
| CN202311679585.XA CN117711499A (en) | 2022-12-09 | 2023-12-08 | Data processing device for assisting in distinguishing benign and malignant thyroid tumors and application thereof |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211580424.0A CN115772571A (en) | 2022-12-09 | 2022-12-09 | Methylation molecular marker for identifying benign and malignant thyroid tumors |
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| CN202211580424.0A Pending CN115772571A (en) | 2022-12-09 | 2022-12-09 | Methylation molecular marker for identifying benign and malignant thyroid tumors |
| CN202311679585.XA Pending CN117711499A (en) | 2022-12-09 | 2023-12-08 | Data processing device for assisting in distinguishing benign and malignant thyroid tumors and application thereof |
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| CN202311679585.XA Pending CN117711499A (en) | 2022-12-09 | 2023-12-08 | Data processing device for assisting in distinguishing benign and malignant thyroid tumors and application thereof |
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