CN116004826A

CN116004826A - Tumor early-stage auxiliary diagnosis marker and application thereof in preparation of products

Info

Publication number: CN116004826A
Application number: CN202211589433.6A
Authority: CN
Inventors: 狄飞飞; 张晶
Original assignee: Nanjing Tengchen Biological Technology Co ltd
Current assignee: Nanjing Tengchen Biological Technology Co ltd
Priority date: 2022-12-12
Filing date: 2022-12-12
Publication date: 2023-04-25

Abstract

The invention discloses a tumor early-stage auxiliary diagnosis marker and application thereof in preparing products. The invention claims a kit comprising a substance for detecting the methylation level of the LZTFL1 gene; the kit is used for distinguishing or assisting in distinguishing thyroid benign tumor from thyroid malignant tumor, thyroid benign tumor and thyroid malignant tumor of different subtypes/different stages; distinguishing or assisting in distinguishing different subtypes/different stages of thyroid malignancy. Compared with benign thyroid tumor, the hypomethylation phenomenon of LZTFL1 gene in thyroid cancer patient tissue is disclosed, and the method has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of thyroid cancer, reducing the death rate of thyroid cancer and guiding the establishment of reasonable clinical treatment scheme.

Description

Tumor early-stage auxiliary diagnosis marker and application thereof in preparation of products

Technical Field

The invention relates to the field of medicine, in particular to an early-stage auxiliary diagnosis marker for tumors and application of the early-stage auxiliary diagnosis marker in preparation of products.

Background

Thyroid cancer (Thyroid cancer) is the most common malignancy of the endocrine system, including papillary Thyroid cancer, follicular Thyroid cancer, undifferentiated Thyroid cancer, and medullary carcinoma. Papillary carcinoma (Papillary thyroid cancer, PTC) is the most common, accounting for more than 90% of all thyroid malignancies [ Xing, migzhao; haugen, bryan R; schlumberger, martin (2013), progress in molecular-based management of differentiated thyroid cancer the Lancet,381 (9871), 1058-1069. The prevalence of adult thyroid nodules is statistically about 5-10%, with the population over 60 being the most severe, up to 50-70% [ Guth S, theune U, aberle J, et al, very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultra sound extraction, eur JClin Invest 2009;39:699-706.]. Imaging examination is a common thyroid diagnosis method, and most of the methods depend on experience judgment of doctors, have certain result errors, and imaging has certain radiation damage to human bodies. Fine needle aspiration biopsy is also a clinically common thyroid cancer diagnostic technique that evaluates the benign or malignant nature of a nodule based on the cytological morphology of the aspirate. Because the cytological features of benign and malignant thyroid tumors often overlap, about 10-30% of fine needle punctures are diagnosed as ambiguous cytologic results [ Cibas ES, ali sz. The 2017Bethesda System for Reporting Thyroid Cytopathology.Thyroid.2017;27 (11):1341-6.]. Uncertain puncture results lead to about 60% of patients suffering from overtreatment or missed diagnosis [ Stewart R, leang YJ, bhatt CR, grodski S, serpell J, lee jc. Quantifying the differences in surgical management of patients with definitive and indeterminate thyroid nodule cytology. Eur J Surg oncol.2020;46 (2):252-7.]. This not only increases the economic and physical burden on the patient, but also occupies a significant amount of public health resources, resulting in a significant financial cost for the healthcare system. Therefore, the identification of benign and malignant thyroid tumors is beneficial to clinicians to adopt more accurate treatment schemes, and has important clinical and public health significance.

Epigenetic is a genetic expression control mode which does not involve DNA sequence changes but can be inherited, and can be inherited to the next generation [ Nicog ou A, merlin F. Epigenetics: A way to bridge the gap between biological fields. Stud Hist Philos Biol Biomed Sci.2017;66:73-82]. DNA methylation is one of the important modes of epigenetic regulation, which means that a methyl group is covalently bonded at the 5' -carbon position of cytosine of a genomic CpG dinucleotide under the action of DNA methylation transferase [ Bird A. Peptides of peptides. Nature 2007;447:396-398]. Numerous studies have shown that DNA methylation can cause changes in chromatin structure, DNA conformation, DNA stability, and the manner in which DNA interacts with proteins, thereby controlling gene expression [ Moore LD, le T, fan g.dna methylation and its basic function.neuroopsymacology.2013; 38:23-38].

The DNA methylation marker is the optimal tumor in-vitro early diagnosis molecular marker at the present stage, and the sensitivity and the specificity of the thyroid cancer diagnosis marker are limited clinically at present, particularly the marker for early diagnosis is lacking, so that the more sensitive and specific early molecular marker is urgently discovered.

Disclosure of Invention

The invention aims to provide a tumor early-stage auxiliary diagnosis marker and application thereof in preparing products.

In a first aspect, the invention claims a kit.

The kit claimed in the invention comprises a substance for detecting the methylation level of the LZTFL1 gene; the application of the kit is at least one of the following:

(1) Distinguishing or assisting in distinguishing between benign thyroid tumors and malignant thyroid tumors;

(2) Distinguishing or assisting in distinguishing between benign thyroid tumors and different subtypes of thyroid malignancy;

(3) Distinguishing or assisting in distinguishing benign thyroid tumors from different stages of thyroid malignancy;

(4) Distinguishing or assisting in distinguishing different subtypes of thyroid malignancy;

(5) Differentiation or assistance in differentiating between different stages of thyroid malignancy.

Further, the different subtypes described in (2) and (4) may be pathological typing, such as histological typing.

Further, the different stages in (3) and (5) may be clinical stages.

In a specific embodiment of the present invention, the differentiation or assistance in differentiating between benign thyroid tumors and thyroid malignant tumors of different subtypes described in (2) may be specifically any of the following: distinguishing or aiding in distinguishing benign thyroid tumors from papillary thyroid cancers, distinguishing or aiding in distinguishing benign thyroid tumors from follicular thyroid cancers, distinguishing or aiding in distinguishing benign thyroid tumors from medullary thyroid cancers, distinguishing or aiding in distinguishing benign thyroid tumors from undifferentiated thyroid cancers.

In a specific embodiment of the present invention, the distinguishing or assisting in distinguishing between benign thyroid tumor and different staged thyroid malignant tumor described in (3) may be specifically any of the following: distinguishing or assisting in distinguishing benign thyroid tumor and thyroid malignant tumor of stage I, distinguishing or assisting in distinguishing benign thyroid tumor and thyroid malignant tumor of stage II, distinguishing or assisting in distinguishing benign thyroid tumor and thyroid malignant tumor of stage III, distinguishing or assisting in distinguishing benign thyroid tumor and thyroid malignant tumor of stage IV.

In a specific embodiment of the present invention, the distinguishing or assisting in distinguishing different subtypes of thyroid malignancy described in (4) may specifically be any of the following: distinguishing or aiding in distinguishing papillary thyroid cancer from follicular thyroid cancer, distinguishing or aiding in distinguishing papillary thyroid cancer from medullary thyroid cancer, distinguishing or aiding in distinguishing follicular thyroid cancer from undifferentiated thyroid cancer, distinguishing or aiding in distinguishing medullary thyroid cancer from medullary thyroid cancer.

In a specific embodiment of the present invention, the distinguishing or assisting in distinguishing the different stages of thyroid malignancy in (5) may specifically be any of the following: distinguishing or assisting in distinguishing between stage I thyroid malignancy and stage II thyroid malignancy, distinguishing or assisting in distinguishing between stage I thyroid malignancy and stage III thyroid malignancy, distinguishing or assisting in distinguishing between stage I thyroid malignancy and stage IV thyroid malignancy, distinguishing or assisting in distinguishing between stage II thyroid malignancy and stage III thyroid malignancy, distinguishing or assisting in distinguishing between stage II thyroid malignancy and stage IV thyroid malignancy, distinguishing or assisting in distinguishing between stage III thyroid malignancy and stage IV thyroid malignancy.

Further, the methylation level of the LZTFL1 gene may be the methylation level of all or part of CpG sites in the fragments of the LZTFL1 gene as shown in (A1) - (A3) below:

(A1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;

(A2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;

(A3) The DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto.

Still further, the all or part of the CpG sites may be any of the following CpG sites:

(B1) Any one or more CpG sites in 3 DNA fragments shown as SEQ ID No.1, SEQ ID No.2 and SEQ ID No.3 in the LZTFL1 gene;

(B2) 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.2 in the LZTFL1 gene;

(B3) 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 LZTFL1 gene;

(B4) 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 LZTFL1 gene;

(B5) All CpG sites on the DNA fragment shown in SEQ ID No.1, 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 LZTFL1 gene;

(B6) All CpG sites 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 in the DNA fragment shown in SEQ ID No.2 in the LZTFL1 gene;

(B7) All or any 5 or any 4 or any 3 or any 2 or any 1 of the following CpG sites shown in 6 on the DNA fragment shown in SEQ ID No.2 in the LZTFL1 gene:

item 1: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 71 st to 72 nd of the 5' end;

item 2: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 100 th to 101 th positions of the 5' end;

item 3: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 153 th to 154 th positions of the 5' end;

item 4: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 181 th to 182 th positions of the 5' end;

item 5: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 265 th to 266 th and 269 th to 270 th of the 5' end;

item 6: the DNA fragment shown in SEQ ID No.2 shows CpG sites from 313 th to 314 th positions of the 5' end.

In particular embodiments of the invention, some adjacent methylation sites are treated as one methylation site when analyzed for DNA methylation using time-of-flight mass spectrometry, because several CpG sites are located on one methylation fragment, the peak pattern is indistinguishable (indistinguishable sites are set forth in Table 6), and thus the methylation level analysis is performed, and related mathematical models are constructed and used.

In the kit, the substance for detecting the methylation level of the LZTFL1 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the LZTFL1 gene.

Further, the partial fragment may be at least one fragment of:

(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

(g4) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g5) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same.

In a specific embodiment of the invention, the primer combination is primer pair a and/or primer pair B and/or primer pair C.

The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.4 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 4; the primer A2 is SEQ ID No.5 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 5;

The primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer B2 is single-stranded DNA shown in SEQ ID No.7 or 32-56 nucleotides of SEQ ID No. 7;

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 in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer C2 is single-stranded DNA shown in SEQ ID No.9 or 32-56 nucleotides of SEQ ID No. 9.

The kit may further comprise a medium storing a mathematical model and/or a method for using the mathematical model, as required.

The mathematical model is obtained according to a method comprising the following steps:

(C1) Detecting the methylation level of genes of n1 type A samples and n2 type B samples respectively;

(C2) And (3) taking the gene methylation level data of all samples obtained in the step (C1), establishing a mathematical model according to the classification modes of the A type and the B type by a two-classification logistic regression method, and determining the threshold value of classification judgment.

Wherein, n1 and n2 can be positive integers more than 10.

The mathematical model using method 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 obtained in the step (D1) into the mathematical model to obtain a detection index; and then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result.

In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.

In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group A type and which group B type are determined according to a specific mathematical model, and no convention is needed.

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 malignancy of different subtypes;

(E5) Thyroid malignancy in different stages.

Further, the different subtypes described in (E2) and (E4) may be pathological typing, such as histological typing.

Further, the different stages described in (E3) and (E5) may be clinical stages.

In a specific embodiment of the present invention, the thyroid benign tumor and thyroid malignant tumor of different subtypes described in (E2) may be specifically any of the following: thyroid benign tumor and papillary thyroid carcinoma, thyroid benign tumor and follicular thyroid carcinoma, thyroid benign tumor and medullary thyroid carcinoma, thyroid benign tumor and thyroid carcinoma, and thyroid undifferentiated carcinoma.

In a specific embodiment of the present invention, the thyroid benign tumor and thyroid malignant tumor of different stages in (E3) may be specifically any of the following: thyroid benign tumor and thyroid malignant tumor of stage I, thyroid benign tumor and thyroid malignant tumor of stage II, thyroid benign tumor and thyroid malignant tumor of stage III, thyroid benign tumor and thyroid malignant tumor of stage IV.

In a specific embodiment of the present invention, the different subtypes of thyroid malignancy described in (E4) may specifically be any of the following: papillary and follicular thyroid carcinoma, papillary and medullary thyroid carcinoma, papillary and undifferentiated thyroid carcinoma, follicular and medullary thyroid carcinoma, follicular and undifferentiated thyroid carcinoma, medullary thyroid carcinoma and undifferentiated thyroid carcinoma.

In a specific embodiment of the present invention, the different stages of thyroid malignancy in (E5) may specifically be any of the following: thyroid malignancy and thyroid malignancy in stage I and stage II, thyroid malignancy and thyroid malignancy in stage I and stage III, thyroid malignancy in stage I and stage IV, thyroid malignancy in stage II and stage III, thyroid malignancy in stage II and stage IV, thyroid malignancy in stage III and thyroid malignancy in stage IV.

In a second aspect, the invention claims a system.

The claimed system includes:

(F1) Reagents and/or instrumentation for detecting the methylation level of the LZTFL1 gene;

In (F1), the reagent for detecting the methylation level of the LZTFL1 gene may be a substance (e.g., a primer pair) for detecting the methylation level of the LZTFL1 gene as described in the first aspect above. The instrument for detecting the methylation level of the LZTFL1 gene may be a time-of-flight mass spectrometry detector. Of course, other conventional reagents for performing time-of-flight mass spectrometry may also be included in the reagents for detecting the methylation level of the LZTFL1 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 LZTFL1 gene methylation level data of n 1A type samples and n 2B type samples obtained by (F1) detection;

the data analysis processing module is configured to receive LZTFL1 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 according to a classification mode of the A type and the B type by a two-classification logistic regression method, and determine a threshold value of classification judgment;

wherein, n1 and n2 can be positive integers more than 10.

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 the 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) LZTFL1 gene methylation level data of the tested person detected;

the data operation module is configured to receive LZTFL1 gene methylation level data of the to-be-detected person from the data input module, and substitutes the LZTFL1 gene methylation level data of the to-be-detected person 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 value determined by 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 a conclusion of whether the type of the sample to be tested 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) Thyroid benign tumors and thyroid malignant tumors of different subtypes;

(E3) Thyroid benign tumors and thyroid malignant tumors of different stages;

(E4) Thyroid malignancy of different subtypes;

(E5) Thyroid malignancy in different stages.

In a third aspect, the invention claims the use of a substance for detecting the methylation level of the LZTFL1 gene as described in the first aspect above for the preparation of a product; the use of the product is at least one of the following:

In a fourth aspect, the invention claims the use of a substance for detecting the methylation level of the LZTFL1 gene and the medium storing a mathematical model and/or a method of using a mathematical model as described in the first aspect above for the preparation of a product; the use of the product is at least one of the following:

In a fifth aspect, the invention claims the use of a medium storing a mathematical model and/or a method of using a mathematical model as described in the first aspect above for the preparation of a product; the use of the product is at least one of the following:

In a sixth aspect, the invention claims the use of the methylated LZTFL1 gene as a marker in the preparation of a product; the use of the product is at least one of the following:

In the third to sixth aspects, the specific meanings of (1) to (5) are as described in the first aspect.

In addition, the invention also discloses a method for distinguishing whether the sample to be detected is an A type sample or a B type sample. The method may comprise the steps of:

(A) The mathematical model may be built as a method comprising the steps of:

(a1) Detecting LZTFL1 gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;

(a2) Taking LZTFL1 gene methylation level data of all samples obtained in the step (a 1), establishing a mathematical model according to a classification mode of A type and B type by a two-classification logistic regression method, and determining a threshold value of classification judgment.

Wherein n1 and n2 in (a 1) are positive integers of 10 or more.

(B) The sample to be tested may be determined as a type a sample or a type B sample according to a method comprising the steps of:

(b1) Detecting the LZTFL1 gene methylation level of the sample to be detected;

(b2) Substituting LZTFL1 gene methylation level data of the sample to be detected obtained in the step (b 1) into the mathematical model to obtain a detection index; and then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result.

In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type 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 application according to the detection method and the fitting mode of DNA methylation, and the mathematical model is determined according to a specific mathematical model without any convention.

In the embodiment of the invention, the model is specifically ln (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained after substituting the methylation value of one or more methylation sites of the sample to be tested into the model by a dependent variable, b0 is a constant, x1-xn is the methylation value of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1-bn is the weight given by the model to the methylation value of each site.

One specific model established in the embodiment of the invention is a model for distinguishing or assisting in distinguishing thyroid benign tumor from thyroid malignant tumor, and the model is specifically as follows: ln (y/(1-y))= 5.696-5.830×lztfl1_b_1-6.507×lztfl1_b_2-1.280×lztflfl1_b_3+0.463×lztfl1_b_4+0.80×lztflztflb_5.6-0.75×lztfl1_b_7. LZTFL1_B_1 is the methylation level of CpG sites shown in the 71 st-72 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; LZTFL1_B_2 is the methylation level of CpG sites shown in the 100 th-101 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; LZTFL1_B_3 is the methylation level of CpG sites shown in 153-154 th position of a 5' end of a DNA fragment shown in SEQ ID No. 2; LZTFL1_B_4 is the methylation level of CpG sites shown in the 181 th to 182 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the LZTFL1_B_5.6 is the methylation level of CpG sites shown in 265 th to 266 th and 269 th to 270 th positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; LZTFL1_B_7 is the methylation level of CpG sites shown in positions 313-314 of the DNA fragment shown in SEQ ID No.2 from the 5' end. The threshold of the model was 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model were thyroid malignancy patients, and patient candidates with less than 0.5 were thyroid benign tumor patients.

In the above aspects, the detecting the methylation level of the LZTFL1 gene is detecting the methylation level of the LZTFL1 gene in a tumor tissue sample.

In the present invention, the methylation level of the methylation sites on the DNA fragments shown in SEQ ID No.1, 2 and 3 in LZTFL1 gene in thyroid malignant tumor tissue is significantly lower than that of thyroid benign tumor.

In the present invention, thyroid cancer of different clinical characteristics such as: methylation levels of methylation sites on DNA fragments shown in SEQ ID nos. 1, 2 and 3 in the LZTFL1 gene in papillary, follicular, medullary and undifferentiated carcinoma tumor tissues are becoming lower.

In the present invention, the methylation level of the methylation sites on the DNA fragments shown in SEQ id nos. 1, 2 and 3 in the LZTFL1 gene in tissues becomes lower with increasing stage of thyroid malignancy.

The LZTFL1 gene described above can be found in particular in Genbank accession numbers: NM-020347.4 (GI: 1519243235), transcript variant 1; NM-001276378.2 (GI: 1889541251), transcript variant 2; NM-001276379.2 (GI: 1889706081), transcript variant 3; NM-001386451.1 (GI: 1894925280), transcript variant 4; NM-001386452.1 (GI: 1894925276), transcript variant 5.

The invention proves that the biopsy sample LZTFL1 methylation can be used as a potential marker for differential diagnosis of thyroid benign tumor and thyroid malignant tumor, different subtypes or thyroid malignant tumor of different stages. The invention has important scientific significance and clinical application value for identifying thyroid benign tumor and thyroid malignant tumor, thyroid malignant tumor of different subtypes or different stages and guiding and formulating 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 following detailed description of the invention is provided in connection with the accompanying drawings that are presented to illustrate the invention and not to limit the scope thereof. The examples provided below are intended as guidelines for further modifications by one of ordinary skill in the art and are not to be construed as limiting the invention in any way.

The experimental methods in the following examples, unless otherwise specified, are conventional methods, and are carried out according to techniques or conditions described in the literature in the field or according to the product specifications. Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.

Example 1 primer design for detection of methylation site of LZTFL1 Gene

The detection selects CpG sites on three fragments (LZTFL 1_A fragment, LZTFL1_B fragment and LZTFL1_C fragment) of the LZTFL1 gene for carrying out correlation analysis on methylation level and thyroid malignant tumor.

The LZTFL 1-A fragment (SEQ ID No. 1) is located in the sense strand of the hg19 reference genome chr3: 45956650-45957080.

The LZTFL 1-B fragment (SEQ ID No. 2) is located in the sense strand of the hg19 reference genome chr3: 45957167-45957756.

The LZTFL 1-C fragment (SEQ ID No. 3) is located in the hg19 reference genome chr3:45958058-45958466, sense strand.

The site information in the lztfl1_a fragment is shown in table 1.

The site information in the lztfl1_b fragment is shown in table 2.

The site information in the lztfl1_c fragment is shown in table 3.

TABLE 1 CpG site information in LZTFL1_A fragment

CpG sites	CpG sites atPositions in the sequence
		LZTFL1_A_1	SEQ ID No.1 from positions 26-27 of the 5' end
LZTFL1_A_2	SEQ ID No.1 from position 32-33 of the 5' end
		LZTFL1_A_3	165 th to 166 th positions from 5' end of SEQ ID No.1
LZTFL1_A_4	SEQ ID No.1 from position 179 to 180 of the 5' end
		LZTFL1_A_5	184 th to 185 th positions from 5' end of SEQ ID No.1
LZTFL1_A_6	SEQ ID No.1 from position 194-195 of the 5' end
		LZTFL1_A_7	272-273 th bit from 5' end of SEQ ID No.1
LZTFL1_A_8	276 th to 277 th positions of SEQ ID No.1 from 5' end
		LZTFL1_A_9	SEQ ID No.1 from positions 313 to 314 of the 5' end
LZTFL1_A_10	SEQ ID No.1 from position 330-331 of the 5' end
		LZTFL1_A_11	349 th to 350 th positions of SEQ ID No.1 from the 5' end
LZTFL1_A_12	SEQ ID No.1 from positions 405-406 of the 5' end

TABLE 2 CpG site information in LZTFL1_B fragment

CpG sites	Position of CpG sites in the sequence
		LZTFL1_B_1	SEQ ID No.2 from position 71-72 of the 5' end
LZTFL1_B_2	SEQ ID No.2 from position 100-101 of the 5' end
		LZTFL1_B_3	153 th to 154 th positions of SEQ ID No.2 from 5' end
LZTFL1_B_4	Positions 181-182 of SEQ ID No.2 from the 5' end
		LZTFL1_B_5	SEQ ID No.2 from positions 265 to 266 of the 5' end
LZTFL1_B_6	SEQ ID No.2 from positions 269-270 of the 5' end
		LZTFL1_B_7	SEQ ID No.2 from positions 313 to 314 of the 5' end
LZTFL1_B_8	SEQ ID No.2 from positions 394-395 of the 5' end
		LZTFL1_B_9	SEQ ID No.2 from position 396-397 of the 5' end
LZTFL1_B_10	SEQ ID No.2 from positions 477-478 of the 5' end
		LZTFL1_B_11	SEQ ID No.2 from the 5' end at positions 498-499
LZTFL1_B_12	545-546 bits of SEQ ID No.2 from the 5' end
		LZTFL1_B_13	564 th to 565 th bits of SEQ ID No.2 from the 5' end

TABLE 3 CpG site information in LZTFL1_C fragment

CpG sites	Position of CpG sites in the sequence
		LZTFL1_C_1	SEQ ID No.3 from positions 26-27 of the 5' end
LZTFL1_C_2	127 th to 128 th bit from 5' end of SEQ ID No.3
		LZTFL1_C_3	SEQ ID No.3 from position 217-218 of the 5' end
LZTFL1_C_4	229-230 th position from 5' end of SEQ ID No.3
		LZTFL1_C_5	SEQ ID No.3 from 344 th to 345 th position of 5' end
LZTFL1_C_6	383-384 bits of SEQ ID No.3 from 5' end

Specific PCR primers were designed for four fragments (lztfl1_a fragment, lztfl1_b fragment, and lztfl1_c fragment) as shown in table 4. SEQ ID No.4, SEQ ID No.6 and SEQ ID No.8 are forward primers; SEQ ID No.5, SEQ ID No.7 and SEQ ID No.9 are reverse primers. The 1 st to 10 th positions of the 5' end in SEQ ID No.4, SEQ ID No.6 and SEQ ID No.8 are nonspecific labels, and the 11 th to 35 th positions are specific primer sequences; the 1 st to 31 st positions of the 5' end in SEQ ID No.5, SEQ ID No.7 and SEQ ID No.9 are nonspecific labels, and the 32 nd to 56 th positions are specific primer sequences. The primer sequences do not contain SNPs and CpG sites.

TABLE 4 LZTFL1 methylation primer sequences

Example 2 LZTFL1 Gene methylation detection and analysis of results

1. Study sample

A total of 380 thyroid benign tumor tissues and 598 thyroid malignant tumor tissues were collected with patient informed consent. Thyroid cancer stage was judged by the American cancer Association (AJCC) eighth edition stage system. Thyroid malignancy includes four major classes, thyroid papillary carcinoma, thyroid follicular carcinoma, medullary carcinoma, and undifferentiated carcinoma, depending on the pathological type. The 598 thyroid malignant tumor patients collected this time include 380 thyroid papillary carcinoma, 138 thyroid follicular carcinoma, 44 thyroid medullary carcinoma, and 36 thyroid undifferentiated carcinoma. According to pathological stage division, 470 patients with stage I, 68 patients with stage II, 24 patients with stage III and 36 patients with stage IV in 598 thyroid malignant tumor patients.

2. Methylation detection

1. Total DNA in tumor tissue is extracted.

2. The total DNA of the tissue samples prepared in step 1 was subjected to bisulfite treatment (see DNA methylation kit instructions for Qiagen). After bisulfite treatment, unmethylated cytosines (C) in the original CpG sites are converted to uracil (U), while methylated cytosines remain unchanged.

3. The DNA treated by the bisulfite in the step 2 is used as a template, 3 pairs of specific primer pairs in the table 4 are adopted to carry out PCR amplification through DNA polymerase according to a reaction system required by a conventional PCR reaction, and all primers adopt a conventional standard PCR reaction system and are amplified according to the following procedure.

The PCR reaction procedure was: 95 ℃,4 min- & gt (95 ℃,20 s- & gt 56 ℃,30 s- & gt 72 ℃ 2 min) 45 cycles- & gt 72 ℃,5 min- & gt 4 ℃ for 1h.

4. Taking the amplified product of the step 3, and carrying out DNA methylation analysis by a time-of-flight mass spectrum, wherein the specific method is as follows:

(1) To 5. Mu.l of the PCR product was added 2. Mu.l of Shrimp Alkaline Phosphate (SAP) solution (0.3 ml SAP [0.5U ] +1.7ml H2O) and then incubated in a PCR apparatus (37 ℃,20 min. Fwdarw. 85 ℃,5 min. Fwdarw. 4 ℃,5 min) according to the following procedure;

(2) Taking out 2 mu.l of the SAP treated product obtained in the step (1), adding the product into a 5 mu l T-clear reaction system according to the instruction, and then incubating for 3 hours at 37 ℃;

(3) Taking the product of the step (2), adding 19 mu l of deionized water, and then carrying out deionized incubation on a rotary shaking table for 1h by using 6 mu g of Resin;

(4) Centrifuging at 2000rpm at room temperature for 5min, and loading 384SpectroCHIP with the micro supernatant by a Nanodispenser mechanical arm;

(5) Time-of-flight mass spectrometry; the data obtained were collected with the spectroacquisition v3.3.1.3 software and visualized by MassArray EpiTyper v 1.2.1.2 software.

Reagents used for the time-of-flight mass spectrometry detection are all kits (T-Cleavage MassCLEAVE Reagent Auto Kit, cat# 10129A); the detection instrument used for the time-of-flight mass spectrometry detection is Massary 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.

Non-parametric tests were used for comparative analysis between the two groups.

The identification effect of a combination of multiple CpG sites on different sample groupings is achieved by logistic regression and statistical methods of the subject curves.

All statistical tests were double-sided, with p-values <0.05 considered statistically significant.

Through mass spectrometry experiments, 29 distinguishable peak patterns were obtained in total. The methylation level at each CpG site of each sample can be automatically obtained by calculating the peak area according to the "methylation level=peak area of methylated fragments/(peak area of unmethylated fragments+peak area of methylated fragments)" formula using SpectroAcquin v3.3.1.3 software.

3. Statistical analysis

Methylation levels were expressed as median, and methylation level differences between two or more groups were compared using a nonparametric test. The differential diagnostic value of single CpG sites and combinations of multiple CpG sites was assessed by the subject's working profile (receiver operating characteristic curve, ROC profile). The difference of P <0.05 on both sides is statistically significant, and all data are statistically analyzed by SPSS 25.0.

4. Analysis of results

1. Analysis of methylation level of LZTFL1 Gene for thyroid benign tumor and thyroid malignant tumor

The methylation level of all CpG sites in the LZTFL1 gene was analyzed using tissue samples of 380 thyroid benign tumors and 598 thyroid malignant tumors as study materials. Specific results of methylation levels of 3 fragments of the LZTFL1 gene for benign thyroid tumors, thyroid malignant tumors, papillary thyroid cancers, follicular thyroid cancers, medullary cancers, undifferentiated cancers, stage I thyroid malignant tumors, stage II thyroid malignant tumors, stage III thyroid malignant tumors, and stage IV thyroid malignant tumor cases are shown in Table 5. The results show that the methylation level median of the thyroid benign tumor LZTFL1 gene is 0.57 (IQR=0.40-0.79), the methylation level median of the thyroid malignant tumor LZTFL1 gene is 0.33 (IQR=0.24-0.65), and the methylation level median of different subtype LZTFL1 genes is in turn: papillary carcinoma 0.33 (iqr=0.23-0.65), follicular carcinoma 0.31 (iqr=0.23-0.62), medullary carcinoma 0.28 (iqr=0.19-0.60), undifferentiated carcinoma 0.25 (iqr=0.14-0.56), the median methylation levels of the LZTFL1 genes at different stages are in order: phase I is 0.34 (iqr=0.25-0.65), phase II is 0.33 (iqr=0.25-0.65), phase iii is 0.30 (iqr=0.20-0.61), phase iv is 0.26 (iqr=0.15-0.59). As a result of comparative analysis of methylation levels between several, it was found that methylation levels of all CpG sites in LZTFL1 gene of thyroid benign tumor were significantly higher than those of LZTFL1 gene in thyroid malignant tumor (Table 5). In addition, several thyroid cancer subtypes listed in the present invention are: the methylation levels of methylation sites on DNA fragments shown in SEQ ID nos. 1, 2 and 3 in the LZTFL1 gene in papillary, follicular, medullary and undifferentiated carcinoma tissues were successively decreasing (table 5), with more and more pronounced differences from thyroid benign tumors. In addition, the methylation level of the methylation sites on the DNA fragments shown by SEQ ID nos. 1, 2 and 3 in the LZTFL1 gene in tissues also showed a decreasing trend with increasing stage of thyroid malignancy (table 5), and the differences from thyroid benign tumors were also more and more evident.

TABLE 5 LZTFL1 Gene methylation levels for thyroid benign tumor and thyroid malignant tumor, each subtype, each stage

Note that: cpG sites in the table are all distinguishable CpG sites.

2. Methylation level of LZTFL1 gene in tumor tissue can distinguish thyroid benign tumor from thyroid malignant tumor of different subtypes

As a result of comparative analysis of the methylation levels of LZTFL1 in 380 cases of thyroid benign tumor and 598 cases of thyroid malignant tumor, it was found that the methylation levels of LZTFL 1-A fragment, LZTFL 1-B fragment and LZTFL 1-C fragment in patients with thyroid malignant tumor, papillary thyroid carcinoma, follicular thyroid carcinoma, medullary thyroid carcinoma and thyroid carcinoma were significantly lower than the methylation levels of the corresponding fragments in patients with thyroid benign tumor (P < 0.05). The specific results are shown in Table 6.

TABLE 6 LZTFL1 Gene methylation level differences between thyroid benign tumors and different subtype thyroid malignant tumors

Note that: cpG sites in the table are all distinguishable CpG sites.

3. The methylation level of LZTFL1 gene in tumor tissue can distinguish thyroid malignant tumors of different subtypes

As a result of comparative analysis of the methylation levels of LZTFL1 in cases of thyroid malignant tumors of different types (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 (P < 0.05) between the methylation levels of LZTFL1 gene in patients with papillary thyroid cancer, follicular thyroid cancer, medullary thyroid cancer and undifferentiated thyroid cancer. The specific results are shown in Table 7.

TABLE 7 LZTFL1 Gene methylation level differences between subtypes of thyroid malignancy

Note that: cpG sites in the table are all distinguishable CpG sites.

4. The methylation level of LZTFL1 gene in tumor tissue can distinguish thyroid benign tumor from thyroid malignant tumor of different stages

As a result of comparative analysis of LZTFL1 methylation levels in 380 cases of thyroid benign tumor and in various stages of thyroid malignant tumor patients (470 cases of patients in stage I, 68 cases of patients in stage II, 24 cases of patients in stage III and 36 cases of patients in stage IV), it was found that the methylation levels of LZTFL 1-A fragment, LZTFL 1-B fragment and LZTFL 1-C fragment were significantly reduced (P < 0.01) with increasing stages. The specific results are shown in Table 8.

TABLE 8 LZTFL1 Gene methylation level differences between thyroid benign tumors and different staged thyroid malignant tumors

Note that: cpG sites in the table are all distinguishable CpG sites.

5. The methylation level of LZTFL1 gene in tumor tissue can distinguish thyroid malignant tumors of different stages

As a result of comparative analysis of LZTFL1 methylation levels of thyroid malignancy patients of different stages (470 cases of phase I patients, 68 cases of phase II patients, 24 cases of phase III patients and 36 cases of phase IV patients), it was found that there was a significant difference (P < 0.05) between the LZTFL1 gene methylation levels of phase I thyroid malignancy, phase II thyroid malignancy, phase III thyroid malignancy and phase IV thyroid malignancy patients. The specific results are shown in Table 9.

TABLE 9 LZTFL1 Gene methylation level differences between different stages of thyroid malignancy

Note that: cpG sites in the table are all distinguishable CpG sites.

6. Establishment of mathematical model for LZTFL1 gene methylation to aid in cancer diagnosis

The mathematical model established by the invention can be used for achieving the following purposes:

(1) Distinguishing thyroid malignant tumor patients from thyroid benign tumor;

(2) Distinguishing between benign thyroid tumors and thyroid malignant tumors of different subtypes;

(3) Distinguishing thyroid benign tumor from thyroid malignant tumor of different stages;

(4) Distinguishing different subtypes of thyroid malignancy;

(5) Different stages of thyroid malignancy are distinguished.

The mathematical model is established as follows:

(A) Data sources: in step one, methylation levels of target CpG sites (combinations of one or more of tables 1 to 3) of tissue samples of 380 cases of thyroid benign tumors and 598 cases of thyroid malignant tumors (380 cases of papillary thyroid carcinomas, 138 cases of thyroid follicular carcinomas, 44 cases of medullary thyroid carcinomas and 36 cases of thyroid undifferentiated carcinomas) are listed (detection method is the same as in step two).

(B) Model building

Any two different types of patient data, namely training sets (for example, thyroid benign tumor and thyroid malignant tumor patients, thyroid benign tumor and thyroid papillary carcinoma patients, thyroid benign tumor and thyroid follicular carcinoma patients, thyroid benign tumor and thyroid medullary carcinoma patients, thyroid benign tumor and thyroid undifferentiated carcinoma patients, thyroid papillary carcinoma and thyroid follicular carcinoma patients, thyroid papillary carcinoma and thyroid medullary carcinoma patients, thyroid papillary carcinoma and thyroid undifferentiated carcinoma patients, thyroid follicular carcinoma and thyroid medullary carcinoma patients, thyroid follicular carcinoma and thyroid undifferentiated carcinoma patients, thyroid medullary carcinoma and thyroid undifferentiated carcinoma patients, thyroid benign tumor and I thyroid malignant tumor patients, thyroid benign tumor and II thyroid malignant tumor patients, thyroid benign tumor and III thyroid malignant tumor patients, I thyroid malignant tumor and II thyroid malignant tumor patients, I thyroid malignant tumor and III thyroid malignant tumor patients, II thyroid malignant tumor and III thyroid malignant tumor patients, IV thyroid malignant tumor and IV thyroid malignant tumor patients) are selected as required to be used for establishing a statistical model by using a statistical regression method of the IV-like data. The numerical value corresponding to the maximum approximate dengue index calculated by the mathematical model formula is a threshold value or is directly set to be 0.5 as the threshold value, the detection index obtained by the sample to be tested after the sample is tested and substituted into the model calculation is more than the threshold value and is classified into one type (B type), less than the threshold value and is classified into the other type (A type), and the detection index is equal to the threshold value and is used as an uncertain gray area. When a new sample to be detected is predicted to judge which type belongs to, firstly detecting methylation levels of one or more CpG sites on the LZTFL1 gene of the sample to be detected by a DNA methylation determination method, then substituting data of the methylation levels into the mathematical model, calculating to obtain a detection index corresponding to the sample to be detected, comparing the detection index corresponding to the sample to be detected with a threshold value, and determining which type of sample the sample to be detected belongs to according to a comparison result.

Examples: as shown in fig. 1, the methylation level of single CpG site or the methylation level of multiple CpG site combinations in the training set LZTFL1 gene is used to establish a mathematical model for distinguishing between class a and class B by using a formula of two classification logistic regression through statistical software such as SAS, R, SPSS. The mathematical model is herein a two-class logistic regression model, specifically: ln (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained by substituting the methylation level of one or more methylation sites of the sample to be tested into the model as a function variable, b0 is a constant, x1-xn is an argument that is the methylation level of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1-bn is a weight given to each methylation site by the model. In specific application, a mathematical model is established according to methylation levels (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, respectively assigning 0 and 1 to y), so that a constant B0 of the mathematical model and weights B1-bn of each methylation site are determined, and a numerical value corresponding to a maximum approximate dengue index calculated by the mathematical model is used as a threshold value or a threshold value divided by 0.5 is directly set. And the detection index, namely the y value, obtained after the sample to be detected is tested and calculated by substituting the sample into the model is classified as B when the y value is larger than the threshold value, and classified as A when the y value is smaller than the threshold value, and the y value is equal to the threshold value and is used as an uncertain gray area. Where class a and class B are the corresponding two classifications (groupings of classifications, which group a is class B, which group is determined from a specific mathematical model, no convention is made here). In predicting a sample of a subject to determine which class the sample belongs to, a biopsy sample (i.e., tumor tissue) of the subject is first collected and then DNA is extracted therefrom. After the extracted DNA is converted by bisulfite, the methylation level of single CpG sites or the methylation level of a plurality of CpG sites of the LZTFL1 gene of a subject is detected by using a DNA methylation determination method, and methylation data obtained by detection are substituted into the mathematical model. If the methylation level of one or more CpG sites of the LZTFL1 gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is larger than a threshold value, the subject judges the attribution type (B class) with the detection index in the training set larger than the threshold value; if the methylation level data of one or more CpG sites of the LZTFL1 gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is smaller than the threshold value, the subject belongs to the class (class A) with the detection index in the training set smaller than the threshold value; if the methylation level data of one or more CpG sites of the LZTFL1 gene of the subject is substituted into the mathematical model, and the calculated value, i.e. the detection index, is equal to the threshold value, the subject cannot be judged to be class A or class B.

Examples: as shown in fig. 2, the following 6 distinguishable CpG sites (lztfl1_b_1, lztfl1_b_2, lztfl1_b_3, lztfl1_b_4, lztfl1_b_5.6, lztfl1_b_7) of lztfl1_b are illustrated for methylation and the use of mathematical modeling to identify thyroid benign tumors and thyroid malignant tumor tissue: the above-mentioned 6 distinguishable CpG site methylation level data of LZTFL1_B, which has been detected in a training set of thyroid benign tumor and thyroid malignant tumor patients (here: 380 thyroid benign tumor and 598 thyroid malignant tumor patients), was used to build 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 constants 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))= 5.696-5.830×lztfl1_b_1-6.507×lztfl1_b_2-1.280×lztflfl1_b_3+0.463×lztfl1_b_4+0.80×lztflfl1_b_5.6-0.75×lztflfl1_b_7, wherein y is a dependent variable, i.e., a detection index obtained by converting after substituting methylation levels of the above 6 distinguishable CpG sites of lztfl1_b of a sample to be measured into a model. LZTFL1_B_5 and LZTFL1_B_6 are located in the same segment, so the average value of methylation levels at these two sites is represented by LZTFL1_B_5.6. Under the condition that 0.5 is set as a threshold value, values obtained by testing methylation levels of the 6 distinguishable CpG sites of LZTFL1_B of the sample to be tested are substituted into a model to calculate, the obtained detection index, namely y value, is smaller than the threshold value and is classified as a thyroid benign tumor patient, the value is larger than the threshold value and is classified as a thyroid malignant tumor patient, and the value equal to the threshold value is not determined as the thyroid benign tumor patient or the thyroid malignant tumor patient. The area under the curve (AUC) calculation for this model was 0.79 (table 14). Specific subject judgment methods are exemplified below, in which biopsies are collected from two subjects (a, B) to extract DNA, the extracted DNA is converted by bisulfite, and methylation levels of 6 distinguishable CpG sites (lztfl1_b_1, lztfl1_b_2, lztfl1_b_3, lztfl1_b_4, lztfl1_b_5.6, and lztfl1_b_7) of the subjects are detected by a DNA methylation measurement method. And substituting the methylation level data information obtained by detection into the mathematical model. The methylation level data of the 6 distinguishable CpG sites of the LZTFL1_B of the first subject are substituted into the mathematical model, and the calculated value is 0.81 or more and 0.5, so that the first subject is judged to be a thyroid malignant tumor patient (consistent with clinical diagnosis); and substituting methylation level data of the 6 distinguishable CpG sites of the LZTFL1_B of the subject B into the mathematical model to calculate a value of 0.39 to be less than 0.5, and judging the thyroid benign tumor patient (consistent with clinical diagnosis) by the subject B.

(C) Model Effect evaluation

According to the above method, a method for finding a thyroid benign tumor and thyroid malignant tumor patient, a thyroid benign tumor and thyroid papillary carcinoma patient, a thyroid benign tumor and thyroid follicular carcinoma patient, a thyroid benign tumor and thyroid medullary carcinoma patient, a thyroid benign tumor and thyroid undifferentiated carcinoma patient, a thyroid papillary carcinoma and thyroid follicular carcinoma patient, a thyroid papillary carcinoma and thyroid undifferentiated carcinoma patient, a thyroid follicular carcinoma and thyroid medullary carcinoma patient, a thyroid follicular carcinoma and thyroid undifferentiated carcinoma patient, a thyroid medullary carcinoma and thyroid undifferentiated carcinoma patient, a thyroid benign tumor and a thyroid malignant tumor patient in stage II, a thyroid benign tumor and a thyroid malignant tumor patient in stage III, a thyroid benign tumor and a thyroid malignant tumor patient in stage IV, a thyroid malignant tumor patient in stage I and a thyroid malignant tumor patient in stage II, a thyroid malignant tumor patient in stage II and a thyroid malignant tumor patient in stage III, a thyroid malignant tumor patient in stage II and a thyroid malignant tumor patient in stage IV, a thyroid malignant tumor is established, and a mathematical curve is performed on a thyroid malignant subject (ROC). The larger the area under the curve (AUC) from the ROC curve, the better the differentiation of the model, the more efficient the molecular marker. The evaluation results after construction of mathematical models using different CpG sites are shown in tables 10, 11, 12 and 13. In tables 10, 11, 12 and 13, 1 CpG site represents a site of any one CpG site in the lztfl1_b amplified fragment, 2 CpG sites represent a combination of any 2 CpG sites in the lztfl1_b amplified fragment, 3 CpG sites represent a combination of any 3 CpG sites in the lztfl1_b amplified fragment, … … and so on. The values in the table are the range of values for the combined evaluation of the different sites (i.e., the results for any combination of CpG sites are within this range).

The above-described results of the study show that the discrimination ability of LZTFL1 gene methylation for each group (thyroid benign tumor and thyroid malignant tumor patients, thyroid benign tumor and thyroid papillary carcinoma patients, thyroid benign tumor and thyroid follicular carcinoma patients, thyroid benign tumor and thyroid medullary carcinoma patients, thyroid benign tumor and thyroid undifferentiated carcinoma patients, thyroid papillary carcinoma and thyroid follicular carcinoma patients, thyroid papillary carcinoma and thyroid medullary carcinoma patients, thyroid papillary carcinoma and thyroid undifferentiated carcinoma patients, thyroid follicular carcinoma and thyroid medullary carcinoma patients, thyroid follicular carcinoma and thyroid undifferentiated carcinoma patients, thyroid benign tumor and phase I thyroid malignant tumor patients, thyroid benign tumor and phase II thyroid malignant tumor patients, thyroid benign tumor and phase III thyroid malignant tumor patients, phase I thyroid malignant tumor and phase II thyroid malignant tumor patients, phase I thyroid malignant tumor and phase III thyroid malignant tumor patients, phase I thyroid malignant tumor and phase IV thyroid malignant tumor patients, phase II thyroid malignant tumor and phase III thyroid malignant tumor patients, phase IV thyroid malignant tumor numbers of the thyroid tumor and phase IV thyroid malignant tumor patients increase with the increase of the number of the thyroid malignant tumor 1 and phase IV of the thyroid tumor patients.

In addition, among the CpG sites shown in tables 1 to 3, there are cases where combinations of a few preferred sites are better in discrimination ability than combinations of a plurality of non-preferred sites. The combination of 6 distinguishable CpG sites, e.g., lztfl1_b_1, lztfl1_b_2, lztfl1_b_3, lztfl1_b_4, lztfl1_b_5.6, lztfl1_b_7 shown in tables 14, 15, 16 and 17, is the preferred site for any 6 combinations in lztfl1_b.

In summary, the CpG sites on the LZTFL1 gene and various combinations thereof, the CpG sites on the LZTFL 1-A fragment and various combinations thereof, the CpG sites on the LZTFL 1-B fragment and various combinations thereof, the LZTFL 1-B_1, LZTFL 1-B_2, LZTFL 1-B_3, LZTFL 1-B_4, LZTFL 1-B_5.6, LZTFL 1-B_7 sites and various combinations thereof, the CpG sites on the LZTFL 1-C fragment and various combinations thereof, and the methylation level of the CpG sites on LZTFL1_ A, LZTFL _B and LZTF1_C and various combinations thereof for thyroid benign and thyroid malignant tumor patients, thyroid benign and papillary thyroid cancer patients, thyroid benign and thyroid follicular cancer patients, thyroid benign and medullary thyroid cancer patients, thyroid benign and thyroid undifferentiated carcinoma patients, thyroid papillary and thyroid follicular cancer patients, thyroid papillary and medullary thyroid cancer patients, thyroid papillary and thyroid undifferentiated carcinoma patients, thyroid follicular and thyroid medullary carcinoma patients, thyroid follicular and thyroid undifferentiated carcinoma patients, thyroid medullary and thyroid undifferentiated carcinoma patients, thyroid benign and I thyroid malignant tumor patients, thyroid benign and II thyroid malignant tumor patients, thyroid benign and III thyroid malignant tumor patients, thyroid benign and IV thyroid malignant tumor patients, I malignant tumor and II thyroid malignant tumor patients, I malignant tumor and III malignant tumor patients, I malignant tumor and IV malignant tumor patients, I malignant tumor and II malignant tumor patients, and III malignant tumor patients Patients with stage III thyroid malignancy and stage IV thyroid malignancy have discrimination ability.

Table 10, cpG sites of LZTFL1_B and combinations thereof for differentiating thyroid benign tumors from thyroid malignant tumors of different subtypes

Table 11, cpG sites of LZTFL1_D and combinations thereof for differentiating thyroid benign tumors from different stages of thyroid malignancy

Table 12, cpG sites of LZTFL1_B and combinations thereof for differentiating patients with different subtype thyroid malignancies

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Table 13, cpG sites of LZTFL1_D and combinations thereof for distinguishing patients with different stage thyroid malignancy

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Table 14, optimal CpG sites of LZTFL1_B and combinations thereof for differentiating thyroid benign tumors from thyroid malignant tumors of different subtypes

Note that: cpG sites in the table are all distinguishable CpG sites.

Table 15, optimal CpG sites of LZTFL1_B and combinations thereof for differentiating thyroid benign tumors from different stages of thyroid malignancy

Note that: cpG sites in the table are all distinguishable CpG sites.

Table 16, optimal CpG sites of LZTFL1_B and combinations thereof for differentiating between different subtypes of thyroid malignancy

Note that: cpG sites in the table are all distinguishable CpG sites.

Table 17, optimal CpG sites of LZTFL1_B and combinations thereof for differentiation between different stages of thyroid malignancy

Note that: cpG sites in the table are all distinguishable CpG sites.

The present invention is described in detail above. It will be apparent to those skilled in the art that the present invention can be practiced in 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 respect to specific embodiments, 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 application of some of the basic features may be done in accordance with the scope of the claims that follow.

Claims

1. A kit comprising a substance for detecting the methylation level of the LZTFL1 gene; the application of the kit is at least one of the following:

2. The kit of claim 1, wherein: the methylation level of the LZTFL1 gene is the methylation level of all or part of CpG sites in fragments shown in the following (A1) - (A3) in the LZTFL1 gene:

3. Kit according to claim 1 or 2, characterized in that: the whole or part of CpG sites are any one of the following CpG sites:

4. A kit according to any one of claims 1-3, wherein: the substance for detecting the methylation level of the LZTFL1 gene comprises a primer combination for amplifying a full or partial fragment of the LZTFL1 gene;

further, the partial fragment is at least one fragment of:

(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;

(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;

(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;

still further, the primer combination is primer pair a and/or primer pair B and/or primer pair C;

5. The kit of any one of claims 1-4, wherein: the kit also contains a medium stored with a mathematical model and/or a mathematical model using method;

(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 mode of the type A and the type B, and determining the threshold value of classification judgment;

the mathematical model using method 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 obtained in the step (D1) into the mathematical model to obtain a detection index; then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to a 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) Thyroid benign tumors and thyroid malignant tumors of different subtypes;

(E3) Thyroid benign tumors and thyroid malignant tumors of different stages;

(E4) Thyroid malignancy of different subtypes;

(E5) Thyroid malignancy in different stages.

6. A system, comprising:

(F2) A device comprising a unit X and a unit Y;

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 malignancy of different subtypes;

(E5) Thyroid malignancy in different stages.

7. Use of a substance according to any one of claims 1-4 for detecting the methylation level of the LZTFL1 gene for the preparation of a product; the use of the product is at least one of the following:

8. Use of a substance according to any one of claims 1-4 for detecting the methylation level of the LZTFL1 gene and of a medium storing a mathematical model and/or a method of using a mathematical model according to claim 5 for the preparation of a product; the use of the product is at least one of the following:

9. Use of a medium storing a mathematical model and/or a method of using a mathematical model as claimed in claim 5 for the manufacture of a product; the use of the product is at least one of the following:

10. Application of methylation LZTFL1 gene as a marker in preparation of products; the use of the product is at least one of the following: