CN113999907A

CN113999907A - Method and kit for identifying thyroid cancer state

Info

Publication number: CN113999907A
Application number: CN202111290622.9A
Authority: CN
Inventors: 陈彦利; 李明明; 蒲珏
Original assignee: Beijing Exellon Medical Technology Co ltd
Current assignee: Beijing Exellon Medical Technology Co ltd
Priority date: 2021-11-02
Filing date: 2021-11-02
Publication date: 2022-02-01

Abstract

Provided herein is a method of identifying a thyroid cancer status in a subject comprising: 1) detecting the methylation levels of biomarker genes NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 in a biological sample from the subject; and 2) comparing the methylation level detected in 1) to a normal methylation level result for the corresponding biomarker gene in the population to determine the thyroid cancer status of the subject. Also provided herein are kits for detecting thyroid cancer. The method and the kit provided by the invention provide a new rapid, reliable and accurate way for screening, predicting, diagnosing and evaluating thyroid cancer.

Description

Method and kit for identifying thyroid cancer state

Technical Field

This document relates to methods and kits for identifying thyroid cancer status, and in particular to methods and kits for identifying thyroid cancer status using gene methylation levels.

Background

Thyroid cancer is a malignant tumor originating from thyroid follicular epithelium or perifollicular epithelial cells, and is the most common malignant tumor of the head and neck, and also the most common malignant tumor of the endocrine system. In recent years, the incidence of thyroid cancer has increased rapidly worldwide, and worldwide health organization international agency for research on cancer (IARC) published worldwide data on cancer in 2020 shows that: thyroid cancer has a global incidence of about 3%, is the ninth cancer incidence, has a mortality rate which is higher than that of the first ten, and has a female incidence rate which is 3 times higher than that of the male. Wherein the thyroid cancer is the seventh place in the new cases of Chinese cancer. According to the data of national tumor registration center, the incidence of thyroid cancer of female in urban areas of China is the 4 th place of all malignant tumors of female. Thyroid cancer will continue to grow at a rate of 20% per year in our country.

According to the diagnosis and treatment standard of thyroid cancer (2018 edition content), thyroid cancer is divided into the following parts according to the origin and differentiation difference of tumors: papillary Thyroid Carcinoma (PTC), thyroid follicular carcinoma (FTC), Medullary Thyroid Carcinoma (MTC), and Anaplastic Thyroid Carcinoma (ATC), with PTC being the most common and accounting for 85% to 90% of all thyroid cancers, and PTC and FTC collectively called Differentiated Thyroid Carcinoma (DTC). Thyroid cancer of different pathological types is significantly different in its pathogenesis, biological behavior, histological morphology, clinical manifestations, therapeutic methods and prognosis. DTC has mild biological behavior and better prognosis. The ATC has extremely high malignancy degree, and the median survival time is only 7-10 months. The prognosis for MTC lies between the two.

The pathogenesis of thyroid cancer mainly comprises the following reasons: abnormal expression of oncogenes, polypeptide growth factors (such as thyroid stimulating hormone), ionizing radiation, iodine deficiency or excess, gender (female incidence is about 3 times greater than male), genetic factors, and several other factors.

According to the thyroid cancer diagnosis and treatment specification (2018 edition), the current common diagnosis and treatment technologies for thyroid cancer are as follows: (1) the clinical manifestations are as follows: symptoms, signs, invasion and metastasis, common complications, etc.; (2) laboratory examination: routine laboratory examination, thyroid hormone, thyroid autoantibody and tumor marker examination; (3) carrying out ultrasonic inspection; (4) imaging examination: CT, MRI, PET-CT, thyroid cancer functional metabolism imaging, etc.; (5) evaluating vocal cord functions; (6) pathological diagnosis of thyroid cancer cells; (7) pathological diagnosis of thyroid cancer tissues; these techniques either lack specificity, have poor sensitivity, depend too much on the clinical experience of the clinician, or have low acceptance by the test population due to radiation, trauma, high cost, etc.

Early stage thyroid cancer is mostly free of obvious symptoms and signs, and small thyroid masses are usually found by thyroid palpation and neck ultrasound examination during physical examination. The incidence of thyroid cancer, particularly papillary thyroid cancer, has increased over the past three decades, but the mortality rate of thyroid cancer has not increased proportionally, indicating that over-diagnosis of thyroid cancer is occurring. New imaging techniques have resulted in the development of over-diagnosis of thyroid cancer, exposing thousands of people to unnecessary, expensive and potentially risky treatments.

Therefore, screening and mining of valuable early thyroid cancer biomarkers becomes a problem to be solved urgently. Molecular markers for early diagnosis of thyroid cancer, which are beginning to shift the eye to, unfortunately, none of them has been known to date to have good sensitivity and specificity. In recent years, the research on the epigenetics of thyroid cancer is relatively fast, particularly on DNA methylation, and researches show that a plurality of specific tumor-related genes have methylation state changes to different degrees in the early stage of thyroid cancer occurrence, so that a new opportunity is provided for the search of early diagnosis markers of thyroid cancer.

Disclosure of Invention

To address the above-described problems, in one aspect, provided herein is a method of identifying a thyroid cancer status in a subject, comprising:

1) detecting the methylation level of a biomarker gene in a biological sample from the subject, wherein the biomarker gene is:

NIS, TSHR, and PTEN;

SLC5A8, RASSF1A and TIMP 3; or

NIS, TSHR, PTEN, SLC5A8, RASSF1A, and TIMP 3; and

2) comparing the methylation level detected in 1) to a normal methylation level of a corresponding biomarker gene in the population to determine the thyroid cancer status of the subject.

In some embodiments, the thyroid cancer status comprises the presence, typing, staging and/or grading of thyroid cancer.

In some embodiments, step 1) comprises extracting DNA from the biological sample and treating with bisulfite, such that unmethylated cytosine residues in the DNA are deaminated while methylated cytosine residues remain unchanged, followed by an amplification reaction using a methylation specific primer pair templated by the biomarker gene or fragment thereof to determine the methylation level.

In some embodiments, the primer pair used in step 1) for the detection of the methylation level of the NIS gene comprises the primers SEQ ID NO: 10 and 11 or primers SEQ ID NO: 14 and 15; the primer pair for detecting the methylation level of the TSHR gene comprises primers SEQ ID NO: 18 and 19 or primers SEQ ID NO: 22 and 23; the primer pair for detecting the methylation level of the PTEN gene comprises primers SEQ ID NO: 26 and 27 or primers SEQ ID NO: 30 and 31. The primer pair for detecting the methylation level of the SLC5A8 gene comprises primers SEQ ID NO: 34 and 35 or primers SEQ ID NO: 38 and 39; the primer pair for detecting the methylation level of the RASSF1A gene comprises primers SEQ ID NO: 42 and 43 or primers SEQ ID NO: 46 and 47; the primer pair for detecting the methylation level of the TIMP3 gene comprises primers SEQ ID NO: 50 and 51 or primers SEQ ID NO: 54, and 55.

In some embodiments, step 1) further comprises the use of a primer pair of SEQ ID NO: 7 and 8, detecting an internal reference gene ACTB.

In some embodiments, the determining the thyroid cancer status of the subject in step 2) is by logistic regression based on the methylation levels of the biomarker genes.

In some embodiments, the biological sample is selected from the group consisting of blood, serum, plasma, lymph, urine, and biopsy tissue of the subject.

In another aspect, provided herein is a kit for detecting a thyroid cancer state in a subject, comprising reagents for detecting the level of methylation of a biomarker gene in a biological sample from the subject, wherein the biomarker gene is:

NIS, TSHR, and PTEN;

SLC5A8, RASSF1A and TIMP 3; or

NIS, TSHR, PTEN, SLC5A8, RASSF1A, and TIMP 3.

In some embodiments, the reagents for detecting a level of methylation comprise a methylation specific primer pair for determining the level of methylation by an amplification reaction and optionally a bisulfite salt.

In some embodiments, the primer pair for NIS gene methylation level detection comprises primers SEQ ID NO: 10 and 11 or primers SEQ ID NO: 14 and 15; the primer pair for detecting the methylation level of the TSHR gene comprises primers SEQ ID NO: 18 and 19 or primers SEQ ID NO: 22 and 23; the primer pair for detecting the methylation level of the PTEN gene comprises primers SEQ ID NO: 26 and 27 or primers SEQ ID NO: 30 and 31. The primer pair for detecting the methylation level of the SLC5A8 gene comprises primers SEQ ID NO: 34 and 35 or primers SEQ ID NO: 38 and 39; the primer pair for detecting the methylation level of the RASSF1A gene comprises primers SEQ ID NO: 42 and 43 or primers SEQ ID NO: 46 and 47; the primer pair for detecting the methylation level of the TIMP3 gene comprises primers SEQ ID NO: 50 and 51 or primers SEQ ID NO: 54, and 55.

In some embodiments, the kit further comprises instructions for processing the detection of the methylation level by logistic regression to determine the thyroid cancer status.

In some embodiments, the thyroid cancer status comprises the presence, typing, staging and/or grading of thyroid cancer. .

The methods and kits provided herein provide a new rapid, reliable, and accurate approach to screening, prognosis, diagnosis, and assessment of thyroid cancer.

Drawings

FIG. 1 shows the Receiver Operating Characteristic (ROC) curve for the "NIS + TSHR + PTEN" combination.

FIG. 2 shows the Receiver Operating Characteristic (ROC) curve for the combination SLC5A8+ RASSF1A + TIMP 3.

FIG. 3 shows the Receiver Operating Characteristic (ROC) curve for the combination "NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3".

Detailed Description

Unless otherwise defined, technical terms used in the present application have the meanings commonly understood by those skilled in the art to which the present invention belongs.

In one aspect, the present disclosure relates to a method of diagnosing thyroid cancer status in a subject comprising the steps of: 1) collecting a biological sample from the subject; 2) detecting the methylation level of a biomarker gene in the biological sample; and 3) comparing the methylation level to a normal methylation level in a population to determine whether the subject has thyroid cancer or a thyroid cancer status, wherein, in some embodiments, the biomarker genes are NIS (SLC5a5, source carrier family 5 member 5), tshr (refractory stimulating hormone receptor), pten (phosphate and hormone homolog) genes. In some embodiments, the biomarker genes are SLC5A8 (source carrier family 5 member 8), RASSF1A (Ras association family member 1), and TIMP3(TIMP metalloseptage inhibitor 3) genes. In a preferred embodiment, the biomarker genes are NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes for determining the thyroid cancer status of the subject.

As used herein, the term "methylation level" refers to the degree to which a particular gene or gene fragment thereof is methylated in a biological sample from a subject, either qualitatively (i.e., the presence or absence of methylation) or quantitatively. For a single DNA sequence, the methylation level can be the ratio of methylated sites to all methylatable sites in the DNA sequence. More generally, for example, for a particular gene or gene fragment (usually numerous) in a biological sample, the methylation level can be the degree to which a particular methylation site is methylated, e.g., the ratio of the gene fragment having methylation at that site to the total gene fragments containing that site. For methylation of a particular site of a gene of interest (e.g., located within a promoter region), the level of methylation also typically has the latter meaning. For the purposes herein, in one particular example, the methylation level can be the Ct value of an amplification reaction, or the Ct value corrected for an internal reference gene, for example, when the "methylation level" of a gene fragment of interest in a biological sample is detected by an amplification reaction (e.g., a PCR amplification reaction) using a methylation specific primer pair.

As used herein, the term "subject" refers to an individual (preferably a human) having or suspected of having a disease, or, in predicting a susceptibility, may also include healthy individuals. The term is often used interchangeably with "patient", "test subject", "treatment subject", and the like.

The term "population" as used herein generally refers to a healthy population. Where reference is made to a particular disease (e.g. thyroid cancer), the "population" may also include individuals who do not have that particular disease but who may have other diseases (e.g. thyroid inflammation, thyroid adenoma, nodular goiter, etc.). In addition, only a part of individuals may be selected as a "population" according to the characteristics such as age, sex, health condition, smoking or not. The "normal methylation level" in a population can be determined by testing a sufficient number of individuals, or can be found in the available clinical literature. In some cases, a normal methylation level refers to no methylation.

In some embodiments, whether a subject has thyroid cancer or a thyroid cancer status can be identified based on the methylation levels of the biomarker NIS, TSHR, and PTEN genes in the subject. In some embodiments, the subject may be identified as having thyroid cancer or a thyroid cancer status based on the methylation levels of the biomarkers SLC5A8, RASSF1A, and TIMP3 gene in the subject. In a more preferred embodiment, the subject may be identified as having thyroid cancer or a thyroid cancer status based on the methylation levels of the biomarkers NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes in the subject. In some embodiments, differences between samples (e.g., sample amount, concentration, etc.) can be eliminated by comparison to the detected values of the internal reference genes.

In the methods of the invention, treatment of the subject may also be scheduled based on the stage of the thyroid cancer, e.g., including performing further tests on the subject, performing surgery, performing drug therapy, and taking no further action.

In some embodiments, detection of methylation levels comprises extracting DNA from a biological sample and treating with bisulfite, followed by a PCR amplification reaction using methylation specific primer pairs. Wherein bisulfite treatment causes the deamination of unmethylated cytosine residues in the DNA duplex to uracil; whereas methylated cytosine residues remain unchanged. Thus, in a subsequent PCR amplification reaction, methylated cytosine residue sites on the template are paired as cytosine residues with guanine residues in the primers, while unmethylated cytosine residue sites are paired as uracil residues with adenine residues in the primers.

The inventors also designed a plurality of primer pairs for methylation detection of biomarker genes to detect the methylation level of a target region within each biomarker gene, wherein the target regions are respectively selected from the group consisting of SEQ ID NOs: 1-6 (corresponding to the DNA sequences of NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3, respectively) and a fragment of at least 15 contiguous bases in length; and the nucleic acid sequences of the primer pairs are respectively identical, complementary or hybridized to the target regions. Provided herein are primer pairs for detecting methylation levels that utilize methylation differences to detect methylation levels of a target region within a biomarker gene, wherein when the target region of the biomarker gene is unmethylated, the primer pair used does not pair-bind efficiently to the target region as a template (by bisulfite treatment) in a PCR amplification reaction and does not (or minimally) produce amplification products; and when the target gene of the biomarker gene is methylated, the primer pair used can be effectively paired and combined with the target region (treated by bisulfite) as a template in a PCR amplification reaction, thereby generating an amplification product. Such differences in amplification reactions can be monitored in real time as the amplification reaction progresses, or can be judged by detecting the amplification products. The present inventors have screened multiple primer pairs (see below) for the biomarker genes through multiple experiments, and the primer pairs can be used alone or in combination to help identify whether the subject has thyroid cancer.

The term "biomarker gene or fragment thereof" will be used herein in reference to detecting methylation levels, since in a PCR amplification reaction the primer pair used does not distinguish between the entire gene or a fragment thereof in the selection of the template, provided that the length of the template is not less than the length of the region to be amplified (in fact, in the course of DNA extraction and subsequent bisulfite treatment, fragmentation of the gene into fragments of different sizes will typically result).

In some preferred embodiments, the present invention measures marker gene methylation using the HeavyMethyl method, and in addition to designing a common Taqman primer, a blocking primer is also designed. The blocking primers are designed on the nucleotide sequence to pair with a template sequence in the region amplified by the corresponding primer pair. The blocking primer may introduce chemical modifications at the 3 ' -OH, such as C3 Spacer (C3 Spacer), C6 Spacer (C6 Spacer), inverted 3 ' end (inverted 3 ' end), 3 ' phosphate (3 ' P), etc., which render the DNA polymerase incapable of amplification. In an embodiment of the method of the invention, the nucleotide sequence of the blocking primer is designed to bind to the unmethylated template (sulfite-treated) but not to the methylated template (sulfite-treated). Thus, when methylation does not occur in the region corresponding to the blocking primer, it can prevent the corresponding amplification reaction from proceeding, thereby improving the specificity of the methylation detection method of the present invention.

In a preferred embodiment, the method further comprises the step of monitoring and/or quantifying the PCR amplification reaction in real time by using a fluorescent probe. The 5' end of the probe used can be a reporting fluorophore such as FAM, JOE, TET, HEX, Cy3, Texas Red, Rox or Cy 5; the quenching group at the 3' end is BHQ1, BHQ2, BHQ3, TAMRA, DABCYL, or MGB.

Detection of the methylation level of the biomarker gene in the methods of the present invention includes detecting the presence or absence of methylation in the biomarker gene, and quantitatively and qualitatively detecting the methylation.

The biological sample is selected from a fluid or tissue extracted from the subject, including blood, serum, plasma, lymph, urine, tissue biopsy, and the like, preferably plasma, serum.

In the method of the present invention, the age of the subject may also be considered for predicting whether the subject has thyroid cancer.

In some embodiments, the methods of the invention further comprise the step of providing a written or electronic report of a thyroid cancer prognosis, and optionally, the report comprises a prognosis regarding the presence or absence or likelihood of thyroid cancer in the subject, or regarding the risk of stratification of thyroid cancer in the subject.

In some embodiments, the methods of the invention further comprise establishing a report of the relative level of biomarker gene methylation for a physician and transmitting such report by mail, fax, mailbox, or the like. In one embodiment, a data stream comprising a report of the methylation level of a biomarker gene is transmitted over the internet.

In some embodiments, statistical methods are used to construct a diagnostic model based on the methylation levels of the biomarker genes. The statistical method may be selected from the following methods: multiple linear regression, lookup tables, decision trees, support vector machines, Probit regression, logistic regression, cluster analysis, neighborhood analysis, genetic algorithms, bayesian and non-bayesian methods, and the like.

In other embodiments, provided herein are predictive or diagnostic models based on biomarker gene methylation levels. The model may be in the form of software code, computer readable format, or written instructions.

With the method of the invention new and important additional information is available which assists the physician in grading the patient's risk of thyroid cancer and planning the diagnostic steps to be taken next. The methods provided herein are similarly also useful for assessing the risk of thyroid cancer in asymptomatic high risk patients, as well as for use as a screening tool for the general population. It is contemplated that the methods of the present invention may be used by clinicians as part of a comprehensive evaluation of other predictive and diagnostic indicators.

The methods of the invention can be used to assess the therapeutic efficacy of existing chemotherapeutic agents and candidate chemotherapeutic agents, as well as other types of cancer treatment modalities. For example, a biological sample can be taken from a subject before or after treatment or during treatment and the detection of the level of biomarker gene methylation is performed as described above, and the change in cancer status in the subject is identified by the detection results, thereby determining the efficacy of the treatment.

The methods of the invention can also be used to identify whether a subject is potentially developing cancer. Detecting the relative levels of biomarker gene methylation in biological samples taken from the subject over time, thereby interpreting changes in biomarker methylation levels that are characteristic of cancer as progressing toward the development of cancer.

The combination of the biomarker genes provides a sensitive, specific and accurate means for predicting the presence of or detecting thyroid cancer in different stages of its progression. Detection of the level of methylation in the biological sample can also be correlated with the presence of a pre-malignant or pre-clinical condition in the patient. Thus, the disclosed methods can be used to predict or detect the presence of thyroid cancer, benign or malignant thyroid tumors, metastatic potential of thyroid cancer, histological type of neoplasms associated with thyroid cancer, indolence or aggressiveness of cancer, and other thyroid cancer features associated with screening, preventing, diagnosing, characterizing, and treating thyroid cancer in a patient in a sample.

The methods of the invention can be used to assess the efficacy of a candidate drug for inhibiting thyroid cancer, to assess the efficacy of a thyroid cancer therapy, to monitor the progression of thyroid cancer, to select an agent or therapy that inhibits thyroid cancer, to monitor treatment of a patient with thyroid cancer, to monitor the status of inhibition of thyroid cancer in a patient, and to assess the oncogenic potential of a test compound by detecting the level of methylation of a biomarker gene in the test animal following exposure to the test compound.

The invention also provides a kit for early diagnosis of thyroid cancer. In some embodiments, the kit comprises reagents for detecting the methylation levels of NIS, TSHR and PTEN genes in a biological sample from a subject, such as a methylation specific primer pair (see below) that detects methylation of at least one nucleotide sequence. In some embodiments, the kit comprises reagents for detecting the methylation level of the SLC5A8, RASSF1A and TIMP3 genes in a biological sample from a subject, such as a methylation specific primer pair (described below) that detects methylation of at least one nucleotide sequence. In a preferred embodiment, the kit comprises reagents for detecting the methylation level of the NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes in a biological sample from a subject, for example a methylation specific primer pair (see below) which detects methylation of at least one nucleotide sequence. .

In some embodiments, the kit may further comprise blocking primers and probes (described above and below for these blocking primers and probes) used in combination with the primer pairs described above.

In some embodiments, the kit may further comprise a DNA extraction reagent and a bisulfite reagent. DNA extraction reagents may include lysis buffer, binding buffer, wash buffer, and elution buffer. Lysis buffers are typically composed of protein denaturants, detergents, pH buffers and nuclease inhibitors. The binding buffer is typically composed of a protein denaturant and a pH buffer. The washing buffer solution is divided into a washing buffer solution A and a washing buffer solution B: the washing buffer solution A consists of a protein denaturant, a nuclease inhibitor, a detergent, a pH buffer and ethanol; the washing buffer B consists of nuclease inhibitor, pH buffer and ethanol. The elution buffer typically consists of a nuclease inhibitor and a pH buffer. The protein denaturant is selected from one or more of guanidinium isothiocyanate, guanidinium hydrochloride and urea; the detergent is selected from one or more of Tween20, IGEPAL CA-630, Triton X-100, NP-40 and SDS; the pH buffering agent is selected from one or more of Tris, boric acid, phosphate, MES and HEPES; the nuclease inhibitor is one or more selected from EDTA, EGTA and DEPC. Bisulfite reagents include bisulfite buffers and protection buffers. Wherein the bisulfite is selected from one or more of sodium bisulfite, sodium sulfite, sodium bisulfite, ammonium bisulfite and ammonium sulfite; the protective buffer solution consists of an oxygen radical scavenger, and the oxygen radical scavenger is one or more selected from hydroquinone, vitamin E derivatives, gallic acid, Trolox, trihydroxybenzoic acid and trihydroxybenzoic acid derivatives.

In some embodiments, the kit may further comprise instructions for use. In some embodiments, the instructions describe how to use the kit to extract biological sample DNA and treat the DNA with bisulfite reagents. In some embodiments, the instructions further set forth how to use the kit to determine whether a subject has thyroid cancer or a thyroid cancer status comprising analyzing the detection results using a statistical model, such as a logistic regression model.

The invention also provides a method for detecting the methylation level of the biomarker gene or the fragment thereof by using the kit, which comprises the following steps: extracting DNA in a biological sample by using a DNA extraction reagent, and treating the extracted DNA by using a bisulfite reagent; and detecting the methylation level of the biomarker gene by using the provided primer pair by using the treated DNA as a template.

The methylation level measurement method can be selected from one or more of the following methods: real-time fluorescence PCR, digital PCR, bisulfite sequencing, methylation specificity PCR, restriction enzyme analysis, high-resolution melting curve technology, gene chip technology and flight time mass spectrum.

The invention is further described below by way of examples.

Example 1: DNA extraction

The DNA extraction reagent consists of a lysis buffer, a binding buffer, a washing buffer and an elution buffer. The lysis buffer consists of protein denaturants, detergents, pH buffers and nuclease inhibitors. The binding buffer consists of a protein denaturant and a pH buffer. The washing buffer solution is divided into a washing buffer solution A and a washing buffer solution B, wherein the washing buffer solution A consists of a protein denaturant, a nuclease inhibitor, a detergent, a pH buffer agent and ethanol; the washing buffer B consists of nuclease inhibitor, pH buffer and ethanol. The elution buffer consists of a nuclease inhibitor and a pH buffer. Wherein the protein denaturant is: guanidine hydrochloride; the detergent is: tween 20; the pH buffer is: Tris-HCl; the nuclease inhibitor is: EDTA.

In this example, plasma DNA was extracted from a plasma sample of a thyroid cancer patient. The extraction method comprises the following steps:

(1) adding lysis buffer solution with the same volume into 1mL of blood plasma, adding proteinase K and Carrier RNA to make the final concentrations of the proteinase K and the Carrier RNA respectively 100mg/L and 1 mu g/mL, shaking and uniformly mixing, and incubating at 55 ℃ for 30 min;

(2) 100. mu.L of magnetic beads (purchased from Life technologies, cat. No.: 37002D) were added and incubated for 1 hour with shaking;

(3) adsorbing the magnetic beads by a magnetic separator, and discarding the supernatant solution;

(4) adding 1mL of cleaning buffer solution A for resuspending the magnetic beads, and shaking and cleaning for 1 min;

(5) adsorbing the magnetic beads by a magnetic separator, and discarding the supernatant;

(6) adding 1mL of cleaning buffer B for resuspending the magnetic beads, and shaking and cleaning for 1 min;

(7) adsorbing the magnetic beads by a magnetic separator, and discarding the supernatant solution;

(8) centrifuging at 10000rpm for 1min, adsorbing magnetic beads with a magnetic separator, and removing residual supernatant;

(9) opening the cover of the centrifugal tube filled with the magnetic beads, placing the centrifugal tube on a metal bath at the temperature of 55 ℃, and airing for 10 min;

(10) adding 100 μ L elution buffer solution to resuspend the magnetic beads, placing on 65 deg.C metal bath, and shaking for elution for 10 min;

(11) adsorbing the magnetic beads by using a magnetic separator, taking out a buffer solution containing the target DNA, quantifying the DNA, and marking;

(12) the eluted DNA is stored in a refrigerator at 4 ℃ for standby or stored in a refrigerator at-20 ℃ for long-term storage.

Example 2: bisulfite treatment of DNA

Bisulfite treating DNA is by using a bisulfite reagent consisting of a bisulfite buffer and a protective buffer; the bisulfite buffer solution is a mixed liquid of sodium bisulfite and water; the protective buffer solution is a mixed liquid of oxygen radical scavenger hydroquinone and water.

In this embodiment, the DNA extracted in example 1 is used as a treatment target, and the bisulfite is used to treat the DNA, which includes the following steps:

(1) preparing a bisulfite buffer: weighing 1g of sodium bisulfite powder, and adding water to prepare 3M buffer solution;

(2) preparing a protection buffer solution: weighing 1g of hydroquinone reagent, and adding water to prepare 0.5M protective buffer solution;

(3) mixing 100 mu L of DNA solution, 200 mu L of bisulfite buffer solution and 50 mu L of protective solution, and shaking and mixing uniformly;

(4) thermal cycling: 5min at 95 ℃, 60min at 80 ℃ and 10min at 4 ℃;

(5) adding 1mL of DNA binding buffer solution into the DNA solution treated by the bisulfite, adding 50 mu L of magnetic beads, and oscillating and incubating for 1 h;

(6) adsorbing the magnetic beads by a magnetic separator, and discarding the supernatant solution;

(7) adding 0.5mL of cleaning buffer solution A for resuspending the magnetic beads, and shaking and cleaning for 1 min;

(8) adsorbing the magnetic beads by a magnetic separator, and discarding the supernatant;

(9) adding 0.5mL of cleaning buffer B for resuspending the magnetic beads, and shaking and cleaning for 1 min;

(10) adsorbing the magnetic beads by a magnetic separator, and discarding the supernatant solution;

(11) centrifuging at 10000rpm for 1min, adsorbing magnetic beads with a magnetic separator, and removing residual supernatant;

(12) placing the centrifugal tube filled with the magnetic beads on a metal bath at 55 ℃, uncovering and airing for 10 min;

(13) adding 50 μ L elution buffer solution to resuspend the magnetic beads, placing on 65 deg.C metal bath, and shaking for elution for 10 min;

(14) and adsorbing the magnetic beads by using a magnetic separator, taking out the buffer solution containing the target DNA, quantifying the DNA, and marking.

Example 3: real-time fluorescence PCR detection of DNA methylation and primer group verification

This example measures methylation levels of biomarker genes, exemplified by real-time fluorescent PCR. Wherein the genes for detecting the methylation biomarkers are NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3, and the reference gene is ACTB. The methylation level was measured in this example using the DNA extraction of example 1 and bisulfite treated DNA of example 2 as template for real-time fluorescent PCR amplification. And 3 multi-hole detection is carried out on the DNA sample to be detected, the negative quality control product, the positive quality control product and the template-free control.

For NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 gene methylation, many sets of primer and probe combinations can be designed, and the performance of each set of probe primer combination may be different, so that the verification through experiments is required.

We designed various primers and probes for NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 gene methylation, which are respectively identical to, complementary to or hybridised to at least 15 nucleotides of the sequences shown in sequences 1-6 of the sequence listing and their complementary sequences; the methylated and unmethylated nucleic acid sequences are then used as templates to verify the effectiveness of the detection methylated primers and probes. The following optimal primer sets and primer sets of the reference gene ACTB (in the methylation primer sets, the primers 1 are all forward primers, and the primers 2 are all reverse primers) are screened out according to the real-time fluorescent PCR amplification result:

primer group for reference gene ACTB

Primer 1: SEQ ID NO: 5'-GTGATGGAGGAGGTTTAGTAAGT-3' ratio

Primer 2: SEQ ID NO: 8: 5'-CCAATAAAACCTACTCCTCCCTT-3'

And (3) probe: SEQ ID NO: 9 '-Cy 5-ACCACCACCCAACACACAATAACAAACACA-BHQ 3-3'

NIS primer set 1

Primer 1: SEQ ID NO: 5'-CGTTGGGTTTTCGTATTC-3' ratio

Primer 2: SEQ ID NO: 11: 5'-CCCAAACTCCGAAAATAAAC-3'

Blocking primers: SEQ ID NO: 12:5 '-TGTATTTGTTTTTATGGAGGTTGTGGAGATT-C3-3'

And (3) probe: SEQ ID NO: 13 '-Texas Red-CCGATCTCCACGACCTCCAT-BHQ 2-3'

NIS primer set 2

Primer 1: SEQ ID NO: 5'-GTCGTTGTTTGTTAGTTTTATG-3'

Primer 2: SEQ ID NO: 155 '-AACCCAAACACATCCAAA-3'

Blocking primers: SEQ ID NO: 16:5 '-TGTGTAGGTGTTGGGTGTGTTGTTGGAGGTTTATT-C3-3'

And (3) probe: SEQ ID NO: 17 '-Texas Red-AACCATAACGATAAACCTCCGACGA-BHQ 2-3'

TSHR primer set 1

Primer 1: SEQ ID NO: 5'-GTAGGATATTGGTTCGTTC-3'

Primer 2: SEQ ID NO: 5'-CCTTCTAAACACCCTATCTA-3'

Blocking primers: SEQ ID NO: 20 '-TTGTTTGTGGATAGTTTATTTTGTGGGGATT-C3-3'

And (3) probe: SEQ ID NO: 21 '-HEX-TCGCACGCACTCCTTATCCA-BHQ 1-3'

TSHR primer set 2

Primer 1: SEQ ID NO: 5'-CGGAGGATGGAGAAATAGTTTC-3' ratio

Primer 2: SEQ ID NO: 235 '-GCCCAAATCCCTAAACAAATCGA-3'

Blocking primers: SEQ ID NO: 24:5 '-TTTTGAGTTTTGTGGAAAATGAGGTTGGTGGA-C3-3'

And (3) probe: SEQ ID NO: 25 '-HEX-ACAACACCAACTACAACAAATCCGCCGA-BHQ 1-3'

PTEN primer set 1

Primer 1: SEQ ID NO: 5'-CGTTCGGAGGATTATTC-3'

Primer 2: SEQ ID NO: 5'-CGACTAAACCTACTTCTC-3' parts by weight

Blocking primers: SEQ ID NO: 28 '-TTGTTGTTGTTAGGTTTTTGGTTGTTGAGGAG-C3-3'

And (3) probe: SEQ ID NO: 29 '-FAM-AACCTAACAACGACGACAACGAA-BHQ 1-3'

PTEN primer set 2

Primer 1: SEQ ID NO: 30: 5'-GGAGAAGTAGGTTTAGTCG-3'

Primer 2: SEQ ID NO: 31: 5'-CCGCCGCTTAACTCTAA-3'

Blocking primers: SEQ ID NO: 32:5 '-AGTTGTTGTAATTATTTAGTAGTTGTTGTAGTA-C3-3'

And (3) probe: SEQ ID NO: 33 '-FAM-CGCAACCGAATAATAACTACTACGACG-BHQ 1-3'

Primer set 1 of SLC5A8

Primer 1: SEQ ID NO: 34: 5'-GTCGTTATCGGTATTTATTAC-3'

Primer 2: SEQ ID NO: 35: 5'-CGACTAACATAAAACTAACG-3'

Blocking primers: SEQ ID NO: 36 '-CGGTATTTATTACGTTTTCGTTGGGGGCGGT-C3-3'

And (3) probe: SEQ ID NO: 37 '-Texas Red-AACAACGCCACGAACACTACG-BHQ 2-3'

Primer set 2 SLC5A8

Primer 1: SEQ ID NO: 5'-GTAGGGGCGATTGTTA-3' ratio

Primer 2: SEQ ID NO: 395 '-TCCCGCGAAAACTAAA-3'

Blocking primers: SEQ ID NO: 40:5 '-TGATTGTTAGTTTTTATTTTGTTTTGGGGTGTGTTT-C3-3'

And (3) probe: SEQ ID NO: 41 '-Texas Red-TATCGACCTCCGAACGCACC-BHQ 2-3'

RASSF1A primer set 1

Primer 1: SEQ ID NO: 5'-GCGTTGAAGTCGGGGTTCG-3' ratio

Primer 2: SEQ ID NO: 43: 5'-CCGATTAAACCCGTACTTC-3'

Blocking primers: SEQ ID NO: 44 '-TTGGGGTTTGTTTTGTGGTTTCGTTTGGTTTGT-C3-3'

And (3) probe: SEQ ID NO: 45:5 '-HEX-CGCTAACAAACGCGAACCGA-BHQ 1-3'

RASSF1A primer set 2

Primer 1: SEQ ID NO: 5'-GGGAGTTTGAGTTTATTGA-3' ratio

Primer 2: SEQ ID NO: 475 '-GATACGCAACGCGTTAACACG-3'

Blocking primers: SEQ ID NO: 48:5 '-CACATTAACACACTCCAACCAAATACAACCCTT-C3-3'

And (3) probe: SEQ ID NO: 49 '-HEX-CGCCCAACGAATACCAACTCC-BHQ 1-3'

TIMP3 primer set 1

Primer 1: SEQ ID NO: 50: 5'-GGTCGATGAGGTAATGC-3'

Primer 2: SEQ ID NO: 51: 5'-GCCGCCTACCTACTACC-3'

Blocking primers: SEQ ID NO: 52:5 '-ACCTACCTACTACCCACTCTACACCACT-C3-3'

And (3) probe: SEQ ID NO: 53 '-FAM-TCTACGCCGCTACCTAAACGAC-BHQ 1-3'

TIMP3 primer set 2

Primer 1: SEQ ID NO: 54: 5'-GAGAGGCGAGTAGTAG-3'

Primer 2: SEQ ID NO: 55: 5'-CACGATAAACCCGAAC-3'

Blocking primers: 56: 5' -TGAGTAGTAGTTTTGGTAGTGGTGGTAGTAGT-C3-3 ' SEQ ID NO '

And (3) probe: SEQ ID NO: 57 '-FAM-TCATTACCGCTACTACCGCCG-BHQ 1-3'

Multiple groups of primers and probes can distinguish methylated and unmethylated templates, and can be used as primers and probes for detecting methylation of NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes respectively. Although the effect of different primer and probe combinations is slightly different, the above primers and probes are suitable for methylation detection of NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes, respectively. Table 1 below shows the results of detection of methylated and unmethylated templates (bisulfite treated) of the above genes using the respective primer and probe combinations. Clearly, each primer and probe combination designed is highly specific for methylated templates.

Table 1 shows the results (Ct, mean) of the detection of methylated and unmethylated DNA templates by the primer sets designed in Table 1

Gene primer set

PTEN-1

PTEN-2

TSHR-1

TSHR-2

NIS-1

NIS-2

Methylated DNA

28.75

29.96

27.3

30.14

28.39

29.65

Unmethylated DNA

No Ct

Gene primer set

TIMP3-1

TIMP3-2

RASSF1A-1

RASSF1A-2

SLC5A8-1

SLC5A8-2

Methylated DNA

29.21

31.53

29.71

30.69

27.66

29.28

Unmethylated DNA

No Ct

Example 4: the kit can detect the sensitivity and specificity of the plasma of thyroid cancer patients and non-thyroid cancer people (including benign disease patients, other types of cancer interference people and healthy people)

Using 826 samples from patients with pathologically defined thyroid cancer ("case group") and 830 samples from patients with defined non-thyroid cancer ("control group"), the control group contained 259 thyroid adenomas, 272 nodular goiters, 198 thyroiditis, 101 samples of other types (specifically, containing 58 samples with no abnormal thyroid health and 43 other cancers: 31 lung cancers, 12 esophageal cancers) (see Table 2), all samples were collected from the general Hospital of the people's liberated military, China. The thyroid cancer samples of the case group included all stages and common typing of the disease. Thyroid cancer patients are diagnosed by neck ultrasound, imaging and pathological diagnosis, the stage of a sample is based on the international TNM stage standard, and the type of the sample is determined according to a tissue biopsy and immunohistochemical method. Control group samples included common types of benign conditions seen throughout the study population and some other types of cancer interfering samples and some thyroid health non-abnormal samples. A complete clinical pathology report was obtained after surgery, including patient age, smoking history, race, staging, typing and encoding collection site for each sample.

TABLE 2 thyroid cancer stage and other characteristics of the collected sample subjects

For detection of methylation of the NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes, DNA was extracted using the DNA extraction method of example 1 followed by bisulfite treatment of the DNA template using the method of example 2, and real-time fluorescence PCR experiments were performed using the primer and probe combination mixture provided in example 3 (primer set 1 was used for each biomarker gene), methylation of the NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes was detected along with the reference gene ACTB, and finally the average Ct values of three duplicate wells of each gene in the control and case group samples were obtained. The methylation level of each gene can be reflected by this Ct value, as described in example 3 above. The primer mixture for detecting the methylation of the NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes comprises the following components:

primer group for reference gene ACTB

Primer 1: SEQ ID NO: 5'-GTGATGGAGGAGGTTTAGTAAGT-3' ratio

Primer 2: SEQ ID NO: 8: 5'-CCAATAAAACCTACTCCTCCCTT-3'

And (3) probe: SEQ ID NO: 9 '-Cy 5-ACCACCACCCAACACACAATAACAAACACA-BHQ 3-3'

NIS primer set 1

Primer 1: SEQ ID NO: 5'-CGTTGGGTTTTCGTATTC-3' ratio

Primer 2: SEQ ID NO: 11: 5'-CCCAAACTCCGAAAATAAAC-3'

Blocking primers: SEQ ID NO: 12:5 '-TGTATTTGTTTTTATGGAGGTTGTGGAGATT-C3-3'

And (3) probe: SEQ ID NO: 13 '-Texas Red-CCGATCTCCACGACCTCCAT-BHQ 2-3'

TSHR primer set 1

Primer 1: SEQ ID NO: 5'-GTAGGATATTGGTTCGTTC-3'

Primer 2: SEQ ID NO: 5'-CCTTCTAAACACCCTATCTA-3'

Blocking primers: SEQ ID NO: 20 '-TTGTTTGTGGATAGTTTATTTTGTGGGGATT-C3-3'

And (3) probe: SEQ ID NO: 21 '-HEX-TCGCACGCACTCCTTATCCA-BHQ 1-3'

PTEN primer set 1

Primer 1: SEQ ID NO: 5'-CGTTCGGAGGATTATTC-3'

Primer 2: SEQ ID NO: 5'-CGACTAAACCTACTTCTC-3' parts by weight

Blocking primers: SEQ ID NO: 28 '-TTGTTGTTGTTAGGTTTTTGGTTGTTGAGGAG-C3-3'

And (3) probe: SEQ ID NO: 29 '-FAM-AACCTAACAACGACGACAACGAA-BHQ 1-3'

Primer set 1 of SLC5A8

Primer 1: SEQ ID NO: 34: 5'-GTCGTTATCGGTATTTATTAC-3'

Primer 2: SEQ ID NO: 35: 5'-CGACTAACATAAAACTAACG-3'

Blocking primers: SEQ ID NO: 36 '-CGGTATTTATTACGTTTTCGTTGGGGGCGGT-C3-3'

And (3) probe: SEQ ID NO: 37 '-Texas Red-AACAACGCCACGAACACTACG-BHQ 2-3'

RASSF1A primer set 1

Primer 1: SEQ ID NO: 5'-GCGTTGAAGTCGGGGTTCG-3' ratio

Primer 2: SEQ ID NO: 43: 5'-CCGATTAAACCCGTACTTC-3'

Blocking primers: SEQ ID NO: 44 '-TTGGGGTTTGTTTTGTGGTTTCGTTTGGTTTGT-C3-3'

And (3) probe: SEQ ID NO: 45:5 '-HEX-CGCTAACAAACGCGAACCGA-BHQ 1-3'

TIMP3 primer set 1

Primer 1: SEQ ID NO: 50: 5'-GGTCGATGAGGTAATGC-3'

Primer 2: SEQ ID NO: 51: 5'-GCCGCCTACCTACTACC-3'

Blocking primers: SEQ ID NO: 52:5 '-ACCTACCTACTACCCACTCTACACCACT-C3-3'

And (3) probe: SEQ ID NO: 53 '-FAM-TCTACGCCGCTACCTAAACGAC-BHQ 1-3'

Descriptive Statistics, Receiver Operating Characteristics (ROC) curves and graphical presentation of plasma biomarker levels were performed using commercially available software packages (IBM SPSS Statistics 24 and MedCalc11.4.2.0, available from IBM and MedCalc, respectively). Statistical differences were determined using the nonparametric Kruskal-Wallis test (ANOVA) followed by Dunn's multiple post-comparison test. For all statistical comparisons, a P value <0.05 was considered statistically significant.

The study samples are shown in table 3 for a total of 1656 samples, and the case group includes 826 patients pathologically identified as thyroid cancer, including patients with common thyroid cancer typing and stages. The control group was 830 samples of patients determined to be non-thyroid cancer, and contained 259 thyroid adenomas, 272 nodular goiters, 198 thyroiditis, 101 samples of other types (specifically, 58 samples of healthy and non-abnormal thyroid gland and 43 other cancers: 31 lung cancer, 12 esophageal cancers). The sample type studied was plasma and the methylation levels of the NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes were determined in 1656 samples above using real-time fluorescent PCR assays. To help determine the ability of these biomarker genes to distinguish symptomatic similar cancers from benign thyroid disease, all samples were obtained from the same clinical population (patients undergoing surgery based on the presence of thyroid adenoma or nodular goiter). All samples were collected prior to any intervention and prior to the known disease state. The disease state is then determined by pathological examination of ex vivo tissues. Plasma was collected using a single sample collection protocol and compliance was monitored. This ensures sample quality and eliminates the possibility of any collection, processing, and biological bias in the sample collection. These samples showed that the average patient age in individuals with thyroid cancer (54.89 years) was higher than those with benign conditions or no abnormalities in health (49.22 years) (table 3). Overall, the distribution of thyroid cancer typing is similar to that seen in all cases of thyroid cancer in the population, with a higher proportion of papillary carcinomas (75.30%) than other typing thyroid carcinomas. The non-thyroid cancer controls in the study represent common benign thyroid disorders, including thyroid adenoma, nodular goiter, thyroiditis, etc., and also include some interfering samples of other types of cancer and some healthy non-abnormal samples.

The multi-marker diagnosis model needs to be performed by using a statistical analysis method, and a methylated gene marker diagnosis model is constructed by taking a logistic regression model as an example to detect thyroid cancer. The training of the logistic regression model is performed as follows: samples were divided into case and control groups and then regression coefficients were optimized using IBM SPSS Statistics 24 software. There is one regression coefficient for each marker, plus one bias parameter, to maximize the likelihood that the logistic regression model will be used to train the data. After training, the set of regression coefficients defines a logistic regression model. One skilled in the art can readily use this type of diagnostic model to predict the likelihood of any new sample being identified as a case or control by placing the methylation levels of the biomarkers into a logistic regression equation.

The inventor compares three different multi-marker combinations, respectively constructs a logistic regression model, and obtains a better combination through comparison. The first of them is 3 marker combinations "NIS + TSHR + PTEN", that is, detecting the methylation level of the methylation biomarker genes NIS, TSHR and PTEN, fitting the Ct values of the methylation of three markers NIS, TSHR and PTEN obtained by detection to a logistic regression equation through SPSS staticistics 24 software, and then drawing a Receiver Operating Characteristic (ROC) curve by Medcalc11.4.2.0 to determine the optimal cut-off value so as to determine whether the receiver has thyroid cancer. The second is to detect the methylation level of 3 marker combinations "SLC 5A8+ RASSF1A + TIMP 3", i.e. the methylation biomarker genes SLC5A8, RASSF1A and TIMP3, and determine the optimal cut-off value by fitting the Ct values of the methylation of the three markers SLC5A8, RASSF1A and TIMP3 to the logistic regression equation by the SPSS staticistics 24 software and then plotting a Receiver Operating Characteristic (ROC) curve with medcalc11.4.2.0 to determine whether the subject has thyroid cancer. The third is that 6 marker combinations "NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3" combined by the aforementioned two marker combinations, i.e. methylation levels of the 6 methylated biomarker genes NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 are detected simultaneously, Ct values of methylation of six markers of NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 detected are fitted to a logistic regression equation by SPSS Statistics 24 software, and optimal cut-off values are determined by plotting a subject working characteristic (ROC) curve with medcalc11.4.2.0 to determine whether the subject has thyroid cancer.

For each of these three combinatorial methods, a 95% confidence interval was chosen using the medcalc11.4.2.0 software, and ROC curves were generated and their area under the curve (AUC) values calculated. Compared with the control group population, the AUC of the three combination methods in the case group sample is greater than 0.90(P value is less than 0.05), the combined AUC of the 'NIS + TSHR + PTEN', 'SLC 5A8+ RASSF1A + TIMP 3' and the 'NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3' are respectively 0.914, 0.906 and 0.951, and the 'NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3' is better than the 'NIS + TSHR + PTEN' and the 'SLC 5A8+ RASSF1A + TIMP 3'. (see FIG. 1, FIG. 2, FIG. 3, and Table 4).

TABLE 4 Area Under Curve (AUC) for three marker combination Receiver Operating Characteristic (ROC) Curve analysis

To evaluate the sensitivity of the three marker combinations for the identification of different stages (especially early stages) of thyroid cancer, the sensitivity of the three combinations in each stage, especially early (the most important stage for marker detection) sample, was compared. The results are shown in tables 5, 6 and 7. The results show that the 'NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3' is superior to the 'NIS + TSHR + PTEN' and the 'SLC 5A8+ RASSF1A + TIMP 3', the three combined sensitivities tend to increase along with the increase of the stage of the thyroid cancer, and the combined diagnosis sensitivity of the 'NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3' reaches over 85 percent in the stages I and II, and has good diagnosis effect on early thyroid cancer.

TABLE 5 sensitivity of "NIS + TSHR + PTEN" combinations in different stages of thyroid cancer

Item	Stage I	Stage II	Stage III	Stage IV	Total number of
						Group entry case	197	211	221	197	826
Distribution of false negatives	56	55	51	44	206
						Sensitivity of the probe	71.57％	73.93％	76.92％	77.66％	75.06％

TABLE 6 sensitivity of the combination SLC5A8+ RASSF1A + TIMP3 in different stages of thyroid cancer

Item	Stage I	Stage II	Stage III	Stage IV	Total number of
						Group entry case	197	211	221	197	826
Distribution of false negatives	33	30	25	19	107
						Sensitivity of the probe	83.25％	85.78％	88.69％	90.36％	87.05％

TABLE 7 sensitivity of "NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3" combinations in different thyroid cancer stages

Item	Stage I	Stage II	Stage III	Stage IV	Total number of
						Group entry case	197	211	221	197	826
Distribution of false negatives	26	21	16	7	70
						Sensitivity of the probe	86.80％	90.05％	92.76％	96.45％	91.53％

To evaluate the sensitivity of the three marker combinations for the identification of different types of thyroid cancer, in particular the more prominent papillary carcinomas, the sensitivity of the three combinations in each common type, in particular papillary carcinoma samples, was compared. The results are shown in tables 8, 9 and 10. It can be seen that "NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3" is superior to the combination of "NIS + TSHR + PTEN" and "SLC 5A8+ RASSF1A + TIMP 3". Wherein the sensitivity of the NiS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP3 and SLC5A8+ RASSF1A + TIMP3 in the nipple cancer diagnosis sample is more than 85%.

TABLE 8 sensitivity of "NIS + TSHR + PTEN" combinations in different thyroid cancer typing

Item	Papillary carcinoma	Follicular cancer	Medullary carcinoma	Undifferentiated cancer	Total number of
						Group entry case	622	119	61	24	826
False negative	150	34	15	7	206
						Sensitivity of the probe	75.88％	71.43％	75.41％	70.83％	75.06％

TABLE 9 sensitivity of the "SLC 5A8+ RASSF1A + TIMP 3" combination in different thyroid cancer typing

Item	Papillary carcinoma	Follicular cancer	Medullary carcinoma	Undifferentiated cancer	Total number of
						Group entry case	622	119	61	24	826
False negative	80	14	9	4	107
						Sensitivity of the probe	87.14％	88.24％	85.25％	83.33％	87.05％

TABLE 10 sensitivity of "NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3" combinations in different thyroid cancer typing

Item	Papillary carcinoma	Follicular cancer	Medullary carcinoma	Undifferentiated cancer	Total number of
						Group entry case	622	119	61	24	826
False negative	51	10	6	3	70
						Sensitivity of the probe	91.80％	91.60％	90.16％	87.50％	91.53％

To evaluate the specificity of the three marker combinations for the control group of non-thyroid cancer populations, the study samples included common thyroid diseases such as thyroid adenoma, nodular goiter, thyroiditis, etc., and also included some other control samples (other types of cancer interfering samples and some healthy non-abnormal samples), the specificity of the three combinations in non-thyroid cancer, especially in disease or other cancer samples that are easily confused with thyroid cancer, was compared. The results are shown in tables 11, 12 and 13. The results show that the combination of 'NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3' is equivalent to 'NIS + TSHR + PTEN', the specificity for adenoma, thyroid nodule and verification diagnosis is more than 90%, benign thyroid adenoma, nodular thyroid tumor and early thyroid cancer can be well distinguished, and the combination is obviously superior to the combination of 'SLC 5A8+ RASSF1A + TIMP 3'.

TABLE 11 specificity of "NIS + TSHR + PTEN" combinations in control samples

Benign	Thyroid adenoma	Nodular goiter	Thyroiditis	Others	Total number of
						Sample size	259	272	198	101	830
False positive	11	9	6	5	31
						Rate of agreement	95.75％	96.69％	96.97％	95.05％	96.27％

TABLE 12 specificity of the SLC5A8+ RASSF1A + TIMP3 combination in control samples

Benign	Thyroid adenoma	Nodular goiter	Thyroiditis	Others	Total number of
						Sample size	259	272	198	101	830
False positive	45	46	31	13	135
						Rate of agreement	82.63％	83.09％	84.34％	87.13％	83.73％

TABLE 13 specificity of "NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3" combinations in control samples

Benign	Thyroid adenoma	Nodular goiter	Thyroiditis	Others	Total number of
						Sample size	259	272	198	101	830
False positive	18	12	7	6	43
						Rate of agreement	93.05％	95.59％	96.46％	94.06％	94.82％

The AUC of the combination of the three markers is more than 0.90. The three combined models were further compared by determining the sensitivity of the model at a fixed value of specificity and the specificity of the model at a fixed value of sensitivity. The results show that in tables 14 and 15, it can be seen that the combination "NIS + TSHR + PTEN + SLC5A8+ RASSF1A + TIMP 3" (Panel 3 described in the tables) is superior to the combination "NIS + TSHR + PTEN" (Panel 1 described in the tables) and "SLC 5A8+ RASSF1A + TIMP 3" (Panel 2 described in the tables).

TABLE 14 sensitivity of logistic regression model for three marker combinations at important specificity thresholds

TABLE 15 specificity of logistic regression model for three marker combinations at important sensitivity thresholds

It is noted that the methylation level measurements provided in this example were obtained using primer set 1 for each biomarker gene, but similar measurements were obtained using the additional primer set 2 provided herein (data not shown).

According to the technical scheme provided by the invention, through combined detection of NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 gene methylation, the sensitivity and specificity of thyroid cancer detection are improved, so that the correctness and reliability of the detection result are ensured. In addition, by detecting the methylation levels of NIS, TSHR, PTEN, SLC5A8, RASSF1A and TIMP3 genes in a sample and analyzing by using a logistic regression equation, whether the sample is positive or not and the risk value are quickly and conveniently judged, and the kit for quickly detecting the thyroid cancer is provided.

The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; those of ordinary skill in the art will understand that: modifications can be made to the technical solutions described in the foregoing embodiments, or some or all of the technical features can be equivalently replaced; the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention, and therefore, the present invention should be covered in the scope of the present specification.

SEQUENCE LISTING

<110> Beijing Aikelen medical science and technology Co., Ltd

<120> method and kit for identifying thyroid cancer status

<130> 21113CI

<160> 57

<170> PatentIn version 3.3

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<213> Homo sapiens

<400> 5

ctgcgagagc gcgcccagcc ccgccttcgg gccccacagt ccctgcaccc aggtttccat 60

tgcgcggctc tcctcagctc cttcccgccg cccagtctgg atcctggggg aggcgctgaa 120

gtcggggccc gccctgtggc cccgcccggc ccgcgcttgc tagcgcccaa agccagcgaa 180

gcacgggccc aaccgggcca tgtcggggga gcctgagctc attgagctgc gggagctggc 240

acccgctggg cgcgctggga agggccgcac ccggctggag cgtgccaacg cgctgcgcat 300

cgcgcggggc accgcgtgca accccacacg gcagctggtc cctggccgtg gccaccgctt 360

ccagcccgcg gggcccgcca cgcacacgtg gtgcgacctc tgtggcgact tcatctgggg 420

cgtcgtgcgc aaaggcctgc agt 443

<210> 6

<211> 550

<212> DNA

<213> Homo sapiens

<400> 6

atccgggggc ggccccgccc cgttacccct tgcccccggc cccgccccct ttttggaggg 60

ccgatgaggt aatgcggctc tgccattggt ctgagggggc gggccccaac agcccgaggc 120

ggggtccccg ggggcccagc gctatatcac tcggccgccc aggcagcggc gcagagcggg 180

cagcaggcag gcggcgggcg ctcagacggc ttctcctcct cctcttgctc ctccagctcc 240

tgctccttcg ccgggaggcc gcccgccgag tcctgcgcca gcgccgaggc agcctcgctg 300

cgccccatcc cgtcccgccg ggcactcgga gggcagcgcg ccggaggcca aggttgcccc 360

gcacggcccg gcgggcgagc gagctcgggc tgcagcagcc ccgccggcgg cgcgcacggc 420

aactttggag aggcgagcag cagccccggc agcggcggca gcagcggcaa tgaccccttg 480

gctcgggctc atcgtgctcc tgggcagctg gagcctgggg gactggggcg ccgaggcgtg 540

cacatgctcg 550

<210> 7

<211> 23

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 7

gtgatggagg aggtttagta agt 23

<210> 8

<211> 23

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 8

ccaataaaac ctactcctcc ctt 23

<210> 9

<211> 30

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 9

accaccaccc aacacacaat aacaaacaca 30

<210> 10

<211> 18

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 10

cgttgggttt tcgtattc 18

<210> 11

<211> 20

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 11

cccaaactcc gaaaataaac 20

<210> 12

<211> 31

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 12

tgtatttgtt tttatggagg ttgtggagat t 31

<210> 13

<211> 20

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 13

ccgatctcca cgacctccat 20

<210> 14

<211> 22

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 14

gtcgttgttt gttagtttta tg 22

<210> 15

<211> 18

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 15

aacccaaaca catccaaa 18

<210> 16

<211> 35

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 16

tgtgtaggtg ttgggtgtgt tgttggaggt ttatt 35

<210> 17

<211> 25

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 17

aaccataacg ataaacctcc gacga 25

<210> 18

<211> 19

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 18

gtaggatatt ggttcgttc 19

<210> 19

<211> 20

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 19

ccttctaaac accctatcta 20

<210> 20

<211> 31

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 20

ttgtttgtgg atagtttatt ttgtggggat t 31

<210> 21

<211> 20

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 21

tcgcacgcac tccttatcca 20

<210> 22

<211> 22

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 22

cggaggatgg agaaatagtt tc 22

<210> 23

<211> 23

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 23

gcccaaatcc ctaaacaaat cga 23

<210> 24

<211> 32

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 24

ttttgagttt tgtggaaaat gaggttggtg ga 32

<210> 25

<211> 28

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 25

acaacaccaa ctacaacaaa tccgccga 28

<210> 26

<211> 17

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 26

cgttcggagg attattc 17

<210> 27

<211> 18

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 27

cgactaaacc tacttctc 18

<210> 28

<211> 32

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 28

ttgttgttgt taggtttttg gttgttgagg ag 32

<210> 29

<211> 23

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 29

aacctaacaa cgacgacaac gaa 23

<210> 30

<211> 19

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 30

ggagaagtag gtttagtcg 19

<210> 31

<211> 17

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 31

ccgccgctta actctaa 17

<210> 32

<211> 33

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 32

agttgttgta attatttagt agttgttgta gta 33

<210> 33

<211> 27

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 33

cgcaaccgaa taataactac tacgacg 27

<210> 34

<211> 21

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 34

gtcgttatcg gtatttatta c 21

<210> 35

<211> 20

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 35

cgactaacat aaaactaacg 20

<210> 36

<211> 31

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 36

cggtatttat tacgttttcg ttgggggcgg t 31

<210> 37

<211> 21

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 37

aacaacgcca cgaacactac g 21

<210> 38

<211> 16

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 38

gtaggggcga ttgtta 16

<210> 39

<211> 16

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 39

tcccgcgaaa actaaa 16

<210> 40

<211> 36

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 40

tgattgttag tttttatttt gttttggggt gtgttt 36

<210> 41

<211> 20

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 41

tatcgacctc cgaacgcacc 20

<210> 42

<211> 19

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 42

gcgttgaagt cggggttcg 19

<210> 43

<211> 19

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 43

ccgattaaac ccgtacttc 19

<210> 44

<211> 33

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 44

ttggggtttg ttttgtggtt tcgtttggtt tgt 33

<210> 45

<211> 20

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 45

cgctaacaaa cgcgaaccga 20

<210> 46

<211> 19

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 46

gggagtttga gtttattga 19

<210> 47

<211> 21

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 47

gatacgcaac gcgttaacac g 21

<210> 48

<211> 33

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 48

cacattaaca cactccaacc aaatacaacc ctt 33

<210> 49

<211> 21

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 49

cgcccaacga ataccaactc c 21

<210> 50

<211> 17

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 50

ggtcgatgag gtaatgc 17

<210> 51

<211> 17

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 51

gccgcctacc tactacc 17

<210> 52

<211> 28

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 52

acctacctac tacccactct acaccact 28

<210> 53

<211> 22

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 53

tctacgccgc tacctaaacg ac 22

<210> 54

<211> 16

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 54

gagaggcgag tagtag 16

<210> 55

<211> 16

<212> DNA

<213> Artificial

<220>

<223> primer

<400> 55

cacgataaac ccgaac 16

<210> 56

<211> 32

<212> DNA

<213> Artificial

<220>

<223> blocking primer

<400> 56

tgagtagtag ttttggtagt ggtggtagta gt 32

<210> 57

<211> 21

<212> DNA

<213> Artificial

<220>

<223> Probe

<400> 57

tcattaccgc tactaccgcc g 21

Claims

1. A method of identifying a thyroid cancer status in a subject comprising:

NIS, TSHR, and PTEN;

SLC5A8, RASSF1A and TIMP 3; or

NIS, TSHR, PTEN, SLC5A8, RASSF1A, and TIMP 3; and

2. The method of claim 1, wherein the thyroid cancer status comprises the presence, typing, staging and/or grading of thyroid cancer.

3. The method of claim 1 or 2, wherein step 1) comprises extracting DNA from the biological sample and bisulfite treating such that unmethylated cytosine residues in the DNA are deaminated while methylated cytosine residues remain unchanged, followed by determining the methylation level using a methylation specific primer pair to perform an amplification reaction using the biomarker gene or fragment thereof as a template.

4. The method of any one of claims 1-3, wherein the primer pair used in step 1) for NIS gene methylation level detection comprises the primers SEQ ID NO: 10 and 11 or primers SEQ ID NO: 14 and 15; the primer pair for detecting the methylation level of the TSHR gene comprises primers SEQ ID NO: 18 and 19 or primers SEQ ID NO: 22 and 23; the primer pair for detecting the methylation level of the PTEN gene comprises primers SEQ ID NO: 26 and 27 or primers SEQ ID NO: 30 and 31. The primer pair for detecting the methylation level of the SLC5A8 gene comprises primers SEQ ID NO: 34 and 35 or primers SEQ ID NO: 38 and 39; the primer pair for detecting the methylation level of the RASSF1A gene comprises primers SEQ ID NO: 42 and 43 or primers SEQ ID NO: 46 and 47; the primer pair for detecting the methylation level of the TIMP3 gene comprises primers SEQ ID NO: 50 and 51 or primers SEQ ID NO: 54, and 55.

5. The method of any one of claims 1-4, wherein step 1) further comprises the use of a primer pair of SEQ ID NO: 7 and 8, detecting an internal reference gene ACTB.

6. The method of any one of claims 1-5, wherein determining the thyroid cancer status of the subject in step 2) is by logistic regression based on the methylation levels of the biomarker genes.

7. The method of any one of claims 1-6, wherein the biological sample is selected from the group consisting of blood, serum, plasma, lymph, urine, and biopsy tissue of the subject.

8. A kit for detecting a thyroid cancer state in a subject comprising reagents for detecting the level of methylation of a biomarker gene in a biological sample from the subject, wherein the biomarker gene is:

NIS, TSHR, and PTEN;

SLC5A8, RASSF1A and TIMP 3; or

NIS, TSHR, PTEN, SLC5A8, RASSF1A, and TIMP 3.

9. The kit of claim 8, wherein the reagents for detecting a level of methylation comprise a methylation specific primer pair and optionally a bisulfite salt for determining the level of methylation by an amplification reaction.

10. The kit of claim 8 or 9, wherein the primer pair for NIS gene methylation level detection comprises the primers SEQ ID NO: 10 and 11 or primers SEQ ID NO: 14 and 15; the primer pair for detecting the methylation level of the TSHR gene comprises primers SEQ ID NO: 18 and 19 or primers SEQ ID NO: 22 and 23; the primer pair for detecting the methylation level of the PTEN gene comprises primers SEQ ID NO: 26 and 27 or primers SEQ ID NO: 30 and 31. The primer pair for detecting the methylation level of the SLC5A8 gene comprises primers SEQ ID NO: 34 and 35 or primers SEQ ID NO: 38 and 39; the primer pair for detecting the methylation level of the RASSF1A gene comprises primers SEQ ID NO: 42 and 43 or primers SEQ ID NO: 46 and 47; the primer pair for detecting the methylation level of the TIMP3 gene comprises primers SEQ ID NO: 50 and 51 or primers SEQ ID NO: 54, and 55.

11. The kit of any one of claims 8-10, wherein the biological sample is selected from the group consisting of blood, serum, plasma, lymph, urine, and biopsy tissue of the subject.

12. The kit of any one of claims 8-11, further comprising instructions for processing the results of the detection of the methylation level by logistic regression to determine the thyroid cancer status.

13. The kit of any one of claims 8-12, wherein the thyroid cancer status comprises the presence, typing, staging and/or grading of thyroid cancer.