CN111424091B - Marker for differential diagnosis of benign and malignant thyroid follicular tumor and application thereof - Google Patents
Marker for differential diagnosis of benign and malignant thyroid follicular tumor and application thereof Download PDFInfo
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Abstract
The invention discloses a group of methylation markers for differential diagnosis and/or prediction of benign and malignant thyroid follicular tumors; the invention further provides application of the methylation marker in preparation of a differential diagnosis tool for benign and malignant thyroid follicular tumors. According to the invention, by using a paraffin tissue specimen with 128 thyroid follicular lesions, 70 thyroid follicular tumor related methylation markers are found, and the sensitivity and specificity are very high when judging whether the thyroid follicular tumor is benign or malignant (the specificity is 100%, the sensitivity is 92.3%, the accuracy is 96.2%, and the AUC value is 0.994); meanwhile, the 70 markers can predict the benign and malignant degree of the borderline thyroid follicular tumor, guide clinical doctors to make a correct diagnosis and treatment strategy and avoid over diagnosis and treatment.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a group of markers for differential diagnosis of benign and malignant thyroid follicular tumors and application thereof.
Background
Thyroid Cancer (TC) is the most common endocrine malignancy, accounting for over 95% of cases. Thyroid cancer mainly comprises four pathological types (papillary thyroid cancer, follicular thyroid cancer, medullary thyroid cancer and anaplastic thyroid cancer, respectively). Thyroid follicular cancer (FTC) accounts for approximately 15% of all TCs, and is a rare type of thyroid cancer. FTC is commonly found in older patients and is more aggressive than Papillary Thyroid Carcinoma (PTC). Pathological diagnosis of FTC depends on typical envelope and/or vascular infiltration, while tumor cells lack the characteristics of papillary carcinoma nuclei.
Thyroid Adenomas (FTA) are the most common benign follicular thyroid lesions, and refer to a group of benign tumors with follicular differentiation, intact envelope, no envelope and vascular infiltration. FTA and FTC cannot be identified from a cytological perspective, and identification of both requires finding the presence or absence of envelopes and/or vascular infiltrates from surgically excised paraffin specimens. Thyroid tumors with uncertain malignant potential (TT-UMP), which are a group of intercalary thyroid follicular lesions with envelopes, have uncertain or suspicious envelopes and/or vascular infiltrates and can not reach the standard for diagnosing malignancy; TT-UMP includes follicular tumors of undetermined malignant potential (FT-UMP) and well-differentiated tumors of undetermined malignant potential of the thyroid (WT-UMP).
Since the treatment strategies of FTA, FTC and TT-UMP are different, correct pathological diagnosis is very important, but in daily pathological diagnosis work, the three are accurately distinguished only by morphology, sometimes the three are very difficult to make accurate diagnosis. Moreover, the good and the malignant of TT-UMP still lack of understanding, and if the good and the malignant of the group of diseases can be effectively judged, clinical doctors can be better guided to make diagnosis and treatment strategies.
Many studies have attempted to find an index for identifying the three by immunohistochemistry, genetic mutation or methylation. However, the existing indexes mainly aim at papillary thyroid carcinoma, the biological indexes aiming at other follicular tumors are very limited, and experimental researches for applying large sample volumes are lacked. In addition, the sensitivity and specificity of the existing benign and malignant judgment indexes are not high; furthermore, the benign and malignant properties of the borderline tumor cannot be predicted by the existing indexes.
Therefore, there is an urgent need in the art to find methylation markers that can aid in the pathological diagnosis of thyroid follicular lesions, and at the same time can be used to predict the malignancy and malignancy of junctional lesions.
Disclosure of Invention
The invention aims to provide a group of methylation markers related to benign and malignant thyroid tumors and application thereof in preparing tools for differential diagnosis and prediction of benign and malignant thyroid tumors.
The above object of the present invention is achieved by the following technical solutions:
the invention carries out methylation detection on paraffin samples of 128 cases (comprising 46 cases of FTA,36 cases of TT-UMP and 46 cases of FTC) thyroid follicular tumors by a bisulfite sequencing technology (RRBS). Firstly, the inventor finds 70 thyroid follicular tumor related methylation markers in a training set (consisting of 33 FTAs and 33 FTCs), then constructs an evaluation model of malignant thyroid cancer potential according to the 70 markers, and verifies the evaluation model of malignant thyroid cancer potential constructed on the basis of the 70 markers in a test set (consisting of 13 FTAs and 13 FTCs). Further, the inventors utilized an evaluation model constructed based on 70 markers for predicting benign and malignant of the borderline thyroid follicular tumor.
First, the present invention provides a set of methylation markers for differential diagnosis and/or prediction of benign and malignant thyroid follicular tumors, said methylation markers comprising a combination of methylation of genomic regions of the TBX15 intergenic region, the LHX4 intron, the MYOG exon, the MYOG 3' UTR, the HMX3 promoter, the KRT85 intergenic region, the GPC5 promoter, the FOXA1 intergenic region, the FOXA1 exon region, the SIX6 promoter, the GSE1 intergenic region, the WNK4 promoter, the HOXB3 promoter, the GATA 2-AS 1 promoter, the CBLN2 promoter, the POU3F3 promoter, the KIAA2012 intron, the LRRTM1 promoter, the FAM95 intergenic region, the SIM2 intron, the PITX2 intron, the crir 2 intergenic region, the rbk 2 promoter, the frm 2 intergenic promoter, the tmrtm 1 promoter, the tmm 95 intergenic region, the gnx 2 region, the mex 3600672 exon 2 promoter, the mex 2 region, the mex 2 promoter, the mex 2 region, the myxo 2 promoter, the mex 2 region, the mex 2 promoter, the mex 2 region, the mex 2 promoter, the mex 2 promoter, the mex region, the mex promoter, the mex 2 region, the promoter, the mex 2, the promoter, and the promoter of myogx gene of myx 2, the promoter of myx gene of myx 2, the promoter of myx gene of myx 4672, the promoter of myx gene of my, MYOG exon, GPC5 promoter, SIX6 promoter, GATA6-AS1 promoter, HOXA5 promoter, MIR3675 exon, KAZALD1 promoter, NEURL1 promoter, LINC00173 intergenic region, AQP5 promoter, KRT86 intron, FRY promoter, KCTD12 promoter, LINC00239 intergenic region, ZC3H18 exon, RFNG promoter, TNPO2 promoter, LYL1 promoter, EPHX3 promoter, ADAMTS10 promoter, LINC01270 intergenic region, FRP OG 1 intergenic region, LINC00887 intergenic region, LINC00884 promoter, LINAH 2 promoter, LINX 10 intergenic region, KAZALD 10 promoter, MROG3672-AS 10 intergenic region, EF 10 promoter, RORA promoter, NUDT16L 4672 exon, LINX 36013672, SNLGALS 10 intergenic region, PRLGALS 10 intergenic region, and PRLGAX 10 intergenic region.
In some of these embodiments, the methylation marker comprises 70 methylation haplotype regions (MHB), the 70 methylation haplotype regions being shown in table 2.
In some of these embodiments, the thyroid follicular tumor comprises thyroid adenoma, thyroid follicular cancer, and thyroid follicular tumor of undetermined malignant potential.
The second aspect of the invention provides an application of the methylation marker in preparing a differential diagnosis tool for benign and malignant thyroid follicular tumors.
In some embodiments, the methylation marker is used for preparing a differential diagnosis tool for benign and malignant thyroid follicular tumors by constructing an evaluation model of malignant thyroid cancer potential.
The third aspect of the invention provides the application of the methylation marker in preparing a tool for evaluating the malignant potential of the thyroid follicular tumor with uncertain malignant potential.
In some embodiments, the methylation marker is used for preparing a tool for evaluating the malignant potential of the thyroid follicular tumor with uncertain malignant potential by constructing an evaluation model of the malignant thyroid cancer potential.
In some of these embodiments, the tools may include reagents, kits, and systems.
In some of these embodiments, the kit comprises reagents for detecting the methylation level of the methylation marker in a sample from a subject.
In some of these embodiments, the reagents are according to reagents used using pyrosequencing, bisulfite conversion sequencing, methylation chip methods, qPCR methods, digital PCR methods, second generation sequencing methods, third generation sequencing methods, genome-wide methylation sequencing methods, DNA enrichment detection methods, simplified bisulfite sequencing techniques, HPLC methods, MassArray, methylation specific PCR, or combinations thereof.
Preferably, the reagents are those employed to simplify bisulfite sequencing techniques.
In the present invention, the system comprises a thyroid follicular tumor benign and malignant diagnosis and evaluation system.
In some of these embodiments, the diagnostic or assessment system comprises a substance that detects the methylation level of the methylation marker in table 2; the diagnosis or evaluation system comprises a data processing device, wherein a diagnosis or evaluation module is arranged in the data processing device, and the diagnosis or evaluation module judges the benign and malignant risk of the thyroid follicular tumor of the subject according to the methylation level of the marker in the sample of the subject.
In some embodiments, the diagnosis or evaluation module obtains the methylation levels corresponding to the 70 methylation markers in the thyroid tumor sample to be tested, forms a sample-marker numerical matrix, obtains a score by inputting an evaluation model of malignant thyroid cancer potential, and identifies benign malignancy of the thyroid tumor or evaluates malignancy potential of the thyroid tumor with uncertain malignancy potential according to the score.
Specifically, the diagnosis module obtains methylation levels corresponding to the 70 methylation markers in the thyroid tumor sample to be detected to form a sample-marker numerical matrix, obtains the probability that each sample is malignant tumor by inputting an evaluation model of malignant thyroid cancer potential, and judges that the model is malignant tumor FTC when the probability of methylation prediction malignant tumor is greater than 0.5, otherwise, the model is benign tumor FTA.
Specifically, the evaluation module detects TT-UMP samples through an RRBS sequencing technology and calculates methylation values corresponding to the 70 markers to form a sample-marker numerical matrix, inputs an evaluation model of malignant thyroid cancer potential to obtain a malignant potential score, and can divide all UMP samples into three categories (three risk levels) according to the methylation score result: 1-low risk, methylation score 0-0.4; 2-intermediate risk, methylation score 0.4-0.6; 3-high risk, methylation score 0.6-1.
In a fourth aspect of the invention, a kit for the differential diagnosis of benign and malignant thyroid follicular tumor is provided, the kit comprising reagents for detecting the methylation level of a marker in a sample of a subject, wherein the marker is a methylation marker shown in table 2.
In some of these embodiments, the subject sample comprises a tissue sample of a thyroid follicular tumor.
In some of these embodiments, the reagents are according to reagents used using pyrosequencing, bisulfite conversion sequencing, methylation chip methods, qPCR methods, digital PCR methods, second generation sequencing methods, third generation sequencing methods, genome-wide methylation sequencing methods, DNA enrichment detection methods, simplified bisulfite sequencing techniques, HPLC methods, MassArray, methylation specific PCR, or combinations thereof.
In some of these embodiments, the reagents are reagents employed to simplify bisulfite sequencing techniques.
In some embodiments, the reagents include enzymatic cleavage reagents, reagents required for methylated linker ligation, bisulfite conversion treatment reagents, primers, and primer amplification reagents, among others.
The cleavage reagent includes methylation-sensitive or non-sensitive restriction enzymes, preferably, MSP I, HaeIII, BanII, HpyCH4V, AluI, SphI and BssSI restriction enzymes. Reagents required for ligation of the methylated linker include buffers, DNA ligase, and ATP. The reagents required by the primer amplification comprise an amplification buffer solution, 4 dNTPs, a primer and DNA polymerase. Both the primers and the reagents are commercially available from commercial companies.
Further, the present invention also provides a kit for predicting the malignant potential of a thyroid follicular tumor with undetermined malignant potential, the kit comprising reagents for detecting the methylation level of a marker in a sample of a subject, the marker being a methylation marker as shown in table 2.
The fifth aspect of the invention provides application of the methylation marker in constructing an evaluation model of malignant thyroid cancer potential.
In some embodiments, the model for assessing the potential of malignant thyroid cancer can be used to identify benign and malignant thyroid tumors, and can also be used to assess the malignant potential of thyroid follicular tumors with uncertain malignant potential.
The invention discloses application of an evaluation model constructed based on the methylation marker in preparation of products for judging benign and malignant thyroid follicular tumor.
Advantageous effects
According to the invention, paraffin tissue specimens of 128 thyroid follicular lesions are used for finding 70 thyroid follicular tumor related methylation markers, and an evaluation model constructed based on the 70 markers has very high sensitivity and specificity (the specificity is 100%, the sensitivity is 92.3%, the accuracy is 96.2%, and the AUC value is 0.994) when judging whether thyroid follicular tumor is benign or malignant; meanwhile, the evaluation model constructed based on the 70 markers can predict the benign and malignant degree of the junctional thyroid follicular tumor, guide clinical doctors to make a correct diagnosis and treatment strategy and avoid excessive diagnosis and treatment. The inventor further utilizes the biomarkers to develop tools for identifying and diagnosing benign and malignant thyroid follicular tumors and tools (reagents, kits and diagnosis or evaluation systems) for evaluating the malignant potential of thyroid follicular tumors with uncertain malignant potential, so that the tools provide reference values for diagnosing or evaluating the benign and malignant thyroid follicular tumors.
Drawings
FIG. 1: the ROC curve shows the accuracy and specificity of the methylation index in the verification set;
FIG. 2: 36 UMP samples were compared for gene mutation and methylation scores in different risk groups.
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art, and the materials and devices used in the present invention are commercially available unless otherwise specified.
The present invention provides methylation detection of paraffin specimens from 128 (including 46 FTA,36 TT-UMP and 46 FTC) thyroid follicular tumors by bisulfite sequencing technology (RRBS). Firstly, the inventor finds 70 thyroid follicular tumor related methylation markers in a training set (consisting of 33 FTA and 33 FTC), and utilizes 70 differential methylation markers to construct an evaluation model of malignant thyroid cancer potential; the constructed methylation marker model is used for identifying the benign and malignant of 26 thyroid follicular tumor samples (pathological detection results of 13 FTA and 13 FTC), and the high sensitivity and specificity of the methylation marker-based model for thyroid benign and malignant differential diagnosis are further verified. Finally, the inventors used an evaluation model constructed by methylation markers to predict benign and malignant status of 36 borderline (malignant potential indeterminate) thyroid follicular tumors, and found that no malignant mutation was detected in any of 7 low-risk samples (0%), no malignant mutation was detected in 5 of 14 cases (35.7%), and no mutation was detected in 11 of 15 high-risk samples (73.3%). The mutation sample proportion of the high-risk group is obviously higher than that of the low-risk group (p is 0.004) and is obviously higher than that of the medium-risk group, which indicates that thyroid tumors belong to the high-risk group, the probability of carrying gene mutation is higher, the malignant potential of the thyroid tumors is obviously higher than that of the medium-risk group and the low-risk group, and patients in the group should be closely followed to avoid developing malignant tumors; and the possibility of carrying gene mutation by patients in a low-risk group is low, and follow-up diagnosis can be carried out to avoid over-treatment.
Example 1: methylation marker screening for benign and malignant thyroid tumors
The inventor collects paraffin wax samples of thyroid follicular tumor diagnosed by Beijing coordination hospital pathology department from 2010 to 2016, and extracts DNA (including 46 FTA,36 TT-UMP and 46 FTC) in 128 cases, and the DNA is purified by magnetic beads (Backman Ampure Xp beads); methylation detection analysis was performed by bisulfite sequencing technique (RRBS), first looking for differentially methylated molecules in a training set (consisting of 33 FTAs and 33 FTCs).
Methylation detection analysis was performed by Shanghai Kun Seiko Biotech, Inc.: taking 100ng of purified sample DNA for enzyme digestion reaction, wherein the reaction system is shown in Table 1, and the reaction temperature is as follows: 3 hours at 37 ℃, 20 minutes at 80 ℃ and 4 ℃. Wherein 37 ℃ is the temperature of enzyme digestion reaction, and 80 ℃ is the temperature of enzyme inactivation. The mixed enzyme contains 7 restriction enzymes: MSP I (NEB, R0106L), HaeIII (NEB, R0108L), BanII (Thermo Scientific)TM，ER0281)、HpyCH4V(NEB，R0620L)、AluI(Thermo ScientificTMER0011), SphI (NEB, R0182L) and BssSI (Thermo Scientific)TMER 1841); connecting the enzyme digestion products by a methylation joint, and purifying the connection products by magnetic beads; bisulfite conversion treatment was performed using EZ DNA Methylation-Gold Kit from ZYMO research; indexingAmplifying the primers, and purifying the amplified product by magnetic beads; quality control of the library: determining the concentration of the library by using a method of Qubit and qPCR, and analyzing the size of the fragment of the library by using a labHip fragment analyzer of PerkinElmer company; sequencing: sequencing was performed with Illumina platform instrument Hiseq X Ten.
TABLE 1 digestion reaction System
| Reaction mixture | Volume/microliter |
| 10x reaction buffer | 4.0 |
| Mixed restriction enzymes | 1.0 |
| 1.2 pg/. mu.L of unmethylated lambda DNA | 0.8 |
| DNA + nuclease-free Water | 34.2 |
| Total of | 40 |
The present invention screens MHB (region with minimum linkage disequilibrium of two adjacent CpG sites r2 greater than 0.5) as candidate methylation markers for FA and FTC samples according to the method of (Guo S, et al (2017) Identification of methylation patterns in depletion of heterologous tissue samples and tumor-of-origin mapping from plasma DNA. Nat Genet 49(4): 635-642) based on multiple DNA methylation databases (ENCOD, TCGA). Not less than 25,000 MHBs per sample have to be detected for downstream analysis, and candidate MHBs have to be detected in not less than 90% of the samples. The candidate markers are MHB with PDR (probability of discrete reads) or MHL (Methylation happy Load (MHL) standard deviation not less than 0.02. finally, 70 statistically different methylated molecular markers are found in the training set (consisting of 33 benign FTA and 33 malignant FTC), wherein 34 are located in the promoter region, 7 are located in the exon region, 6 are located in the intron region, 2 are located in the 3' -UTR region, and 21 are located in the intergenic region, and the specific contents are shown in Table 2.
TABLE 270 methylation markers and their specific positions
Example 2 construction of an evaluation model of malignant thyroid cancer potential Using 70 differentially methylated markers
After the sequencing result of each sample in example 1 is subjected to quality control analysis, sequences of 70 marker segments are selected, and calculation is performed according to a methylation value calculation mode corresponding to each marker (the methylation value calculation mode refers to Guo S, et al (2017) Identification of methylation mapping blocks in restriction of methylation groups in samples and tumor tissue-of-origin mapping from place DNA. Nat Genet 49 (635-) (642)), and if the depth of a certain sequencing site in a segment is less than 10 ×, the methylation value of the segment is NA. A sample-marker numerical matrix is formed, and some exemplary data are shown in table 3. The construction of the model was done by Shanghai Kun Seiko Biotechnology Inc.
TABLE 310 marker methylation value matrix for thyroid tumor samples
……
Constructing a model:
the methylation level matrices (markers) of 33 FTAs and 33 FTCs used in the model construction above, and the classification information of 66 samples was stored as a first column with sample names and a second column with a matrix (pheno) of classification information (FTC name p.ftc, FTA name n.fta).
Opening the R program package, and importing the classification information matrix and the corresponding methylation level matrix of xx samples
Delay (storage path of classification information matrix), sep ═ T ═ as. is ═ T, head ═ T, check
markers ═ ead.delim ("storage path of methylation level matrix", sep ═ T ", as.is ═ T, head ═ T, check
Converting the methylation level matrix into a matrix with the row name as the sample name and the column name as the name of the methylation marker
imput＝t(markers)
Complement NA value by proximity value complement method
library(DMwR)
imputed＝knnImputation(imput)
Setting modeling parameters
library(caret,quietly＝T)
ctrl<-trainControl(method＝"repeatedcv",savePredictions＝T,classProbs＝T,number＝3,repeats＝10,allowParallel＝TRUE)
Construction of random forest model
mod_rf<-train(imputed,pheno,method＝'rf',trControl＝ctrl)
Example 3 methylation markers for benign and malignant identification of thyroid tumors
In this example, 26 samples of thyroid follicular tumor were identified as benign or malignant using the model constructed in example 2. The process is as follows:
a sample-marker numerical matrix was constructed similar to that of table 3 for 26 samples, according to the method of example 2.
Opening the R program package, and importing 26 samples to be evaluated-a numerical matrix of markers
Delaim ("storage path of sample-marker matrix", sep ═ T ", as.is ═ T, row.names ═ 1, head ═ T, check.names ═ F)
Converting the matrix into a matrix with the row name as the sample name and the column name as the name of the methylation marker
imput＝t(valdata)
And supplement the NA value by the approach value supplement method
library(DMwR)
imputed＝knnImputation(imput)
Then, the established evaluation model (RFmodelFTC) for the malignant thyroid cancer potential is introduced
model readRDS (RFmodelFTC storage path)
Model evaluation was then initiated to determine whether each sample was more likely to be malignant or benign
library(randomForest)
class＝predict(model,imputed,type＝"response")
And calculating the probability that each sample is malignant tumor
probs＝predict(model,imputed,type＝"prob")
And (3) drawing an ROC curve according to the tumor probability value predicted by the methylation level by using a random forest model, wherein the AUC of the area under the curve is 0.994, the sensitivity is 92.3%, the specificity is 100%, the accuracy is 96.2%, the cut-off value of the optimal diagnosis is 0.5, namely when the probability of the methylation prediction malignant tumor is more than 0.5, the model judges that the malignant tumor is FTC, and otherwise, the model judges that the malignant tumor is benign FTA (figure 1). Comparing the final judgment result with the pathological detection results (13 cases of FTA and 13 cases of FTC), 26 cases of samples are found, only one methylation marker model is inconsistent with the pathological diagnosis (see Table 4), and the ROC result further proves that the combination of 70 methylation markers has good effect of identifying the benign or malignant methylated tumors.
TABLE 426 identification of benign and malignant patients with thyroid tumors
| Sample (I) | Pathological judgment | Methylation prediction | Methylation assessment of malignancy probability |
| 1 | p.ftc | p.ftc | 0.596 |
| 2 | p.ftc | p.ftc | 0.546 |
| 3 | p.ftc | p.ftc | 0.692 |
| 4 | n.fta | n.fta | 0.418 |
| 5 | n.fta | n.fta | 0.246 |
| 6 | n.fta | n.fta | 0.378 |
| 7 | n.fta | n.fta | 0.22 |
| 8 | n.fta | n.fta | 0.49 |
| 9 | n.fta | n.fta | 0.266 |
| 10 | p.ftc | p.ftc | 0.886 |
| 11 | p.ftc | p.ftc | 0.508 |
| 12 | p.ftc | p.ftc | 0.784 |
| 13 | p.ftc | n.fta | 0.462 |
| 14 | p.ftc | p.ftc | 0.68 |
| 15 | n.fta | n.fta | 0.172 |
| 16 | n.fta | n.fta | 0.298 |
| 17 | n.fta | n.fta | 0.314 |
| 18 | n.fta | n.fta | 0.074 |
| 19 | n.fta | n.fta | 0.408 |
| 20 | n.fta | n.fta | 0.168 |
| 21 | n.fta | n.fta | 0.36 |
| 22 | p.ftc | p.ftc | 0.752 |
| 23 | p.ftc | p.ftc | 0.762 |
| 24 | p.ftc | p.ftc | 0.572 |
| 25 | p.ftc | p.ftc | 0.762 |
| 26 | p.ftc | p.ftc | 0.702 |
Example 4 methylation markers for the assessment of TT-UMP malignancy potential
The method explores whether the malignant thyroid cancer potential evaluation model constructed based on 70 methylation markers can be used for prediction of TT-UMP malignant potential. A sample-marker numerical matrix was constructed similar to the UMP sample of table 3, according to the method of example 2.
Opening the R-program package, importing the methylation marker numerical matrix of the UMP sample
Delaim ("storage path of sample-marker matrix", sep ═ T ", as.is ═ T, row.names ═ 1, head ═ T, check.names ═ F)
Converting the matrix into a matrix with the row name as the sample name and the column name as the name of the methylation marker
imput＝t(valdata)
And supplement the NA value by the approach value supplement method
library(DMwR)
imputed＝knnImputation(imput)
Then, the established evaluation model (RFmodelFTC) for the malignant thyroid cancer potential is introduced
model readRDS (RFmodelFTC storage path)
Scoring for malignancy potential:
probs＝predict(model,imputed,type＝"prob")
based on methylation scoring results, all UMP samples can be classified into three categories (three risk classes): 1-low risk, methylation score 0-0.4; 2-intermediate risk, methylation score 0.4-0.6; 3-high risk, methylation score 0.6-1. The methylation scores were compared with the results of known detection of genetic mutations associated with thyroid carcinogenesis (Table 5), and no malignant mutation was detected in any of 7 low-risk samples (0%), 5 of 14 at-risk samples (35.7%), and 11 of 15 high-risk samples (73.3%). The high risk group had a significantly higher proportion of mutant samples than the low risk group (p 0.004) and significantly higher than the medium risk group (fig. 2). Indicating that thyroid tumors belonging to the high risk group have a significantly higher malignant potential than the low risk group and the medium risk group, and patients in this group should be closely followed up to avoid developing malignant tumors; and the possibility of carrying gene mutation by patients in a low-risk group is low, and follow-up diagnosis can be carried out to avoid over-treatment.
TABLE 5 results of methylation scoring and Gene mutation detection
Among them, TP53, PIK3CA, TSHR, NRAS, HRAS, GNAS, PPARG-PAX8 and KRAS are mutated genes related to thyroid carcinogenesis in the prior art.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (2)
1. The application of a reagent for detecting the methylation level of a methylation marker in preparing a kit for differential diagnosis of benign and malignant thyroid follicular tumors, wherein the methylation marker comprises the following methylation markers: chr, and a, chr, and a, chr, and a, chr, The chr19:52104749:52104928, chr2:128158537:128158621, chr21:48087183:48088183, chr22:38071168:38071189, chr7:26415938:26416562, chr1:16862044:16862199, chr10: 10: 10:10, chr10: 10: 10: chr10: 10:10, chr10: 10:10, chr10: 10: 10:10, chr10: 10: 10:10, chr10: 10:10, chr10: 10: 10:10, chr10: 10: 10:10, chr10: 10: 10:10, chr10: 10: 10:10, chr10: 10.
2. Use of an agent for detecting the methylation level of a methylation marker in the preparation of a kit for assessing the malignant potential of a follicular thyroid tumor of indefinite malignant potential, said methylation marker comprising the following methylation markers: chr, and a, chr, and a, chr, and a, chr, The chr19:52104749:52104928, chr2:128158537:128158621, chr21:48087183:48088183, chr22:38071168:38071189, chr7:26415938:26416562, chr1:16862044:16862199, chr10: 10: 10:10, chr10: 10: 10: chr10: 10:10, chr10: 10:10, chr10: 10: 10:10, chr10: 10: 10:10, chr10: 10:10, chr10: 10: 10:10, chr10: 10: 10:10, chr10: 10: 10:10, chr10: 10: 10:10, chr10: 10.
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