CN117778566A - Marker for predicting thyroid cancer metastasis and application thereof - Google Patents
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Abstract
The invention provides a marker combination for predicting thyroid cancer metastasis and application thereof. In particular, the present invention provides the use of a gene, mRNA, cDNA, protein or a detection reagent thereof for a risk marker for thyroid cancer metastasis assessment for preparing/establishing a diagnostic reagent or kit/device for judging the risk of thyroid cancer metastasis occurrence. Research shows that the thyroid cancer metastasis risk marker can be used as a marker for early judging thyroid cancer metastasis of thyroid cancer patients, has high sensitivity and specificity, can rapidly diagnose thyroid cancer metastasis at a relatively early disease progression stage, and provides powerful assistance for early therapeutic intervention of diseases.
Description
Technical Field
The invention relates to the field of medical diagnosis, in particular to a marker for predicting thyroid cancer metastasis and application thereof.
Background
The number of the global incidences of thyroid cancer is about 56.7 ten thousand cases each year, and the incidence rate is ninth to various cancer types. The global female incidence rate is about 10.2/10 ten thousand people, which is 3 times that of men. Since the 1980 s, the incidence of thyroid cancer has been continuously rising in many countries. This is mainly due to the improved detection and diagnosis of Papillary Thyroid Carcinoma (PTC) which increases the detection rate.
About 84% of thyroid cancer patients are PTC, which is the most common thyroid malignancy. The average incidence of PTC in the united states was about 6.66% and mortality based on morbidity was 0.20% between 1974 and 2013. Although PTC is considered an indolent tumor, some cancer cells metastasize to perithyroidic lymph nodes, mainly including central Lymph Node Metastasis (LNM) and cervical lateral LNM. Typically, the LNM occurs first in the central region and then in the outer regions. LNM is an important indicator of PTC prognosis, range and surgical mode, as well as an important risk factor for high recurrence rate and low survival rate in patients.
Several studies have been proposed for LNM risk assessment of PTC patients. Tumor size, tumor location, tumor expansion, microcalcifications and hashimoto are considered independent risk factors for LNM. Some people combine the risk factors with Thyroid Stimulating Hormone (TSH), antithyroid globulin antibody (TGAb) and other blood markers to construct an LNM predictive model of a PTC patient.
In recent years, radiology has been attracting attention in terms of accurate diagnosis. Radiohistology-based methods have also been proposed to predict the LNM of PTC patients by converting ultrasound images into data that can be mined. These methods extract the intensity, boundary, texture, wavelet, etc. features from the ultrasound image and establish a relationship between these high-throughput features and the LNM state.
In the above studies, the area under the curve (AUC) test set for individual subject operating characteristics (ROC) was approximately between 0.67 and 0.78, both based on clinical statistics and on imaging histology, since the integrity of the extracted image features was difficult to guarantee, and the predictive performance of LNM was not ideal.
Thus, there is an urgent need in the art to develop methods that enable more effective, early and accurate early screening and diagnosis of thyroid cancer lymphatic metastasis, enabling timely intervention therapy for thyroid cancer lymphatic metastasis.
Disclosure of Invention
It is an object of the present invention to provide a more efficient, early and accurate method for early screening and diagnosis of thyroid cancer metastasis.
In a first aspect of the invention, there is provided the use of a gene, mRNA, cDNA, protein, or a detection reagent thereof, for a marker for risk of thyroid cancer metastasis, for the preparation of a diagnostic reagent or kit for diagnosing whether a thyroid cancer patient has thyroid cancer metastasis;
wherein the thyroid cancer metastasis risk marker is selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30.
In another preferred embodiment, the thyroid cancer metastasis risk marker further comprises any marker selected from the group consisting of: (B1) TPO; (B2) TIMP1; (B3) SLC26A7; (B4) SERPINA1; (B5) SLC34A2.
In another preferred embodiment, the markers A1 to A8 are selected from table a:
table A
In another preferred embodiment, the markers of group B are selected from table B:
table B
In another preferred embodiment, the gene, mRNA, cDNA, or protein of any one of the markers shown in table a and/or table B of the thyroid cancer metastasis risk markers is of human origin.
In another preferred embodiment, the thyroid cancer metastasis risk marker further comprises a combination of 8 markers selected from group a and 5 markers selected from group B.
In another preferred embodiment, the diagnostic reagent or kit is used to detect a thyroid cancer metastasis risk marker in thyroid carcinoma in situ tissue.
In another preferred embodiment, the thyroid cancer comprises papillary thyroid cancer.
In another preferred embodiment, the thyroid cancer metastasis comprises thyroid cancer lymphatic metastasis;
in another preferred embodiment, the thyroid cancer lymphatic metastasis comprises: central lymphatic metastasis of thyroid cancer, cervical lateral lymphatic metastasis of thyroid cancer, or a combination thereof.
In another preferred embodiment, the detection reagent comprises:
(a) A specific antibody, a specific binding molecule, directed against the thyroid cancer metastasis risk marker; and/or
(b) Primers or primer pairs, probes or chips (e.g., nucleic acid chips or protein chips) for specifically amplifying the mRNA or cDNA of the thyroid cancer metastasis risk marker.
In a second aspect of the present invention, there is provided a kit comprising a detection reagent for detecting a gene, mRNA, cDNA, protein, or a combination thereof, of a thyroid cancer metastasis risk marker in a test sample;
wherein the thyroid cancer metastasis risk marker is selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30.
In another preferred embodiment, the detection reagent is coupled to or carries a detectable label.
In another preferred embodiment, the detectable label is selected from the group consisting of: chromophores, chemiluminescent groups, fluorophores, isotopes or enzymes.
In another preferred embodiment, the antibody is a monoclonal antibody or a polyclonal antibody.
In another preferred embodiment, the diagnostic reagent comprises an antibody, a primer, a probe, a sequencing library, a nucleic acid chip (e.g., a DNA chip), or a protein chip.
In another preferred embodiment, the nucleic acid chip comprises a substrate and specific oligonucleotide probes spotted on the substrate, wherein the specific oligonucleotide probes comprise probes specifically binding to polynucleotides (mRNA or cDNA) of any of the thyroid cancer metastasis risk markers.
In another preferred embodiment, the protein chip comprises a substrate and specific antibodies spotted on the substrate, wherein the specific antibodies comprise specific antibodies against the thyroid cancer metastasis risk markers.
In another preferred embodiment, the antibody is a monoclonal antibody or a polyclonal antibody.
In another preferred embodiment, the kit contains the gene, mRNA, cDNA and/or protein of the thyroid cancer metastasis risk marker as a control or quality control.
In another preferred embodiment, the kit further comprises a label or instructions stating that the kit is used to (a) determine the risk of developing thyroid cancer metastasis and/or (b) evaluate the effectiveness of a treatment for thyroid cancer metastasis.
In another preferred embodiment, the reagents comprise primers, probes, gRNA or a combination thereof, more preferably a primer pair or probe for PCR, qPCR, RT-PCR.
In another preferred embodiment, the detection of the thyroid cancer metastasis risk marker can be performed by the following method: sequencing, PCR, or a combination thereof.
In another preferred embodiment, the detection of the thyroid cancer metastasis risk marker is quantitatively detectable.
In a third aspect of the present invention, there is provided a detection method comprising the steps of:
(a) Providing a detection sample;
(b) Detecting the expression level of a thyroid cancer metastasis risk marker gene in the detection sample, and marking the expression level as C1; and
(c) Comparing the concentration of the marker for risk of thyroid cancer metastasis C1 with a control reference value C0, wherein the marker for risk of thyroid cancer metastasis is selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30;
if the detection result of the thyroid cancer metastasis risk of the detected object meets the following conditions, prompting that the thyroid cancer metastasis risk of the object is high:
when the expression level of a certain marker in the test subjects of table a and table B is significantly different from the reference value or standard value, the risk of thyroid cancer metastasis in the thyroid cancer patient is high.
In a fourth aspect of the invention, there is provided an apparatus for early screening for thyroid cancer metastasis, the apparatus comprising:
(a) The input module is used for inputting the expression data of the characteristic genes of a certain thyroid cancer patient;
wherein the characteristic gene comprises a gene selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30;
(b) The processing module is used for analyzing and calculating the input characteristic genes so as to obtain risk judgment; through the analysis of differential expression conditions, the thyroid cancer metastasis risk of the thyroid cancer patient is indicated to be high when the obvious differential expression conditions appear; when the differential expression condition is not obvious, the thyroid cancer metastasis risk of the thyroid cancer patient is low; and
(c) And the output module is used for outputting the auxiliary screening result.
In another preferred embodiment, the device further comprises a detection module for detecting mRNA levels, protein levels, or protein activity of the risk marker.
In another preferred embodiment, the detection module is selected from the group consisting of: ELISA analyzers, PCR detectors, sequencers, or combinations thereof.
In a fifth aspect of the invention, there is provided a method of detecting the combined expression level of a thyroid cancer metastasis risk marker combination, comprising the steps of:
(a) Providing a detection sample;
(b) Extracting total RNA of the sample;
(c) Performing reverse transcription on the product RNA obtained in step (b);
(d) Performing fluorescent quantitative PCR on the reverse transcription product obtained in the step (c), thereby obtaining the expression level of the risk marker gene;
wherein the risk marker is selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30.
In another preferred embodiment, the method is a non-diagnostic, non-therapeutic method.
In another preferred embodiment, the method is an in vitro method.
It is understood that within the scope of the present invention, the above-described technical features of the present invention and technical features specifically described below (e.g., in the examples) may be combined with each other to constitute new or preferred technical solutions. And are limited to a space, and are not described in detail herein.
Drawings
FIG. 1 shows two-dimensional intercalation of thyroid tumor cells (principal components 1 and 2); cells are based on whether they are derived from patients with central/cervical tumor metastasis (positive) or not (negative).
FIG. 2 shows AUROC values of a random forest model using different numbers of genes as inputs; genes are selected based on the mutual information between gene expression and class tags.
Fig. 3 shows AUROC curves of the final random forest model using 13 genes as inputs.
Fig. 4 shows the expression levels of the final 13 gene signature of tumor cells from the positive/negative group.
Detailed Description
Through extensive and intensive studies, the present inventors have for the first time opened a method and apparatus for efficiently and accurately screening for thyroid cancer metastasis in an early stage based on a characteristic gene. Specifically, the inventor constructs a simple, accurate and efficient early screening system for thyroid cancer metastasis by constructing single cell transcriptome maps of central metastatic thyroid cancer patients, lateral metastatic thyroid cancer patients and in-situ cancer tissues without any metastasis, and carrying out weight sequencing on genes according to mutual information scores of the genes, so as to unexpectedly screen out a plurality of characteristic genes closely related to thyroid cancer metastasis. The method and the screening system can effectively distinguish the patients with the thyroid cancer metastasis from the patients with the thyroid cancer metastasis, and facilitate corresponding therapeutic intervention on the patients with the thyroid cancer metastasis as early as possible. The present invention has been completed on the basis of this finding.
The invention uses the tumor cell expression data of in-situ thyroid cancer as the input for constructing a prediction model, and uses at least 13 genes as the input, and finally the model has 92.9 percent of accuracy for classifying the metastasis and non-metastasis of the patients just admitted to the hospital, which shows that the model has strong performance. Implementation of this model may help clinicians make timely diagnoses of thyroid cancer metastasis. Therapeutic intervention may be performed early and prevent the progression of thyroid cancer metastasis. The significance of the work is to have the opportunity to solve the classification screening of early thyroid cancer metastasis, and has great clinical significance for the management of thyroid cancer metastasis of patients and hospitals.
Terminology
The term "sample" or "specimen" as used herein refers to a material that is specifically associated with a subject from which particular information about the subject can be determined, calculated, or inferred. The sample may be composed in whole or in part of biological material from the subject.
As used herein, the term "expression" includes the production of mRNA from a gene or gene portion, and includes the production of a protein encoded by RNA or gene portion, and also includes the presence of a detection substance associated with expression. For example, cDNA, binding of a binding ligand (e.g., an antibody) to a gene or other oligonucleotide, protein or protein fragment, and chromogenic portions of the binding ligand are included within the term "expressed". Thus, an increase in half-pel density on immunoblots, such as Western blots, is also within the term "expression" based on biological molecules.
As used herein, the term "reference value" or "control reference value" refers to a value that is statistically relevant to a particular result when compared to the result of an analysis. In a preferred embodiment, the reference value is determined based on the expression of mRNA and/or protein in comparison to a thyroid cancer lymphatic metastasis risk marker and statistically analyzed. Some of these studies are shown in the examples section herein. However, the studies from the literature and the user experience of the methods disclosed herein can also be used to produce or adjust the reference value. Reference values may also be determined by considering conditions and results that are particularly relevant to the patient's population, medical history, genetics, age, and other factors.
Thyroid cancer lymphatic metastasis risk marker
As used herein, the term "thyroid cancer metastasis risk marker of the present invention" refers to one or more markers shown in table a and/or table B.
In the present invention, the terms "a thyroid cancer metastasis risk marker protein of the present invention", "a thyroid cancer lymphatic metastasis risk marker protein of the present invention", "a polypeptide of the present invention", or "a marker shown in table a and/or table B" are used interchangeably, and refer to any one or more of the thyroid cancer lymphatic metastasis risk markers of the present invention.
In the present invention, the terms "thyroid cancer metastasis risk marker gene", "thyroid cancer lymphatic metastasis risk marker gene", "polynucleotide of thyroid cancer lymphatic metastasis risk marker" are used interchangeably, and refer to the nucleotide sequence of any one of the thyroid cancer metastasis risk markers shown in table a and/or table B.
It is understood that substitution of nucleotides in the codon is acceptable when encoding the same amino acid. It is further understood that nucleotide substitutions are also acceptable when conservative amino acid substitutions are made by the nucleotide substitutions.
In case that information of a thyroid cancer metastasis risk marker is obtained, a nucleic acid sequence encoding it can be constructed therefrom, and a specific probe can be designed based on the nucleotide sequence. The full-length nucleotide sequence or a fragment thereof can be obtained by PCR amplification, recombinant methods or artificial synthesis. For the PCR amplification method, primers can be designed based on the nucleotide sequence of the thyroid cancer metastasis risk marker disclosed in the present invention, particularly, the open reading frame sequence, and amplified to obtain the relevant sequence using a commercially available cDNA library or a cDNA library prepared according to a conventional method known to those skilled in the art as a template. When the sequence is longer, it is often necessary to perform two or more PCR amplifications, and then splice the amplified fragments together in the correct order.
Once the relevant sequences are obtained, recombinant methods can be used to obtain the relevant sequences in large quantities. This is usually done by cloning it into a vector, transferring it into a cell, and isolating the relevant sequence from the propagated host cell by conventional methods.
Furthermore, the sequences concerned, in particular fragments of short length, can also be synthesized by artificial synthesis. In general, fragments of very long sequences are obtained by first synthesizing a plurality of small fragments and then ligating them.
At present, it is entirely possible to obtain the DNA sequences encoding the proteins of the invention (or fragments, derivatives thereof) by chemical synthesis. The DNA sequence may then be introduced into a variety of existing DNA molecules (e.g., vectors) and cells known in the art.
The polynucleotide sequences of the present invention can be used to express or produce recombinant thyroid cancer metastasis risk markers by conventional recombinant DNA techniques.
Detection method
Based on differential expression in-situ cancer cells of thyroid cancer metastasis risk markers, the invention also provides a corresponding method for judging thyroid cancer metastasis risk.
The present invention relates to diagnostic assays for the quantitative and positional detection of protein levels or mRNA levels of thyroid cancer metastasis risk markers. Such tests are well known in the art. The level of human thyroid cancer metastasis risk marker protein or mRNA level detected in the assay can be used to determine (including aid in determining) whether there is a risk of thyroid cancer metastasis.
A preferred method is to perform a quantitative PCR/qPCR/RT-PCR assay on mRNA or cDNA.
One preferred method is to quantitatively detect mRNA or cDNA, sequencing.
Polynucleotides of a thyroid cancer lymphatic metastasis risk marker are useful for diagnosis of thyroid cancer lymphatic metastasis risk. A part or all of the polynucleotides of the present invention can be immobilized as probes on a microarray or DNA chip for differential expression analysis and gene diagnosis of genes in analysis.
In addition, the invention can also detect at the protein level. For example, antibodies to a thyroid cancer lymphatic metastasis risk marker may be immobilized on a protein chip for detection of thyroid cancer lymphatic metastasis risk proteins in a sample.
Detection kit
Based on the correlation between the risk of thyroid cancer metastasis and the risk of thyroid cancer metastasis, the risk of thyroid cancer metastasis marker can be used as a judgment marker for the risk of thyroid cancer metastasis.
The invention also provides a kit for judging the risk of thyroid cancer metastasis, which comprises a detection reagent for detecting the gene, mRNA, cDNA, protein or the combination thereof of the thyroid cancer metastasis risk marker. Preferably, the kit contains an antibody or immunoconjugate of the invention against a risk marker for thyroid cancer metastasis, or an active fragment thereof; or a primer or primer pair, probe or chip containing mRNA or cDNA specifically amplifying a thyroid cancer metastasis risk marker.
In another preferred embodiment, the kit further comprises a label or instructions.
The main advantages of the invention include:
(1) Compared with the existing thyroid cancer metastasis detection method, the marker combination established by the invention has higher specificity and more accurate detection result.
(2) By adopting the prediction model provided by the invention, the multi-part puncture of a patient with high risk of thyroid cancer metastasis is avoided, the pain of the patient is greatly reduced, and medical resources are saved.
(3) The prediction model provided by the invention can be used for guiding the selection of the clinical operation scheme more accurately, so that the treatment scheme is more accurate.
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. The experimental procedure, which does not address the specific conditions in the examples below, is generally followed by routine conditions, such as, for example, sambrook et al, molecular cloning: conditions described in the laboratory Manual (New York: cold Spring Harbor Laboratory Press, 1989) or as recommended by the manufacturer. Percentages and parts are weight percentages and parts unless otherwise indicated.
Materials and methods
The invention uses EPCAM and TG double gene expression as pointer gene to separate all tumor cells. According to the gene expression profile of tumor cells, in order to construct a predictive model, first, a selection of highly variable genes is adopted to remove less informative genes and increase computational efficiency. We then use the mutual information score as a metric to select the largest duty cycle gene for constructing the random forest classifier. AUROC is used to evaluate model performance to finalize model selection, while other metrics are also calculated, including recall scores, precision scores, etc. All models were evaluated using 5-fold cross-validation.
Example 1 sequencing sample preparation
The samples were selected from six central metastatic patients of thyroid cancer, eight lateral cervical metastatic patients, and four in situ cancer patients who did not find any metastasis pathologically, and tissue samples were obtained by:
1. after the pathological diagnosis of papillary thyroid carcinoma, the central part of the thyroid tumor in operation is cut after the patient passes through the ethics of a hospital, 5mm x 5mm tissues with the size are cut, the tissue is preserved in tissue preservation solution and is preserved in liquid nitrogen, and then flow cytometry and single cell sequencing analysis are carried out.
2. After the pathological diagnosis of papillary thyroid cancer patients passes through the ethics of a hospital, the central part of a thyroid tumor in operation is cut off, so that an in-vitro thyroid tumor tissue which is penetrated by ultrasonic guidance is obtained, and the in-vitro thyroid tumor tissue is stored in tissue preservation solution and in liquid nitrogen, and then flow cytometry and single cell sequencing analysis are carried out.
EXAMPLE 2 Single cell sequencing
Single cell sequencing and V (D) J library A10X Genomics Chromium Controller Instrument, chromium Single Cell 5' library, gel bead kit and V (D) J enrichment kit (10X Genomics, pleasanton, calif.) were used. The cell suspension was concentrated to 1,000 cells/μl and about 10,000 of them were loaded into each channel to generate single cell gel beads in emulsion (GEM). The output is mRNA barcodes of 6,000 cells per sample. After the reverse transcription step, the GEM was destroyed, the barcode cDNA was purified, amplified, and used to construct 5' gene expression and TCR and BCR enrichment libraries. For 5' library construction, the amplified barcode cDNA was fragmented, A-tailed, ligated to adaptors and index PCR amplified. For the V (D) J library, human T cell and B cell V (D) J sequences were enriched from amplified cdnas, fragmented, a tail, adaptor ligated and index PCR amplified.
The final library was quantified using a Qubit high sensitivity DNA assay (No. q33231; thermo Fisher Scientific, waltham, MA, USA). Library size distribution was determined in a Bioanalyzer 2200 (Agilent Technologies, santa Clara, CA, USA) using a high sensitivity DNA chip. All libraries were sequenced on an Illumina sequencer (Illumina, san Diego, CA, USA) using a 150bp paired-end run.
Example 3 Gene selection and clustering
After obtaining sequencing raw data, we aligned and barcode demultiplexed single Cell sequencing data using the Cell Ranger v.3.0.2vdj procedure (10X Genomics). The expression matrix data was then analyzed under Scanpy computational framework. The data were filtered according to QC standards. Cells with <500 detected genes and <5 cells/dataset detected genes were removed. Cells with >10% of total expression levels of mitochondrial gene expression were also excluded. The double peak was avoided by removing cells with the first 5% total transcripts (UMI). The Scrublets software is also used to remove the doublet.
Genes were then selected for down-scaling and clustering based on their variability. All data were pooled, the first 2,000 variable genes were selected, and total UMI counts and mitochondrial gene expression ratios per cell were regressed to eliminate the impact of these factors on clustering. The BBKNN method is used to perform batch correction using a donor as a batch key. Leiden clustering was performed in Scanpy, with a resolution set to 1.
Example 4 modeling
The model construction of the invention adopts a basic result based on a machine learning algorithm, and thyroid carcinoma cells in situ are selected as the input of the model.
(1) Feasibility of finding disease predictors: all tumor cells were sorted from single cell transcriptome data using EPCAM and TG dual gene expression as indicator genes and PCR analysis was performed (fig. 1); the results show that these tumor cells show PCR spatial differences, proving feasibility.
(2) The genes are ordered according to their mutual information score, limiting the number of genes to a relatively small range.
(3) Random forest classifiers are constructed based on different numbers of features (genes) and performance is evaluated, with the observation that at feature basis factors of 13, the performance evaluation of the model is inflection points (fig. 2).
(4) The first 13 genes with the highest mutual information scores were selected to obtain a classifier model under which the area of AUROC reached 0.929 (fig. 3).
(5) The expression of these 13 genes was plotted in tumor cells from the metastatic/non-metastatic group, and was found to be all different based on the Wilcoxon test (fig. 4). The 10 genes are 8 genes selected from table a and 5 genes selected from table B, respectively: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30; (B1) TPO; (B2) TIMP1; (B3) SLC26A7; (B4) SERPINA1; (B5) SLC34A2. Wherein the weight of each gene is shown in the tables A and B.
Example 5 model verification
Model inputs were 8 genes selected from table a and 5 genes selected from table B: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30; (B1) TPO; (B2) TIMP1; (B3) SLC26A7; (B4) SERPINA1; (B5) SLC34A2.
The invention uses the established model to carry out 100 verification tests on different sample sets (80% test set and 20% training set) sorted by 4:1, and the AUROC value of each time is above 92%, which proves that the model has better robustness and supports the reliability of conclusion (the experimental result is shown in Table C).
TABLE C verification of result data
Discussion of the invention
In the present invention, a determination method is provided for analyzing the risk of metastasis of thyroid cancer, and an analysis apparatus for predicting the occurrence risk of metastasis of thyroid cancer is developed.
In the present invention, the present inventors have unexpectedly found a novel thyroid cancer metastasis risk marker for the first time, which comprises (A1) PKHD1L1 in group (a); (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30.
In the invention, by selecting the markers selected from the group (A) and the group (B) in thyroid carcinoma cells as biomarkers to evaluate the risk of thyroid carcinoma metastasis, objective diagnosis of thyroid carcinoma metastasis is realized, and the diagnosis of thyroid carcinoma metastasis has higher sensitivity and specificity.
All documents mentioned in this application are incorporated by reference as if each were individually incorporated by reference. Further, it will be appreciated that various changes and modifications may be made by those skilled in the art after reading the above teachings, and such equivalents are intended to fall within the scope of the claims appended hereto.
Claims (10)
1. The use of a gene, mRNA, cDNA, protein, or a detection reagent thereof for a risk marker for thyroid cancer metastasis, for preparing a diagnostic reagent or kit for diagnosing whether a thyroid cancer patient has thyroid cancer metastasis;
wherein the thyroid cancer metastasis risk marker is selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30.
2. The use of claim 1, wherein the thyroid cancer metastasis risk marker further comprises any marker selected from the group B: (B1) TPO; (B2) TIMP1; (B3) SLC26A7; (B4) SERPINA1; (B5) SLC34A2.
3. The use of claim 1, wherein the thyroid cancer metastasis risk marker further comprises a combination of 8 markers selected from group a and 5 markers selected from group B.
4. The use according to claim 1, wherein the diagnostic reagent or kit is for detecting a thyroid cancer metastasis risk marker in thyroid cancer tissue in situ.
5. The use of claim 1, wherein the thyroid cancer comprises papillary thyroid cancer.
6. A kit comprising a detection reagent for detecting a gene, mRNA, cDNA, protein, or a combination thereof for a thyroid cancer metastasis risk marker in a sample to be tested;
wherein the thyroid cancer metastasis risk marker is selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30.
7. A method of detection comprising the steps of:
(a) Providing a detection sample;
(b) Detecting the expression level of a thyroid cancer metastasis risk marker gene in the detection sample, and marking the expression level as C1; and
(c) Comparing the concentration of the marker for risk of thyroid cancer metastasis C1 with a control reference value C0, wherein the marker for risk of thyroid cancer metastasis is selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30;
if the detection result of the thyroid cancer metastasis risk of the detected object meets the following conditions, prompting that the thyroid cancer metastasis risk of the object is high:
when the expression level of a certain marker in the test subjects of table a and table B is significantly different from the reference value or standard value, the risk of thyroid cancer metastasis in the thyroid cancer patient is high.
8. An apparatus for early screening for thyroid cancer metastasis, the apparatus comprising:
(a) The input module is used for inputting the expression data of the characteristic genes of a certain thyroid cancer patient;
wherein the characteristic gene comprises a gene selected from the group consisting of:
(A) Any one of the markers selected from A1 to A8, or a combination thereof: (A1) PKHD1L1; (A2) CRABP1; (A3) MT1G; (A4) NUPR1; (A5) BMP8A; (A6) DIO1; (A7) RPS4Y1; (A8) IGHV3-30;
(b) The processing module is used for analyzing and calculating the input characteristic genes so as to obtain risk judgment; through the analysis of differential expression conditions, the thyroid cancer metastasis risk of the thyroid cancer patient is indicated to be high when the obvious differential expression conditions appear; when the differential expression condition is not obvious, the thyroid cancer metastasis risk of the thyroid cancer patient is low; and
(c) And the output module is used for outputting the auxiliary screening result.
9. The apparatus of claim 8, further comprising a detection module for detecting mRNA levels, protein levels, or protein activity of the risk markers.
10. The apparatus of claim 9, wherein the detection module is selected from the group consisting of: ELISA analyzers, PCR detectors, sequencers, or combinations thereof.
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