CN115807081A - Gene marker for diagnosing thyroid cancer and application thereof - Google Patents

Abstract

The invention relates to a gene marker for diagnosing thyroid cancer and application thereof. The invention provides a reagent or a kit for detecting gene mRNA expression and/or protein expression, and application thereof in preparing products for diagnosing thyroid cancer or identifying benign and malignant thyroid nodules. The gene combination can realize early diagnosis and identification of benign and malignant thyroid with high sensitivity and high specificity, and effectively solves the problem of over-diagnosis and over-treatment of the existing thyroid cancer.

Description

Gene marker for diagnosing thyroid cancer and application thereof

Technical Field

The invention relates to thyroid cancer diagnosis, in particular to a gene marker for diagnosing thyroid cancer and application thereof.

Background

Thyroid cancer is a malignant tumor that originates in the follicular epithelium of the thyroid gland. In recent years, the detection rate of thyroid nodules has become higher. Ultrasound examination of thyroid nodules highly suspected of being malignant requires access to the next diagnostic step, fine needle aspiration cytology (FNA), but 40% of FNAs have indeterminate cytology characteristics and require further diagnosis by surgery. If molecular diagnostics can accurately judge the malignancy and the goodness of the nodule, the patient can be saved from surgery.

The efficacy of current molecular diagnostic methods is to be improved.

Gene Expression Classifier is the most widely used molecular diagnostic technique for thyroid nodules, but its Positive Predictive Value (PPV) is only 47%. ThyroSeqv2 is a gene mutation-based thyroid cancer diagnostic method, which detects H/K/NRAS gene mutation and RET/PTC gene rearrangement frequently carried by benign nodules, and PPV is 42-77%. The ThyroSeqv3 is used for identifying the nodular property of the FNA specimen with uncertain cytological characteristics by detecting 112 thyroid-associated gene point mutations, gene fusion, copy number variation or mRNA expression of genes, and the PPV is 66 percent. Furthermore, DDMS (Diagnostic DNA Methylation Signature) is a molecular Diagnostic method based on DNA Methylation and is used for identifying benign and malignant thyroid tissues, but some samples are not suitable for detection by the method.

Disclosure of Invention

In a first aspect, the present invention provides reagents for detecting mRNA expression and/or protein expression of a gene comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes.

In one or more embodiments, the agent is used to identify benign or malignant thyroid nodules.

In one or more embodiments, the agent comprises one or more selected from the group consisting of: genes or transcripts or proteins thereof, primer molecules, probe molecules and immunological molecules.

In one or more embodiments, the reagent further comprises one or more selected from the group consisting of: RNA extraction reagent, magnetic beads with OligodT, DNA probe for hybridization of rRNA, RNaseH for digestion of DNA/RNA hybrid chain, DNaseI, T4 DNA polymerase and Klenow DNA polymerase.

In one or more embodiments, the transcript sequence is the sequence of one or more or all transcripts of the corresponding gene.

In one or more embodiments, the reagent comprises a primer molecule that hybridizes to the gene or transcript thereof. The primer molecules can amplify transcripts or fragments of the genes. The primer molecule is at least 9bp.

In one or more embodiments, the agent comprises a probe molecule that hybridizes to the gene or its transcript. In one or more embodiments, the probe further comprises a detectable substance. In one or more embodiments, the detectable species is a5 'fluorescent reporter and a 3' labeled quencher. In one or more embodiments, the fluorescent reporter gene is selected from Cy5, FAM, and VIC. The probe molecule is at least 12bp.

In one or more embodiments, the agent comprises an immune molecule, such as an antibody or antigen-binding fragment thereof, that is a protein expression product of a gene.

In a second aspect the invention provides a medium bearing content selected from one or more of the following: a gene, its sequence, its transcript sequence, its protein sequence, its transcriptional information, its mRNA expression and/or protein expression information, comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes.

In one or more embodiments, the medium is used to align with gene mRNA expression and/or protein expression detection data to determine the mRNA expression and/or protein expression level of the gene.

In one or more embodiments, the medium is used to identify benign and malignant nodules of the thyroid.

In one or more embodiments, the medium is a support on which the content is printed, including cards, such as paper, plastic, metal, glass cards.

In one or more embodiments, the medium is a computer readable medium storing the content and a computer program that, when executed by a processor, performs the steps of: and comparing the mRNA expression and/or protein expression detection data of the sample with the content to obtain the mRNA expression amount and/or protein expression amount of the gene in the sample. The mRNA expression amount and/or the protein expression amount of the gene are used for diagnosing thyroid cancer or identifying benign and malignant thyroid nodules; preferably, the expression level is put into a model to calculate a score for diagnosing thyroid cancer or identifying benign and malignant thyroid nodules.

In another aspect, the present invention provides the use of a gene, its mRNA expression and/or protein expression product, a reagent or a device for detecting its mRNA expression and/or protein expression, in the preparation of a product for diagnosing thyroid cancer or identifying benign and malignant thyroid nodules, the gene comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes.

In one or more embodiments, the gene further comprises:

(1) ATF7IP2 and PLVAP, or

(2) SIRPB2, or

(3) ATF7IP2, PLVAP, and SIRPB2.

In one or more embodiments, the reagent or device comprises: reagents or devices for detecting gene mRNA expression and/or protein expression by Mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization, serial Analysis of Gene Expression (SAGE), gene expression analysis by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics, or proteomics techniques.

In one or more embodiments, the reagent comprises one or more selected from a primer molecule, a probe molecule, and an immune molecule.

In one or more embodiments, the agent comprises a primer molecule that hybridizes to the gene or transcript thereof. The primer molecule is capable of amplifying a transcript of the gene or a fragment thereof. The primer molecule is at least 9bp.

In one or more embodiments, the agent comprises a probe molecule that hybridizes to the gene or its transcript. In one or more embodiments, the probe further comprises a detectable substance. In one or more embodiments, the detectable species is a5 'fluorescent reporter and a 3' labeled quencher. In one or more embodiments, the fluorescent reporter gene is selected from Cy5, FAM, and VIC. The probe molecule is at least 12bp.

In one or more embodiments, the agent comprises an immune molecule, such as an antibody or antigen-binding fragment thereof, that is a protein expression product of a gene.

In one or more embodiments, the device comprises a medium as described in any embodiment herein.

In one or more embodiments, the kit is a minimally invasive diagnostic kit.

In one or more embodiments, the sample is from a tissue, cell, e.g., thyroid tissue, of a subject. In one or more embodiments, the sample is a thyroid nodule fine needle biopsy.

In one or more embodiments, the kit further comprises a reverse transcription reagent.

In one or more embodiments, the kit further comprises PCR reaction reagents. Preferably, the PCR reaction reagent comprises DNA polymerase, PCR buffer solution, dNTP and Mg ²⁺ 。

In one or more embodiments, the kit further comprises an immunoreactive reagent. Preferably, the immunoreaction reagent comprises a sealing liquid, a washing liquid and an enzyme labeling reagent.

In one or more embodiments, the diagnosing comprises: calculating a score according to the detection result of the gene mRNA expression and/or protein expression, and diagnosing thyroid cancer or identifying benign and malignant thyroid nodules according to the score; preferably, a score is calculated by a model based on the mRNA expression level and/or the protein expression level of the gene, and thyroid cancer is diagnosed or thyroid nodule benign or malignant is identified based on the score. In one or more embodiments, the calculation is calculated by constructing a support vector regression model.

In still another aspect, the present invention provides a method for diagnosing thyroid cancer or thyroid nodules benign and malignant, comprising:

(1) Detecting mRNA expression and/or protein expression of genes in a sample, said genes comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPN 7, S100A10, PB 3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes selected from said three genes,

(2) The score is calculated, preferably by a model calculation,

(3) Diagnosing thyroid cancer or identifying benign or malignant thyroid nodules according to the score.

In one or more embodiments, the gene further comprises:

(a) ATF7IP2 and PLVAP, or

(b) SIRPB2, or

(c) ATF7IP2, PLVAP, and SIRPB2.

In one or more embodiments, the method further comprises RNA or protein extraction and/or quality control prior to step (1).

In one or more embodiments, the detecting of step (1) is performed by a method selected from the group consisting of: mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization, serial Analysis of Gene Expression (SAGE), analysis of gene expression by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics, or proteomics techniques.

In one or more embodiments, step (1) comprises performing the detecting using a nucleic acid molecule, primer molecule, probe molecule, immune molecule and/or a medium as described herein.

In one or more embodiments, the sample is from a tissue, cell, e.g., thyroid tissue, of a subject. In one or more embodiments, the sample is a thyroid nodule fine needle biopsy.

In one or more embodiments, the score in step (2) is calculated by a support vector machine regression model.

In one or more embodiments, step (3) comprises: when the score meets a threshold, the subject is identified as having or not having thyroid cancer or a nodule thereof as benign or malignant.

In another aspect of the present invention, there is provided a kit for identifying thyroid cancer or thyroid nodule benign or malignant, comprising: one or more of a gene, its mRNA expression and/or protein expression product, a reagent or device for detecting its mRNA expression and/or protein expression, said gene comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes.

In one or more embodiments, the gene further comprises:

(a) ATF7IP2 and PLVAP, or

(b) SIRPB2, or

(c) ATF7IP2, PLVAP, and SIRPB2.

In one or more embodiments, the reagent or device comprises: reagents or devices for detecting gene mRNA expression and/or protein expression by Mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization, serial Analysis of Gene Expression (SAGE), gene expression analysis by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics, or proteomics techniques.

In one or more embodiments, the reagent comprises one or more selected from the group consisting of a primer molecule, a probe molecule, and an immune molecule.

In one or more embodiments, the agent comprises a primer molecule that hybridizes to the gene or transcript thereof. The primer molecules can amplify transcripts or fragments of the genes. The primer molecule is at least 9bp.

In one or more embodiments, the agent comprises a probe molecule that hybridizes to the gene or its transcript. In one or more embodiments, the probe further comprises a detectable substance. In one or more embodiments, the detectable species is a5 'fluorescent reporter and a 3' labeled quencher. In one or more embodiments, the fluorescent reporter gene is selected from Cy5, FAM, and VIC. The probe molecule is at least 12bp.

In one or more embodiments, the agent comprises an immune molecule, such as an antibody or antigen-binding fragment thereof, that is a protein expression product of a gene.

In one or more embodiments, the device comprises a medium as described in any embodiment herein.

In one or more embodiments, the kit is a minimally invasive diagnostic kit.

In one or more embodiments, the kit further comprises a reverse transcription reagent, e.g., reverse transcriptase, buffer.

In one or more embodiments, the kit further comprises PCR reaction reagents. Preferably, the PCR reaction reagent comprises DNA polymerase, PCR buffer solution, dNTP and Mg ²⁺ 。

In one or more embodiments, the kit further comprises an immunoreactive reagent. Preferably, the immunoreaction reagent comprises a sealing liquid, a washing liquid and an enzyme labeling reagent.

In one or more embodiments, the diagnosing comprises: calculating a score according to the detection result of the gene mRNA expression and/or protein expression, and diagnosing thyroid cancer or identifying benign and malignant thyroid nodules according to the score; preferably, the diagnosing comprises: and calculating the mRNA expression quantity and/or the protein expression quantity of the gene, then calculating a score through the model, and diagnosing thyroid cancer or identifying benign and malignant thyroid nodules according to the score. In one or more embodiments, the calculation is calculated by constructing a support vector regression model.

In one or more embodiments, the kit is suitable for use as described in any of the embodiments herein.

In another aspect, the present invention provides an apparatus for diagnosing thyroid cancer or identifying benign or malignant thyroid nodules, the apparatus comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of:

(1) Obtaining mRNA expression amount and/or protein expression amount of genes in a sample, wherein the genes comprise: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPN 7, S100A10, PB 3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes selected from said three genes,

(2) The score is calculated, preferably by a model calculation,

(3) Diagnosing thyroid cancer or identifying benign or malignant thyroid nodules according to the score.

In one or more embodiments, the gene further comprises:

(a) ATF7IP2 and PLVAP, or

(b) SIRPB2, or

(c) ATF7IP2, PLVAP, and SIRPB2.

In one or more embodiments, the detecting of step (1) is performed by a method selected from the group consisting of: mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization, serial Analysis of Gene Expression (SAGE), analysis of gene expression by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics, or proteomics techniques.

In one or more embodiments, step (1) comprises performing the detecting using a nucleic acid molecule, primer molecule, probe molecule, immune molecule and/or a medium as described herein.

In one or more embodiments, the sample is from a tissue, cell, e.g., thyroid tissue, of a subject. In one or more embodiments, the sample is a thyroid nodule fine needle biopsy.

In one or more embodiments, the score in step (2) is calculated by a support vector machine regression model.

In one or more embodiments, step (3) comprises: when the score meets a threshold, the subject is identified as having or not having thyroid cancer or a nodule thereof as benign or malignant.

The sensitivity of the invention for identifying benign and malignant thyroid gland by gene combination can reach 90%, and the specificity reaches 100%. Compared with the existing technology for diagnosing benign and malignant thyroid gland by molecules, the gene marker provided by the invention has the highest Positive Predictive Value (PPV) of malignant thyroid nodules, and the Positive Predictive Value (PPV) reaches 100%. Effectively solves the problem of over-diagnosis and over-treatment of the existing thyroid cancer.

Drawings

Fig. 1 shows that gene combination 1 constructs an SVR prediction model ROC curve for all 20 thyroid nodule samples.

Fig. 2 shows that gene combination 2 constructs an SVR prediction model ROC curve for all 20 thyroid nodule samples.

Fig. 3 shows that gene combination 3 constructs an SVR prediction model ROC curve for all 20 thyroid nodule samples.

Detailed Description

The invention aims to provide a method for detecting thyroid benign and malignant nodules by using mRNA expression quantity and/or protein expression quantity of genes, aiming at the problems that the current thyroid benign and malignant nodule molecular diagnosis accuracy is not high or all samples cannot be covered due to technical reasons and the like.

The inventors found that the properties of thyroid nodules were related to mRNA expression and/or protein expression of the following genes: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes. The invention provides mRNA expression and/or protein expression detection for the genes of a sample, and a mathematical model is used for distinguishing benign nodules and malignant nodules of thyroid based on the mRNA expression and/or protein expression detection result, so that the aim of accurately diagnosing the thyroid cancer nodules is fulfilled. The test results described herein are typically mRNA expression levels and/or protein expression levels, and may be absolute or relative amounts of mRNA expression and/or protein expression. The above genes are referred to herein as "thyroid cancer-associated genes". The above genes, their mRNA expression and/or protein expression products, or their fragments that can characterize mRNA expression and/or protein expression are collectively referred to as "thyroid cancer markers". ENTREZID of the above gene is shown in the following table.

The terms "benign" and "malignant" as used herein refer to the nature of thyroid nodules. Generally, benign is manifested by slow growth of nodules, uniform texture, good mobility, smooth surface, cystic changes, no enlargement of lymph nodes, no calcification, etc. Malignancy manifests as uncontrolled malignant cell growth, spread and tissue infiltration. Ultrasound signs that suggest that thyroid nodules are malignant include: the height of the nodule is greater than the width, lack of vocal cord, micro-calcification, irregular boundaries, reduced echo, solid nodules, abundant blood flow inside the nodule, etc. In an embodiment of the invention, the malignant thyroid nodule is Papillary Thyroid Carcinoma (PTC).

In the present invention, early diagnosis refers to the possibility of finding cancer before metastasis, preferably before morphological changes in tissues or cells can be observed. In addition to early diagnosis of thyroid malignancies, advanced diagnosis of thyroid malignancies is also included, and thyroid malignancy screening, risk assessment, prognosis, disease identification are also included. The term "biopsy" refers to a diagnostic study of a sample to determine whether a patient presenting with a nodule has a condition that can be classified as benign or malignant.

The term "diagnosis" also encompasses the term "prognosis", as well as the application of the procedure involved at two or more time points, to monitor the diagnosis and/or prognosis over time. Furthermore, the term diagnosis includes: a. prediction (determining whether a patient will likely develop a thyroid malignancy), b. prognosis (predicting whether a patient is likely to have better or worse outcome at a preselected time in the future), c. therapy selection, d. treatment drug monitoring, e. relapse monitoring.

Methods for performing mRNA expression and/or protein expression detection on a sample are well known in the art and include, but are not limited to: mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization, serial Analysis of Gene Expression (SAGE), analysis of gene expression by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics, or proteomics techniques. In certain embodiments of the methods provided herein, the biological sample can be concentrated and/or purified prior to detecting the amount of mRNA expression and/or the amount of protein expression. Concentration and/or purification methods for DNA, RNA, proteins are known in the art.

The present invention provides a reagent or a device for detecting the expression of mRNA and/or the expression of protein of a gene related to thyroid cancer by the above-mentioned method. Illustratively, reagents for real-time quantitative PCR, RT-PCR detection of mRNA expression and/or protein expression toAt least one pair of primers for specifically amplifying genes; the reagent for detecting protein expression by immunoassay at least comprises an antibody which is specifically combined with a gene protein expression product; the reagent for detecting mRNA expression and/or protein expression by in situ hybridization at least comprises a probe which is hybridized with a nucleic acid sequence of a gene or a transcript thereof; products for microarray detection of mRNA expression and/or protein expression include: protein chips and gene chips; wherein, the protein chip comprises an antibody which is specifically combined with the gene protein expression product, and the gene chip comprises a probe which is hybridized with the nucleic acid sequence of the gene or the transcript thereof. The invention also relates to other reagents required in the process of the detection method. For example, the reaction solution for PCR comprises Taq DNA polymerase (e.g., hot-start Taq DNA polymerase), PCR buffer, dNTPs, KCl, and MgCl ₂ (e.g., 1.0-10.0 mM) and (NH) ₄ ) ₂ SO ₄ 。

The term "biological sample" as used herein refers to any sample of biological origin that may contain one or more markers. Examples of biological samples include tissues, organs, or bodily fluids such as whole blood, plasma, serum, tissue, lavage, or any other sample used for detecting disease. The term "subject" as used herein refers to a mammal, preferably a human.

The transcripts described herein are one or more or all transcripts (mRNA) of the corresponding gene. Herein, the transcript expression amount (or mRNA expression amount) indicates the amount of mRNA produced by gene transcription. Methods for detecting transcripts are well known in the art, e.g., mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization. RNA sequencing as used in the exemplary embodiments herein includes: RNA extraction-mRNA purification-mRNA fragmentation-ctDNA synthesis-end repair-addition of A base-ligation Adapter-PCR amplification-library quality inspection-high-throughput sequencing. Obtaining RNA-seq original off-machine reads.

The amount of protein expression of a gene can be detected by detecting one or more or all of the protein expression products (proteins or polypeptides) of the gene, such as Mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), microarray, serial Analysis of Gene Expression (SAGE), gene expression analysis by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics, or proteomics techniques. Immunoassays include antigen antibody reaction assays including, but not limited to, ELISA, IHC, and the like.

The invention also provides a kit for identifying thyroid cancer or thyroid nodule benign and malignant, which comprises a reagent or a device for detecting the mRNA expression and/or protein expression of the thyroid cancer related gene, and optionally further comprises the thyroid cancer related gene, and mRNA expression and/or protein expression products thereof. Optionally, the kit may comprise instructions for use in any form.

As described above, the reagent or device for detecting transcription comprises a primer molecule or a probe molecule that hybridizes to the gene or its transcript. The primer molecule is capable of amplifying a transcript of the gene or a fragment thereof. Such fragments are specific fragments known in the art that recognize the gene or its transcript. Primers, probes or screening immune molecules that can be used to detect the amount of mRNA expression and/or the amount of protein expression of a gene are readily designed based on the genetic information provided herein. The probe may be DNA, RNA, DNA-RNA chimeras, PNA or other derivatives. The length of the probe is not limited as long as specific hybridization and specific binding with a target nucleotide sequence are achieved. The kit also contains other reagents required for transcript detection including, but not limited to: RNA extraction reagent, magnetic beads with OligodT, DNA probe for hybridization of rRNA, RNaseH for digestion of DNA/RNA hybrid chain, DNaseI, T4 DNA polymerase and Klenow DNA polymerase.

As another example, the reagent or device for detecting expression of a gene protein can comprise an immune molecule. An immune molecule is a molecule that reacts antigen-antibody with the gene protein expression product, such as an antibody or antigen-binding fragment thereof, including monoclonal antibodies, polyclonal antibodies. The present invention may employ any method well known in the art for making such antibodies. The kit also contains other reagents required for expression detection, including but not limited to: HRP, diluent, buffer, color developing agent and the like.

The gene protein expression product of the invention also includes any functional equivalent of the gene protein expression product. The functional equivalents include conservative variation proteins of gene protein expression products, or active fragments thereof, or active derivatives thereof, allelic variants, natural mutants, induced mutants, proteins encoded by DNA that is capable of hybridizing with a gene or its transcript sequence.

Conservative modifications of one or more amino acids in a protein do not affect the function of the protein. One of skill in the art understands that changing a single amino acid or a small percentage of amino acids or individual additions, deletions, insertions, substitutions to an amino acid sequence is a conservative modification, where a change in the protein results in a protein with a similar function. Conservative substitution tables providing functionally similar amino acids are well known in the art.

The gene protein expression products of the invention also include non-conservative modifications to the amino acid sequence, as long as the modified protein still retains the biological activity of the gene protein expression product. The number of amino acids mutated in the modified protein is usually 10 or less. The modification may also be a detectable (e.g., isotopic) label.

Determining the amount of the gene protein expression product can further comprise fragmenting the protein. Fragmentation of the protein can be accomplished by trypsin digestion. Thus, the kit may further comprise trypsin.

Determining the amount of the gene protein expression product optionally further comprises normalization of the protein measurement. For example, protein measurements can be normalized by one or more housekeeping proteins. Thus, the kit may further comprise a housekeeping protein standard. The term "normalization" or "standard" as used herein refers to the expression of a difference value from a standard value to adjust for effects due to sample handling, sample preparation, and technical variations. For example, when measuring the expression of a differentially expressed protein, the absolute value of protein expression can be expressed in terms of the absolute value of standard protein expression that is substantially constantly expressed. This prevents sample preparation and measurement technique variations from impeding the measurement of the amount of protein concentration in the sample.

In the method provided by the invention, after the detection data is obtained by using the kit for detecting mRNA expression and/or protein expression, the score is calculated through a model, and the thyroid cancer is diagnosed or the benign and malignant thyroid nodules are identified according to the score.

Calculations are sometimes implemented by building models. Exemplary calculations include: calculating the mRNA expression (e.g. FPKM or TPM) from the results of RNA sequencing, (optionally) normalizing the mRNA expression, and constructing a mathematical model (e.g. support vector model) and determining the threshold. In a specific diagnosis process, the transcription amount of the thyroid cancer-associated gene to be detected is substituted into a model to obtain a score, and the score is compared with a threshold value to diagnose the thyroid cancer.

In a specific embodiment, the calculating comprises the steps of:

(1) Comparing the transcript sequencing data to a reference genome (e.g., GRCh 38) and outputting a comparison reading; the comparative readings are then used for gene mRNA quantification (e.g.using HT-Seq-Count software) and the genes are annotated; the expression level of mRNA of the gene was calculated as FPKM (Fragments Per Kilobase of exon model Per Million mapped Fragments) or (Transcripts Per Million). The FPKM calculation formula is as follows:

FPKM＝RCg*10 ⁹ /RCpc*L

RCg is the number of reads compared to the gene; RCpc is the number of reads of all genes compared; l is the length of the exon (exon) of the gene. The above quantification and FPKM or TPM calculation processes are examples only and mRNA quantification and calculation can be performed by one skilled in the art using any known methods and techniques.

Optionally (2) normalizing the FPKM, which is calculated as follows:

newValue＝(oldValue-min)/(max-min)

oldValue is the original FPKM value; min is the minimum FPKM of the gene in all samples; max is the maximum FPKM of the gene in all samples; newValue is the normalized FPKM value.

(3) A Support Vector Regression (SVR) model is constructed. In an exemplary preferred embodiment, the gene hyperparameters are set such that the kernel Function (kernel) is a Radial Basis Function (RBF), gamma is 0.001, and c is 100.0. Malignant and benign nodule class labels are set (e.g., malignant set to 0; benign set to 1).

The model first maps data (e.g., normalized FPKM) from its feature space to a higher dimensional space using a gaussian kernel function (also referred to as RBF in SVR) with the specific formula: k (x, y) = exp (- γ | | x-y | | non-conducting phosphor ² ). Then a series of alpha and b are found (for example, using the SMO (Sequential Minimal Optimization) algorithm), and a weight vector w is calculated based on these alpha and a separating hyperplane is obtained. Finally, predicting the category of the sample based on each characteristic value mapped to the high-dimensional space of the sample, wherein the prediction value is not less than 0.5 and is a benign nodule; otherwise, malignant nodules are predicted. The concrete formula is as follows:

f(x)＝w ^T x+b

x is the eigenvalue matrix of each sample mapped to the high dimensional space and the constant b is the intercept. w is a ^T x denotes multiplying corresponding elements of two vectors of values and then adding them all together.

In some embodiments, step (3) can be implemented directly using the SVR model in the scimit-spare (version 0.24.2) library.

Similar to the calculation and diagnosis of mRNA, calculation and diagnosis of protein expression can be obtained by one skilled in the art based on the prior art and the teachings herein. For example, the method comprises the following steps: calculating an expression amount from the protein expression assay result, (optionally) normalizing the expression amount, and constructing a mathematical model (e.g., a support vector model) and determining a threshold value. In a specific diagnosis process, the protein expression level of the thyroid cancer-associated gene of the subject is detected, and the protein expression level is substituted into a model to obtain a score, which is compared with a threshold value to diagnose the thyroid cancer.

The methods provided herein are less invasive and constitute little or no risk of adverse effects. And thus can be used to diagnose, monitor and provide clinical management in subjects not exhibiting any symptoms of thyroid disease and subjects classified as low risk for thyroiditis. For example, the methods disclosed herein can be used to diagnose thyroid cancer in a subject that has not developed thyroid cancer and/or has not developed thyroid cancer in the past but has not been confirmed. Similarly, the methods disclosed herein can be used as a prophylactic measure to diagnose a healthy subject classified as developing thyroid cancer.

The present invention also provides a medium bearing content selected from one or more of the following: thyroid cancer associated gene, sequence thereof, transcript sequence thereof, protein sequence thereof, transcriptional information thereof, mRNA expression and/or protein expression information thereof as described herein. Transcriptional information includes, but is not limited to: one or more exons contained in the transcript, temporal and spatial characteristics of transcription, reference transcription levels, diagnostic thresholds for transcription, and the like. mRNA expression and/or protein expression information includes, but is not limited to: protein structural characteristics, expression temporal-spatial characteristics, reference protein expression amount, expression diagnosis threshold value and the like of one or more expression products. The medium is used for comparing with mRNA expression and/or protein expression detection data of the gene to determine the mRNA expression amount and/or protein expression amount of the gene, so as to identify benign and malignant thyroid nodules. The medium may be a carrier on which the content is printed, including cards, such as paper, plastic, metal, glass cards. Alternatively, the medium is a computer readable medium or device storing the content and a computer program which, when executed by a processor, performs the steps of: and comparing the mRNA expression and/or protein expression detection data of the sample with the content, thereby obtaining the mRNA expression amount and/or protein expression amount of the gene in the sample. The mRNA expression quantity and/or protein expression quantity of the gene is put into a model to calculate a score for diagnosing thyroid cancer or identifying benign and malignant thyroid nodules. For example, the medium may record the names of the genes related to thyroid cancer, or their sequences, transcript sequences, protein sequences, etc. as described herein, and by comparing the transcriptome data or proteome data of the sample with these contents, the mRNA expression level and/or protein expression level of these genes in the sample can be obtained, thereby diagnosing thyroid cancer or identifying benign and malignant thyroid nodules.

Examples

Example 1, experimental procedure

1) mRNA library construction scheme

RNA extraction-mRNA purification-mRNA fragmentation-ctDNA synthesis-end repair-addition of A base-ligation Adapter-PCR amplification-library quality inspection-high-throughput sequencing. Obtaining RNA-seq original off-machine reads.

2) Data processing

(1) Comparing RNA-seq original off-machine reads to a GRCh38 reference genome by using STAR (version 2.6.0 c) software, and outputting comparison reads (bam format files); quantifying gene mRNA expression level of the bam file by using HT-Seq-Count (version 0.6.1p1) software, and using GENCODE v22 for gene annotation; then, the gene mRNA expression level FPKM (Fragments Per Kibase of exon model Per Million mapped Fragments) was calculated. The calculation formula is as follows:

FPKM＝RCg*10 ⁹ /RCpc*L

RCg is the number of reads compared to the gene; RCpc is the number of reads of all genes compared; l is the length of the exon (exons) of the gene.

(2) Normalized FPKM, the calculation formula is as follows:

newValue＝(oldValue-min)/(max-min)

old FPKM value of oldValue; min is the minimum FPKM of the gene in all samples; max is the maximum FPKM of the gene in all samples; newValue is the normalized FPKM value.

(3) A Support Vector Regression (SVR) model is constructed. The hyperparametric combinations of the three groups of genes are the same, the kernel Function (kernel) is a Radial Basis Function (RBF), the gamma is 0.001, and the C is 100.0. The malignant nodule class label is set to 0; the benign nodule class label is set to 1. The model first maps the data (i.e., normalized FPKM) from its feature space to a higher dimensional space using a gaussian kernel function, also known as RBF in SVR, with the specific formula: k (x, y) = exp (- γ | | x-y | | non-conducting phosphor ² )

Then, a series of alpha and b are solved by using an SMO (Sequential Minimal Optimization) algorithm, and a weight vector w is calculated based on the alpha to obtain a separating hyperplane. Finally, predicting the category of the sample based on each characteristic value mapped to the high-dimensional space of the sample, wherein the predicted value is not less than 0.5 and is predicted to be a benign nodule; otherwise, malignant nodules are predicted. The concrete formula is as follows:

f(x)＝w ^T x+b

x is the eigenvalue matrix of each sample mapped to the high dimensional space and the constant b is the intercept. w is a ^T x denotes multiplying corresponding elements of two vectors of values and then adding them all together.

Step (3) can be directly realized by using an SVR model in a scimit-spare (version 0.24.2) library.

20 samples of thyroid nodules (10 malignant nodules and 10 benign nodules) were subjected to RNA-seq, and the mRNA expression levels were converted to FPKM and normalized according to the procedure described above. For the normalized FPKM values of the following three groups of genes, we predicted the benign and malignant of each sample by leave-one-out cross validation (LOO CV) based on the SVR model, i.e. 19 cases were used as training set and 1 case was used as test set. The predicted value is not less than 0.5, and the benign nodule is predicted; otherwise, malignant nodules are predicted.

Gene set 1 comprises: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, TBCPPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, SNURF, SLSTAT 6, STEAP4, SUMF1, SH 1D10C, TEK, THIS 2, VAMP 3926, TMxBB 3926, TUxV 3926.

Gene set 2 comprises: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB2, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, TBCPPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, SLSTAT 6, STEAP4, SUMF1, SH 1D10C, TEK, THIS 2, VAMMP 39119, VAzMP 3, TMAXP 24, TMXP 3934.

Gene set 3 comprises: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB2, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, TBCPPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, SH 1D10C, TEK, THIS 2, VAMP 39119, TMxMP 24, SIR 3934, TMxPLMP 3, SIR 24, TMxFP 3934.

Example 2 Gene combination 1

LOO CV was used to predict the benign and malignant nature of each sample based on the SVR model for gene combination 1. The predicted results are shown in Table 1. A prediction value of not less than 0.5 is predicted to be benign, whereas malignant is predicted. All 10 benign nodules are correctly predicted, 9 malignant nodules are correctly predicted, and the model prediction accuracy (ACCURACY, ACC) of the gene combination reaches 95%.

TABLE 1 LOO CV prediction of Gene combination 1 in the test set

Sample ID	Type of sample	Malignancy prediction	Predicted results
				s0018T	Malignancy	-0.142	Malignancy
s023T	Malignancy	0.037	Malignancy
				s052T	Malignancy	0.100	Malignant disease
s060T	Malignancy	0.710	Benign
				s186T	Malignancy	-0.127	Malignancy
s187T	Malignancy	0.234	Malignancy
				s195T	Malignancy	0.346	Malignancy
s210T	Malignancy	0.296	Malignancy
				s282T	Malignant disease	-0.090	Malignancy
s283T	Malignancy	0.207	Malignant disease
				s1007T	Benign	0.811	Benign
s1008T	Benign	0.719	Benign
				s1010T	Benign	0.773	Benign
s1013T	Benign	0.609	Benign
				s1016T	Benign	1.141	Benign of the body
s1022T	Benign	0.751	Benign
				s1023T	Benign	0.867	Benign
s1024T	Benign	0.717	Benign
				s1027T	Benign	0.923	Benign
s1029T	Benign	0.878	Benign

The SVR prediction model was constructed using all 20 thyroid nodule samples for gene combination 1, the area under the ROC curve was 1.00, and 95% CI was 1.00 to 1.00 (FIG. 1). When the benign/malignant threshold was 0.5, the sensitivity was 90% and the specificity was 100%.

Example 3 Gene set 2

LOO CV was used to predict the malignancy and goodness of each sample based on the SVR model for the second set of genes. The predicted results are shown in Table 2. A prediction value of not less than 0.5 is predicted to be benign, whereas a prediction to be malignant is made. All 10 benign nodules are correctly predicted, 9 malignant nodules are correctly predicted, and the model prediction accuracy (ACCURACY, ACC) of the gene combination reaches 95%.

TABLE 2 prediction of Gene combinations 2 in the test set

The SVR prediction model was constructed using all 20 thyroid nodule samples for the second group of genes, the area under the ROC curve was 1.00, and the 95% CI was 1.00-1.00 (FIG. 2). When the benign/malignant threshold was 0.5, the sensitivity was 90% and the specificity was 100%.

Example 4 Gene combination 3

LOO CV was used to predict the malignancy and goodness of each sample based on the SVR model for the third set of genes. The predicted results are shown in Table 3. A prediction value of not less than 0.5 is predicted to be benign, whereas malignant is predicted. All 10 benign nodules are correctly predicted, 9 malignant nodules are correctly predicted, and the model prediction accuracy (ACCURACY, ACC) of the gene combination reaches 95%.

TABLE 3 prediction of Gene combination 3 in the test set

The SVR prediction model was constructed using all 20 thyroid nodule samples for the third group of genes, and the area under the ROC curve was 1.00 and 95% CI was 1.00-1.00 (FIG. 3). When the benign/malignant threshold was 0.5, the sensitivity was 90% and the specificity was 100%.

Claims (10)

1. An agent for detecting mRNA expression and/or protein expression of a gene comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPN 7, S100A10, PB 3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes selected from said three genes,

preferably, the agent comprises one or more selected from:

a gene or a transcript or protein thereof, the sequence of said transcript, protein being the sequence of one or more or all transcripts, proteins of the corresponding gene,

primer molecules and/or probe molecules which hybridize to the genes or transcripts thereof, and

an immune molecule that is the protein expression product of a gene.

2. A medium bearing one or more of the following: a gene, its sequence, its transcript sequence, its protein sequence, its transcriptional information, its mRNA expression and/or protein expression information, comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPN 7, S100A10, PB 3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes selected from said three genes,

preferably, the first and second electrodes are formed of a metal,

the medium being a support on which the content is printed, e.g. paper, plastic, metal, glass card, and/or

The transcript sequences, protein sequences are one or more or all of the transcript sequences, protein sequences of the corresponding gene, and/or

The medium is a computer readable medium storing the content and a computer program which, when executed by a processor, performs the steps of: and comparing the mRNA expression and/or protein expression detection data of the sample with the content, thereby obtaining the mRNA expression and/or protein expression amount of the gene in the sample.

3. Use of a gene, its mRNA expression and/or protein expression product, an agent or device for detecting its mRNA expression and/or protein expression, in the manufacture of a product for diagnosing thyroid cancer or identifying benign or malignant thyroid nodules, said gene comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes.

4. The use according to claim 3,

the sequence of the mRNA expression and/or protein expression product is the sequence of one or more or all mRNA expression and/or protein expression of the corresponding gene;

the reagent or device comprises: reagents or devices for detecting mRNA expression and/or protein expression of a gene by Mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization, serial Analysis of Gene Expression (SAGE), analysis of gene expression by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics, or proteomics techniques, and/or

The diagnosis includes: calculating a score according to the detection result of the mRNA expression and/or the protein expression of the gene, and diagnosing thyroid cancer or identifying benign and malignant thyroid nodules according to the score; preferably, a score is calculated by the model according to the mRNA expression amount of the gene, and thyroid cancer is diagnosed or thyroid nodule benign or malignant is identified according to the score.

5. The use according to claim 3 or 4,

the agent comprises one or more selected from: primer molecules hybridizing to said genes or their transcripts, probe molecules hybridizing to said genes or their transcripts, immunological molecules of the protein expression products of genes, and/or

The apparatus comprising the medium of claim 2.

6. The use of claim 3 or 4, wherein the kit further comprises one or more selected from the group consisting of: reverse transcription reagent, PCR reaction reagent and immunoreaction reagent.

7. A kit comprising a reagent or device for detecting mRNA expression and/or protein expression of a gene, optionally further comprising said gene, mRNA expression and/or protein expression products thereof, said gene comprising: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPTPN 7, S100A10, SH3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes.

8. The kit of claim 7,

the reagent or device comprises: reagents or devices for detecting mRNA expression and/or protein expression of genes by Mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization, serial Analysis of Gene Expression (SAGE), analysis of gene expression by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics, or proteomics techniques, and/or

The sequence of the mRNA expression and/or protein expression product is the sequence of one or more or all mRNA expression and/or protein expression products of the corresponding gene, and/or

The kit is for use according to any one of claims 3 to 6.

9. The kit of claim 7 or 8,

the agent comprises one or more selected from: primer molecules hybridizing to said genes or their transcripts, probe molecules hybridizing to said genes or their transcripts, immunological molecules of the protein expression products of genes, and/or

Said device comprising the medium of claim 2, and/or

The kit further comprises one or more selected from the group consisting of: reverse transcription reagent, PCR reaction reagent and immunoreaction reagent.

10. An apparatus for diagnosing thyroid cancer or identifying benign or malignant thyroid nodules, the apparatus comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of:

(1) Obtaining mRNA expression amount and/or protein expression amount of genes in a sample, wherein the genes comprise: ACSL5, ACTR3B, AIM, ASB2, C15orf62, C2CD4B, CCDC, CCNB2, CD200, CD3G, CEBPD, CIITA, CSK, DLEU7, DYNLT3, ELF4, FABP3, HLA-E, HRH, IL12RB1, IL17C, ITGB, KRTAP5-9, LAT2, LGALS1, LPIN1, OAS3, PCYT1B, PITX, PTR, PTPTPN 7, S100A10, PB 3BP4, SIGLEC14, SIGLEC7, SLC29A3, SLC2A10, SLC5A5, SLFN13, AFURF, STAT6, STEAP4, SUMF1, PB 1D10C, TEK, THIS 2, VAMP 1, VAMP 6, VAMP 397, SIR 2A10, ATxP 26, and optionally two of said genes selected from said three genes,

(2) The score is calculated, preferably by a model calculation,

(3) Diagnosing thyroid cancer or identifying benign or malignant thyroid nodules according to the scores,

preferably, the first and second electrodes are formed of a metal,

the detection of step (1) is carried out by a method selected from the group consisting of: mass Spectrometry (MS), liquid chromatography-selective reaction monitoring/mass spectrometry (LC-SRM-MS), reverse transcriptase-polymerase chain reaction (RT-PCR), real-time quantitative PCR, RNA sequencing, microarray, in situ hybridization, serial Analysis of Gene Expression (SAGE), analysis of gene expression by Massively Parallel Signature Sequencing (MPSS), immunoassay, immunohistochemistry (IHC), transcriptomics or proteomics, and/or

Step (1) comprises carrying out said detection using a reagent according to claim 1, a medium according to claim 2 or a kit according to any one of claims 7 to 9, and/or

The sample is derived from the tissue and/or cells of the subject, and/or

The score in the step (2) is calculated by a regression model of a support vector machine, and/or

The step (3) comprises the following steps: when the score meets a threshold, the subject is identified as having thyroid cancer or a nodule thereof as malignant.

Images