Marker combination for predicting thyroid cancer metastasis and application thereof

Abstract

The invention provides a marker combination for predicting thyroid cancer metastasis and application thereof. Specifically, the invention provides application of a gene, mRNA, cDNA and protein of the risk marker combination for thyroid cancer metastasis judgment or a detection reagent of the gene, the mRNA, the cDNA and the protein, and is used for preparing / establishing a diagnostic reagent or a kit / equipment for judging the thyroid cancer metastasis occurrence risk. Researches show that the thyroid cancer metastasis risk marker combination can be used as a marker for early judgment of thyroid cancer metastasis of a thyroid cancer patient, has high sensitivity and specificity, can quickly diagnose thyroid cancer metastasis at a relatively early disease progress stage, and provides powerful assistance for early treatment intervention of diseases.

Description

Technical Field

[0001] This invention relates to the field of medical diagnostics, and more specifically to a combination of biomarkers for predicting thyroid cancer metastasis and their applications. Background Technology

[0002] Globally, there are approximately 567,000 new cases of thyroid cancer each year, ranking ninth in incidence among all cancer types. Women are about three times more likely to develop the disease than men, with an incidence rate of 10.2 per 100,000 people. Since the 1980s, the incidence of thyroid cancer has been steadily rising in many countries, primarily due to improvements in detection and diagnostic methods, particularly in the detection rate of papillary thyroid carcinoma (PTC).

[0003] PTC accounts for approximately 84% of thyroid cancer cases, making it the most common type of thyroid malignancy. Between 1974 and 2013, the average incidence of PTC in the United States was approximately 6.66%, with an incidence-related mortality rate of 0.20%. Although PTC is generally considered a slow-becoming tumor, some cancer cells can metastasize to adjacent lymph nodes, particularly the central and lateral cervical lymph nodes. Lymph node metastasis (LNM), which typically occurs first in the central region, is a key factor in PTC prognosis, treatment strategy, and recurrence rate, and is also associated with lower survival rates.

[0004] Several methods have been proposed to assess the risk of lung neoplasm (LNM) in patients with post-traumatic hypertension (PTC), including considering factors such as tumor size, location, extension, microcalcifications, and Hashimoto's lesions. Some studies have also included blood markers such as TSH and TGAb. Radiomics has gained attention in precision diagnostics in recent years, and radiomics-based techniques have been proposed to predict LNM in PTC patients by converting ultrasound images into analyzable data. These techniques extract features, including intensity, margins, texture, and wavelets, from ultrasound images and establish correlations between these high-throughput features and LNM status. In previous studies, whether based on clinical data or radiomics, the predictive performance of LNM has been less than ideal due to the difficulty in ensuring the completeness of image feature extraction; the area under the single receiver operating characteristic (ROC) curve (AUC) in the test set was approximately between 0.67 and 0.78.

[0005] Therefore, there is an urgent need to develop a simpler, earlier, more effective, and more accurate method for screening and diagnosing lymph node metastasis in thyroid cancer, so as to achieve timely intervention and treatment of lymph node metastasis in thyroid cancer. Summary of the Invention

[0006] The purpose of this invention is to provide a simpler, more effective, earlier, and more accurate method for early screening and diagnosis of thyroid cancer metastasis.

[0007] In a first aspect of the present invention, the use of a gene, mRNA, cDNA, protein, or a detection reagent thereof for a risk marker of thyroid cancer metastasis is provided for the preparation of a diagnostic reagent or kit, said diagnostic reagent or kit being used to determine whether a thyroid cancer patient has thyroid cancer metastasis.

[0008] The aforementioned biomarkers for thyroid cancer metastasis include: (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1.

[0009] In another preferred embodiment, the markers A1 to A3 are selected from Table A:

[0010]

[0011] In another preferred embodiment, the gene, mRNA, cDNA, or protein of any of the biomarkers shown in Table A of thyroid cancer metastasis risk biomarkers is derived from a human.

[0012] In another preferred embodiment, the diagnostic reagent or kit is used to detect thyroid cancer metastasis risk markers in thyroid protocarcinoma tissue.

[0013] In another preferred embodiment, the thyroid cancer includes papillary thyroid carcinoma.

[0014] In another preferred embodiment, the thyroid cancer metastasis includes thyroid cancer lymph node metastasis;

[0015] In another preferred embodiment, the thyroid cancer lymph node metastasis includes: central lymph node metastasis of thyroid cancer, lateral neck lymph node metastasis of thyroid cancer, or a combination thereof.

[0016] In another preferred embodiment, the detection reagent includes: (a) a specific antibody or specific binding molecule against the thyroid cancer metastasis risk marker; and / or (b) primers or primer pairs, probes, or chips (such as nucleic acid chips or protein chips) that specifically amplify the mRNA or cDNA of the thyroid cancer metastasis risk marker.

[0017] In another preferred embodiment, the detection reagent includes primer pairs or probes that specifically amplify the mRNA or cDNA of the thyroid cancer metastasis risk marker.

[0018] In another preferred embodiment, the primer pairs include primer pairs selected from the group consisting of: primer pairs for amplifying RPS4Y1: SEQ ID NO:1, SEQ ID NO:2; primer pairs for amplifying PKHD1L1: SEQ ID NO:3, SEQ ID NO:4; primer pairs for amplifying CRABP1: SEQ ID NO:5, SEQ ID NO:6; or combinations thereof.

[0019] In another preferred embodiment, the detection reagent further includes the following internal reference primer pair: SEQ ID NO:7, SEQ ID NO:8.

[0020] In another preferred embodiment, the diagnostic reagent or kit is used to detect the expression level of a biomarker for thyroid cancer metastasis in the sample to be tested.

[0021] In a second aspect of the invention, a kit is provided, the kit containing a detection reagent for detecting genes, mRNA, cDNA, proteins, or combinations thereof of thyroid cancer metastasis risk markers in a sample to be tested;

[0022] The aforementioned biomarkers for thyroid cancer metastasis include: (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1.

[0023] In another preferred embodiment, the detection reagent includes: (a) a specific antibody or specific binding molecule against the thyroid cancer metastasis risk marker; and / or (b) primers or primer pairs, probes, or chips (such as nucleic acid chips or protein chips) that specifically amplify the mRNA or cDNA of the thyroid cancer metastasis risk marker.

[0024] In another preferred embodiment, the detection reagent includes primer pairs or probes that specifically amplify the mRNA or cDNA of the thyroid cancer metastasis risk marker.

[0025] In another preferred embodiment, the primer pairs include primer pairs selected from the group consisting of: primer pairs for amplifying RPS4Y1: SEQ ID NO:1, SEQ ID NO:2; primer pairs for amplifying PKHD1L1: SEQ ID NO:3, SEQ ID NO:4; primer pairs for amplifying CRABP1: SEQ ID NO:5, SEQ ID NO:6; or combinations thereof.

[0026] In another preferred embodiment, the detection reagent further includes the following internal reference primer pair: SEQ ID NO:7, SEQ ID NO:8.

[0027] In another preferred embodiment, the detection reagent is coupled with or carries a detectable marker.

[0028] In another preferred embodiment, the detectable marker is selected from the group consisting of chromophores, chemiluminescent groups, fluorophores, isotopes, or enzymes.

[0029] In another preferred embodiment, the antibody is a monoclonal antibody or a polyclonal antibody.

[0030] In another preferred embodiment, the diagnostic reagent includes antibodies, primers, probes, sequencing libraries, nucleic acid chips (such as DNA chips), or protein chips.

[0031] In another preferred embodiment, the nucleic acid chip includes a substrate and specific oligonucleotide probes spotted on the substrate, the specific oligonucleotide probes including probes that specifically bind to polynucleotides (mRNA or cDNA) of any of the said thyroid cancer metastasis risk markers.

[0032] In another preferred embodiment, the protein chip includes a substrate and specific antibodies spotted on the substrate, the specific antibodies including specific antibodies against the thyroid cancer metastasis risk marker.

[0033] In another preferred embodiment, the antibody is a monoclonal antibody or a polyclonal antibody.

[0034] In another preferred embodiment, the kit contains genes, mRNAs, cDNAs, and / or proteins of thyroid cancer metastasis risk markers as controls or quality control products.

[0035] In another preferred embodiment, the kit further includes a label or instructions indicating that the kit is used for (a) assessing the risk of thyroid cancer metastasis and / or (b) evaluating the efficacy of treatment for thyroid cancer metastasis.

[0036] In another preferred embodiment, the reagents include primers, probes, gRNA, or combinations thereof, more preferably primer pairs or probes for PCR, qPCR, or RT-PCR.

[0037] In another preferred embodiment, the thyroid cancer metastasis risk marker can be detected by sequencing, PCR, or a combination thereof.

[0038] In another preferred embodiment, the detection of the thyroid cancer metastasis risk marker can be quantitative.

[0039] In a third aspect of the present invention, a detection method is provided, comprising the steps of:

[0040] (a) Provide a test sample;

[0041] (b) Detect the expression levels of thyroid cancer metastasis risk marker genes in the test samples, denoted as C1; and

[0042] (c) Compare the concentration C1 of the biomarker for thyroid cancer metastasis risk with a control reference value C0, wherein the biomarker for thyroid cancer metastasis risk includes:

[0043] (A) Any marker selected from A1 to A8, or a combination thereof: (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1;

[0044] The risk of thyroid cancer metastasis in a subject is considered high if the test results meet the following criteria:

[0045] When the expression level of a certain biomarker in the subjects tested is significantly different from the reference or standard value, the thyroid cancer patients have a high risk of thyroid cancer metastasis.

[0046] In a fourth aspect of the invention, a device for early screening of thyroid cancer metastasis is provided, the device comprising:

[0047] (a) An input module, wherein the input module is used to input expression data of characteristic genes of a thyroid cancer patient;

[0048] The characteristic genes mentioned include: (A1)RPS4Y1; (A2)PKHD1L1; and (A3)CRABP1;

[0049] (b) A processing module, which analyzes and calculates the input feature genes to obtain a risk assessment; through differential expression analysis, it is found that when (A1)RPS4Y1 is significantly downregulated and (A2)PKHD1L1 and (A3)CRABP1 are significantly upregulated, it indicates a high risk of thyroid cancer metastasis in the thyroid cancer patient; conversely, it indicates a low risk of thyroid cancer metastasis in the thyroid cancer patient.

[0050] (c) Output module, which is used to output the auxiliary screening results.

[0051] In another preferred embodiment, the device further includes a detection module for detecting the mRNA level, protein level, or protein activity of the risk biomarker.

[0052] In another preferred embodiment, the detection module is selected from the group consisting of: ELISA analyzer, PCR detector, sequencer, or combinations thereof.

[0053] In a fifth aspect of the invention, a method for detecting the expression level of a combination of biomarkers for thyroid cancer metastasis is provided, comprising the steps of:

[0054] (a) Provide a test sample;

[0055] (b) Extract total RNA from the sample;

[0056] (c) Reverse transcribe the RNA product obtained in step (b);

[0057] (d) Perform real-time PCR on the reverse transcription products obtained in step (c) to obtain the expression levels of risk marker genes;

[0058] The risk markers include: (A1)RPS4Y1; (A2)PKHD1L1; and (A3)CRABP1.

[0059] In another preferred embodiment, the method is a non-diagnostic and non-therapeutic method.

[0060] In another preferred embodiment, the method is an in vitro method.

[0061] It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be described in detail here. Attached Figure Description

[0062] Figure 1 Two-dimensional embeddings of thyroid tumor cells are shown (major components 1 and 2); the cells are based on whether they originated from patients with central / neck tumor metastases (positive) or not (negative).

[0063] Figure 2 The AUROC values ​​of random forest models using different numbers of genes as input are shown; genes are selected based on mutual information between gene expression and class labels.

[0064] Figure 3 The AUROC curve of the final random forest model using 3 genes as input is shown.

[0065] Figure 4 The expression levels of the final 3-gene signatures from tumor cells from the positive / negative groups were shown. Detailed Implementation

[0066] Through extensive and in-depth research and screening, the inventors unexpectedly discovered a more effective, rapid, and accurate method and device for early screening of thyroid cancer metastases based on characteristic genes. Specifically, the inventors found that the 3-gene combination (RPS4Y1, PKHD1L1, and CRABP1) of this invention has a very high accuracy rate in early detection or screening, which is simpler, more effective, faster, and more accurate than existing single-gene or multi-gene combinations for detecting thyroid metastases. It also significantly reduces the detection cost of multi-gene (>3) combination detection. Based on this, a simple, accurate, and efficient early screening system for thyroid cancer metastases was constructed. The method and screening system of this invention can effectively distinguish between patients with metastatic thyroid cancer and those without metastasis, facilitating early treatment intervention for patients with metastatic thyroid cancer. This invention was completed based on this.

[0067] This invention uses tumor cell expression data of orthotopic thyroid carcinoma as input to construct a predictive model, and uses at least three genes containing the present invention (RPS4Y1, PKHD1L1, and CRABP1) as input. The resulting model achieves a 92.5% accuracy rate in classifying newly admitted patients as metastatic or non-metastatic, demonstrating strong performance. The implementation of this model may help clinicians make timely diagnoses of thyroid cancer metastasis. Treatment intervention can be initiated early and prevent the progression of thyroid cancer metastasis. The significance of this work lies in its potential to address the classification and screening of early thyroid cancer metastasis, and has significant clinical implications for the management of thyroid cancer metastasis by both patients and hospitals.

[0068] the term

[0069] As used in this article, the term "sample" or "sample" refers to material specifically associated with a subject from which specific information relating to the subject can be determined, calculated, or inferred. A sample may consist wholly or partially of biological material from the subject.

[0070] As used herein, the term "expression" includes the production of mRNA from a gene or gene segment, and includes the production of proteins encoded by RNA or a gene or gene segment, as well as the appearance of detection substances associated with expression. For example, the binding of cDNA, binding ligands (such as antibodies) to genes or other oligonucleotides, proteins, or protein fragments, and the chromogenic portion of the binding ligand are all included within the scope of the term "expression." Therefore, an increase in the density of half-points on an immunoblot such as a Western blot also falls within the scope of the biologically molecular-based term "expression."

[0071] As used herein, the term "reference value" or "control reference value" refers to a value that is statistically relevant to a specific outcome when compared with the results of an analysis. In a preferred embodiment, the reference value is determined based on a statistical analysis of the mRNA expression and / or protein expression of a marker for the risk of lymph node metastasis in thyroid cancer. Some such studies are shown in the Examples section of this document. However, studies from the literature and user experience with the methods disclosed herein can also be used to produce or adjust reference values. Reference values ​​can also be determined by considering circumstances and outcomes that are particularly relevant to the patient's ethnicity, medical history, genetics, age, and other factors.

[0072] Risk markers for lymph node metastasis in thyroid cancer

[0073] As used herein, the term "thyroid cancer metastasis risk marker of the present invention" refers to one or more of the markers shown in Table A.

[0074] In this invention, the terms "thyroid cancer metastasis risk marker protein of the present invention", "thyroid cancer lymph node metastasis risk marker protein of the present invention", "protein of the present invention", "polypeptide of the present invention", "characteristic gene of the present invention" or "marker shown in Table A" are used interchangeably and all refer to having any one or more of the thyroid cancer lymph node metastasis risk markers of the present invention.

[0075] In this invention, the terms "thyroid cancer metastasis risk marker gene", "thyroid cancer lymph node metastasis risk marker gene", and "thyroid cancer lymph node metastasis risk marker polynucleotide" are used interchangeably and all refer to the nucleotide sequence of any of the thyroid cancer metastasis risk markers shown in Table A.

[0076] It should be understood that nucleotide substitutions in a codon are acceptable when encoding the same amino acid. Furthermore, it should be understood that nucleotide substitutions are also acceptable when they result in conserved amino acid substitutions.

[0077] Once information on thyroid cancer metastasis risk markers is available, the nucleic acid sequence encoding it can be constructed, and specific probes can be designed based on the nucleotide sequence. The full-length nucleotide sequence or fragments thereof can typically be obtained using PCR amplification, recombinant methods, or synthetic methods. For PCR amplification, primers can be designed based on the nucleotide sequence of the thyroid cancer metastasis risk marker disclosed in this invention, especially the open reading frame sequence, and the relevant sequence can be amplified using a commercially available cDNA library or a cDNA library prepared according to conventional methods known to those skilled in the art. When the sequence is long, two or more PCR amplifications are often required, and then the fragments amplified from each amplification are spliced ​​together in the correct order.

[0078] Once the relevant sequence is obtained, it can be obtained in large quantities using recombination methods. This typically involves cloning it into a vector, transferring it into cells, and then isolating the sequence from the proliferated host cells using conventional methods.

[0079] In addition, sequences can be synthesized artificially, especially when the fragment length is short. Typically, long sequences can be obtained by first synthesizing multiple small fragments and then joining them.

[0080] Currently, the DNA sequence encoding the protein of this invention (or its fragments, derivatives) can be obtained entirely through chemical synthesis. This DNA sequence can then be introduced into various existing DNA molecules (such as vectors) and cells known in the art.

[0081] Using conventional recombinant DNA technology, the polynucleotide sequence of the present invention can be used to express or produce recombinant thyroid cancer metastasis risk markers.

[0082] Detection methods

[0083] Based on the differential expression of thyroid cancer metastasis risk markers in in situ cancer cells, this invention also provides a corresponding method for assessing the risk of thyroid cancer metastasis.

[0084] This invention relates to diagnostic test methods for quantitatively and locally detecting protein or mRNA levels of risk markers for thyroid cancer metastasis. These tests are well known in the art. The protein or mRNA levels of human thyroid cancer metastasis risk markers detected in the tests can be used to determine (including as a supplementary determination) whether there is a risk of thyroid cancer metastasis.

[0085] A preferred method is to perform quantitative detection of mRNA or cDNA by PCR / qPCR / RT-PCR.

[0086] A preferred method is to perform quantitative detection by sequencing mRNA or cDNA.

[0087] Polynucleotides that serve as risk markers for lymph node metastasis in thyroid cancer can be used for the diagnosis of this risk. Some or all of the polynucleotides of this invention can be immobilized as probes on microarrays or DNA chips for differential gene expression analysis and gene diagnosis.

[0088] Furthermore, this invention can also perform detection at the protein level. For example, antibodies against thyroid cancer lymph node metastasis risk markers can be immobilized on a protein chip for detecting thyroid cancer lymph node metastasis risk proteins in a sample.

[0089] Test kit

[0090] Based on the correlation between thyroid cancer metastasis risk markers and the risk of thyroid cancer metastasis, thyroid cancer metastasis risk markers can be used as markers for assessing the risk of thyroid cancer metastasis.

[0091] This invention also provides a kit for assessing the risk of thyroid cancer metastasis. The kit contains a detection reagent for detecting genes, mRNA, cDNA, proteins, or combinations thereof of thyroid cancer metastasis risk markers. Preferably, the kit contains antibodies or immunoconjugates of the anti-thyroid cancer metastasis risk markers of this invention, or their active fragments; or primers or primer pairs, probes, or chips that specifically amplify the mRNA or cDNA of thyroid cancer metastasis risk markers.

[0092] In another preferred embodiment, the kit also includes a label or instructions.

[0093] The main advantages of this invention include:

[0094] (a) Compared with existing methods for detecting thyroid cancer metastasis, the biomarker combination established in this invention uses fewer biomarkers yet achieves very high detection accuracy, greatly optimizing the detection process.

[0095] (b) The predictive model provided by this invention avoids multiple biopsies for patients at high risk of thyroid cancer metastasis, greatly reducing patient suffering and saving medical resources.

[0096] (c) The predictive model provided by this invention can more accurately guide the selection of clinical surgical plans, making the treatment plan more precise.

[0097] (d) This invention achieves very good detection results using only 3 genes (RPS4Y1, PKHD1L1 and CRABP1), significantly reducing detection costs, detection process and detection time.

[0098] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Experimental methods in the following embodiments, unless otherwise specified, are generally performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or as recommended by the manufacturer. Unless otherwise stated, percentages and parts are weight percentages and parts by weight.

[0099] Materials and methods

[0100] This invention utilizes the expression of the EPCAM and TG dual genes as guide genes to screen all tumor cells. In constructing the predictive model, genes with highly variable expression are first selected to eliminate genes with low information content and optimize computational efficiency, based on the gene expression profile of tumor cells. Next, mutual information scores are used as a metric to select the dominant genes for building the random forest model classifier. AUROC is used to evaluate model performance and ultimately determine model selection; other evaluation metrics, including but not limited to recall and precision, are also calculated. All models are evaluated using 5-fold cross-validation.

[0101] Example 1 Sequencing Sample Preparation

[0102] Samples were selected from six patients with central metastases of thyroid cancer, eight patients with lateral neck metastases, and four patients with carcinoma in situ without pathological metastases. Tissue samples were obtained through the following methods:

[0103] 1. For patients pathologically diagnosed with papillary thyroid carcinoma, after passing hospital ethics review, a 5mm*5mm tissue sample was harvested from the center of the thyroid mass during surgery, placed in tissue preservation solution, and then preserved in liquid nitrogen for subsequent flow cytometry and single-cell sequencing analysis.

[0104] 2. For patients pathologically diagnosed with papillary thyroid carcinoma, after passing hospital ethics review, the central part of the thyroid mass during surgery was removed to obtain excised thyroid tumor tissue via ultrasound-guided puncture. The tissue was then preserved in tissue preservation solution and liquid nitrogen for subsequent flow cytometry and single-cell sequencing analysis.

[0105] Example 2 Single-cell sequencing

[0106] Single-cell sequencing and V(D)J library preparation were performed using a 10X Genomics Chromium Controller Instrument, Chromium Single Cell 5' Library, Gel Bead Kit, and V(D)J Enrichment Kit (10X Genomics, Pleasanton, CA). Cell suspensions were concentrated to 1,000 cells / μL, and approximately 10,000 cells were loaded into each channel to generate single-cell gel beads in an emulsion (GEM). The output was mRNA barcodes from 6,000 cells per sample. Following a reverse transcription step, the GEM was destroyed, and 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 with adapters, and indexed PCR amplification was performed. For the V(D)J library, human T cell and B cell V(D)J sequences were enriched from the amplified cDNA, fragmented, A-tailed, ligated with adapters, and indexed PCR amplification was performed.

[0107] The final libraries were quantified using the Qubit high-sensitivity DNA assay (No. Q33231; Thermo Fisher Scientific, Waltham, MA, USA). Library size distribution was determined using a high-sensitivity DNA microarray on a Bioanalyzer 2200 (Agilent Technologies, Santa Clara, CA, USA). All libraries were sequenced on an Illumina sequencer (Illumina, San Diego, CA, USA) using 150 bp paired ends.

[0108] Example 3. qRT-PCR experiment

[0109] RNA Extraction: First, total RNA was extracted from thyroid cancer in situ cancer cell samples using an RNA extraction kit (e.g., Takara Bio, Kusatsu, Japan). Follow the kit's instructions carefully to ensure high-quality RNA extraction.

[0110] cDNA synthesis: The extracted total RNA was reverse transcribed into the corresponding cDNA. The reverse transcription reaction was performed using a reverse transcription kit (e.g., Vazyme, Nanjing, China) according to the manufacturer's instructions. This step converts the RNA into stable cDNA for subsequent PCR analysis.

[0111] qPCR Preparation: Prepare the qPCR reaction system to measure the expression level of the target gene. Using a SYBR Green qPCR kit (e.g., Vazyme) and following the manufacturer's instructions, mix the cDNA with appropriate primers and reagents.

[0112] Real-time PCR analysis: Place the qPCR reaction system into a real-time PCR instrument, such as the Bio-Rad Laboratories CFX96 Touch Real-time PCR Detection System (RRID: SCR_008426). Set an appropriate PCR program to measure changes in fluorescence signal, which will reflect the expression level of the target gene. Perform real-time PCR analysis to obtain quantitative gene expression data.

[0113] Examples of primers used (primer F (forward) and primer R (reverse)) are shown in Table B below:

[0114] Table B

[0115] RPS4Y1-primer F TGGATGCTTGACAAACTAACGG SEQ ID NO:1 RPS4Y1-primer R AGGAAGACGATCAGAGGAAGAC SEQ ID NO:2 PKHD1L1-primer F TGGAGTTGATAACGCTGAGTTG SEQ ID NO:3 PKHD1L1-primer R CCGGCATTGCTCTAGTATAGCAT SEQ ID NO:4 CRABP1 primer F GCAGCAGCGAGAATTTCGAC SEQ ID NO:5 CRABP1-primer R CGTGGTGGATGTCTTGATGTAGA SEQ ID NO:6 GAPDH (internal reference) - primer F AGATCATCAGCAATGCCTCCT SEQ ID NO:7 GAPDH (Internal Reference) - Primer R TGAGTCCTTCCACGATACCAA SEQ ID NO:8

[0116] Example 4: Gene Selection and Clustering

[0117] After obtaining the raw sequencing data, the single-cell sequencing data were aligned and demultiplexed using the Cell Ranger v.3.0.2vdj workflow (10X Genomics). The expression matrix data was then analyzed using the Scanpy computational framework. Data was filtered according to QC standards, removing cells with <500 detected genes and <5 genes detected in a single cell / data set. Cells with mitochondrial gene expression >10% of total expression were also excluded. Bimodal peaks were avoided by removing cells with the top 5% of total transcripts (UMI). Scrublets software was also used to remove bimodal peaks.

[0118] Then, genes were selected for dimensionality reduction and clustering based on their variability. All data were merged, the top 2,000 variable genes were selected, and the total UMI count and mitochondrial gene expression ratio for each cell were regressed to eliminate the influence of these factors on clustering. The BBKNN method was used to perform batch correction using donors as batch keys. Leiden clustering was performed in Scanpy with a resolution of 1.

[0119] Example 5 Model Establishment

[0120] The model of this invention is constructed using a basic method that relies on machine learning algorithms, with thyroid in situ cancer cells as the model input.

[0121] (1) Feasibility of exploring disease predictive factors: Using EPCAM and TG dual gene expression as guide genes, all tumor cells were screened from single-cell transcriptome data. After feature screening, mRNA was extracted from a new set of 400 thyroid carcinoma in situ biopsy specimens. After qPCR experiments, the data were integrated and PCA (principal component analysis) was performed. Figure 1 The results showed that these tumor cells exhibited spatial differences in PCA analysis, confirming the feasibility of the method.

[0122] (2) Gene sorting: Genes are sorted according to their mutual information scores, and the number of genes is limited to a relatively small range.

[0123] (3) Classifier Construction and Performance Evaluation: Random forest model classifiers were constructed based on different numbers of features (genes), and their performance was evaluated. It was observed that when the number of feature genes reached 4, the model performance evaluation showed an inflection point (…). Figure 2 ).

[0124] (4) Classifier model for the first 3 genes: The classifier model was constructed using the 3 genes with the highest mutual information scores. Under this model, the area of ​​AUROC reached approximately 0.975. Figure 3 ).

[0125] (5) Gene expression mapping: The expression of these three genes in tumor cells of the metastatic and non-metastatic groups was depicted, and differences in their expression were found based on the Wilcoxon test. Figure 4 These three genes are the same as the three genes in Table A: (A1)RPS4Y1, (A2)PKHD1L1, and (A3)CRABP1. The weight of each gene in the model is shown in Table A.

[0126] Table A

[0127]

[0128]

[0129] Example 6 Model Validation

[0130] The model uses three specific genes from Table A as input parameters: (A1)RPS4Y1, (A2)PKHD1L1, and (A3)CRABP1.

[0131] This invention used the established model to conduct 100 validation experiments on different sample sets (80% test set, 20% training set) with a 4:1 sorting ratio, and the average AUROC value reached approximately 0.975. These results (experimental data are shown in Table C) demonstrate the strong robustness of the model and support the solidity and reliability of its conclusions.

[0132] Table C shows the verification results.

[0133]

[0134]

[0135]

[0136] Example 6 Comparative Example

[0137] In step (2) of Example 4, the top four genes are RPS4Y1, PKHD1L1, CRABP1 and MTT1G.

[0138] Classifier models were constructed using either 4 genes or any combination of 3 of the 4 genes. The classification results for each model are shown in Table 3.

[0139] Table 3

[0140]

[0141]

[0142] As shown in Table 3, the biomarker gene RPS4Y1 is the most important and the 3-gene (RPS4Y1, PKHD1L1 and CRABP1) classifier model of this invention has a very good detection effect.

[0143] discuss

[0144] In this invention, an assessment method is introduced for analyzing the potential risk of thyroid cancer metastasis, and an analytical device capable of predicting the risk of thyroid cancer metastasis is further designed.

[0145] In this invention, the inventors unexpectedly identified a new combination of biomarkers for the risk of thyroid cancer metastasis, including the following: (A1) RPS4Y1, (A2) PKHD1L1 and (A3) CRABP1.

[0146] In the implementation of this invention, by selecting the biomarkers in the above-mentioned thyroid in situ cancer cells, an objective assessment of the risk of thyroid cancer metastasis is achieved, demonstrating high sensitivity and specificity in the diagnosis of thyroid cancer metastasis.

[0147] Specifically, this invention uses single-cell sequencing for feature screening and qPCR for retraining and feature contribution ranking. The three genes with the highest mutual information scores (RPS4Y1, PKHD1L1, and CRABP1) were selected to construct the model, and the model was validated. This demonstrates that the three-gene classification model of this invention has excellent classification and detection performance, with a higher detection accuracy than other three-gene combinations, and a significantly lower detection cost than combinations with more genes (>3).

[0148] All documents mentioned in this invention are incorporated herein by reference as if each document were individually incorporated by reference. Furthermore, it should be understood that after reading the foregoing teachings of this invention, those skilled in the art can make various alterations or modifications to this invention, and these equivalent forms also fall within the scope defined in the appended documents.

Claims

1. Use of a gene, mRNA, cDNA, protein, or detection reagent thereof of a thyroid cancer metastasis risk marker, characterized in that, A diagnostic reagent or kit for determining whether a thyroid cancer patient has a risk of thyroid cancer metastasis; The thyroid cancer metastasis risk marker comprises (A1) RPS4Y1, (A2) PKHD1L1, and (A3) CRABP1.

2. Use according to claim 1, characterized in that, The diagnostic reagent or kit is used for detecting the thyroid cancer metastasis risk marker in a thyroid cancer tissue.

3. Use according to claim 1, characterized in that, The thyroid cancer comprises a thyroid papillary carcinoma.

4. The use according to claim 1, characterized in that, The diagnostic reagent or kit is used for detecting the expression level of the thyroid cancer metastasis risk marker in a sample to be tested.

5. A kit characterized in that, The kit comprises a detection reagent for detecting the gene, mRNA, cDNA, protein, or combination thereof of the thyroid cancer metastasis risk marker in the sample to be tested. The thyroid cancer metastasis risk marker comprises (A1) RPS4Y1, (A2) PKHD1L1, and (A3) CRABP1.

6. The kit of claim 5, wherein The detection reagent comprises (a) a specific antibody or specific binding molecule for the thyroid cancer metastasis risk marker; and / or (b) a primer or primer pair, probe, or chip for specifically amplifying the mRNA or cDNA of the thyroid cancer metastasis risk marker.

7. The kit of claim 5, wherein The kit further comprises a label or instruction indicating that the kit is used for (a) determining the risk of thyroid cancer metastasis, and / or (b) evaluating the therapeutic effect on thyroid cancer metastasis.

8. A device for early screening of thyroid cancer metastasis, characterized by, The device comprises: (a) an input module for inputting the expression data of a characteristic gene of a thyroid cancer patient; The characteristic gene comprises (A1) RPS4Y1, (A2) PKHD1L1, and (A3) CRABP1. (b) a processing module for analyzing and calculating the input characteristic gene, thereby obtaining a risk evaluation; wherein when (A1) RPS4Y1 is significantly down-regulated, and (A2) PKHD1L1 and (A3) CRABP1 are significantly up-regulated, it indicates that the thyroid cancer patient has a high risk of thyroid cancer metastasis; otherwise, it indicates that the thyroid cancer patient has a low risk of thyroid cancer metastasis; and (c) an output module for outputting the risk evaluation result.

9. The apparatus of claim 8, wherein, The device further comprises a detection module for detecting the mRNA level, protein level, or protein activity of the risk marker.

10. The apparatus of claim 9, wherein, The detection module is selected from the group consisting of an ELISA analyzer, a PCR detector, a sequencer, or a combination thereof.

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