Marker combination for predicting metastasis of thyroid cancer and application thereof

Abstract

This invention provides a combination of biomarkers for predicting thyroid cancer metastasis and their applications. Specifically, this invention provides the use of genes, mRNAs, cDNAs, proteins, or detection reagents thereof for a combination of risk biomarkers for assessing thyroid cancer metastasis, for the preparation / establishment of diagnostic reagents or kits / devices for assessing the risk of thyroid cancer metastasis. Studies have shown that the combination of thyroid cancer metastasis risk biomarkers can serve as biomarkers for early assessment of thyroid cancer metastasis in patients, exhibiting high sensitivity and specificity. It can rapidly diagnose thyroid cancer metastasis at a relatively early stage of disease progression, providing strong support for early treatment intervention.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of medical diagnosis, more particularly to a marker combination for predicting thyroid cancer metastasis and use thereof. BACKGROUND

[0002] There are about 567,000 cases of thyroid cancer worldwide each year, ranking ninth in the incidence of all cancer types. The incidence in women is about three times that of men, at 10.2 / 100,000 people. Since the 1980s, the incidence of thyroid cancer has been steadily rising in many countries, mainly due to the improvement of detection and diagnosis methods, especially the improved discovery rate of papillary thyroid carcinoma (PTC).

[0003] PTC accounts for about 84% of thyroid cancer patients and is the most common type of thyroid malignancy. The average incidence of PTC in the United States from 1974 to 2013 was about 6.66%, and the mortality rate related to the incidence was 0.20%. Although PTC is usually considered a slow-growing tumor, some cancer cells can metastasize to adjacent lymph nodes, especially the central and lateral neck lymph nodes. Lymph node metastasis (LNM) usually occurs in the central region and is a key factor in the prognosis, treatment strategy, and recurrence rate of PTC, and is also associated with lower survival rates.

[0004] Studies have proposed using several methods to assess the risk of LNM in PTC patients, including considering factors such as tumor size, location, extension, microcalcification, and Hashimoto's disease, and some studies also include blood markers such as TSH and TGAb. Radiomics has attracted attention in precision diagnosis in recent years, and radiomics-based techniques have also been proposed to predict LNM in PTC patients by converting ultrasound images into analyzable data. These techniques extract features from ultrasound images, including intensity, edge, texture, and wavelet, and establish a link between these high-throughput features and LNM status. In previous studies, whether based on clinical data or imageomics, the prediction performance of LNM was not ideal due to the difficulty in ensuring the completeness of image feature extraction, with the area under the single receiver operating characteristic curve (ROC) (AUC) in the test set being about 0.67 to 0.78.

[0005] Therefore, there is an urgent need in this field to develop a method that can more simply, more effectively, more early, and more accurately screen and diagnose thyroid cancer lymph node metastasis to achieve timely intervention treatment for thyroid cancer lymph node metastasis. SUMMARY

[0006] The present application provides a method for more simply, more effectively, more early, and more accurately screening and diagnosing thyroid cancer metastasis in the early stage.

[0007] In a first aspect of the present application, there is provided a use of a gene, mRNA, cDNA, protein, or detection reagent thereof of a thyroid cancer metastasis risk marker for preparing a diagnostic reagent or kit for determining whether a thyroid cancer patient has thyroid cancer metastasis;

[0008] In the present application, the thyroid cancer metastasis risk marker comprises (A1) RPS4Y1, (A2) PKHD1L1, and (A3) CRABP1.

[0009] In another preferred embodiment, the detection reagent comprises a primer pair, which comprises a marker primer pair for specifically amplifying mRNA or cDNA of the thyroid cancer metastasis risk marker combination.

[0010] In another preferred embodiment, the kit further comprises a reference primer pair for amplifying a reference gene.

[0011] In another preferred embodiment, the marker primer pair comprises:

[0012] (P1) a primer pair for amplifying RPS4Y1: SEQ ID NO: 1 and SEQ ID NO: 2;

[0013] (P2) a primer pair for amplifying PKHD1L1: SEQ ID NO: 3 and SEQ ID NO: 4;

[0014] (P3) a primer pair for amplifying CRABP1: SEQ ID NO: 5 and SEQ ID NO: 6; and / or

[0015] The reference primer pair is SEQ ID NO: 7 and SEQ ID NO: 8.

[0016] In another preferred embodiment, the detection reagent further comprises a probe used in cooperation with the primer pair,

[0017] The probe comprises: a probe 1 used in cooperation with the primer pair for amplifying RPS4Y1: SEQ ID NO: 9; a probe 2 used in cooperation with the primer pair for amplifying PKHD1L1: SEQ ID NO: 10; a probe 3 used in cooperation with the primer pair for amplifying CRABP1: SEQ ID NO: 11; and a probe 4 used in cooperation with the primer pair for amplifying a reference gene: SEQ ID NO: 12.

[0018] In another preferred embodiment, the diagnostic reagent or kit is used for detecting the thyroid cancer metastasis risk markers in the thyroid cancer tissue.

[0019] In another preferred embodiment, the markers of A1 to A3 are selected from the group consisting of:

[0020]

[0021] In another preferred embodiment, the thyroid cancer metastasis risk markers are any one of the markers shown in Table 1.

[0022] In another preferred embodiment, the diagnostic reagent or kit is used for detecting the thyroid cancer metastasis risk markers in the thyroid cancer tissue.

[0023] In another preferred embodiment, the thyroid cancer comprises thyroid papillary carcinoma.

[0024] In another preferred embodiment, the thyroid cancer metastasis comprises thyroid cancer lymph node metastasis.

[0025] In another preferred embodiment, the thyroid cancer lymph node metastasis comprises thyroid cancer central lymph node metastasis, thyroid cancer lateral neck lymph node metastasis, or a combination thereof.

[0026] In another preferred embodiment, the detection reagent comprises: (a) a specific antibody, a specific binding molecule against the thyroid cancer metastasis risk markers; and / or (b) a primer or primer pair, a probe or a chip (such as a nucleic acid chip or a protein chip) that specifically amplifies the mRNA or cDNA of the thyroid cancer metastasis risk markers.

[0027] In another preferred embodiment, the detection reagent comprises a primer pair or a probe, which is a primer pair or a probe that specifically amplifies the mRNA or cDNA of the thyroid cancer metastasis risk markers.

[0028] In another preferred embodiment, the primer pair comprises a primer pair selected from the group consisting of: a primer pair for amplifying RPS4Y1: SEQ ID NO: 1, SEQ ID NO: 2; a primer pair for amplifying PKHD1L1: SEQ ID NO: 3, SEQ ID NO: 4; a primer pair for amplifying CRABP1: SEQ ID NO: 5, SEQ ID NO: 6; or a combination thereof.

[0029] In another preferred embodiment, the detection reagent further comprises the following internal reference primer pair: SEQ ID NO: 7, SEQ ID NO: 8.

[0030] In another preferred embodiment, the diagnostic reagent or kit is used for detecting the expression level of the thyroid cancer metastasis risk marker in the sample to be tested.

[0031] In a second aspect of the present application, a kit is provided, which comprises a detection reagent for detecting the gene, mRNA, cDNA, protein, or a combination thereof of the thyroid cancer metastasis risk marker in a sample to be tested.

[0032] The thyroid cancer metastasis risk marker comprises (A1) RPS4Y1, (A2) PKHD1L1, and (A3) CRABP1.

[0033] In another preferred embodiment, the detection reagent comprises: (a) specific antibodies, specific binding molecules for 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.

[0034] In another preferred embodiment, the detection reagent comprises primer pairs or probes, which comprise primer pairs or probes that specifically amplify the mRNA or cDNA of the thyroid cancer metastasis risk marker.

[0035] In another preferred embodiment, the detection reagent comprises primer pairs, which comprise marker primer pairs for specifically amplifying the mRNA or cDNA of the combination of the thyroid cancer metastasis risk markers.

[0036] In another preferred embodiment, the kit further comprises: internal reference primer pairs for amplifying internal reference genes.

[0037] In another preferred embodiment, the internal reference gene is GAPDH.

[0038] In another preferred embodiment, the marker primer pairs comprise:

[0039] (P1) primer pairs for amplifying RPS4Y1: SEQ ID NO: 1 and SEQ ID NO: 2;

[0040] (P2) primer pairs for amplifying PKHD1L1: SEQ ID NO: 3 and SEQ ID NO: 4;

[0041] (P3) primer pairs for amplifying CRABP1: SEQ ID NO: 5 and SEQ ID NO: 6; and / or

[0042] The internal reference primer pair is SEQ ID NO: 7 and SEQ ID NO: 8.

[0043] In another preferred embodiment, the detection reagent further comprises a probe used in cooperation with the primer pair,

[0044] The probe comprises: probe 1 used in cooperation with the primer pair for amplifying RPS4Y1: SEQ ID NO: 9; probe 2 used in cooperation with the primer pair for amplifying PKHD1L1: SEQ ID NO: 10; probe 3 used in cooperation with the primer pair for amplifying CRABP1: SEQ ID NO: 11; and probe 4 used in cooperation with the primer pair for amplifying the internal reference gene: SEQ ID NO: 12.

[0045] In another preferred embodiment, the primer pair comprises a primer pair selected from the group consisting of: a primer pair for amplifying RPS4Y1: SEQ ID NO: 1, SEQ ID NO: 2; a primer pair for amplifying PKHD1L1: SEQ ID NO: 3, SEQ ID NO: 4; a primer pair for amplifying CRABP1: SEQ ID NO: 5, SEQ ID NO: 6; or a combination thereof. In another preferred embodiment, the detection reagent further comprises the following internal reference primer pair: SEQ ID NO: 7, SEQ ID NO: 8.

[0046] In another preferred embodiment, the detection reagent is coupled with or carries a detectable label.

[0047] In another preferred embodiment, the detectable label is selected from the group consisting of a chromophore, a chemiluminescent group, a fluorophore, an isotope, or an enzyme.

[0048] In another preferred embodiment, the antibody is a monoclonal antibody or a polyclonal antibody.

[0049] In another preferred embodiment, the diagnostic reagent comprises an antibody, a primer, a probe, a sequencing library, a nucleic acid chip (such as a DNA chip), or a protein chip.

[0050] In another preferred embodiment, the nucleic acid chip comprises a substrate and specific oligonucleotide probes spotted on the substrate, wherein the specific oligonucleotide probes comprise probes specific to the polynucleotides (mRNA or cDNA) of any of the thyroid cancer metastasis risk markers.

[0051] In another preferred embodiment, the protein chip comprises a substrate and specific antibodies spotted on the substrate, wherein the specific antibodies comprise antibodies specific to the thyroid cancer metastasis risk markers.

[0052] In another preferred embodiment, the antibody is a monoclonal antibody or a polyclonal antibody.

[0053] In another preferred embodiment, the kit comprises the gene, mRNA, cDNA and / or protein of the thyroid cancer metastasis risk marker as a control or quality control.

[0054] In another preferred embodiment, the kit further comprises a label or instructions indicating that the kit is used for (a) judging the risk of thyroid cancer metastasis, and / or (b) evaluating the therapeutic effect of thyroid cancer metastasis.

[0055] In another preferred embodiment, the reagent comprises primers, probes, gRNA or a combination thereof, more preferably a primer pair or a probe for PCR, qPCR, RT-PCR.

[0056] In another preferred embodiment, the detection of the thyroid cancer metastasis risk marker can be detected by sequencing, PCR, or a combination thereof.

[0057] In another preferred embodiment, the detection of the thyroid cancer metastasis risk marker can be quantitatively detected.

[0058] In a third aspect of the present application, a detection method is provided, comprising the steps of:

[0059] (a) providing a detection sample;

[0060] (b) detecting the expression amount of the thyroid cancer metastasis risk marker gene in the detection sample, denoted as C1; and

[0061] (c) comparing the concentration C1 of the thyroid cancer metastasis risk marker with a control reference value C0, wherein the thyroid cancer metastasis risk marker comprises:

[0062] (A) any marker selected from A1 to A8, or a combination thereof: (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1;

[0063] If the detection result of the thyroid cancer metastasis risk of the detection object meets the following conditions, it is suggested that the object has a high risk of thyroid cancer metastasis:

[0064] When a marker in the detection object has a significant difference in the expression level of the marker in Table 1 and the reference value or standard value, the thyroid cancer patient has a high risk of thyroid cancer metastasis.

[0065] In a fourth aspect of the present application, a device for early screening of thyroid cancer metastasis is provided, comprising:

[0066] (a) an input module for inputting expression data of certain thyroid cancer patient characteristic genes;

[0067] wherein the characteristic genes comprise: (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1;

[0068] (b) a processing module or a risk assessment module configured to: for the inputted characteristic genes, perform analysis calculation to obtain a risk assessment; and find through differential expression analysis that, when (A1) RPS4Y1, (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

[0069] (c) an output module for outputting the auxiliary screening result.

[0070] In another preferred embodiment, the risk assessment module is configured to, when performing risk assessment, use a random forest model to perform risk assessment on the inputted expression data.

[0071] In another preferred embodiment, the risk assessment module is configured to use the following random forest model formula shown in formula (I) to perform risk assessment:

[0072] (I)

[0073] wherein the random forest model is a trained and optimized model, T is the number of decision trees, x1, x2 and x3 correspond to the ΔCt values of the characteristic genes (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1, respectively.

[0074] In another preferred embodiment, in the random forest model, T = 41; and / or the weights of the (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1 markers integrated into the model are 0.484, 0.281 and 0.235, respectively.

[0075] In another preferred embodiment, when y ≥ 0.5, it indicates that the thyroid cancer patient has a high risk of metastasis, otherwise it indicates that the thyroid cancer patient has a low risk of metastasis.

[0076] In another preferred embodiment, the expression data is obtained using a detection reagent:

[0077] wherein the detection reagent comprises:

[0078] (P1) a primer pair for amplifying RPS4Y1: SEQ ID NO: 1 and SEQ ID NO: 2, and a probe 1: SEQ ID NO: 9 used in conjunction;

[0079] (P2) a primer pair for amplifying PKHD1L1: SEQ ID NO: 3 and SEQ ID NO: 4, and a probe 2: SEQ ID NO: 10 used in conjunction;

[0080] (P3) a primer pair for amplifying CRABP1: SEQ ID NO: 5 and SEQ ID NO: 6, and a probe 3: SEQ ID NO: 11 used in conjunction; and

[0081] the internal reference primer pair is SEQ ID NO: 7 and SEQ ID NO: 8, and a probe 4: SEQ ID NO: 12 used in conjunction.

[0082] In another preferred embodiment, the device further comprises a detection module for detecting the mRNA level, protein level, or protein activity of the risk marker.

[0083] In another preferred embodiment, the sample to be tested is a thyroid cancer tissue.

[0084] In another preferred embodiment, the detection module is selected from the group consisting of an ELISA analyzer, a PCR detector, a sequencer, or a combination thereof.

[0085] In a fifth aspect of the present application, a method for detecting the expression level of a combination of risk markers of thyroid cancer metastasis is provided, comprising the steps of:

[0086] (a) providing a detection sample;

[0087] (b) extracting total RNA from the sample;

[0088] (c) reverse transcribing the product RNA obtained in step (b);

[0089] (d) performing fluorescent quantitative PCR on the reverse transcription product obtained in step (c) to obtain the expression level of the risk marker genes;

[0090] wherein the combination of risk markers comprises: (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1.

[0091] In another preferred embodiment, the method is a non-diagnostic and non-therapeutic method.

[0092] In another preferred embodiment, the method is an in vitro method.

[0093] It should be understood that, in the scope of the present application, each of the technical features of the present application described above and each of the technical features specifically described hereinafter (e.g. in the examples) can be combined with each other to form new or preferred technical solutions. Due to the limited space, they are not listed one by one here. BRIEF DESCRIPTION OF DRAWINGS

[0094] Figure 1 Two-dimensional embedding of thyroid tumor cells (principal components 1 and 2) are shown; cells are based on whether they came from a patient with central / neck tumor metastasis (positive) or not (negative).

[0095] Figure 2 AUROC values of random forest models using different numbers of genes as input are shown; genes were selected based on mutual information between gene expression and class label.

[0096] Figure 3 AUROC curve of the final random forest model using 3 genes as input is shown.

[0097] Figure 4 Development of the algorithm for predicting thyroid cancer metastasis and the process of clinical experiments of the present application are shown.

[0098] Figure 5 Distribution of ΔCt values of three genes (RPS4Y1, PKHD1L1 and CRABP1) in the total dataset of three centers (Nantong (Center B), Zhongshan (Center A) and Xuzhou (Center C)) between metastasis and non-metastasis groups (RPS4Y1: P-value < 0.00001; PKHD1L1: P-value < 0.00001; CRABP1: P-value < 0.00001) is shown.

[0099] Figure 6 Receiver operating characteristic (ROC) curve of the random forest model in predicting metastasis occurrence in the three datasets is shown.

[0100] Figure 7 Matrix analysis of the random forest model on the dataset of Center B is shown.

[0101] Figure 8 Matrix analysis of the random forest model on the dataset of Center A is shown.

[0102] Figure 9 Matrix analysis of the random forest model on the dataset of Center B is shown.

[0103] Figure 10 Receiver operating characteristic (ROC) curve of the determination of 355 thyroid papillary carcinoma samples by the kit of the present application is shown. DETAILED DESCRIPTION

[0104] The present inventors have made extensive and in-depth research, and unexpectedly found a more effective, rapid and accurate method and device for early screening of thyroid cancer metastasis based on characteristic genes. Specifically, the present inventors found that the expression amount change or difference (such as ΔCt) of the 3-gene combination (RPS4Y1, PKHD1L1 and CRABP1) relative to the expression amount of the internal reference gene has a very high accuracy of early detection or screening, and the detection of thyroid metastatic cancer is more simple, effective, rapid and accurate than the existing single gene or multi-gene combination. The method of the present application significantly reduces the detection cost of multi-gene (>3) combination detection. In addition, the present application also constructs a kit and screening device for simple, accurate and efficient early screening of thyroid cancer metastasis. The method and screening device (or system) of the present application can not only effectively distinguish thyroid cancer metastasis patients from non-metastasis patients, but also has very excellent detection stability, anti-endogenous interference, anti-exogenous interference, clinical diagnosis specificity, clinical diagnosis sensitivity and clinical reproducibility. Therefore, it is suitable for early corresponding typing and treatment intervention of thyroid cancer metastasis patients. On this basis, the present application is completed.

[0105] The present application uses the tumor cell expression data of in situ thyroid cancer as the input for constructing the prediction model, and uses at least the 3 genes (RPS4Y1, PKHD1L1 and CRABP1) of the present application as the input. The accuracy of the model for classifying metastasis and non-metastasis of newly admitted patients is 92.5%, indicating that it has strong performance. The implementation of the model can help clinicians make timely thyroid cancer metastasis diagnosis. Treatment intervention can be performed at an early stage and prevent thyroid cancer metastasis progression. This work has the opportunity to solve the classification screening of early thyroid cancer metastasis, and has important clinical significance for the management of thyroid cancer metastasis for patients and hospitals.

[0106] Terms

[0107] The term "sample" or "specimen" as used herein refers to material that is specifically associated with a subject from which particular information about the subject can be determined, calculated, or inferred. The specimen can consist entirely or in part of biological material from the subject.

[0108] As used herein, the term "expression" includes the production of mRNA from a gene or portion of a gene, and includes the production of a protein encoded by the RNA or portion of the gene, and also includes the appearance of a detectable substance associated with expression. For example, cDNA, binding of a binding partner (such as an antibody) to a gene or other oligonucleotide, binding of a protein or protein fragment, and visualization of a colored moiety of a binding partner are all included within the scope of the term "expression". Thus, an increase in the intensity of a band on an immunoblot, such as a Western blot, is also within the scope of the term "expression" as used herein in reference to a biological molecule.

[0109] As used herein, the term "reference value" or "control reference value" refers to a value that is statistically associated with a particular result when compared to the results of an analysis. In preferred embodiments, reference values are determined from studies comparing mRNA expression and / or protein expression of the thyroid cancer lymph node metastasis risk marker, and subjected to statistical analysis. Some such studies are shown in the Examples section herein. However, studies from the literature and user experience with the methods disclosed herein can also be used to generate or adjust reference values. Reference values can also be determined by considering circumstances and results that are particularly relevant to the patient's population, medical history, genetics, age, and other factors.

[0110] As used herein, "Ct value" or "Cycle Threshold" refers to the cycle number at which the fluorescence signal of the amplified product reaches a set fluorescence threshold in quantitative PCR. In simple words, Ct value represents the cycle number at which the amplification of the starting template reaches a certain amount of product. The higher the concentration of the starting template amount, the smaller the Ct value; the lower the concentration of the starting template amount, the larger the Ct value.

[0111] As used herein, "ΔCt value" refers to the value of the gene to be tested (R gene) compared to the internal control, for example, the Ct value of GAPDH (the internal control selected by the present invention) is added or subtracted, and the ΔCt value of the R gene is Ct (R gene) - Ct (GAPDH) gene.

[0112] Thyroid cancer lymph node metastasis risk marker

[0113] 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 1.

[0114] In the present 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", "marker gene of the present invention", or "marker shown in Table 1" are used interchangeably and all refer to any one or more of the thyroid cancer lymph node metastasis risk markers of the present invention.

[0115] In the present application, the terms "thyroid cancer metastasis risk marker gene", "thyroid cancer lymph node metastasis risk marker gene", and "polynucleotide of a thyroid cancer lymph node metastasis risk marker" are used interchangeably and refer to the nucleotide sequence of any one of the thyroid cancer metastasis risk markers shown in Table 1.

[0116] It is to be understood that substitution of nucleotides in a codon is acceptable when it codes for the same amino acid. Also, it is to be understood that a change in nucleotides is acceptable when it results in a conservative substitution of amino acids.

[0117] In the case where information on a thyroid cancer metastasis risk marker is obtained, a nucleic acid sequence encoding it can be constructed therefrom, and a specific probe can be designed based on the nucleotide sequence. The nucleotide full-length sequence or a fragment thereof can be obtained by PCR amplification, recombination, or artificial synthesis. For PCR amplification, primers can be designed based on the nucleotide sequence of the thyroid cancer metastasis risk marker disclosed in the present application, particularly the open reading frame sequence, and a commercially available cDNA library or a cDNA library prepared according to a conventional method known to those skilled in the art can be used as a template to amplify the relevant sequence. When the sequence is long, it is often necessary to perform two or more PCR amplifications, and then the fragments amplified in each amplification are ligated in the correct order.

[0118] Once the relevant sequence is obtained, it can be obtained in large quantities by recombination. This is usually done by cloning it into a vector, transferring it into a cell, and then isolating the relevant sequence from the proliferated host cell by a conventional method.

[0119] In addition, the relevant sequence can be synthesized by artificial synthesis, particularly when the length of the fragment is short. Usually, a long fragment of a sequence can be obtained by synthesizing a plurality of small fragments and then ligating them.

[0120] At present, it is possible to obtain a DNA sequence encoding the protein (or a fragment or derivative thereof) of the present application entirely by chemical synthesis. Then, the DNA sequence can be introduced into various existing DNA molecules (e.g., vectors) and cells known in the art.

[0121] The polynucleotide sequence of the present application can be used to express or produce a recombinant thyroid cancer metastasis risk marker by conventional recombinant DNA technology.

[0122] RPS4Y1, PKHD1L1, and CRABP1

[0123] RPS4Y1 is a component of ribosomes and is involved in protein synthesis.

[0124] PKHD1L1 gene is involved in regulating cell growth and differentiation, which is essential for tissue development and normal function of organs.

[0125] CRABP1 is involved in the metabolism of retinoic acid, a molecule that regulates cell differentiation and growth. In various cancers, including thyroid cancer, dysregulation of the retinoic acid pathway can affect tumor behavior. Some studies suggest that changes in CRABP1 expression can be associated with the development of thyroid cancer, which can affect tumor growth and treatment response.

[0126] Detection methods

[0127] Based on the differential expression of the thyroid cancer metastasis risk marker in situ cancer cells, the present application also provides a corresponding method for judging the metastasis risk of thyroid cancer.

[0128] The present application relates to diagnostic test methods for quantitatively and qualitatively detecting the protein level or mRNA level of the thyroid cancer metastasis risk marker. These tests are well known in the art. The protein level or mRNA level of the human thyroid cancer metastasis risk marker detected in the test can be used to determine (including auxiliary determination) whether there is a risk of thyroid cancer metastasis.

[0129] A preferred method is to quantitatively detect mRNA or cDNA by PCR / qPCR / RT-PCR.

[0130] A preferred method is to quantitatively detect mRNA or cDNA by sequencing.

[0131] The polynucleotide of the thyroid cancer lymph node metastasis risk marker can be used for the diagnosis of the risk of thyroid cancer lymph node metastasis. Part or all of the polynucleotide of the present application can be fixed on a microarray or DNA chip as a probe for differential expression analysis and gene diagnosis in analysis.

[0132] In addition, the present application can also be detected at the protein level. For example, antibodies against the thyroid cancer lymph node metastasis risk marker can be fixed on a protein chip for detecting thyroid cancer lymph node metastasis risk proteins in the sample.

[0133] Detection kit

[0134] Based on the correlation between the thyroid cancer metastasis risk marker and the risk of thyroid cancer metastasis, the thyroid cancer metastasis risk marker can be used as a marker for judging the risk of thyroid cancer metastasis.

[0135] The present application also provides a kit for judging the metastasis risk of thyroid cancer, which comprises a detection reagent for detecting the gene, mRNA, cDNA, protein, or combination thereof of the metastasis risk marker of thyroid cancer. Preferably, the kit comprises the antibody or immunoconjugate against the metastasis risk marker of thyroid cancer of the present application, or the active fragment thereof; or the primer or primer pair, probe or chip for specifically amplifying the mRNA or cDNA of the metastasis risk marker of thyroid cancer.

[0136] In another preferred embodiment, the kit further comprises a label or instruction.

[0137] The main advantages of the present application include:

[0138] (a) Compared with the existing detection methods for the metastasis of thyroid cancer, the present application uses a combination of fewer markers, i.e. has a very high detection accuracy, and greatly optimizes the detection process.

[0139] (b) Using the prediction model provided by the present application, multi-site puncture of high-risk patients with metastasis of thyroid cancer is avoided, greatly reducing the pain of patients and saving medical resources.

[0140] (c) Using the prediction model provided by the present application, the selection of clinical surgical plans can be more accurately guided, making the treatment plan more precise.

[0141] (d) The present application only uses 3 genes (RPS4Y1, PKHD1L1 and CRABP1) to achieve very good detection effect, significantly reducing the detection cost, reducing the detection process and detection time.

[0142] The present application will be further described in conjunction with specific examples. It should be understood that these examples are only used to illustrate the present application and not used to limit the scope of the present application. The experimental methods in the following examples are not specified, which are usually carried out according to the conventional conditions, such as the conditions described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or the conditions recommended by the manufacturer. Unless otherwise specified, percentages and parts are weight percentages and weight parts.

[0143] Materials and methods

[0144] The present application utilizes the expression of EPCAM and TG double genes as a guide gene to screen all tumor cells. In the process of constructing the prediction model, first, genes with high expression variation are selected to eliminate genes with low information content and optimize computational efficiency, according to the gene expression profile of tumor cells. Next, the mutual information score is used as a measure to select the dominant genes for constructing the random forest model classifier. AUROC is used to evaluate the performance of the model and finally determine the selection of the model, while other evaluation indicators are also calculated, including but not limited to recall rate and accuracy. All models have been evaluated by 5-fold cross-validation.

[0145] Example 1 Sequencing sample preparation

[0146] The samples were selected from six patients with central metastasis of thyroid cancer, eight patients with lateral metastasis of neck and four patients with in situ carcinoma without any pathological metastasis, and the tissue samples were obtained by the following methods:

[0147] 1. After the hospital ethics, the patients diagnosed as papillary carcinoma of thyroid cancer by pathology were cut at the center of the thyroid lump during surgery, with a size of about 5mm*5mm tissue, in tissue preservation liquid, and stored in liquid nitrogen, followed by flow cytometry and single cell sequencing analysis.

[0148] 2. After the hospital ethics, the patients diagnosed as papillary carcinoma of thyroid cancer by pathology were cut at the center of the thyroid lump during surgery, obtaining ex vivo thyroid tumor tissue by ultrasound-guided puncture, in tissue preservation liquid, and stored in liquid nitrogen, followed by flow cytometry and single cell sequencing analysis.

[0149] Example 2 Single cell sequencing

[0150] Single-cell sequencing and V(D)J library were performed using the 10X Genomics Chromium Controller Instrument, Chromium Single Cell 5' Library, Gel Bead in emulsion (GEM) Kit, and V(D)J Enrichment Kit (10X Genomics, Pleasanton, CA). Cell suspension was concentrated to 1,000 cells / µL, and about 10,000 of them were loaded into each channel to generate single-cell GEMs. The output was 6,000 cells of mRNA barcoding per sample. After the reverse transcription step, GEMs were broken, barcoded cDNA was purified, amplified, and used to construct 5' gene expression and TCR and BCR enrichment libraries. For 5' library construction, amplified barcoded cDNA was fragmented, A-tailed, ligated to adapters, and subjected to index PCR amplification. For V(D)J library, human T cell and B cell V(D)J sequences were enriched from amplified cDNA, fragmented, A-tailed, adapter-ligated, and subjected to index PCR amplification.

[0151] Final libraries were quantified using Qubit High Sensitivity DNA Assay (No. Q33231; Thermo Fisher Scientific, Waltham, MA, USA). Library size distribution was determined in a Bioanalyzer 2200 (Agilent Technologies, Santa Clara, CA, USA) using High Sensitivity DNA Chips. All libraries were sequenced on an Illumina sequencer (Illumina, San Diego, CA, USA) using 150 bp paired-end run.

[0152] Example 3. qRT-PCR experiments

[0153] RNA extraction: First, total RNA was extracted from the thyroid cancer in situ carcinoma cell samples using an RNA extraction kit (e.g., Takara Bio, Utsu, Japan). Follow the instructions provided by the kit to ensure high-quality RNA extraction.

[0154] cDNA synthesis: The extracted total RNA was reverse-transcribed into corresponding cDNA. Use a reverse transcription kit (e.g., Vazyme, Nanjing, China) to perform the reverse transcription reaction according to the manufacturer's recommendations. This step converts RNA into stable cDNA for subsequent PCR analysis.

[0155] qPCR preparation: Prepare the qPCR reaction system to measure the expression level of the target gene. Mix the cDNA with appropriate primers and reagents using the SYBR Green qPCR kit (e.g. Vazyme) and follow the manufacturer's instructions.

[0156] Real-time PCR analysis: Place the qPCR reaction system into a real-time PCR instrument, such as the CFX96 Touch Real-Time PCR Detection System (RRID:SCR_008426) from Bio-Rad Laboratories. Set up the appropriate PCR program to measure the change in fluorescence signal, which will reflect the expression level of the target gene. Perform real-time PCR analysis to obtain quantitative gene expression data.

[0157] The primers (primer F (forward) and primer R (reverse)) and probes used are shown in Table 2 below:

[0158] Table 2

[0159]

[0160] Example 4 Gene selection and clustering

[0161] After obtaining the sequencing raw data, the single-cell sequencing data was aligned and barcode demultiplexed using the Cell Ranger v. 3.0.2 vdj pipeline (10X Genomics). Then the expression matrix data was analyzed under the Scanpy computing framework. The data was filtered according to the QC standard. Cells with <500 detected genes and <5 cells per detected gene in the dataset were removed. Cells with mitochondrial gene expression accounting for >10% of the total expression level were also excluded. Double peaks were avoided by removing cells with the top 5% total transcripts (UMI). The Scrublets software was also used to remove double peaks.

[0162] Then the genes were selected for dimensionality reduction and clustering according to the variability of the genes. All data was combined, the top 2,000 variable genes were selected, and the total UMI count and mitochondrial gene expression ratio of each cell were regressed out 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 setting of 1.

[0163] Example 5 Model establishment

[0164] The construction of the model of the present application adopts a basic method relying on machine learning algorithms, with thyroid in situ cancer cells as the model input.

[0165] (1) Feasibility of disease predictors: Using EPCAM and TG dual gene expression as a guide gene, all tumor cells were screened from single-cell transcriptome data, and after feature screening, a new set of 400 thyroid carcinoma pathological puncture samples was used for mRNA extraction, and after qPCR experiment, the data was 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.

[0166] (2) Gene ranking: Genes were ranked according to the mutual information score of the genes, and the number of genes was limited to a relatively small range.

[0167] (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 appeared a turning point ( Figure 2 ).

[0168] (4) Top 3 gene classifier model: The top 3 genes with the largest mutual information score were selected to construct a classifier model. Under this model, the area of AUROC reached about 0.975 ( Figure 3 ).

[0169] (5) Gene expression plot: The expression of these 3 genes in tumor cells of the metastasis group and the non-metastasis group was plotted, and it was found that based on the Wilcoxon test, their expressions were different. The 3 genes include the 3 genes of Table 3: (A1) RPS4Y1, (A2) PKHD1L1, (A3) CRABP1. The weight proportion of each gene in the model is shown in Table 3.

[0170] Table 3

[0171]

[0172] Example 6 Model validation

[0173] The model uses 3 specific genes from Table 3 as input parameters: (A1) RPS4Y1, (A2) PKHD1L1, (A3) CRABP1.

[0174] The established model was used to perform 100 validation tests on different sample sets (80% test set, 20% training set) sorted by 4 to 1, and the average AUROC value reached about 0.975. These results (see Table 4 for experimental result data) demonstrate the strong robustness of the model and support the stability and reliability of its conclusions.

[0175] Table 4 Validation result data

[0176]

[0177]

[0178]

[0179] Example 6 Comparative Example

[0180] In step (2) of Example 4, the top four genes were RPS4Y1, PKHD1L1, CRABP1 and MT1G, respectively.

[0181] Classifier models were constructed using 4 genes or any 3 genes of the 4 genes, respectively. The classification results under each model are shown in Table 5.

[0182] Table 5

[0183]

[0184] As can be seen from the results in Table 5, the marker gene RPS4Y1 is the most important, and the 3-gene (RPS4Y1, PKHD1L1 and CRABP1) classifier model of the present application performs best in the test AUROC, i.e. has very good detection effect.

[0185] Example 7

[0186] Test tissue preparation and data collection

[0187] For transcriptional level analysis, 157 patients were selected from A Center, including 107 patients with lymph node metastasis (49 patients with neck metastasis and 58 patients with central metastasis) and 50 patients without metastasis. The primary tumor tissues and their paired peripheral tissues (more than 0.5 cm away from the tumor edge) were removed by surgery and transferred to liquid nitrogen for storage. All patients were first-time visitors and had not received any anti-cancer treatment before surgery. In addition to collecting paired specimens, detailed clinical information was also collected, including gender, age, tumor size, differentiation degree, lymph node metastasis, TNM stage and other related factors. The acquisition of all patient samples was approved by the hospital ethics committee, and all participants signed the informed consent form.

[0188] The inclusion criteria for patient selection were: (1) first-time thyroid surgery; (2) postoperative pathology confirmed as papillary thyroid carcinoma; (3) no history of other malignant tumors.

[0189] Participants for building the prediction model were extracted from the electronic health record database, and a total of 458 patients were selected for developing the prediction model (B center dataset). In the clinical validation, 366 participants were recruited from two centers (A and C center datasets). Among them, 349 participants underwent qRT-PCR testing to assess RNA expression. Due to insufficient information from histopathological examination, 17 participants were excluded from further analysis; in addition, 10 participants were excluded due to insufficient RNA concentration. Therefore, a total of 339 participants were included in the final analysis. These 339 participants were classified according to the metastasis status, with 185 participants in the non-metastasis group and 154 participants in the metastasis group. In the metastasis group, further analysis of the metastatic location was performed on 24 participants. Among them, 17 participants were classified as central region metastasis, and 7 participants were classified as neck metastasis. However, due to missing information on metastatic location, 130 participants were not included in the metastatic location analysis.

[0190] The algorithm development for predicting thyroid cancer metastasis and the process of clinical experiment are shown in Figure 4 The distribution of ΔCT values of the three genes (RPS4Y1, PKHD1L1, and CRABP1) in the metastasis and non-metastasis groups in the Nantong (center B), Zhongshan (center A), and Xuzhou (center C) datasets is shown in Figure 5

[0191] AI-aided algorithm development and evaluation

[0192] To predict thyroid cancer metastasis, five machine learning algorithms were used: Random Forest (RF), XGBoost, Support Vector Machine (SVM), Deep Learning (DL), and Logistic Regression. These models were chosen for their excellent performance in classification tasks and their ability to handle complex relationships in the dataset. The development process first divided the dataset into training and validation sets. For each model, hyperparameter tuning was performed through 5-fold cross-validation to optimize performance and prevent overfitting. The models were trained using the training data, and their performance was then evaluated on the validation data. After hyperparameter tuning, the model performance was further evaluated using clinical data, which served as the test set.

[0193] ​Model evaluation uses multiple key metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUROC). Accuracy measures the overall correctness of the model, calculating the proportion of correctly classified instances. Precision represents the proportion of true positives among all samples predicted as positive, reflecting the model's ability to avoid false positives. Recall measures the proportion of true positive cases correctly identified, indicating the model's sensitivity. F1 score is the harmonic mean of precision and recall, balancing the trade-off between the two. AUROC reflects the model's ability to distinguish between different cases at all classification thresholds, providing a comprehensive performance evaluation.

[0194] Each machine learning model is compared on each independent dataset using these metrics to evaluate their generalizability and robustness in predicting thyroid cancer metastasis. The best-performing model is determined based on the highest scores in these evaluation metrics, particularly the AUROC value.

[0195] Experimental Results

[0196] The performance evaluation results of five machine learning models - Random Forest (RF), XGBoost, Support Vector Machine (SVM), Deep Learning (DL), and Logistic Regression - in predicting thyroid cancer metastasis occurrence on three different datasets (Center B, Center A, and Center C) are shown in Tables 6-8 and Figures 6-9 The evaluation metrics include accuracy, precision, recall, F1 score, and the area under the ROC curve (AUROC).

[0197] Table 6. Test results of each model on Center B's data

[0198]

[0199] Table 7. Test results of each model on Center A's data

[0200]

[0201] Table 8. Test results of each model on Center C's data

[0202]

[0203] As shown in Tables 6-8, the random forest model outperformed other models on all datasets. In the center B dataset, the random forest achieved the highest accuracy (0.949), precision (0.896), recall (0.976), F1 score (0.934), and AUROC value (0.992). Similarly, the random forest performed well in the center A and center C datasets, with accuracy of 0.894 and 0.886, respectively, and AUROC values of 0.911 and 0.953, respectively. In contrast, the logistic regression and deep learning models performed poorly overall across all three datasets, particularly in terms of recall and AUROC metrics.

[0204] Figure 6 The receiver operating characteristic (ROC) curves of the random forest model in predicting metastasis occurrence in the three datasets are shown. The area under the curve (AUC) is highest for the center B dataset, reaching 0.992, followed by center C (0.953) and center A (0.911). The high AUC values in all datasets indicate that the model (AI algorithm model combined with the three genes) has strong ability to distinguish between metastatic and non-metastatic cases.

[0205] Figure 7 The matrix analysis of the random forest model on the center B dataset is shown. The results show that the model correctly classified 271 non-metastatic and 164 metastatic cases, with only a small number of misclassifications (19 false positives and 4 false negatives).

[0206] Figure 8 The matrix analysis of the random forest model on the center A dataset is shown. The results show that the model correctly classified 17 true negatives and 34 true positives, with only a small number of misclassification errors (3 false positives and 3 false negatives).

[0207] Figure 9 The matrix analysis of the random forest model on the center C dataset is shown. The results show that the model correctly classified 151 non-metastatic and 99 metastatic cases, with 14 false positives and 18 false negatives.

[0208] The above results show that the random forest model of the present application has high accuracy in different clinical environments under the three gene indicators.

[0209] Example 8 Human RPS4Y1, PKHD1L1, CRABP1 gene detection kit (qPCR-fluorescent probe method)

[0210] In this example, a gene detection kit based on the three gene markers of the present application developed by the inventors of the present application is provided. The kit is based on the real-time fluorescent PCR platform.

[0211] The kit is used for qualitatively detecting the expression of RPS4Y1, PKHD1L1, CRABP1 and internal reference GAPDH genes in tumor tissue samples of patients with papillary thyroid carcinoma (PTC) fixed by neutral formalin and embedded by paraffin (FFPE).

[0212] The kit sets the internal reference GAPDH gene, which can prompt the quality of the added DNA template, monitor the DNA amplification in each reaction well, and add dUTP and UNG enzyme for anti-pollution.

[0213] The kit establishes a random forest model, which uses the amplification of RPS4Y1, PKHD1L1 and CRABP1 genes to predict the metastasis of thyroid tumors.

[0214] The main components of the kit are shown in Table 9.

[0215] Table 9

[0216]

[0217] Detection method

[0218] Preparation of PCR pre-reaction solution

[0219] According to the amount of reaction sample, the qPCR reaction solution and primer probe mixed solution in the kit are placed on ice to melt, and after complete melting, they are slightly shaken and mixed, and centrifuged at low speed for 10 sec.

[0220] Each qPCR reaction requires 15 μL of qPCR reaction solution, 1 μL of enzyme mixed solution and 4 μL of primer probe mixed solution. According to the sample quantity, add the corresponding proportion of the above reagents to the centrifugal tube. Vortex the qPCR pre-reaction solution and centrifuge briefly to remove the liquid residue on the tube wall.

[0221] Preparation of PCR reaction plate

[0222] Add 20 μl of qPCR pre-reaction solution to the selected 8-link PCR tube well. Then add 5 μl of extraction template to the corresponding well of the PCR tube.

[0223] Seal with a tube cover, centrifuge at low speed, so that the mixed solution flows into the bottom of the tube and no bubbles appear. The sealed PCR tube can be placed at 2-8℃ for no more than 2 hours.

[0224] The reaction system of each reaction during PCR detection is shown in Table 10.

[0225] Table 10

[0226]

[0227] PCR instrument setup

[0228] Applied Biosystems 7500 PCR instrument

[0229] 1) Loading qPCR reaction plates

[0230] The PCR pre-reaction solution does not contain ROX or other dyes, therefore the reference fluorescence is set to "None". RPS4Y1 is set to the FAM fluorescence channel, PKHD1L1 to the VIC fluorescence channel, CRABP1 to the ROX fluorescence channel, and GAPDH to the Cy5 channel, as shown in Table 11.

[0231] Table 11: Reaction Procedure

[0232]

[0233] 2) Setting analysis conditions

[0234] In most cases, the baseline start point and end cycle number set automatically by the instrument can be used to analyze the results. When baseline problems occur, manual adjustment is required for each channel (e.g., when the PKHD1L1 target baseline in the VIC channel is uneven, select Analysis Setting > CT Setting, select the VIC channel, uncheck Automatic Baseline, and change the Baseline Start Cycle and End Cycle, for example, change the Start Cycle from 3 to 2, return to the instrument analysis interface, click Analyze in the upper right corner, and re-analyze to observe whether the VIC channel baseline is flat).

[0235] Set the thresholds for RPS4Y1, PKHD1L1, CRABP1, and GAPDH to 25000 (these can be fine-tuned depending on the specific circumstances).

[0236] SLAN-96P PCR Analysis System by Hongshi

[0237] 1) Loading qPCR reaction plates

[0238] The PCR pre-reaction solution does not contain ROX or other dyes. RPS4Y1 uses the FAM fluorescence channel, PKHD1L1 uses the VIC fluorescence channel, CRABP1 uses the ROX fluorescence channel, and GAPDH uses the Cy5 channel, as shown in Table 11.

[0239] 2) Setting analysis conditions

[0240] Analyzing the results, in most cases the baseline start point and end point cycle number set automatically by the instrument can be used. However, when baseline problems occur, manual adjustment of each channel is required.

[0241] Set the thresholds for RPS4Y1, PKHD1L1, CRABP1, and GAPDH to 0.12 (these thresholds can be fine-tuned depending on the specific circumstances).

[0242] Applied Biosystems Q5 PCR instrument in use

[0243] 1) Loading qPCR reaction plates

[0244] The PCR pre-reaction solution does not contain ROX or other dyes, so the reference fluorescence is set to "None". RPS4Y1 is set to the FAM fluorescence channel, PKHD1L1 to the VIC fluorescence channel, CRABP1 to the ROX fluorescence channel, and GAPDH to the Cy5 channel, as shown in Table 11.

[0245] 2) Setting analysis conditions

[0246] Analyzing the results, in most cases the baseline start point and end point cycle number set automatically by the instrument can be used. However, when baseline problems occur, manual adjustment of each channel is required.

[0247] Set the thresholds for RPS4Y1, PKHD1L1, CRABP1, and GAPDH to 15000 (these can be fine-tuned depending on the specific circumstances).

[0248] Test results

[0249] The ΔCT values ​​of RPS4Y1, PKHD1L1, CRABP1 targets and GAPDH for each test sample were obtained.

[0250] The calculation methods for the ΔCt values ​​of each marker are as follows:

[0251] ΔCt value = Ct value of the gene to be tested - Ct value of the internal reference. The Ct value of the gene to be tested refers to the Ct value corresponding to the gene signal detected in the sample; the Ct value of the internal reference refers to the Ct value of the internal reference signal corresponding to the sample.

[0252] Calculate based on the random forest model of the detection results

[0253] Random forest model calculation:

[0254]

[0255] Random forests consist of multiple decision trees, each decision tree ( Using the ΔCt value Output prediction results . The ΔCt value ( ) consists of the ΔCt values of the RPS4Y1, PKHD1L1, and CRABP1 genes.

[0256]

[0257] The final prediction of the random forest is obtained from the above formula, T where is the number of decision trees. In the present invention, T = 41. If the average of the positive probabilities output by all decision trees is greater than 0.5, the prediction is metastasis; otherwise, the prediction is non - metastasis.

[0258] Detection effect of the kit

[0259] It is specified to use the thyroid cancer metastasis calculation software supporting the kit. Input the ΔCt values of the RPS4Y1, PKHD1L1, and CRABP1 genes to obtain the positive probability.

[0260] Positive result: If the positive probability is greater than 0.5, the sample is judged to be positive.

[0261] Negative result: If the positive probability is less than or equal to 0.5, the sample is judged to be negative.

[0262] Invalid result: If the average Ct value of GAPDH > 32.0, it indicates that the current result is invalid. It is recommended to re - check for invalid results.

[0263] The above kit was used to measure 355 cases of papillary thyroid carcinoma samples, and the ROC curve analysis was performed on the measurement results. The analysis results are as Figure 10 shown.

[0264] When the positive probability threshold is 0.5, the sensitivity of papillary thyroid carcinoma metastasis is 0.843, the specificity is 0.952, and the AUC value is 0.926.

[0265] Example 9 Kit performance test

[0266] Data such as the specimen type, within - laboratory precision, between - batch precision, detection limit, anti - interference ability, stability, cross - contamination, clinical or diagnostic sensitivity and specificity, and clinical reproducibility of the kit in Example 8 are shown below.

[0267] 9.1 Specimen type

[0268] The clinical samples of the thyroid cancer metastasis detection kit are mainly thyroid tissue sections.

[0269] The standard formalin - fixed paraffin - embedding procedure often causes nucleic acid fragmentation. To minimize the possibility of RNA fragmentation, the sample treatment should be carried out according to the following operating steps:

[0270] After tissue removal, it should be immersed in a 4%-10% formalin solution as soon as possible;

[0271] The fixation time is best between 14 and 24 hours. A fixation time that is too long will lead to more severe RNA fragmentation, which is not conducive to downstream experiments.

[0272] The sample must be thoroughly dehydrated before being coated.

[0273] Fresh FFPE tissue sections should be used, with a section thickness not exceeding 10 μm. Sections that are too thick may result in low RNA yield. The number of sections used in each preparation should not exceed 8, and the surface area should not exceed 250 mm². 2 .

[0274] If there is no initial sample information, it is recommended that the number of slices used in the initial preparation should not exceed 2 slices. The number of slices used in subsequent preparations should be adjusted according to the RNA yield and purity, but should not exceed 8 slices.

[0275] In addition, fresh thyroid tissue can also be used as a clinical sample for this kit.

[0276] 9.2 Precision in the laboratory

[0277] To determine the repeatability of thyroid cancer metastasis detection at different time points, this study primarily analyzed in-laboratory precision and calculated the CV values ​​within each group.

[0278] Thyroid metastasis detection kit batches: X4410010, X4411020, X4412030;

[0279] Instruments: ABI 7500, SLAN, ABI Q5.

[0280] The CV value for in-laboratory precision must be ≤10%.

[0281] Method: The precision of the results of the same test sample (high, medium and low precision reference) is measured by the same (group of) operators on the same instrument in the same laboratory, using the same method and the same type and batch of reagents, over a period of time (one month).

[0282] The specific design is as follows: the laboratory uses one reagent kit batch number to evaluate the precision of samples at multiple concentration levels, running two analytical batches daily (morning and afternoon), with two parallel samples of each concentration in each batch processed for testing, and the testing is conducted for 20 days (non-continuous). That is, 20×2×2, ultimately obtaining 80 data points for each concentration.

[0283] Results: The CV values ​​of the differences in CT values ​​among the 80 data points obtained from the 20-day tests of the three batches were all ≤10%.

[0284] 9.3 Inter-batch precision

[0285] To determine the reproducibility of thyroid cancer metastasis detection across different batches, this study mainly analyzed three batches and calculated the CV values ​​within each group.

[0286] Thyroid metastasis detection kit batches: X4410010, X4411020, X4412030;

[0287] Instruments: ABI 7500, SLAN, ABI Q5.

[0288] The CV value for batch-to-batch precision must be ≤10%.

[0289] Results: The CV values ​​of the three batches of reagents in Laboratory A were all ≤10% within 5 days.

[0290] 9.4 Detection Limit

[0291] Since the determination method of this kit is based on ΔCT value, conventional clinical detection limits are not applicable. In order to determine the detection limit for thyroid cancer metastasis, this invention mainly verifies the detection limit of the kit from the perspective of reagent sensitivity.

[0292] Thyroid metastasis detection kit batches: X4410010, X4411020, X4412030;

[0293] Instruments: ABI 7500, SLAN, ABI Q5.

[0294] The detection limit for each target of the reagent is ≥1 copy / μL. The detection limit is determined as follows:

[0295] Experiments were conducted using fake virus templates: lenti RPS4Y1 fake virus product, lenti PKHD1L1 fake virus product, and lentiCRABP1 fake virus product.

[0296] Preliminary detection limit test: Based on the pseudovirus calibration data, pseudoviruses for the three targets were diluted using nucleic acid release agent V2, with four concentrations for each target: 2 copies / μL, 1 copy / μL, 0.5 copies / μL, and 0.25 copies / μL. The detection rate of these templates was tested using the first batch of reagents. Each template was tested in triplicate, and the detection rate was statistically analyzed.

[0297] The preliminary test results of the detection limit are shown in Table 12.

[0298] Table 12

[0299]

[0300] The lowest concentration template with a detection rate of 100% was selected as the initial test reagent sensitivity for subsequent experiments, i.e., 0.5 copies / μL.

[0301] Detection limit depth testing: Based on the pseudovirus calibration data, pseudoviruses for the three targets were diluted using nucleic acid release agent V2, with four concentrations for each target: 1 copies / μL, 0.5 copies / μL, and 0.25 copies / μL. Two batches of reagents were used to test the detection of the above templates. Each template was tested in 20-well replicates, and the detection rate was statistically analyzed.

[0302] The results of the detection limit depth test are shown in Table 13.

[0303] Table 13

[0304]

[0305] The lowest concentration template with a detection rate of 95% was selected as the initial test reagent sensitivity for subsequent experiments, i.e., 1 copy / μL.

[0306] Detection limit confirmation test: Based on calibration data, pseudoviruses targeting three targets were diluted using nucleic acid release agent V2, with four concentrations for each target: 2 copies / μL, 1.5 copies / μL, 1 copy / μL, and 0.5 copies / μL. The detection of these templates was tested using three batches of reagents. Each template was tested in five replicates, and the detection rate was statistically analyzed.

[0307] The results of the detection limit confirmation test are shown in Table 14 below.

[0308] Table 14

[0309]

[0310] In summary, the detection limits for the three targets RPS4Y1, PKHD1L1, and CRABP1 are all 1 copy / μL.

[0311] The three targets (1 copies / μL) were tested and confirmed using three different instruments: ABI 7500, SLAN, and ABI Q5, with three different batches of reagents. The results are shown in Table 15.

[0312] Table 15

[0313]

[0314] Results: Validation showed that the detection rate of 1 copy / μL for the three targets RPS4Y1, PKHD1L1, and CRABP1 was ≥95% in 20 repeated detections. Therefore, the reagent sensitivity of this kit can reach 1 copy / μL.

[0315] 9.5 Anti-interference capability

[0316] 9.5.1 Resistance to endogenous interference

[0317] The kit was tested to determine whether it could accurately detect endogenous interfering substances in their presence by using 2 mg / mL hemoglobin, 37 mM / L triglycerides, and 60 mg / mL albumin as simulated endogenous interfering substances.

[0318] Thyroid metastasis detection kit batches: X4410010, X4411020, X4412030;

[0319] Instruments: ABI 7500, SLAN, ABI Q5.

[0320] The CT value of the sample diluted with interfering substances after extraction should be within 0.5 compared with the control.

[0321] Methods: 2 mg / mL hemoglobin, 37 mM / L triglycerides and 60 mg / mL albumin were used as endogenous interfering substances. The three substances were mixed according to their concentrations to form an endogenous interfering substance, named GRW-1.

[0322] T7 RNA templates were diluted to high, medium, and low concentrations using GRW-1 and nucleic acid release agent V2, respectively. Total RNA extraction was performed using a paraffin-embedded tissue section total RNA extraction kit, and the extracted products were tested using this kit. Each concentration was tested in triplicate, and the CT values ​​of the three targets, RPS4Y1, PKHD1L1, and CRABP1, were calculated.

[0323] Results: Endogenous interfering agents were prepared using 2 mg / mL hemoglobin, 37 mM / L triglycerides, and 60 mg / mL albumin. These three agents were mixed according to their concentrations to form the endogenous interfering agent. The RNA was extracted using a total RNA extraction kit from paraffin-embedded tissue sections, and the extracted products were tested using this kit. Compared with the control without interfering agents, the difference in detection ΔCT for high, medium, and low template levels was ≤0.5. For example, the batch-SLAN endogenous interfering agent test results are shown in Table 16.

[0324] As can be seen, the kit of the present invention has very good resistance to endogenous interference.

[0325] Table 16

[0326]

[0327] 9.5.2 Resistance to external interference

[0328] The kit was tested to determine whether it could accurately detect substances in the presence of exogenous interfering substances by using 21.7 mmol / L ethanol, an equal volume of paraffin, an equal volume of formalin (4%-10%), and 35 mmol / L xylene as exogenous interfering substances.

[0329] Thyroid metastasis detection kit batches: X4410010, X4411020, X4412030;

[0330] Instruments: ABI 7500, SLAN, ABI Q5.

[0331] The CT value of the sample diluted with interfering substances after extraction should be within 0.5 compared with the control.

[0332] Methods: 21.7 mmol / L ethanol, an equal volume of paraffin, an equal volume of formalin (4%-10%), and 35 mmol / L xylene were used as exogenous interfering substances. The above four substances were mixed according to their concentrations to form an exogenous interfering substance, named GRW-2.

[0333] T7 RNA templates were diluted to high, medium, and low concentrations using GRW-2 and nucleic acid release agent V2, respectively. Total RNA was extracted using a paraffin-embedded tissue section total RNA extraction kit, and the extracted products were tested using this kit.

[0334] Results: Total RNA was extracted from paraffin-embedded tissue sections using a total RNA extraction kit with 21.7 mmol / L ethanol, an equal volume of paraffin, an equal volume of formalin (4%–10%), and 35 mmol / L xylene as exogenous interfering substances. The extracted products were then tested using this kit. Compared with the control without interfering substances, the difference in detection ΔCT for high, medium, and low template levels was ≤0.5. For example, the batch-SLAN exogenous interfering substance test results are shown in Table 17.

[0335] As can be seen, the kit of the present invention has very good resistance to endogenous interference.

[0336] Table 17

[0337]

[0338] 9.6 Stability

[0339] The purpose of this study is to ensure the stability of all components of the kit (including positive and negative controls) during the usage period.

[0340] Thyroid metastasis detection kit batches: X4410010, X4411020, X4412030;

[0341] Instrument: ABI 7500.

[0342] The kit is required to have a linearity R-value after being stored under appropriate storage conditions for a certain period of time. 2 ≥0.99, 90%≤ amplification efficiency≤110%; its accuracy in detecting positive and negative reference standards is 100%; its precision detection CV value is ≤10%; its reagent detection limit is at least 1 copy / μL.

[0343] Methods: Three batches of original reagent kits were stored at 2-8℃. The point at which the products were placed under these storage conditions was set as the zero point. Samples were taken on days 0, 3, and 7 from the date of storage, and reaction solutions were prepared according to the instructions. Nucleic acid extraction from the accompanying reference samples was performed using the Tiangen paraffin-embedded tissue section total RNA extraction kit, following the instructions. Nucleic acid from each reference sample was tested five times. After template loading, amplification was performed using an ABI 7500 real-time PCR instrument. Experimental results: The target threshold was set at 25000. The performance of the three batches of reagents after opening was statistically analyzed: linearity, limit of detection, accuracy, and precision.

[0344] Results: The linearity of the three batches of reagents was detected after 7 days of storage in the dark at 2-8℃ (4℃). The correlation coefficients R of each target RPS4Y1, PKHD1L1, and CRABP1 were also measured. 2 ≥0.99, 90%≤Amplification Efficiency≤110%, therefore the kit's stability linearity test is qualified; the detection rate of each target RPS4Y1, PKHD1L1, and CRABP1 against the detection limit reference in 20 wells is above 95%. Therefore, the kit's stability detection limit test is qualified; the ΔCt values ​​of each target RPS4Y1, PKHD1L1, and CRABP1 against the internal standard are all within the negative / positive interpretation range, with a negative / positive concordance rate of 100%. Therefore, the kit's stability accuracy test is qualified; the coefficient of variation (CV) of ΔCt for each target in 10 repeated detections against the precision reference is ≤5% (Table 18). Therefore, the kit's stability precision test is qualified.

[0345] Table 18

[0346]

[0347] 9.7 Cross-contamination

[0348] The test kit for detecting thyroid cancer metastasis is designed to detect whether cross-contamination occurs during the extraction and instrumentation processes.

[0349] Thyroid metastasis detection kit batches: X4410010, X4411020, X4412030;

[0350] Instrument: ABI 7500.

[0351] The kit is required to produce a positive result for positive samples and a negative result for negative samples, i.e., a 100% positive-negative concordance rate.

[0352] Methods: Twenty positive samples and twenty negative samples were mixed and cross-placed. Total RNA was extracted using the Tiangen Paraffin-Embedded Tissue Section Total RNA Extraction Kit according to the manufacturer's instructions. The extracted RNA was then analyzed using this kit, and the results were statistically analyzed.

[0353] Results: As shown in Table 19, the test values ​​of the 20 positive samples and the 20 negative samples were consistent with their positive and negative values, and there was no cross-contamination.

[0354] Table 19

[0355]

[0356] 9.8 Clinical diagnostic specificity

[0357] Clinical samples were used to validate the specificity of the thyroid cancer metastasis detection kit.

[0358] Thyroid metastasis detection kit batch number: X4410010;

[0359] Instrument: ABI 7500.

[0360] Test using the following clinical samples that have already tested negative:

[0361] (1) Thyroid tissue sections from 150 patients who did not have thyroid cancer;

[0362] (2) Thyroid tissue sections from 150 patients with thyroid cancer but without metastasis;

[0363] (3) Thyroid tissue from 50 patients who did not have thyroid cancer;

[0364] (4) Thyroid tissue from 50 patients with thyroid cancer but without metastasis.

[0365] The specimens used for specific testing came from Zhongshan Hospital affiliated with Fudan University.

[0366] Requirement: Specificity greater than or equal to 95%.

[0367] Results: The results of this study are shown in Table 20.

[0368] Table 20

[0369]

[0370] The test results in Table 20 show that no metastasis was detected in any of the samples. Therefore, the kit of the present invention has a specificity of greater than 95% for thyroid tissue sections or fresh tissue from healthy individuals.

[0371] 9.9 Clinical diagnostic sensitivity

[0372] For thyroid tissue sections or fresh tissue from healthy individuals, the specificity is greater than 95%.

[0373] Thyroid metastasis detection kit batch number: X4410010;

[0374] Instrument: ABI 7500.

[0375] Test using the following clinical samples that have been confirmed to be positive or negative:

[0376] Thyroid tissue sections from 150 patients with thyroid cancer that had metastasized;

[0377] Thyroid tissue samples from 100 patients with thyroid cancer that had metastasized.

[0378] The specimens used for specific testing came from Zhongshan Hospital affiliated with Fudan University.

[0379] Requirement: Detection rate greater than or equal to 95%.

[0380] Results: The results of this study are shown in Table 21.

[0381] Table 21

[0382]

[0383] Table 21 shows that metastasis was detected in all samples. Therefore, the kit of the present invention has a detection rate of greater than 95% for thyroid tissue sections or fresh tissue from patients.

[0384] 9.10 Clinical reproducibility

[0385] Clinical samples were used to validate the reproducibility of the thyroid cancer metastasis detection kit.

[0386] Thyroid metastasis detection kit batch number: X4410010;

[0387] Instrument: ABI 7500.

[0388] Methods: Multiple serial slides of 20 known samples prepared by the main laboratory were sent to four other qPCR laboratories within 7 days of arrival. Samples were stored at -20°C upon receipt and extracted for testing within one week. Each qPCR laboratory used the same batch of this kit for experiments, and the consistency of results from the five qPCR laboratories was ultimately compared.

[0389] Requirement: Accuracy greater than or equal to 95%.

[0390] Results: The experimental results are shown in Table 22.

[0391] Table 22

[0392]

[0393] Table 22 shows that positive samples were correctly identified at all five locations. Therefore, the kit of the present invention can accurately identify positive samples at all five locations in thyroid tissue section samples from patients.

[0394] discuss

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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).

[0399] Thyroid cancer is one of the most common endocrine malignancies, with a significant increase in global incidence in recent years [1, 2]. Papillary thyroid carcinoma (PTC) accounts for approximately 90% of all thyroid cancers and is a well-differentiated type. Although the prognosis of PTC is generally good, it is often accompanied by lymph node metastasis (LNM), which complicates surgical management and increases the risk of recurrence. The reported incidence of central lymph node metastasis in PTC is 30% to 65%, while the incidence of lateral lymph node metastasis is approximately 20% to 30%, but can be as high as 50% in some more aggressive lesions.

[0400] Preoperative assessment of LNM primarily relies on neck ultrasound. However, ultrasound has limited diagnostic accuracy, particularly in detecting central cervical lymph node metastasis. Studies have shown that central and lateral metastases are misreported or missed in 78.6% and 42.3% of cases, respectively, leading to changes in surgical plans in 65.4% of patients due to intraoperative ultrasound examination by the surgeon. This low accuracy frequently results in unnecessary fine-needle aspiration biopsy (FNA) and prophylactic lymph node dissection (LND). Therefore, a non-invasive, accurate, and efficient method for preoperative prediction of thyroid LNM is urgently needed.

[0401] This invention aims to combine genomic data and advanced artificial intelligence (AI) algorithms to construct a lymph node dissection (LNM) prediction model based on the gene expression profile of primary thyroid tumors. This approach may significantly improve the accuracy of preoperative assessment of LNM risk, thereby optimizing surgical planning, reducing unnecessary lymph node dissections, and lowering the likelihood of recurrence and reoperation, ultimately improving the overall prognosis of patients.

[0402] Predicting local non-tumor masses (LNMs) in post-traumatic cardiac diagnoses (PTCs) is crucial for effective patient management, especially where ultrasound detection presents challenges in certain anatomical regions. Central LNMs are particularly difficult to detect via ultrasound due to anatomical limitations and the lack of typical LNM features such as microcalcifications and angiogenesis. To address these limitations, machine learning (ML) and radiomics have shown considerable promise. For example, Liu et al. extracted 614 features from ultrasound images, applied radiomics to predict LNMs, and developed a support vector machine (SVM) model with an AUC of 0.78 on the training set and 0.73 on the test set. Similarly, Chang et al. developed a nomogram integrating deep learning, clinical features, and ultrasound features, achieving AUCs between 0.809 and 0.829 in the validation cohort. A recent meta-analysis reported a pooled AUC of 0.84 for radiomics-based models, while the AUC for models combining radiomics with clinical data was only 0.81, indicating that incorporating clinical data generally does not significantly improve diagnostic performance. However, in multicenter and cross-device settings, the performance of both clinical and conventional radiomics models declined significantly. Independent testing showed that the AUCs of clinically based models and conventional radiomics models were 0.67 and 0.56, respectively. These findings underscore the need for more robust and reproducible diagnostic tools for LNM diagnosis.

[0403] Gene expression profiling has become a powerful method for diagnosing lymph node metastasis (LNM), offering higher reproducibility by reducing reliance on image quality. For example, Cerruti et al. identified differentially expressed genes using sequence-enhanced gene expression analysis (SAGE), achieving a diagnostic accuracy of 80% in LNM detection. Another study using single-cell RNA sequencing developed an 11-gene signature and incorporated it into a nomogram predicting LNM in patients with post-traumatic tumor (PTC), with AUC values ​​ranging from 0.812 to 0.864 across different datasets. Furthermore, a predictive model integrating clinical characteristics such as age, sex, and tumor diameter with genetic markers like RET fusions reported an AUC of 0.724, further emphasizing the prognostic significance of BRAFV600E and RET mutations as indicators of metastasis. Compared to these previous studies, our three-gene expression model offers a more efficient and cost-effective approach. Its simplified application enhances accuracy, precision, and AUC values, making it a superior tool for LNM diagnosis in clinical practice.

[0404] The three-gene expression model of this invention demonstrates extremely high accuracy and stability in predicting lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC). Compared to other methods, such as models based on radiomics or traditional clinical features, the model of this invention consistently maintained high AUC values ​​(0.91 and 0.95, respectively) in external validation across multiple clinical centers, significantly outperforming previously reported models. Furthermore, traditional models often exhibit inconsistent results in multi-center and cross-device applications, while the model of this invention, based on gene expression profiling, is less affected by external factors and possesses better cross-scenario applicability and reproducibility. Therefore, the model of this invention not only provides higher diagnostic accuracy but also demonstrates stability in different clinical settings, becoming a reliable tool for LNM diagnosis.

[0405] 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. The use of a reagent for detecting the level of a combination of mRNA or cDNA markers for thyroid cancer metastasis risk, characterized in that, This is used to prepare a diagnostic reagent or kit for determining whether a patient with thyroid cancer has thyroid cancer metastasis. The combination of thyroid cancer metastasis risk markers is as follows: (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1; The detection reagent includes primer pairs, which include: marker primer pairs for specifically amplifying the mRNA or cDNA of the combination of thyroid cancer metastasis risk markers, and internal reference primer pairs for amplifying internal reference genes. The thyroid cancer mentioned is papillary thyroid carcinoma.

2. The use as described in claim 1, characterized in that, The internal reference gene is GAPDH.

3. The use as described in claim 1, characterized in that, The marker primer pair includes: (P1) Primer pair used to amplify RPS4Y1: SEQ ID NO:1 and SEQ ID NO:2; (P2) Primer pair used for amplifying PKHD1L1: SEQ ID NO:3 and SEQ ID NO:4; (P3) Primer pairs for amplifying CRABP1: SEQ ID NO:5 and SEQ ID NO:6; and / or The internal reference primer pair is SEQ ID NO:7 and SEQ ID NO:

8.

4. The use as described in claim 3, characterized in that, The detection reagent also includes a probe used in conjunction with the primer pair. The probes include: probe 1 (SEQ ID NO: 9) used in conjunction with the primer pair for amplifying RPS4Y1; probe 2 (SEQ ID NO: 10) used in conjunction with the primer pair for amplifying PKHD1L1; probe 3 (SEQ ID NO: 11) used in conjunction with the primer pair for amplifying CRABP1; and probe 4 (SEQ ID NO: 12) used in conjunction with the primer pair for amplifying the internal reference gene.

5. The use as described in claim 1, characterized in that, The diagnostic reagent or kit is used to detect thyroid cancer metastasis risk markers in thyroid protocarcinoma tissue.

6. The use as described in claim 1, characterized in that, The diagnostic reagent or kit obtains the assessment result of thyroid cancer metastasis risk by detecting the difference ΔCt between the Ct value of each of the three thyroid cancer metastasis risk markers (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1 in qPCR amplification and the Ct value of the internal reference gene in thyroid protocarcinoma tissue.

7. A device for early screening of thyroid cancer metastasis, characterized in that, The device includes: (a) An input module, which is used to input expression data of a combination of characteristic genes of a thyroid cancer patient; The characteristic gene combinations mentioned above are: (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1; The expression data are the ΔCt values ​​of each characteristic gene obtained by PCR amplification using primer pairs. The ΔCt value is the difference between the Ct value of the characteristic gene and the Ct value of the internal reference signal. The primer pairs include: marker primer pairs for specifically amplifying the mRNA or cDNA of the combination of thyroid cancer metastasis risk markers; and internal reference primer pairs for amplifying internal reference genes. (b) A processing module or risk assessment module, configured to: analyze and calculate the input characteristic genes to obtain a risk assessment; wherein, when (A1) RPS4Y1, (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; and (c) Output module, the output module being used to output the risk assessment result; The thyroid cancer mentioned is papillary thyroid carcinoma.

8. The device as described in claim 7, characterized in that, The risk assessment module is configured to use a random forest model to assess the risk of the input data during risk assessment.

9. The device as described in claim 8, characterized in that, The risk assessment module is configured to perform risk assessment using the random forest model formula shown in equation (I): (I) Wherein, the random forest model is a trained and optimized model, T is the number of decision trees, and x1, x2 and x3 correspond to the ΔCt values ​​of feature genes (A1) RPS4Y1; (A2) PKHD1L1; and (A3) CRABP1, respectively.

10. The device as claimed in claim 7, characterized in that, The expression data were obtained using detection reagents: The detection reagents include: (P1) Primer pair for amplifying RPS4Y1: SEQ ID NO:1 and SEQ ID NO:2 and probe 1 used in conjunction: SEQ ID NO:9; (P2) Primer pair for amplifying PKHD1L1: SEQ ID NO:3 and SEQ ID NO:4 and probe 2 used in conjunction: SEQ ID NO:10; (P3) Primer pairs for amplifying CRABP1: SEQ ID NO:5 and SEQ ID NO:6, and probe 3 used in conjunction: SEQ ID NO:11; and The internal reference primer pair is SEQ ID NO:7 and SEQ ID NO:8, and the probe 4 used in conjunction is SEQ ID NO:

12.

11. The device as claimed in claim 7, characterized in that, The device also includes a detection module for detecting the mRNA level of the risk biomarker.

12. The device as claimed in claim 11, characterized in that, The detection module includes a PCR detector.

Images