Compositions, uses and systems for pre-operative diagnosis and / or risk assessment of thyroid papillary carcinoma

Abstract

The present application relates to the technical field of diagnosis and risk assessment of thyroid papillary carcinoma, and particularly relates to a composition, application and system for preoperative diagnosis and / or risk assessment of thyroid papillary carcinoma. The composition for preoperative diagnosis and / or risk assessment of thyroid papillary carcinoma comprises reagents for measuring the expression level of CLDN16 protein and / or the mRNA expression level of the gene encoding CLDN16 protein. Through the composition, the mRNA expression level of CLDN16 in thyroid tissue can be detected preoperatively, which is used for accurate diagnosis of the primary lesion and metastatic lesion of thyroid papillary carcinoma, and simultaneously, the disease risk of thyroid papillary carcinoma is accurately predicted to guide clinical decision-making.

Description

Technical Field

[0001] This invention relates to the field of diagnostic and risk assessment technology for papillary thyroid carcinoma, and more particularly to a composition, application, and system for preoperative diagnosis and / or risk assessment of papillary thyroid carcinoma. Background Technology

[0002] Thyroid cancer is the most common malignant tumor of the endocrine system, with its global incidence rate increasing by 3.6% annually, making it one of the fastest-growing malignant tumors. Global cancer statistics for 2020 show 586,000 new cases of thyroid cancer, ranking 9th in global cancer incidence. Among women, the incidence rate is 10.1 per 100,000, making it the 5th most common cancer among women worldwide, accounting for 3.8% of all new malignant tumor cases. The 2018 National Cancer Registry Annual Report shows that the incidence rate of thyroid cancer in my country is 13.17 per 100,000, and the incidence rate in women is 20.28 per 100,000, ranking 7th among malignant tumors and 4th among female malignant tumors, respectively, and is one of the fastest-growing malignant tumors. Therefore, thyroid cancer is gradually becoming a pressing public health issue in my country that needs to be addressed.

[0003] Common pathological types of thyroid cancer include papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC). PTC accounts for over 90% of all thyroid cancers. In papillary thyroid carcinoma, the most common clinical manifestation is a thyroid nodule. Ultrasound-guided fine-needle aspiration cytology (FNAB) is particularly important for the diagnosis of thyroid nodules. Although it greatly improves the detection rate and diagnostic accuracy of thyroid cancer, 25%–30% of FNA samples remain undiagnosed. This portion is termed "indeterminate cytology," namely atypical or follicular lesions of uncertain significance (FNA category III) and follicular tumors or suspected follicular tumors (FNA category IV). Both of these carry a significant risk of malignancy (10%–30% and 25%–40%, respectively). According to a study, considering the diameter of small nodules and the diagnostic experience of pathologists, approximately 17% (10%-26%) of femtosecond thyroid nodules (FNAB) are indeterminate, and 6% (1%-11%) are undiagnostic [9, 10]. Patients with indeterminate or undiagnostic results often undergo repeated FNABs or unnecessary diagnostic surgeries, leading to lifelong thyroid hormone replacement therapy and associated surgical complications. Therefore, accurate diagnosis of benign or malignant thyroid nodules is crucial. This not only allows patients with thyroid cancer to receive timely and effective treatment, improving their survival rate, but also prevents overtreatment of patients with benign thyroid nodules. Meanwhile, papillary thyroid carcinoma (PTC) is a relatively low-invasive tumor with a good prognosis, with a five-year survival rate exceeding 90%. Therefore, some studies suggest that some low-risk PTC cases, especially papillary thyroid microcarcinomas, can be treated with close follow-up instead of surgery. However, in clinical practice, up to 10% of PTC patients are prone to metastasis, resulting in high tumor recurrence and mortality rates. The lack of effective preoperative predictive models for this group of patients means that relying solely on doctors' clinical experience for follow-up or surgical treatment of thyroid cancer patients often leads to overtreatment of low-risk patients and delayed surgical intervention for some high-risk patients, resulting in increased surgical scope and complexity. Therefore, given the variability in the prognosis of thyroid cancer, there is an urgent need to establish a preoperative risk assessment system to accurately differentiate thyroid cancers at different risk levels, guiding clinical surgical decisions and postoperative follow-up plans.

[0004] The applicant's Chinese invention patent application (Publication No.: CN114606315A, Publication Date: 2022-06-10) discloses the application of a biomarker for papillary thyroid carcinoma (PTC) and a PIM1 gene inhibitor in the preparation of anti-PTC agents. The biomarker is the PIM1 protein regulated by the BRAF V600E mutation. This invention studies the role of the PIM1 gene and the BRAF V600E mutation in the pathological development of PTC, finding that PIM1 may play an important carcinogenic role in the occurrence and development of PTC. Furthermore, the role of PIM1 in PTC is regulated by upstream BRAF V600E mutation and NOX4; that is, BRAF V600E mutation regulates NOX4, and then NOX4 regulates PIM1.

[0005] The applicant's Chinese invention patent application (Publication No.: CN112852960A, Publication Date: 2021-05-28) discloses a biomarker for papillary thyroid carcinoma (PTC) and the application of a PIM1 gene inhibitor in the preparation of anti-PTC agents. The biomarker is the PIM1 gene and its expression product. This invention studies the role of the PIM1 gene in the pathological development of PTC and finds that PIM1 may play an important carcinogenic role in the occurrence and development of PTC. Furthermore, PIM1 expression is significantly correlated with NOX4 expression, meaning that PIM1 is regulated by NOX4 in PTC, thereby exerting its role in oxidative damage. Therefore, this invention has significant theoretical importance and value for the theoretical study of the pathogenesis of PTC. Moreover, the biomarker in this invention can serve as a diagnostic marker for PTC, thereby improving the accuracy of PTC diagnosis and providing a theoretical basis for the research of PTC diagnostic products.

[0006] Chinese invention patent application (publication number CN111808950A, publication date: 2020-10-23) discloses a miRNA biomarker associated with papillary thyroid carcinoma, namely miRNA-503-5p; it also provides the application of the miRNA biomarker in the preparation of tools for predicting thyroid cancer risk or diagnosing papillary thyroid carcinoma. This invention analyzes miRNA data for papillary thyroid carcinoma from the GEO and TCGA databases, screens out miRNAs associated with papillary thyroid carcinoma, and verifies their expression in clinical samples using quantitative real-time PCR, providing a basis for the diagnosis and treatment of papillary thyroid carcinoma. The results demonstrate that miRNA-503-5p can effectively distinguish between papillary thyroid carcinoma specimens and normal specimens.

[0007] The Claudin family comprises more than 20 transmembrane proteins, which are key components of tight junctions. They act as physical barriers, preventing molecules from freely passing through the paracellular space between epithelial or endothelial cell sheets, and play a crucial role in maintaining cell polarity and signal transduction. Claudin-16, a member of the claudin family, is a tight junction protein that plays an important role in maintaining cell polarity, cell alignment, adhesion, paracellular transport, and ion permeability in various epithelial systems; however, it lacks clinical applications in the diagnosis and risk assessment of papillary thyroid carcinoma. Summary of the Invention

[0008] To address the aforementioned technical problems, the present invention aims to provide a composition for preoperative diagnosis and / or risk assessment of papillary thyroid carcinoma. This composition allows for the preoperative detection of CLAUDIN-16 mRNA expression levels in thyroid tissue, enabling accurate diagnosis of primary and metastatic lesions of papillary thyroid carcinoma, as well as precise prediction of disease risk, thus guiding clinical decision-making.

[0009] To achieve the above objectives, the present invention adopts the following technical solution:

[0010] A composition for preoperative diagnosis and / or risk assessment of papillary thyroid carcinoma, the composition comprising reagents for measuring the expression level of CLAUDIN-16 (NCBI Gene: 10686) protein and / or the mRNA expression level of the gene encoding the CLAUDIN-16 protein.

[0011] Preferably, the composition further includes reagents for measuring the expression level of the BRAF V600E mutant protein and / or the mRNA expression level of the gene encoding the BRAF V600E mutant protein.

[0012] Preferably, the reagent used to measure protein expression levels comprises an antibody, oligopeptide, ligand, PNA, or aptamer, which can specifically bind to the protein.

[0013] Preferably, the reagent for measuring mRNA expression levels comprises primers, probes, or antisense nucleotides, which can specifically bind to the mRNA of the gene encoding the marker protein; the testing method includes any one or a combination of at least two of PCR, gene chip, and next-generation high-throughput sequencing.

[0014] Furthermore, the present invention also discloses the use of the described composition in the preparation of reagents for preoperative diagnosis and / or risk assessment of papillary thyroid carcinoma.

[0015] Furthermore, the present invention also discloses the use of the described composition in the preparation of a system for preoperative diagnosis and / or risk assessment of papillary thyroid carcinoma.

[0016] Furthermore, the present invention also discloses a system for preoperative diagnosis and / or risk assessment of papillary thyroid carcinoma, the system comprising:

[0017] 1) The composition or the kit described herein;

[0018] 2) An apparatus for testing protein expression levels and / or mRNA expression levels of genes encoding proteins using the aforementioned composition;

[0019] 3) Computer equipment, the computer equipment including a memory and a processor, the memory storing a preoperative diagnostic model and / or risk assessment model for papillary thyroid carcinoma; the processor performing precise diagnosis of primary and metastatic lesions of papillary thyroid carcinoma and / or precise prediction of disease risk of papillary thyroid carcinoma using the preoperative diagnostic model and / or risk assessment model.

[0020] Preferably, the subject sample is blood, serum, or plasma.

[0021] Preferably, protein expression levels are measured using antibodies, oligopeptides, ligands, or aptamers that can specifically bind to the corresponding proteins.

[0022] Preferably, the measurement or comparison of protein expression levels is performed using at least one of the following: protein chip assay, immunoassay, ligand binding assay, MALDI-TOF (matrix-assisted laser desorption / ionization time-of-flight mass spectrometry), SELDI-TOF (surface-enhanced laser desorption / ionization time-of-flight mass spectrometry), complement fixation assay, 2D electrophoresis, liquid chromatography-mass spectrometry (LC-MS), and liquid chromatography-mass spectrometry / mass spectrometry (LC-MS / MS).

[0023] Preferably, the measurement of mRNA expression levels is performed using reverse transcription polymerase chain reaction (RT-PCR), ribonuclease protection assay, RNA blotting, DNA microarray, or next-generation high-throughput sequencing.

[0024] Preferably, the method for measuring the mRNA expression level of the gene encoding the CLAUDIN-16 protein includes the following steps:

[0025] 1) After breaking the mRNA into short fragments of 200 to 700 nt, the first eDNA strand is synthesized using the mRNA as a template and a six-base random primer.

[0026] 2) Add buffer, dNTPs, RNase H and DNA polymerase I to synthesize the second eDNA strand;

[0027] 3) After purification with the QiaQuick PCR kit and elution with EB buffer, end repair was performed, polyA was added and sequencing adapters were ligated, fragment size was selected by agarose gel electrophoresis, and finally PCR amplification was performed to build a sequencing library.

[0028] 4) Construct a transcriptome library, perform quality control by gel electrophoresis, and then sequence the transcriptome to obtain the sequencing results;

[0029] 5) After sequencing, unqualified sequences in the raw data are filtered out to obtain valid data for subsequent analysis.

[0030] Preferably, FastQC-based data quality control is performed on both the raw data before sequencing and the clean data after filtering. The data quality before and after filtering is evaluated from the perspectives of GC ratio, sequence length, and base sequence quality. HISAT2 is used for reference genome alignment to align the RNA-Seq information to the human genome and obtain detailed gene expression information. At the same time, based on the Hisat2 alignment results, HTSeq is used to perform expression level statistics according to MappedReads to obtain the number of counts for each gene in each sample. Preferably, based on the number of counts for each gene, the FPKM value is used as a measure of gene expression level to statistically obtain the expression status of each gene in each sample.

[0031] As a preferred method, the detection method for CLAUDIN-16 protein is as follows: using an antibody against CLAUDIN-16, IHC staining is performed using SP immunohistochemical staining and a comprehensive scoring method is employed for evaluation.

[0032] 1) Scoring based on the number of positive cells: 0 points for no positive cells, 1 point for 1-10%, 2 points for 11-50%, 3 points for 51-80%, and 4 points for 81-100%.

[0033] 2) Staining intensity scoring: no staining is 0 points, pale yellow is 1 point, brownish yellow is 2 points, and brownish brown is 3 points;

[0034] 3) The product of the two items is the total score. 0 points is negative (-), 1-4 points is weak positive (+), 5-8 points is positive (++), and 9-12 points is strong positive (+++).

[0035] As a preferred method, the construction of a preoperative diagnostic model for papillary thyroid carcinoma is as follows: The optimal cutoff point for positive and negative CLAUDIN-16 mRNA expression, FPKM=1.25, is determined by ROC curve. The expression level of CLAUDIN-16 mRNA in the sample is detected. When FPKM≥1.25, 1 point is scored. At the same time, BRAFV600E in the sample is detected. When BRAFV600E mutation is detected, 1 point is scored. When the score is ≥1 point, it is judged as malignant, and when it is <1 point, it is judged as benign. This model is used for the preoperative diagnosis of papillary thyroid carcinoma.

[0036] As a preferred method, the preoperative risk assessment model for papillary thyroid carcinoma is constructed as follows: The prediction model is built using Libsvm 3.20 with the MATLAB 2022a modeling platform; the model is debugged using C-SVC, RBF kernel function and grid search method; the grid c boundary, grid c step size, grid g boundary and grid g step size are -8 to 8, 0.5, -8 to 8 and 0.5 respectively; positive values ​​indicate high-risk PTC, and negative values ​​indicate non-high-risk PTC; the model formula is as follows: Plabel = sgn(Σni = 0 wi exp(−gamma|(xi-x)|2+b)).

[0037] Furthermore, the risk assessment and prediction model incorporates the expression level of CLAUDIN-16 and the mutation status of BRAF V600E, as well as patient age and gender, for modeling. During the modeling process, for model optimization, a grid search method is used to select parameters for the nonlinear SVM model. When screening indicators for the constructed model, thresholding, enumeration, backward, and forward algorithms are used sequentially, and necessary improvements are made to the indicator screening algorithms based on the characteristics of these commonly used algorithms. In evaluating the model's effectiveness, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC are used, with accuracy being the most important criterion.

[0038] This invention, by employing the aforementioned technical solution, detects the mRNA expression level of CLAUDIN-16 in thyroid tissue preoperatively and combines it with BRAF V600E mutations to accurately diagnose primary and metastatic lesions of papillary thyroid carcinoma, while also accurately predicting the disease risk of papillary thyroid carcinoma and guiding clinical decision-making. Attached Figure Description

[0039] Figure 1 This study investigates the expression and molecular function of the CLAUDIN-16 gene in papillary thyroid carcinoma cells.

[0040] Figure 2This section describes the expression of the CLAUDIN-16 gene in cases of papillary thyroid carcinoma.

[0041] Figure 3 ROC curves for CLAUDIN-16, BRAF V600E mutations and their combination in differentiating benign from malignant papillary thyroid carcinoma.

[0042] Figure 4 Molecular subtyping of CLAUDIN-16-binding BRAF V600E mutations in papillary thyroid carcinoma. Detailed Implementation

[0043] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0044] I. Experimental Methods:

[0045] 1. Obtaining and culturing thyroid cancer cell lines

[0046] The 8505C thyroid cancer cell line was obtained from the German Culture Collection of Microorganisms (DSMZ), while Nthy, KHM-5M, KMH2, C643, CAL62, BCPAP, TPC1, and KTC1 were purchased from the China Cell Bank (Shanghai, China). All cell lines were cultured in RPMI-1640 medium containing 10% fetal bovine serum and 1% penicillin / streptomycin in an incubator at 37% humidity and 5% CO2.

[0047] 2. Detection of the migration and invasion capabilities of papillary thyroid carcinoma cell lines:

[0048] Using RNA interference sequences, a CLAUDIN-16 knockdown PTC cell model was constructed. The migration and invasion abilities of the cells were examined through transwell migration and invasion experiments. At the same time, the expression levels of N-Cadherin and E-Cadherin, proteins related to the epithelial-mesenchymal transition (EMT) pathway, were examined through Western blotting experiments.

[0049] 3. Detection technology for CLAUDIN-16 mRNA:

[0050] rRNA was removed using the Epicentre® Ribo-zero rRNA Removal Kit (Human) for whole transcriptome sequencing. After adding fragmentation buffer, mRNA was fragmented into short fragments of 200 to 700 nt. Using the mRNA as a template, the first eDNA strand was synthesized using six-base random hexamers. A second eDNA strand was synthesized using buffer, dNTPs, RNase H, and DNA polymerase I. After purification using the QiaQuick PCR kit and elution with EB buffer, end repair was performed, poly A was added, and sequencing adapters were ligated. Fragment size selection was then performed using agarose gel electrophoresis, followed by PCR amplification to construct a sequencing library. The transcriptome library was constructed using NEBNext® Ultra™, and after quality control via gel electrophoresis, it was sequenced using an Illumina Novo-Seq 6000. After sequencing, Cutadapt was used to filter out unsuitable sequences (sequencing adapters, low-quality sequences, etc.) from the raw data, obtaining clean data for subsequent analysis. The filtering criteria used in this study were as follows: 1. Filtering out contaminated sequences. 2. Filtering out sequences with a large number of sequencing failures (sequences containing a large number of N's). 3. Filtering out sequences with excessively low sequencing quality (20% of a sequence having a Q value below 20, i.e., single base errors greater than 1%). 4. Sequences that were too short (50 bp). Fast QC-based data quality control was performed on both the raw data before sequencing and the clean data after filtering, evaluating the data quality before and after filtering from the perspectives of GC ratio, sequence length, and base sequence quality. HISAT2 was used for reference genome alignment, aligning RNA-Seq information to the human genome to obtain detailed gene expression information. Simultaneously, based on the Hisat2 alignment results, HTSeq was used to perform expression level statistics based on mapped reads, obtaining the number of counts for each gene in each sample. To obtain more accurate detailed information on gene expression, we used the FPKM value (Fragments Per Kilobase Million, which is standardized based on the original reads count of a gene) as a measure of gene expression based on the number of counts for each gene, and statistically obtained the expression of each gene in each sample.

[0051] 4. Detection technology for CLAUDIN-16 protein:

[0052] Using the CLAUDIN-16 antibody, IHC staining was determined by SP immunohistochemical staining and a comprehensive scoring method: 1) Score for the number of positive cells: no positive cells = 0 points, 1-10% = 1 point, 11-50% = 2 points, 51-80% = 3 points, 81-100% = 4 points; 2) Score for staining intensity: no staining = 0 points, pale yellow = 1 point, brownish yellow = 2 points, brownish brown = 3 points; 3) The product of the two scores is the total score: 0 points is negative (-), 1-4 points is weakly positive (+), 5-8 points is positive (++), and 9-12 points is strongly positive (+++).

[0053] 5. Construct a preoperative diagnostic model for papillary thyroid carcinoma.

[0054] The optimal cutoff point for positive and negative CLAUDIN-16 mRNA expression was determined using ROC curves: FPKM=1.25. The expression level of CLAUDIN-16 mRNA in the samples was detected, and 1 point was scored when FPKM≥1.25. At the same time, BRAF V600E in the samples was detected, and 1 point was scored when BRAF V600E mutation was detected. When the score is ≥1 point, it is judged as malignant, and when it is <1 point, it is judged as benign. This method is used for the preoperative diagnosis of papillary thyroid carcinoma.

[0055] 6. Construct a preoperative risk assessment model for papillary thyroid carcinoma.

[0056] Support Vector Machines (SVM) were used to build a predictive model for PTC risk assessment. The model was built using Libsvm 3.20 (https: / / www.csie.ntu.edu.tw / ~cjlin / libsvm / ) with the MATLAB 2022a (MathWorks, USA) modeling platform. The model was debugged using C-SVC, the RBF kernel function, and a grid search method. The grid boundary c, grid step size c, grid boundary g, and grid step size g were -8 to 8, 0.5, -8 to 8, and 0.5, respectively. Since patients were divided into two groups, positive values ​​represented high-risk PTC, and negative values ​​represented non-high-risk PTC.

[0057] Modeling formula: Plabel = sgn(Σni = 0 wi exp(−gamma|(xi-x)|2+b))

[0058] This risk assessment and prediction model incorporates CLAUDIN-16 expression levels (related variable), BRAF V600E mutation status (mutated / unmutated), patient age (continuous variable), and gender (male / female) for modeling. During modeling, for model optimization, a grid search method was used to select parameters for the nonlinear SVM model. When selecting indicators for the constructed model, thresholding, enumeration, backward, and forward algorithms were used sequentially, and necessary improvements were made to the indicator selection algorithms based on the characteristics of these commonly used algorithms. In evaluating the model's effectiveness, we mainly used indicators such as accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC, with accuracy being the most important criterion.

[0059] Relevant parameters of the SVM model for assessing the risk of papillary thyroid carcinoma:

[0060] Parameters: [5×1 double]

[0061] nr_class: 2

[0062] totalSV: ​​289

[0063] rho: -0.6861

[0064] Label: [2×1 double]

[0065] ProbA: []

[0066] ProbB: []

[0067] nSV: [2×1 double]

[0068] sv_coef: [289×1 double]

[0069] SVs: [289×4 double]

[0070] 7. Detection methods for BRAF V600E mutations

[0071] The detection of BRAF V600E mutation can be performed using existing technologies. See the research progress on the clinical application of BRAF V600E in papillary thyroid carcinoma published in "Advances in Clinical Medicine, 2022, 12(9), 8499-8507"; or the Chinese invention patent application filed by the applicant (publication number: CN114606315A, publication date: 2022-06-10).

[0072] II. Experimental Results:

[0073] 1. Expression and molecular function of the CLAUDIN-16 gene in papillary thyroid carcinoma cells.

[0074] Figure 1 The expression and molecular function of the CLAUDIN-16 gene in papillary thyroid carcinoma cells were shown; among which: Figure 1 Example A shows that CLAUDIN-16 protein is highly expressed in papillary thyroid carcinoma cells (BCPAP, TPC1, KTC1), with expression levels significantly higher than in other thyroid cancer cell lines; Figure 1 Example B shows that the expression level of CLAUDIN-16 mRNA in papillary thyroid carcinoma lines (BCPAP, TPC1, KTC1) is significantly higher than that in normal thyroid cell lines (Nthy). Figure 1 C,1D examples show that knockout of the CLAUDIN-16 gene significantly inhibits the migration and invasion of papillary thyroid carcinoma cells; Figure 1 The E,1F example shows that after CLAUDIN-16 gene knockout, N-Cadherin expression is significantly reduced and E-Cadherin expression is significantly increased, indicating that it inhibits the EMT function of papillary thyroid carcinoma cells.

[0075] 2. Expression of the CLAUDIN-16 gene in papillary thyroid carcinoma.

[0076] Figure 2 The expression of the CLAUDIN-16 gene in papillary thyroid carcinoma is shown in the following cases: Figure 2 AC examples show that CLAUDIN-16 mRNA is highly expressed in papillary thyroid carcinoma cases in the TCGA database, GSE27155 database and Chinese clinical data, with expression levels significantly higher than in normal thyroid tissue and benign thyroid tumors. Figure 2 DF examples show that in the GSE33630, GSE29265 and GSE27155 databases, the expression level of CLAUDIN-16 mRNA in papillary thyroid carcinoma pathology is significantly higher than that in other types of thyroid tumors (undifferentiated thyroid carcinoma, medullary thyroid carcinoma, follicular thyroid carcinoma). Figure 2 Example G shows that in clinical data from China, CLAUDIN-16 protein expression was significantly higher in papillary thyroid carcinoma than in other types of thyroid tumors (undifferentiated thyroid carcinoma, follicular thyroid carcinoma).

[0077] 3. Preoperative diagnostic value of CLAUDIN-16 combined with BRAF V600E mutation in papillary thyroid carcinoma

[0078] Table 1

[0079]

[0080] Table 1 shows the diagnostic value of determining RPKM=1.25 as the cutoff point for CLAUDIN-16 mRNA, combined with the molecular combination of BRAF V600E mutations, in diagnosing the benign or malignant nature of papillary thyroid carcinoma: the diagnostic accuracy reached 89.4% in TCGA samples, 96.6% in Chinese clinical data, 93.9% in BRAF V600E wild-type clinical samples, and 97.7% in thyroid biopsy samples, significantly higher than the accuracy of existing mainstream international thyroid cancer diagnostic models.

[0081] 4. ROC curves of CLAUDIN-16 and BRAF V600E mutations and their combination in differentiating benign from malignant papillary thyroid carcinoma.

[0082] like Figure 3 As shown, Figure 3 Example A shows that in the TCGA database, the diagnostic efficiency (ROC=0.925) of CLAUDIN-16 combined with BRAF V600E for differentiating between benign and malignant thyroid nodules is significantly higher than that of CLAUDIN-16 (ROC=0.922) or BRAF V600E (ROC=0.742) alone. Figure 3 Example B, shown in our center's sample, also indicates that the diagnostic efficiency (ROC=0.976) of CLAUDIN-16 combined with BRAF V600E for differentiating between benign and malignant thyroid nodules is significantly higher than that of CLAUDIN-16 (ROC=0.937) or BRAF V600E (ROC=0.870) alone.

[0083] 5. Molecular subtyping of CLAUDIN-16 combined with BRAF V600E mutation in papillary thyroid carcinoma

[0084] like Figure 4 As shown, Figure 4 Example A shows that the CLAUDIN-16 gene combined with the BRAF V600E mutation can be used for molecular subtyping of papillary thyroid carcinoma, which can be divided into 4 subtypes. These 4 subtypes can reflect different clinical features of papillary thyroid carcinoma and are used to distinguish the risk level of papillary thyroid carcinoma. Figure 4 Example B shows the differentiation degree of four molecular subtypes of papillary thyroid carcinoma. These four subtypes can effectively distinguish the clinical risk of thyroid cancer.

[0085] 6. Predictive value of CLAUDIN-16 in preoperative risk assessment for papillary thyroid carcinoma

[0086] Table 2

[0087]

[0088] Table 2 illustrates a preoperative risk assessment model for papillary thyroid carcinoma, which achieved a prediction accuracy of 92.0% in the training set of 174 cases and 100% in the validation set of 126 cases.

[0089] The foregoing description of embodiments of the present invention, through which those skilled in the art are able to implement or use the present invention, will be readily apparent to those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novelty disclosed herein.

Claims

1. The use of a composition in the preparation of a reagent for preoperative diagnosis and / or risk assessment of papillary thyroid carcinoma, characterized in that, The composition consists of the following reagents: (1) Reagents for measuring the expression level of CLAUDIN-16 protein and / or the mRNA expression level of the gene encoding CLAUDIN-16 protein; as well as, (2) Reagents for measuring mutations at the V600E site of the BRAF gene.

2. The application according to claim 1, characterized in that, The reagent used to measure protein expression levels includes antibodies, oligopeptides, ligands, PNAs, or aptamers, and the reagent can specifically bind to the protein. And / or, the reagent for measuring mRNA expression levels comprises primers, probes, or antisense nucleotides, which can specifically bind to the mRNA of the gene encoding the marker protein; the testing method includes any one or a combination of at least two of PCR, gene chip, and next-generation high-throughput sequencing.

3. A system for preoperative diagnosis and / or risk assessment of papillary thyroid carcinoma, the system comprising: 1) The composition used in the method according to any one of claims 1-2; 2) An apparatus for testing protein expression levels and / or mRNA expression levels of genes encoding proteins using the aforementioned composition; 3) Computer equipment, the computer equipment including a memory and a processor, the memory storing a preoperative diagnostic model and / or risk assessment model for papillary thyroid carcinoma; the processor performing precise diagnosis of primary and metastatic lesions of papillary thyroid carcinoma and / or precise prediction of disease risk of papillary thyroid carcinoma using the preoperative diagnostic model and / or risk assessment model.

4. The system according to claim 3, characterized in that, The subject sample is blood, serum, or plasma.

5. The system according to claim 3, characterized in that, Protein expression levels are measured or compared using at least one of the following methods: protein chip assay, immunoassay, ligand binding assay, MALDI-TOF (matrix-assisted laser desorption / ionization time-of-flight mass spectrometry), SELDI-TOF (surface-enhanced laser desorption / ionization time-of-flight mass spectrometry), complement fixation assay, 2D electrophoresis, liquid chromatography-mass spectrometry (LC-MS), and liquid chromatography-mass spectrometry / mass spectrometry (LC-MS / MS). And / or, mRNA expression levels are measured using reverse transcription polymerase chain reaction (RT-PCR), ribonuclease protection assay, RNA blotting, DNA microarray, or next-generation high-throughput sequencing.

6. The system according to claim 3, characterized in that, The mRNA expression level of the gene encoding the CLAUDIN-16 protein was measured using next-generation high-throughput sequencing, including the following steps: 1) After breaking the mRNA into short fragments of 200 to 700 nt, the first eDNA strand is synthesized using the mRNA as a template and a six-base random primer. 2) Add buffer, dNTPs, RNase H and DNA polymerase I to synthesize the second eDNA strand; 3) After purification with the QiaQuick PCR kit and elution with EB buffer, end repair was performed, polyA was added and sequencing adapters were ligated, fragment size was selected by agarose gel electrophoresis, and finally PCR amplification was performed to build a sequencing library. 4) Construct a transcriptome library, perform quality control by gel electrophoresis, and then sequence the transcriptome to obtain the sequencing results; 5) After sequencing, unqualified sequences in the raw data are filtered out to obtain valid data for subsequent analysis.

7. The system according to claim 6, characterized in that, FastQC-based data quality control was performed on both raw data before sequencing and clean data after filtering. The data quality before and after filtering was evaluated from the perspectives of GC ratio, sequence length, and base sequence quality. HISAT2 was used for reference genome alignment to align RNA-Seq information to the human genome and obtain detailed gene expression information. At the same time, based on the Hisat2 alignment results, HTSeq was used to perform expression level statistics according to MappedReads to obtain the number of counts for each gene in each sample.

8. The system according to claim 7, characterized in that, Based on the number of counts for each gene, the FPKM value was used as a measure of gene expression to statistically obtain the expression status of each gene in each sample.

9. The system according to claim 3, characterized in that, The detection method for CLAUDIN-16 protein is as follows: using an antibody against CLAUDIN-16, IHC staining is performed via SP immunohistochemical staining, and a comprehensive scoring method is used to determine the staining outcome. 1) Scoring based on the number of positive cells: 0 points for no positive cells, 1 point for 1-10%, 2 points for 11-50%, 3 points for 51-80%, and 4 points for 81-100%. 2) Staining intensity scoring: no staining is 0 points, pale yellow is 1 point, brownish yellow is 2 points, and brownish brown is 3 points; 3) The product of the two items is the total score. 0 points is negative -, 1-4 points is weak positive +, 5-8 points is positive ++, and 9-12 points is strong positive +++.

10. The system according to any one of claims 6-9, characterized in that, The method for constructing a preoperative diagnostic model for papillary thyroid carcinoma is as follows: The optimal cutoff point for positive and negative CLAUDIN-16 mRNA expression was determined by ROC curve analysis (FPKM=1.25). The expression level of CLAUDIN-16 mRNA in the samples was detected, and 1 point was scored when FPKM≥1.

25. BRAFV600E was also detected in the samples, and 1 point was scored when BRAFV600E mutation was detected. A score ≥1 point was considered malignant, and a score <1 point was considered benign. This method is used for the preoperative diagnosis of papillary thyroid carcinoma. And / or, the method for constructing a preoperative risk assessment model for papillary thyroid carcinoma is as follows: The prediction model was built using Libsvm 3.20 with the MATLAB 2022a modeling platform; the model was debugged using C-SVC, RBF kernel function and grid search method; the grid c boundary, grid c step size, grid g boundary and grid g step size were -8 to 8, 0.5, -8 to 8 and 0.5 respectively; positive values ​​represent high-risk PTC, and negative values ​​represent non-high-risk PTC; the model formula is as follows: Plabel = sgn(Σni = 0 wi exp(−gamma|(xi-x)|2+b)).

11. The system according to claim 10, characterized in that, The risk assessment and prediction model incorporated CLDN16 expression levels, BRAF V600E mutation status, patient age, and gender for modeling. During modeling, for model optimization, a grid search method was used to select parameters for the nonlinear SVM model. When screening indicators for the constructed model, thresholding, enumeration, backward, and forward algorithms were used sequentially, and the indicator screening algorithms were improved based on the characteristics of these commonly used algorithms. In evaluating the model's effectiveness, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC were used, with accuracy being the most important criterion.

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