Biomarker for auxiliary diagnosis of thyroid cancer and application

Abstract

The invention provides a biomarker WSCD2 gene for auxiliary diagnosis of thyroid cancer, the expression of the biomarker WSCD2 gene in thyroid cancer tissue is obviously lower than that in normal thyroid cancer tissue, and the expression level of the biomarker WSCD2 gene is in negative correlation with stages, T stages and N stages of thyroid cancer and is in positive correlation with progression-free lifetime of a patient; the expression level can be detected through various methods. The invention further provides application of the WSCD2 gene in auxiliary diagnosis of thyroid cancer, and important reference can be provided for early diagnosis of thyroid cancer, tumor progression evaluation, prognosis judgment and guidance of an immunotherapy scheme. Through high-throughput data analysis, a machine learning method and clinical verification, the WSCD2 is successfully screened as a key biomarker of the thyroid cancer, and through multiple verification, the high sensitivity and specific diagnosis potential of the WSCD2 in the thyroid cancer are proved, so that the WSCD2 has important significance for improving early diagnosis and individualized treatment strategies of the thyroid cancer.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a biomarker for assisting in the diagnosis of thyroid cancer and its application. Background Art

[0002] Thyroid cancer (THCA) is one of the most rapidly increasing endocrine malignancies worldwide in recent years. While most patients have a good prognosis with early diagnosis and standard treatment, a significant proportion remain at risk of recurrence, metastasis, and even poor prognosis. Therefore, achieving early identification, accurate classification, and personalized treatment decisions for thyroid cancer remains a key focus in both clinical and scientific research.

[0003] Currently, the clinical diagnosis of thyroid cancer relies primarily on imaging examinations (such as ultrasound, CT, and MRI), fine needle aspiration (FNA), and serum thyroid function tests. However, these methods generally have certain limitations: on the one hand, they have limited ability to identify some borderline lesions or low-grade malignant lesions, with low sensitivity and specificity; on the other hand, they lack molecular support for assessing tumor heterogeneity, prognostic stratification, and treatment response.

[0004] With the advancement of genomics, transcriptomics, and bioinformatics, a growing number of researchers are focusing on the discovery and application of biomarkers related to thyroid cancer. These biomarkers could theoretically reveal the molecular mechanisms of tumor development and progression, aid in diagnosis, assess prognosis, and even predict the efficacy of immunotherapy and chemotherapy. However, despite the reported success of some biomarkers, their accuracy, stability, and universal applicability in real-world clinical settings remain insufficient for widespread application.

[0005] Current technologies in this field still face the following prominent problems:

[0006] Lack of highly specific and sensitive biomarkers: Existing thyroid cancer-related markers are easily interfered with by other benign diseases (such as nodular goiter and Hashimoto's thyroiditis), making them unable to effectively distinguish between benign and malignant lesions, resulting in high false positive or false negative rates.

[0007] Poor sample representativeness of biomarker discovery methods: Traditional biomarker discovery often relies on single or small sample cohorts and lacks multi-center, multi-sample validation support, making it difficult to ensure its applicability and robustness in large populations;

[0008] Single analysis model and weak data integration capabilities: Most studies are based solely on the transcriptome, lacking comprehensive analysis of multidimensional data such as proteomics, immunology, and clinical characteristics, and failing to form a closed-loop screening and validation process;

[0009] Insufficient consideration of tumor immune microenvironmental factors: Immune-infiltrating cells, immune escape mechanisms, immune checkpoint expression, and other factors play an important role in the progression of thyroid cancer, but existing studies have largely ignored the interaction between these factors and candidate biomarkers.

[0010] Insufficient clinical translational validation of markers: Although existing studies have identified some potential candidate genes, most remain at the database prediction and theoretical analysis stage, lacking systematic empirical links such as immunohistochemistry, patient sample verification, and drug response prediction, resulting in weak translational capabilities.

[0011] Therefore, there is an urgent need for a new strategy that combines large-scale samples, multi-omics integration, systematic analysis, cross-validation and experimental verification to build a reliable thyroid cancer biomarker screening and validation framework, thereby improving the molecular diagnosis level of thyroid cancer and providing an effective biological basis for precision treatment. Summary of the Invention

[0012] The first technical problem to be solved by the present invention is to provide a biomarker for assisting in the diagnosis of thyroid cancer, so as to overcome the problem in the existing technology of lacking molecular markers with high sensitivity and strong specificity for diagnosing thyroid cancer, and making it difficult to accurately assess clinical characteristics such as tumor staging, progression and prognosis.

[0013] To overcome the above-mentioned defects of the prior art, the present invention provides a biomarker for assisting in the diagnosis of thyroid cancer, characterized in that the biomarker is the WSCD2 gene, the expression level of which in thyroid cancer tissue is significantly lower than that in normal thyroid tissue, and is verified by immunohistochemistry with a P value of <0.001. "A comprehensive search of PubMed, Web of Science, Google Scholar and patent databases (as of April 2025) found no reports on the correlation between the WSCD2 gene and the diagnosis, staging and prognosis of thyroid cancer," the search keyword being ("WSCD2 AND thyroid cancer").

[0014] Compared with the existing technology, the present application provides a biomarker for auxiliary diagnosis of thyroid cancer, which has the following advantages: clear technical principle: the present invention mines and integrates large-scale multi-omics databases, combines differential expression analysis, functional enrichment analysis and cross-screening of multiple machine learning algorithms, and identifies the WSCD2 gene as a core candidate gene significantly downregulated in thyroid cancer. Its expression pattern in different samples shows good distinguishing ability and is closely related to clinical stage and survival prognosis; the technical effect is direct and stable: the diagnostic sensitivity and specificity of WSCD2 in thyroid cancer provided by the present invention are better than those of traditional single tumor markers. The expression level of this gene is negatively correlated with the tumor stage and positively correlated with the patient's progression-free survival, which can effectively assist in tumor risk assessment and follow-up management; technical features synergistic association: in the technical solution of the present invention, changes in the expression level of the WSCD2 gene can be detected by multiple detection methods (including RNA sequencing, The WSCD2 gene can be accurately measured by quantitative fluorescence PCR, immunohistochemistry, etc. The biological characteristics of the gene are highly correlated with clinical information (staging, T / N staging, progression-free survival, etc.), forming a technical closed loop of "molecular markers-detection methods-clinical indicators"; significantly improving existing solutions: compared with existing diagnostic methods that rely on imaging or conventional immunohistochemical markers (such as BRAF, GAL3, etc.), the molecular marker WSCD2 provided by the present invention has higher diagnostic consistency and lower false positive rate, and shows unique advantages in tumor immune microenvironment and drug sensitivity assessment, which helps to achieve more accurate individualized treatment plan formulation; in summary, the present invention successfully solves the problem of lack of efficient and specific molecular diagnostic markers in the existing technology by systematically screening and verifying the expression characteristics of the WSCD2 gene in thyroid cancer, which not only improves the auxiliary diagnosis level of thyroid cancer, but also has broad translational application potential.

[0015] In one possible embodiment, the nucleotide sequence of the biomarker is as shown in SEQ ID NO: 1.

[0016] In one possible embodiment, the expression level of the biomarker in thyroid cancer tissue is negatively correlated with the stage, T stage, and N stage of thyroid cancer, and is positively correlated with the patient's progression-free survival (PFS).

[0017] Compared with the existing technology, using the above technical solution, the WSCD2 gene was identified as significantly downregulated in thyroid cancer in multi-omics big data analysis, and its expression level was highly statistically correlated with tumor stage and patient prognosis, indicating that this gene is not only a molecule reflecting the disease status, but also an indicator factor of disease progression. By introducing WSCD2 as a molecular marker, the present invention constructs a highly sensitive and specific auxiliary diagnosis and risk assessment tool, which not only helps to achieve early identification of thyroid cancer, but also can assist in the assessment of disease stage, prognostic trends and response potential to immunotherapy, thereby providing a scientific basis for individualized clinical management.

[0018] In one possible embodiment, the expression level of the WSCD2 gene is detected by at least one of the following methods:

[0019] Gene expression profile chip detection;

[0020] RNA sequencing;

[0021] Real-time fluorescence quantitative PCR, the PCR primer pair includes:

[0022] The primer sequences of the internal reference gene Actb are shown in SEQ ID NO: 2 and SEQ ID NO: 3, specifically:

[0023] Actb-F:TATGCTCTCCCTCACGCCATCC,

[0024] Actb-R: GTCACGCACGATTTCCCTCTCAG; Primer pair for the internal reference gene Actb:

[0025] The primer sequences of the target gene WSCD2 are shown in SEQ ID NO:4 and SEQ ID NO:5, specifically:

[0026] WSCD2-F:gtggacaaatgcgtggactt,

[0027] WSCD2-R:atgcaacggttgtcttggac;

[0028] Immunohistochemistry.

[0029] Compared with the existing technology, the above-mentioned technical scheme can significantly improve the detection sensitivity and accuracy of WSCD2 expression levels, thereby realizing multi-level and multi-dimensional quantification of the expression of thyroid cancer-related molecules. The above-mentioned technical scheme of the present invention evaluates the expression changes of WSCD2 at the transcriptome level (including RNA sequencing and chip detection) and protein level (including immunohistochemistry), which can fully capture the dynamic regulatory pattern presented in the disease state, and verify each other to enhance the robustness of the results. It can accurately evaluate the expression level of WSCD2 under different sample types and clinical conditions, and enhance its operability and application breadth as a biomarker.

[0030] The second technical problem to be solved by the present invention is to provide the application of the biomarker to solve the problems in the existing technology that diagnostic markers lack clear functional annotations, lack support for actual transformation scenarios, and are difficult to form clinical application paths.

[0031] In order to overcome the above-mentioned defects of the prior art, the present invention provides an application of the biomarker, which includes the application of the biomarker in assisting the diagnosis of thyroid cancer.

[0032] Compared with the existing technology, the application of a biomarker for assisting in the diagnosis of thyroid cancer in the present application has the following advantages: the present invention replaces the single diagnostic method or means relying on imaging and clinical symptoms in the existing technology with molecular level detection of the WSCD2 gene, which can achieve earlier and more accurate diagnosis of thyroid cancer. By combining multi-dimensional analysis of genomics, immunology and clinical information, the application scheme provided by the present invention provides an operational and verifiable molecular marker application path for clinical practice.

[0033] In one possible embodiment, low expression of the WSCD2 gene is closely related to the occurrence, development, metastasis and poor prognosis of thyroid cancer, and is used for early diagnosis of thyroid cancer, tumor progression assessment, prognosis judgment and guidance of immunotherapy regimens.

[0034] Compared with existing technologies, the above-mentioned technical solution can accurately diagnose patients in the early stages of thyroid cancer by detecting low expression of the WSCD2 gene. Through its correlation with tumor progression and immune response, it can further help clinicians assess the severity and prognosis of the disease. The expression of WSCD2 can also serve as the basis for personalized immunotherapy to improve treatment efficacy.

[0035] In one possible embodiment, the application includes the use of the biomarker in the preparation of a product for assisting in the diagnosis of thyroid cancer.

[0036] Compared with the existing technology, the above-mentioned technical solution can simplify the diagnostic process of thyroid cancer, apply the WSCD2 biomarker to the development of auxiliary diagnostic products, and provide clinicians with faster, more convenient and accurate detection methods, avoiding the complexity and high cost of traditional diagnostic methods, and providing clinicians with more standardized detection tools. Through the standardized application of biomarkers, the consistency of thyroid cancer diagnosis in different hospitals and laboratories can be ensured, reducing misdiagnosis caused by diagnostic differences.

[0037] In one possible embodiment, the product includes a reagent or a kit.

[0038] In one possible embodiment, the kit includes:

[0039] (1) Specific antibody: Primary antibody against WSCD2 protein, selected from rabbit polyclonal antibody (Cat. No. orb620966, Biorbyt, titer ≥1:100) or rabbit monoclonal antibody (Cat. No. S0001, AFFINITY, titer ≥1:200), used at a concentration of 1:100-1:200 diluted in PBS buffer (pH 7.4);

[0040] (2) Secondary antibody for immunohistochemistry: horseradish peroxidase (HRP)-labeled goat anti-rabbit IgG (Cat. No. S0002, AFFINITY), diluted 1:200 in PBS buffer containing 3% BSA;

[0041] (3) DAB colorimetric reagent: a ready-to-use colorimetric solution containing 3,3'-diaminobenzidine tetrahydrochloride (DAB) (Cat. No. G1212, Sevier), with a color development time of 30-60 seconds;

[0042] (4) Antigen retrieval solution: citric acid buffer (pH 6.0, product number G1202, Solebro), retrieval conditions: high pressure microwave heating for 5 minutes at medium-high heat and 15 minutes at medium-low heat;

[0043] (5) Blocking solution: 3% bovine serum albumin (BSA, product number SW3015, Solebro Corporation) in PBS;

[0044] (6) Positive and blank controls: The positive control was a normal thyroid tissue section with high expression of WSCD2, and the blank control was a thyroid cancer tissue section without the addition of primary antibody;

[0045] (7) Supporting reagents: including hematoxylin stain (product number H8070, Solebol), neutral resin mounting medium (product number 1004160, Sinopharm Group) and gradient dehydrating agents (xylene, anhydrous ethanol).

[0046] (8) Result interpretation criteria:

[0047] Positive signal: brown-yellow granule deposition in the cytoplasm / membrane (DAB staining);

[0048] Negative judgment: the staining intensity is less than 10% of the positive control or consistent with the blank control;

[0049] Clinical relevance threshold: a staining intensity score ≤ 2 points (based on the H-score semiquantitative system) indicates a high risk of thyroid cancer.

[0050] The H-score scoring criteria are: staining intensity (0-3 points) × positive cell ratio (0-100%), with a total score of 0-300 points and a threshold of ≤2 points based on ROC curve analysis.

[0051] Compared with the existing technology, the above technical solution is adopted. By providing a kit containing specific antibodies and a complete reagent formula, the present invention can achieve accurate detection of WSCD2 gene expression levels, and can provide an efficient, low-cost, and simple-to-operate solution for the diagnosis of thyroid cancer. The sensitivity and specificity of the detection are improved. The specific antibodies can accurately identify the WSCD2 protein, thereby ensuring the accuracy of the test results; and it provides a wider range of application scenarios. Through the applicability of immunohistochemistry reagents and the design for paraffin sections, the kit can be widely used in different samples and clinical environments, improving the flexibility and adaptability of the detection; further reducing the complexity of experimental operations. By providing complete reagents and standardized experimental procedures, it can reduce the operator's dependence on experimental steps, reduce operational errors and result deviations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Volcano plot screening for differentially expressed genes (DEGs) related to thyroid cancer (THCA);

[0053] Figure 2 Results of screening feature-related biomarkers for machine learning algorithms: Figure 2 A represents the important biomarker genes screened by random forest (RF); Figure 2 B is the gene importance ranking of the random forest (RF) algorithm; Figure 2 C is the potential biomarker screened by LASSO regression; Figure 2 D is the diagnostic marker gene screened by SVM-RFE; Figure 2 EH is the relevant gene module screened by WGCNA analysis; Figure 2 I is the WSCD2 gene highly correlated with THCA screened by four algorithms;

[0054] Figure 3 The AUC value verification results of WSCD2 gene in different data sets are as follows: Figure 3AF is the ROC curve and AUC value of the training set, test set and independent data set;

[0055] Figure 4 Multiple validations of the low and specific expression of the WSCD2 gene in thyroid cancer tissues: A. Expression of WSCD2 in various tumor tissues, including thyroid cancer, and normal tissues in the GEPIA database. B. In the Timer2.0 database, WSCD2 expression levels are highest in human thyroid tissue. CHPA immunohistochemistry demonstrates reduced expression of WSCD2 in thyroid cancer. D. PCR results indicate that WSCD2 expression is significantly lower in papillary thyroid carcinoma cell lines (IHH4 and BCPAP) than in the control group (Nthy). E. Immunohistochemistry of 60 paired thyroid cancer and adjacent adjacent tissues confirms a significant decrease in WSCD2 expression in thyroid cancer.

[0056] Figure 5 Correlation analysis of WSCD2 gene expression with clinical characteristics and survival: Figure 5 AC are the relationship diagrams between WSCD2 gene expression and thyroid cancer stage, T stage, and N stage; Figure 5 D is the survival analysis of WSCD2 gene expression and patients' progression-free survival (PFS);

[0057] Figure 6 This is a diagram of the regulatory relationship between WSCD2 gene expression and the immune microenvironment: Figure 6 AB are schematic diagrams of the correlation between WSCD2 gene expression and immune cell infiltration: Figure 6 C is the correlation analysis diagram of WSCD2 gene expression and tumor mutation burden (TMB); Figure 6 D is the correlation between the expression levels of WSCD2 gene and immune checkpoint genes;

[0058] Figure 7 AD is the predicted result of immunotherapy by TIDE algorithm; Figure 7 EL is the result of WSCD2 expression level and drug sensitivity analysis;

[0059] Figure 8 A flowchart of an embodiment. DETAILED DESCRIPTION

[0060] First, those skilled in the art should understand that these embodiments are merely used to explain the technical principles of the embodiments of the present application and are not intended to limit the scope of protection of the embodiments of the present application. Those skilled in the art may adjust them as needed to suit specific application scenarios.

[0061] The present invention provides a biomarker for auxiliary diagnosis of thyroid cancer. The biomarker is the WSCD2 gene, the expression level of which in thyroid cancer tissue is significantly lower than that in normal thyroid tissue.

[0062] As a preferred embodiment, the nucleotide sequence of the biomarker is shown in SEQ ID NO: 1.

[0063] As a preferred solution, the expression level of the biomarker in thyroid cancer tissue is negatively correlated with the stage, T stage, and N stage of thyroid cancer, and the expression of WSCD2 is positively correlated with the patient's progression-free survival.

[0064] As a preferred solution, the expression level of the WSCD2 gene is detected by at least one of the following methods:

[0065] Gene expression profile chip detection;

[0066] RNA sequencing;

[0067] Real-time fluorescence quantitative PCR, the PCR primer pair includes:

[0068] The primer sequences of the internal reference gene Actb are shown in SEQ ID NO: 2 and SEQ ID NO: 3, specifically:

[0069] Actb-F:TATGCTCTCCCTCACGCCATCC,

[0070] Actb-R: GTCACGCACGATTTCCCTCTCAG; Primer pair for the internal reference gene Actb:

[0071] The primer sequences of the target gene WSCD2 are shown in SEQ ID NO:4 and SEQ ID NO:5, specifically:

[0072] WSCD2-F:gtggacaaatgcgtggactt,

[0073] WSCD2-R:atgcaacggttgtcttggac;

[0074] Immunohistochemistry.

[0075] The present invention also provides an application of the above-mentioned biomarker, which includes the application of the biomarker in assisting the diagnosis of thyroid cancer.

[0076] As a preferred solution, low expression of the WSCD2 gene is closely related to the occurrence, development, metastasis and poor prognosis of thyroid cancer, and is used for early diagnosis of thyroid cancer, tumor progression assessment, prognosis judgment and guidance of immunotherapy programs.

[0077] As a preferred solution, the application includes the use of the biomarker in the preparation of a product for assisting in the diagnosis of thyroid cancer.

[0078] As a preferred solution, the product includes a reagent or a kit.

[0079] As a preferred solution, the kit includes:

[0080] (1) Specific antibody: Primary antibody against WSCD2 protein, selected from rabbit polyclonal antibody (Cat. No. orb620966, Biorbyt, titer ≥1:100) or rabbit monoclonal antibody (Cat. No. S0001, AFFINITY, titer ≥1:200), used at a concentration of 1:100-1:200 diluted in PBS buffer (pH 7.4);

[0081] (2) Secondary antibody for immunohistochemistry: horseradish peroxidase (HRP)-labeled goat anti-rabbit IgG (Cat. No. S0002, AFFINITY), diluted 1:200 in PBS buffer containing 3% BSA;

[0082] (3) DAB colorimetric reagent: a ready-to-use colorimetric solution containing 3,3'-diaminobenzidine tetrahydrochloride (DAB) (Cat. No. G1212, Sevier), with a color development time of 30-60 seconds;

[0083] (4) Antigen retrieval solution: citric acid buffer (pH 6.0, product number G1202, Solebro), retrieval conditions: high pressure microwave heating for 5 minutes at medium-high heat and 15 minutes at medium-low heat;

[0084] (5) Blocking solution: 3% bovine serum albumin (BSA, product number SW3015, Solebro Corporation) in PBS;

[0085] (6) Positive and blank controls: The positive control was a normal thyroid tissue section with high expression of WSCD2, and the blank control was a thyroid cancer tissue section without the addition of primary antibody;

[0086] (7) Supporting reagents: including hematoxylin stain (product number H8070, Solebol), neutral resin mounting medium (product number 1004160, Sinopharm Group) and gradient dehydrating agents (xylene, anhydrous ethanol).

[0087] (8) Result interpretation criteria:

[0088] Positive signal: brown-yellow granule deposition in the cytoplasm / membrane (DAB staining);

[0089] Negative judgment: the staining intensity is less than 10% of the positive control or consistent with the blank control;

[0090] Clinical relevance threshold: Staining intensity score ≤ 2 points (based on the H-score semi-quantitative system) indicates a high risk of thyroid cancer. The H-score scoring standard is: staining intensity (0-3 points) × positive cell ratio (0-100%), with a total score of 0-300 points. The threshold ≤ 2 points is based on ROC curve analysis.

[0091] The following provides more specific examples that combine actual test methods and data to further explain and expand the technical solutions of the present invention:

[0092] Example:

[0093] In this embodiment, the overall experimental process is as follows Figure 8 shown.

[0094] This study obtained gene expression profiles of multiple cohorts of thyroid carcinoma (THCA) from the Gene Expression Omnibus (GEO, https: / / www.ncbi.nlm.nih.gov / geo / ) to identify key biomarkers associated with thyroid cancer. The selection criteria for the dataset are as follows:

[0095] (a) The data sample must contain thyroid tumor tissue and normal thyroid tissue;

[0096] (b) The selected dataset must be based on the expression profile of the chip;

[0097] (c) The research species is Homo sapiens;

[0098] (d) Each dataset should contain at least 10 samples.

[0099] Under these screening conditions, the present invention ultimately selected nine GEO datasets: GSE53157, GSE35570, GSE33630, GSE29265, GSE6004, GSE65144, GSE3467, and GSE3678, as training sets, and GSE60542 as a test set. To further validate the results, the present invention also selected RNA sequencing data from The Cancer Genome Atlas (TCGA, https: / / portal.gdc.cancer.gov / ) as an independent validation dataset. All data were obtained from public databases, and the research protocol received ethics approval from the First Affiliated Hospital of Ningbo University, eliminating the need for further ethical review.

[0100] Differentially expressed genes (DEGs) screening

[0101] During the data processing phase, the present invention used the Combat algorithm to perform background normalization and batch effect correction on the training data. Next, the differentially expressed genes (DEGs) were screened using the limma package in R programming, and the differential expression of the DEGs was visually displayed using a volcano plot. Statistical significance was determined by an adjusted P value < 0.05 and |log2FC| ≥ 1, ensuring that the expression differences of the selected genes between thyroid cancer tissue and normal thyroid tissue were highly significant.

[0102] Machine learning algorithms screen for feature-related biomarkers

[0103] In this embodiment, the present invention adopts four advanced machine learning algorithms, including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine recursive feature elimination (SVM-RFE) and weighted gene co-expression network analysis (WGCNA), for screening biomarkers closely related to thyroid cancer (THCA). The RF algorithm helps to screen out genes with diagnostic value by evaluating the importance of each feature to the prediction model. LASSO regression eliminates irrelevant features by setting a penalty term, thereby improving the stability and prediction accuracy of the model. SVM-RFE further optimizes the feature selection process through the support vector machine model to identify potential important genes. WGCNA identifies gene modules with synergistic effects in thyroid cancer by constructing a gene co-expression network. (LASSO regression uses 10-fold cross validation to select λ=λ.min; random forest sets the number of decision trees to 500)

[0104] Combining the results of the above four algorithms, the present invention used the R package pROC to draw the ROC curve and calculated the AUC value to evaluate the diagnostic efficacy of the screened biomarkers, with the significance standard being P < 0.05.

[0105] Validation of diagnostically relevant gene signatures

[0106] During the validation of the gene markers, the present invention selected the GSE60542 dataset as the validation set and independently verified it using TCGA data. Differential expression analysis of the candidate genes was performed, and their diagnostic efficacy in THCA patients was further evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. Furthermore, the present invention used GEPIA (http: / / gepia.cancer-pku.cn / ) and Timer 2.0 (http: / / timer.cistrome.org / ) to analyze the expression data of the candidate genes in various in vivo tissues and different types of cancer. Furthermore, the present invention analyzed the expression of the candidate genes in thyroid cancer tissue using the Human Protein Atlas (https: / / www.proteinatlas.org / ). For additional validation, this study used immunohistochemistry (IHC) to detect the expression levels of the candidate markers in 60 paired thyroid cancer and adjacent adjacent tissue samples. PCR was also used to further verify the expression levels of the candidate genes in thyroid cancer cell lines and normal thyroid cell lines.

[0107] Analysis of the relationship between candidate genes and clinical outcomes:

[0108] This study downloaded clinical and survival data from thyroid carcinoma (THCA) patients from the UCSC database and combined them with transcriptome data for comprehensive analysis. By stratifying the samples into low-risk and high-risk groups based on target gene expression levels, the relationship between candidate gene expression and clinical characteristics and survival of THCA patients was further analyzed using R software.

[0109] Immune microenvironment analysis of candidate genes

[0110] In order to further explore the impact of candidate genes in THCA on the immune microenvironment, the present invention quantitatively analyzed the percentage of 22 types of immune cells in THCA samples based on GEO and TCGA data using the CIBERSORT algorithm, and plotted the correlation analysis diagram between candidate genes and immune cells using the "corrplot" package, revealing the relationship between immune cells and candidate genes in THCA samples. At the same time, the present invention also used the TCGA data portal to obtain mutation data of THCA patients, and analyzed these data using "maftools" to calculate the tumor mutation burden (TMB) score. In addition, the expression level of immune checkpoint biomarkers is closely related to the efficacy of immune checkpoint blockade therapy. Therefore, the present invention further used the R package "immuneeconv" to analyze the co-expression of candidate biomarkers and immune checkpoint biomarkers.

[0111] Immunotherapy and drug sensitivity analysis

[0112] To further explore the clinical application of candidate biomarkers, this study used the TIDE algorithm to predict the efficacy of immune checkpoint blockade therapy. Using the "pRRophetic" package in R software, the researchers estimated the half-inhibitory concentration (IC50) of conventional chemotherapy drugs based on risk factors in high-risk and low-risk groups. This analysis helps predict the sensitivity of thyroid cancer patients to different chemotherapy drugs, thus providing a theoretical basis for personalized treatment.

[0113] The above is a specific research method in an embodiment of the present invention, covering the steps from data collection to analysis of various candidate genes. Through the above method, the present invention aims to screen out biomarkers related to thyroid cancer in order to provide an effective diagnostic auxiliary tool for clinical practice.

[0114] Test results:

[0115] Screening of differentially expressed genes (DEGs)

[0116] In order to screen differentially expressed genes (DEGs) associated with thyroid carcinoma (THCA), the present invention used the limma package in R programming to analyze the integrated gene chip data (GSE53157, GSE35570, GSE33630, GSE29265, GSE6004, GSE65144, GSE3467 and GSE3678). The screening criteria were adjusted P value less than 0.05 and |log2FC| value greater than 1. Table 1 shows the source and specific information of the transcriptome data. Based on the above criteria, 828 DEGs were finally identified, including 425 up-regulated genes and 403 down-regulated genes (such as Figure 1 To more intuitively display the expression differences of DEGs, heat maps were drawn using the R packages “pheatmap” and “ggrepel”.

[0117] Table 1: Transcriptome data sources and details

[0118]

[0119] Screening feature-related biomarkers through machine learning

[0120] In further analysis, the study used four machine learning algorithms, including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine recursive feature elimination (SVM-RFE) and weighted gene co-expression network analysis (WGCNA), to screen out potential biomarkers associated with thyroid cancer (THCA); 20 important biomarker genes (such as Figure 2AB); at the same time, LA SSO regression screened 40 potential biomarkers from DEGs (λ.min=0.00417, e.g. Figure 2 C); SVM-RFE algorithm further identified 40 genes for diagnosis (such as Figure 2 D); the WGCNA method identified eight highly correlated gene modules by performing hierarchical clustering on the rlog-transformed data after batch control, among which the core genes of the turquoise module showed a strong correlation with tumor samples (such as Figure 2 By combining the results of the four algorithms, we finally obtained the WSCD2 gene that is highly correlated with THCA (such as Figure 2 I).

[0121] Validation of diagnostically relevant gene signatures

[0122] Studies have shown that the WSCD2 gene is closely related to THCA; to verify its diagnostic value, the present invention measured an AUC value of 96.4% (95% CI: 0.943-0.981) in the training set, an AUC value of 97.8% (95% CI: 0.930-1.000) in the test set, and an AUC value of 95.1% (95% CI: 0.931-0.968) in the independent data set (e.g. Figure 3 AF); In addition, in the Timer 2.0 database, WSCD2 expression was significantly different in various tumor tissues including thyroid cancer, among which WSCD2 was highest in thyroid tissue (as shown in Figure 5). Figure 4 In the GEPIA database, WSCD2 is expressed at the highest level in human thyroid tissue (as shown in Figure 4 HPA immunohistochemistry showed that WSCD2 expression was reduced in THCA (as shown in B); Figure 4 C); PCR results showed that the expression of WSCD2 in papillary thyroid carcinoma cell lines (IHH4, BCPAP) was significantly lower than that in the control group (Nthy) (as shown in Figure 4 D); Immunohistochemistry of 60 paired thyroid cancer and adjacent adjacent tissues confirmed that WSCD2 expression was significantly decreased in thyroid cancer (as shown in Figure 4 E).

[0123] Association of candidate genes with clinical outcomes

[0124] Clinical correlation analysis showed that the expression of WSCD2 gene was significantly correlated with the stage, T stage and N stage of THCA (e.g. Figure 5 AD); specifically, high expression of WSCD2 was associated with low THCA stage; survival analysis results showed that high expression of WSCD2 was positively correlated with patients' progression-free survival (PFS).

[0125] Correlation analysis between WSCD2 and immune microenvironment

[0126] Immune cell co-expression analysis showed that the infiltration levels of various immune cells in THCA were closely related to the expression of WSCD2 (e.g. Figure 6 AB); tumor mutation burden (TMB) analysis results showed that WSCD2 expression was negatively correlated with TMB (as shown in Figure 6 In addition, co-expression analysis showed that WSCD2 expression levels in THCA were significantly negatively correlated with multiple immune checkpoints (e.g. Figure 6 D).

[0127] Immunotherapy and drug sensitivity analysis

[0128] TIDE algorithm prediction results showed that THCA patients with low WSCD2 expression may have a better response to immunotherapy (e.g. Figure 7 In order to explore the potential of WSCD2 in clinical applications, the present invention further used the "pRRophetic" package in R software to evaluate the half-inhibitory concentration (IC50) of more than 200 conventional chemotherapy drugs according to the high-risk and low-risk groups of patients. The analysis results showed that 67 drugs had significant therapeutic effects on WSCD2 expression in THCA patients (such as Figure 7 EL).

[0129] The above examples further demonstrate that the present invention, through a variety of technical approaches, including high-throughput data analysis, machine learning methods, and clinical validation, has successfully screened WSCD2, a key biomarker associated with thyroid cancer (THCA), and verified its high sensitivity and specific diagnostic potential in thyroid cancer. Further analysis of the immune microenvironment and immunotherapy response reveals the close relationship between WSCD2 and the tumor microenvironment, providing new insights into thyroid cancer immunotherapy. Furthermore, drug sensitivity analysis provides a theoretical basis for the development of personalized treatment strategies, with significant clinical application prospects.

[0130] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "in the present embodiment", "specific example", or "some examples" means that the specific features, mechanisms, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, mechanisms, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0131] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A biomarker for assisting in the diagnosis of thyroid cancer, characterized in that: The biomarker is the WSCD2 gene, and its mRNA expression level in thyroid cancer paraffin sections and thyroid cancer cell lines is significantly lower than that in normal thyroid tissue.

2. The biomarker for assisting in the diagnosis of thyroid cancer according to claim 1, characterized in that: The nucleotide sequence of the biomarker is shown in SEQ ID NO:

1.

3. The biomarker for assisting in the diagnosis of thyroid cancer according to claim 1, wherein The expression level of the biomarker in thyroid cancer tissue is negatively correlated with the stage, T stage, and N stage of thyroid cancer, and is positively correlated with the patient's progression-free survival.

4. The biomarker for assisting in the diagnosis of thyroid cancer according to claim 1, characterized in that: The expression level of the WSCD2 gene is detected by at least one of the following methods: Gene expression profile chip detection; RNA sequencing; Real-time fluorescence quantitative PCR, the PCR primer pair includes: The primer sequences of the internal reference gene Actb are shown in SEQ ID NO: 2 and SEQ ID NO: 3, specifically: Actb-F:TATGCTCTCCCTCACGCCATCC, Actb-R: GTCACGCACGATTTCCCTCTCAG; Primer pair for the internal reference gene Actb: The primer sequences of the target gene WSCD2 are shown in SEQ ID NO:4 and SEQ ID NO:5, specifically: WSCD2-F:gtggacaaatgcgtggactt, WSCD2-R:atgcaacggttgtcttggac; Immunohistochemistry.

5. A use of the biomarker according to any one of claims 1 to 4, characterized in that: The application includes the application of the biomarker in assisting the diagnosis of thyroid cancer.

6. The use of the biomarker according to claim 5, characterized in that: Low expression of the WSCD2 gene is closely related to the occurrence, development, metastasis and poor prognosis of thyroid cancer, and is used for early diagnosis of thyroid cancer, tumor progression assessment, prognosis judgment and guidance of immunotherapy programs.

7. The use of the biomarker according to claim 5, characterized in that: The application includes the use of the biomarker according to any one of claims 1 to 4 in the preparation of a product for assisting in the diagnosis of thyroid cancer.

8. The use of the biomarker according to claim 7, characterized in that: The product includes a reagent or a kit.

9. The use of the biomarker according to claim 8, characterized in that: The kit comprises: (1) Specific antibody: Primary antibody against WSCD2 protein, selected from rabbit polyclonal antibody or rabbit monoclonal antibody, diluted in PBS buffer (pH 7.4) at a concentration of 1:100-1:200; (2) Secondary antibody for immunohistochemistry: horseradish peroxidase-labeled goat anti-rabbit IgG, diluted 1:200 in PBS buffer containing 3% BSA; (3) DAB colorimetric reagent: a ready-to-use colorimetric solution containing 3,3'-diaminobenzidine tetrahydrochloride, with a color development time of 30-60 seconds; (4) Antigen retrieval solution: citric acid buffer, retrieval conditions: high pressure microwave heating for 5 minutes at medium-high temperature and 15 minutes at medium-low temperature; (5) Blocking solution: 3% bovine serum albumin in PBS; (6) Positive and blank controls: The positive control was a normal thyroid tissue section with high expression of WSCD2, and the blank control was a thyroid cancer tissue section without the addition of primary antibody; (7) Supporting reagents: including hematoxylin stain, neutral resin mounting medium and gradient dehydrating agent, wherein the gradient dehydrating agent includes xylene and anhydrous ethanol; The interpretation criteria include: (8) Result interpretation criteria: Positive signal: brownish-yellow granule deposition in the cytoplasm / membrane; Negative judgment: the staining intensity is less than 10% of the positive control or consistent with the blank control; Clinical relevance threshold: A staining intensity score of ≤2 based on the H-score semiquantitative system indicates a high risk of thyroid cancer; The H-score scoring criteria were: staining intensity (0-3) × positive cell ratio 0-100%, total score 0-300, and the threshold ≤ 2 points based on ROC curve analysis.

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