Biomarkers for predicting the risk of recurrence of papillary thyroid carcinoma and their applications

Abstract

The present invention provides a biomarker for predicting the risk of recurrence of papillary thyroid carcinoma and its application. By using proteomics methods, by analyzing proteins with significantly different abundance levels in the blood of two groups of people with recurrent and non-recurrent papillary thyroid carcinoma after surgical treatment of papillary thyroid carcinoma, biomarkers that can be used to predict the risk of recurrence of papillary thyroid carcinoma are screened out, and a multi-marker joint detection model is further constructed, which can achieve accurate, non-invasive and efficient prediction of the risk of recurrence of papillary thyroid carcinoma to meet clinical needs.

Description

Technical Field

[0001] The present invention relates to the technical field of proteomics screening for diagnostic markers of papillary thyroid carcinoma, and in particular to a biomarker for predicting the recurrence risk of papillary thyroid carcinoma and its application. Background Art

[0002] Papillary thyroid carcinoma (PTC) originates from thyroid follicular epithelial cells and is the most common malignant tumor of the endocrine system, accounting for approximately 80% of all thyroid cancer cases.

[0003] Surgical resection is the core treatment for patients with PTC. However, despite PTC being an indolent tumor with an overall favorable prognosis and high survival rate, some patients experience recurrence and metastasis after surgery, often accompanied by lymph node metastasis (LNM). This complicates postoperative management and increases the risk of recurrence. The reported incidence of central LN metastasis in PTC ranges from 30% to 65%, while the incidence of lateral LN metastasis is approximately 20% to 30%, though it may be as high as 50% in some more aggressive lesions. Furthermore, once recurrence occurs after surgery, two-thirds of patients often develop iodine resistance and multi-organ distant metastasis, significantly impairing their quality of life. The risk of mortality is significantly correlated with the risk of recurrence. If patients who do not benefit from surgical treatment or who experience progression are promptly evaluated for risk, and their treatment options are adjusted promptly (e.g., radioiodine therapy, adjuvant chemoradiotherapy, secondary surgical resection, targeted therapy, or immunotherapy), overall survival and quality of life can be significantly improved. Therefore, finding specific markers to predict the recurrence risk of papillary thyroid carcinoma and developing new diagnostic methods are research hotspots and difficulties in the treatment of papillary thyroid carcinoma, and also have important clinical significance and social benefits.

[0004] Proteomics is the study of protein composition, localization, changes, and interactions within cells, tissues, or organisms, encompassing the study of protein expression patterns and proteome functional patterns. With the advancement of mass spectrometry, liquid chromatography coupled to mass spectrometry (LC-MS / MS) has become the predominant tool in proteomics research. This advancement in proteomics is crucial for identifying disease diagnostic markers, screening drug targets, and conducting toxicology studies, leading to its widespread application in medical research. Despite numerous reports and patents on the discovery of novel tumor markers in recent years, these remain largely at the laboratory research stage, with limited clinical application and market penetration. Furthermore, in most cases, a single indicator is insufficient for in vitro diagnosis of tumor recurrence risk. Only a combination of multiple diagnostic tests, integrating multiple dimensions, can enhance predictive accuracy. Therefore, identifying new diagnostic markers for PTC recurrence risk and constructing predictive models combining multiple markers holds significant clinical value. Summary of the Invention

[0005] In response to the problems existing in the prior art, the present invention provides a biomarker for predicting the risk of recurrence of papillary thyroid carcinoma and its application. By using the proteomics method, by analyzing the proteins with significantly different abundance levels in the blood of two groups of people with recurrent and non-recurrent papillary thyroid carcinoma after surgical treatment of papillary thyroid carcinoma, the biomarkers that can be used to predict the risk of recurrence of papillary thyroid carcinoma are screened out, and a multi-marker joint detection model is further constructed, which can achieve accurate, non-invasive and efficient prediction of the risk of recurrence of papillary thyroid carcinoma to meet clinical needs.

[0006] On the one hand, the present invention provides a use of a marker for preparing a reagent for predicting the recurrence risk of papillary thyroid carcinoma, wherein the marker includes any one or more of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB.

[0007] The proteomic markers provided by the present invention can accurately predict the risk of recurrence of patients with papillary thyroid carcinoma after surgical treatment, thereby enabling earlier identification of people at high risk of recurrence. This can help doctors predict in advance whether treatment plans need to be adjusted in a timely manner, thereby reducing the risk of recurrence or metastasis of papillary thyroid carcinoma and truly benefiting patients with papillary thyroid carcinoma.

[0008] The present invention uses proteomics methods to collect plasma samples from patients who experience recurrence of papillary thyroid carcinoma within a short period of time (e.g., within 1 to 5 years) after surgical treatment, as well as patients who do not experience recurrence within a short period of time. The different samples are analyzed using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS / MS). Based on orthogonal partial least squares discriminant analysis and significance analysis methods, proteins with significant differences between patients with recurrent papillary thyroid carcinoma and patients without recurrence are first screened. Ultimately, 10 differential proteins with a significant correlation with the risk of recurrence of papillary thyroid carcinoma are screened. These 10 proteins can be used to distinguish patients with recurrent papillary thyroid carcinoma and patients without recurrence, have a certain diagnostic efficacy, and can therefore be used to predict the risk of recurrence of papillary thyroid carcinoma.

[0009] Among them, the SFTPB is a protein or amino acid sequence with a UniProt database number of P07988; ORM1 is a protein or amino acid sequence with a UniProt database number of P02763; CD36 is a protein or amino acid sequence with a UniProt database number of P16671; DCD is a protein or amino acid sequence with a UniProt database number of P81605; SELL is a protein or amino acid sequence with a UniProt database number of P14151; PLIN1 is a protein or amino acid sequence with a UniProt database number of O60240; CTBS is a protein or amino acid sequence with a UniProt database number of Q01459; SERPINB1 is a protein or amino acid sequence with a UniProt database number of P30740; KRT19 is a protein or amino acid sequence with a UniProt database number of P08727; and CRYAB is a protein or amino acid sequence with a UniProt database number of P02511.

[0010] The present inventors also surprisingly discovered that some of the protein markers obtained through proteomic screening are already known markers for other cancers. For example, SELL, previously reported for predicting colorectal cancer metastasis, was found in this screening to be also useful for predicting recurrence risk after surgical treatment of papillary thyroid carcinoma. This demonstrates that the protein markers for many different cancers are not completely separate or unrelated; in fact, they exhibit numerous cross-relationships or influences. Many protein markers can be used for both early-stage cancer prediction and prognostic diagnosis, and even for prediction and diagnosis of multiple cancers at different stages. Therefore, the field of proteomics holds many new capabilities yet to be explored, and the market prospects are vast.

[0011] Furthermore, the markers include SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS and SERPINB1.

[0012] To improve the diagnostic efficacy of papillary thyroid carcinoma recurrence risk, it is necessary to combine different differentially expressed proteins to construct a diagnostic model. These proteins are ranked according to their importance, and different numbers of differentially expressed proteins with the highest rankings are selected for combination. Ultimately, eight protein markers were screened. A model based on these eight protein markers has demonstrated good risk prediction capabilities in the diagnosis of papillary thyroid carcinoma recurrence risk. Data from clinical papillary thyroid carcinoma samples showed that using only these eight biomarkers to predict papillary thyroid carcinoma recurrence risk achieved an AUC value of 0.913, demonstrating good diagnostic performance.

[0013] Furthermore, the recurrence risk refers to recurrence of papillary thyroid carcinoma within three years after treatment; the recurrence of papillary thyroid carcinoma includes recurrence of papillary thyroid carcinoma in situ or adjacent areas, and metastasis of papillary thyroid carcinoma.

[0014] It can be understood that the short term of "recurrence within three years after treatment of papillary thyroid carcinoma" here is not an absolute and unchanging time node, but a time node currently summarized based on clinical experience. With the passage of time or the improvement of other treatment methods, this time node or the length of time may change. For example, recurrence of papillary thyroid carcinoma after treatment may occur within one year, within 360 days, or within half a year, within 180 days, or within two years, within two and a half years, within three and a half years, etc.

[0015] In some embodiments, the recurrence of papillary thyroid carcinoma refers to the recurrence of papillary thyroid carcinoma in situ or in adjacent areas within three years after radical tumor resection, or the metastasis of papillary thyroid carcinoma, such as lymph node metastasis, lung metastasis, etc.

[0016] Furthermore, the reagent is used to detect the content of biomarkers in a body fluid sample; the body fluid sample includes any one or more of saliva, blood, urine, plasma, serum, and cerebrospinal fluid.

[0017] In some embodiments, the reagent for predicting the recurrence risk of papillary thyroid carcinoma is a detection reagent prepared with the biomarker as the detection target, such as sample pretreatment reagents, antigens or antibodies, and other biological reagents and kits suitable for the detection of the biomarker; it can also be developed into a standardized reagent or kit suitable for the biomarker.

[0018] Furthermore, the reagent is used to detect the presence or relative abundance or concentration of biomarkers in a body fluid sample.

[0019] The present invention uses blood screening to develop biomarkers that predict the risk of recurrence of papillary thyroid cancer. These biomarkers show significant differences in the blood of people at high risk of recurrence of papillary thyroid cancer and people at low risk of recurrence of papillary thyroid cancer. By collecting blood samples, these biomarkers in the individual's blood can be detected to predict or assist in diagnosing the possibility of recurrence of papillary thyroid cancer in the individual, or these biomarkers in the blood of a certain group can be detected, and then the group can be divided into people at high risk of recurrence of papillary thyroid cancer and people at low risk of recurrence of papillary thyroid cancer.

[0020] Furthermore, the detection method includes a radiometric method, an immunological method, a fluorescence method, a flow cytometry method, a latex turbidimetry method, a biochemical method, an enzymatic method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method or a photoelectric conversion method.

[0021] The presence or absence of a marker, or the level of a marker, is a relative concept. For example, when comparing a high-risk group for papillary thyroid cancer recurrence with a low-risk group, the levels of these specific markers are compared relative to the baseline of the high-risk and low-risk groups. For some markers, the high-risk group may have higher levels than the low-risk group, and this increase may be statistically significant, such as a significant or highly significant increase. Therefore, when assessing the prognosis of a single marker, if the probability of a particular risk increases, the marker's level may change. This change may be a relative increase or decrease, and this relative increase or decrease may be significant, or even highly significant. Therefore, regardless of the testing method, a predetermined cutoff value can be used as a standard. A value above this cutoff value is considered a change in the level, and such a result can be used for prognostic or diagnostic purposes.

[0022] Therefore, in some aspects, the markers described herein can be obtained by detecting the marker content in a sample using any known method, such as liquid chromatography, gas chromatography, mass spectrometry, LC-MS, gas chromatography-mass spectrometry (GC-MS), chromatography-mass spectrometry (CC-MS), liquid chromatography-tandem mass spectrometry (LC-MS-MS), nuclear magnetic resonance spectroscopy (NMR), immunochromatographic test strips, immunoreaction chips, capillary electrophoresis, infrared spectroscopy, and the like. As long as the protein marker content in a sample can be detected, it can be used to diagnose high-risk and low-risk groups for papillary thyroid carcinoma recurrence. As long as the protein marker content in a sample can be detected, it can be used to predict or diagnose the probability of a particular disease. It will be understood that the detection here involves testing an individual sample, then comparing it with a pre-set standard, and using the comparison results to determine or predict the disease status. For example, it can be used to predict the probability of papillary thyroid carcinoma recurrence. This prediction or diagnosis is based on whether or not recurrence of papillary thyroid carcinoma will occur within a certain period of time. Of course, such detection can be continuous, with changes in the content of certain substances used to infer the progression of the disease.

[0023] In some embodiments, the relative abundance is the peak area of ​​the biomarker in the detection spectrum obtained by high-performance liquid chromatography-tandem mass spectrometry. For example, if the average peak area of ​​a biomarker measured in a control sample is 100 and the average peak area measured in a sample of a patient at high risk of recurrence of papillary thyroid cancer is 600, then the abundance of the biomarker in the sample is considered to be 6 times that in the control sample.

[0024] In another aspect, the present invention provides a kit for predicting the recurrence risk of papillary thyroid carcinoma, wherein the kit comprises a detection reagent for the biomarker for the use described above.

[0025] In another aspect, the present invention provides a biomarker combination for predicting the risk of recurrence of papillary thyroid carcinoma, comprising SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, and SERPINB1.

[0026] On the other hand, the present invention provides a system for predicting the recurrence risk of papillary thyroid carcinoma, the system comprising a data analysis module, the data analysis module being used to analyze the detection values ​​of markers, the markers comprising any one or more of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB.

[0027] Furthermore, the markers include SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS and SERPINB1; the recurrence risk refers to recurrence within three years after treatment of papillary thyroid carcinoma; the recurrence of papillary thyroid carcinoma includes recurrence of papillary thyroid carcinoma in situ or adjacent areas, and metastasis of papillary thyroid carcinoma.

[0028] Furthermore, the data analysis module uses the detection values ​​of markers of known samples as a training set, and divides the samples into a papillary thyroid carcinoma recurrence group and a papillary thyroid carcinoma non-recurrence group according to whether the papillary thyroid carcinoma recurs. The relationship between the detection values ​​of the papillary thyroid carcinoma recurrence group and the papillary thyroid carcinoma non-recurrence group is analyzed to construct a model.

[0029] In some embodiments, a combined diagnostic model for predicting the risk of recurrence of papillary thyroid cancer is constructed by combining multiple machine learning methods, and it is preliminarily confirmed that the concentration changes of any one of the screened new biomarkers alone can be used to distinguish between people at high and low risk of recurrence of papillary thyroid cancer, indicating that these biomarkers have extremely high diagnostic value.

[0030] In some embodiments, the constructed model equation is:

[0031]

[0032] Where Y is the predicted value, i represents the i-th biomarker, m represents the number of biomarkers (m=8), Xi represents the detection value of the i-th biomarker (μg / mL), Ki represents the coefficient of the i-th biomarker, and b is a constant of 2.6366832. The coefficients of the eight biomarkers are:

[0033]

[0034] When Y≤0.4823323, the risk of recurrence of papillary thyroid cancer in the tested subject is low; when Y>0.4823323, the risk of recurrence of papillary thyroid cancer in the tested subject is high.

[0035] Furthermore, the system also includes a data storage module, a data input interface and a data output interface; the data storage module is used to store the detection values ​​of biomarkers; the data input interface is used to input the detection values ​​of biomarkers, and the data output interface is used to output the prediction results.

[0036] Furthermore, the detection value is the presence or absence, relative abundance or concentration value of each biomarker.

[0037] In another aspect, the present invention provides a use of a marker for preparing a reagent for predicting whether a patient with papillary thyroid carcinoma will not relapse after surgery, will relapse in situ, or will have distant metastasis, wherein the marker comprises any one or more of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB.

[0038] The present invention attempts to apply protein markers used to predict recurrence risk to differentiate between patients with in situ recurrence and those who develop metastasis. It was found that each protein marker can be used to distinguish between patients with papillary thyroid cancer who do not relapse after surgery, those who relapse in situ, and those who develop metastasis. The term "metastasis" encompasses patients with only metastasis as well as those with both in situ recurrence and metastasis.

[0039] When a three-classification combined diagnostic model was constructed using eight markers, namely SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, and SERPINB1, the accuracy of distinguishing between patients with in situ recurrence and patients with metastasis reached about 65%.

[0040] By adding KRT19 to the existing eight markers, a three-category combined diagnostic model consisting of nine protein markers was constructed. The diagnostic efficacy of this model, which distinguishes between in situ recurrence and metastasis in patients with papillary thyroid carcinoma after surgery, can be further improved, reaching an accuracy of approximately 78%. This model can be directly used to predict whether patients with papillary thyroid carcinoma will not relapse after surgery, will relapse in situ, or will metastasize.

[0041] Furthermore, the reagent is used to predict whether a patient with papillary thyroid carcinoma will not relapse, relapse in situ, or metastasize within three years after surgery.

[0042] Furthermore, the reagent is used to detect the content of biomarkers in a body fluid sample; the body fluid sample includes any one or more of saliva, blood, urine, plasma, serum, and cerebrospinal fluid.

[0043] Furthermore, the reagent is used to detect the presence or relative abundance or concentration of biomarkers in a body fluid sample.

[0044] In another aspect, the present invention provides a kit for predicting whether a patient with papillary thyroid carcinoma will not relapse after surgery, will relapse in situ, or will metastasize. The kit comprises a detection reagent for the biomarker for the purpose described above.

[0045] In another aspect, the present invention provides a biomarker combination for predicting whether a patient with papillary thyroid carcinoma will not relapse after surgery, relapse in situ, or metastasize, the combination comprising SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, and SERPINB1.

[0046] In another aspect, the present invention provides a system for predicting whether a patient with papillary thyroid carcinoma will not relapse after surgery, will relapse in situ, or will metastasize. The system includes a data analysis module, which is used to analyze the detection values ​​of markers, wherein the markers include any one or more of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB.

[0047] Furthermore, the markers include SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1 and KRT19.

[0048] Furthermore, the occurrence of metastasis also includes simultaneous in situ recurrence and metastasis. As long as metastasis occurs, it is classified into the metastasis group.

[0049] Furthermore, the data analysis module uses the detection values ​​of markers of known samples as a training set, and divides patients with papillary thyroid carcinoma into a non-recurrence group, an in situ recurrence group, and a distant metastasis group according to their post-operative conditions. The relationship between the detection values ​​of the non-recurrence group, the in situ recurrence group, and the distant metastasis group is analyzed to construct a model.

[0050] Furthermore, the model is constructed based on the gradient boosting algorithm.

[0051] Unlike generalized linear regression, the gradient boosting algorithm cannot output model formulas and cutoff values. All calculations are done directly by machine learning. The test values ​​can be directly input into the software system to obtain the prediction results.

[0052] Furthermore, the system also includes a data storage module, a data input interface and a data output interface; the data storage module is used to store the detection values ​​of biomarkers; the data input interface is used to input the detection values ​​of biomarkers, and the data output interface is used to output the prediction results.

[0053] The beneficial effects of the present invention are:

[0054] 1. The present invention has screened 10 new biomarkers that can predict the risk of recurrence of papillary thyroid carcinoma and developed a new protein marker combination. It can effectively evaluate and diagnose patients at high risk of recurrence of papillary thyroid carcinoma, effectively distinguish between high-risk and low-risk groups for recurrence of papillary thyroid carcinoma, and more accurately identify patients at high risk of recurrence of papillary thyroid carcinoma. Compared with traditional detection methods, it reduces the risk of misdiagnosis and missed diagnosis, and provides strong support for early detection and intervention of the disease.

[0055] 2. The combined differential diagnosis model of eight biomarkers constructed in the present invention is convenient and fast, and the test results are highly consistent with the clinical gold standard test results. At the same time, it significantly reduces the cost of predicting the recurrence risk of papillary thyroid cancer and has good application prospects.

[0056] 3. Based on the biomarkers screened to predict the risk of recurrence of papillary thyroid carcinoma, a three-classification model was further constructed that can simultaneously distinguish between the non-recurrence group, the in situ recurrence group and the metastasis group, providing a more effective and accurate predictive diagnostic model.

[0057] Detailed description

[0058] (1) Diagnosis or testing

[0059] The diagnosis or detection here refers to the detection or testing of biomarkers in a sample, or the content of a target biomarker, such as the absolute content or relative content, and then the presence or amount of the target marker is used to indicate whether the individual providing the sample may have or suffer from a certain disease, or the possibility of having a certain disease. The meanings of diagnosis and detection here are interchangeable. The result of such a test or diagnosis cannot be directly used as a direct result of being ill, but is an intermediate result. If a direct result is obtained, other auxiliary means such as pathology or anatomy are required to confirm that the patient has a certain disease. For example, the present invention provides a variety of new biomarkers related to the risk of recurrence of papillary thyroid carcinoma, and changes in the content of these markers are directly correlated with whether the patient belongs to a group at high risk of recurrence of papillary thyroid carcinoma.

[0060] (2) Association between markers, biomarkers, or differentially expressed proteins and the risk of recurrence in papillary thyroid cancer

[0061] The terms "marker," "biomarker," and "differential protein" have the same meaning in this invention. Association here refers to a direct correlation between the presence or change in the level of a biomarker in a sample and a specific disease. For example, a relative increase or decrease in the level indicates a higher likelihood of the individual having the disease compared to a healthy population.

[0062] The simultaneous presence of multiple markers in a sample, or the relative changes in their levels, indicate a higher likelihood of the individual having the disease compared to healthy individuals. This means that among marker types, some are strongly associated with disease, while others are weakly associated, or even unrelated to a particular disease. One or more markers with strong correlations can be used as diagnostic markers, while markers with weaker correlations can be combined with stronger markers to diagnose a disease, increasing the accuracy of test results.

[0063] The numerous biomarkers in serum discovered by the present invention can be used to distinguish between people at high and low risk of recurrence of papillary thyroid carcinoma. The markers here can be used alone as single markers for direct detection or diagnosis. The selection of such markers indicates that the relative change in the content of the marker has a strong correlation with the risk of recurrence of papillary thyroid carcinoma. Of course, it is understandable that one or more markers with a strong correlation with the risk of recurrence of papillary thyroid carcinoma can be selected for simultaneous detection. It is normal to understand that in some ways, selecting biomarkers with strong correlation for detection or diagnosis can achieve a certain standard of accuracy, such as 60%, 65%, 70%, 80%, 85%, 90% or 95% accuracy, which means that these markers can obtain intermediate values ​​for diagnosing a certain disease, but it does not mean that a certain disease can be directly confirmed.

[0064] Of course, differentially expressed proteins with larger ROC values ​​can also be selected as diagnostic markers. The so-called strength is generally calculated and confirmed using algorithms, such as the contribution rate or weight analysis of markers to the risk of recurrence in papillary thyroid cancer. Such calculation methods can include significance analysis (p-value or FDR value) and fold change. Multivariate statistical analysis mainly includes principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA), and of course other methods such as ROC analysis are also included. Of course, other model prediction methods are also possible. When selecting specific biomarkers, the differentially expressed proteins disclosed in this invention can be selected, or other existing well-known marker combinations can be selected or combined to make predictions through model methods.

[0065] (3) Definition of disease terms

[0066] Papillary thyroid carcinoma: Papillary thyroid carcinoma is the most common and least malignant type. It accounts for approximately 85% of thyroid cancers and can occur at any age, but is more common in children or young women (under 40 years old). Some patients have undergone neck X-rays during childhood. The tumor grows slowly and can remain confined to the thyroid gland for several years. Lesions can spread from the primary site to other parts of the gland and cervical lymph nodes via intraglandular lymphatic vessels, or remain confined for several years, making it easy to overlook their nature. The incidence of papillary thyroid carcinoma is increasing annually. The development of papillary thyroid carcinoma can be influenced by hormonal, genetic, and environmental factors, such as radiation, goitrogenic substances, and iodine deficiency. Hashimoto's thyroiditis can also contribute to the development of papillary thyroid carcinoma.

[0067] Surgical resection is the preferred treatment for papillary thyroid carcinoma. Depending on the extent of the thyroid tumor, either a unilateral thyroid lobe resection with isthmus resection or total thyroidectomy may be performed. Depending on the presence of cervical lymph node metastasis, either central or cervical lymph node dissection may be performed.

[0068] Recurrence of papillary thyroid cancer occurs when, after a period of clinically disease-free status (i.e., no evidence of tumor), patients have undergone curative treatment (surgical resection) and then reappear in the primary tumor site or elsewhere in the body. Recurrence may result from residual cancer cells that were not completely eliminated during treatment or from previously undetected micrometastases. Recurrence of papillary thyroid cancer can occur months to years after treatment, but 80% of recurrences occur within 2-3 years after surgery. The risk of recurrence decreases significantly after 5 years. Recurrence of papillary thyroid cancer is closely related to tumor stage, treatment compliance, and postoperative management. Although the prognosis is good, some patients may still experience recurrence, which typically requires regular monitoring, standardized treatment, and long-term follow-up to reduce the risk. This approach is relatively tedious. The primary route of metastasis for papillary thyroid cancer is lymph node metastasis, although distant metastasis, such as lung metastasis and bone metastasis, can also occur in a small number of cases. After recurrence or metastasis, surgery, iodine-131 therapy or targeted therapy should be selected for treatment according to the specific situation. If it can be discovered as early as possible, the recurrence can be controlled as soon as possible and the prognosis can be significantly improved.

[0069] (4) The gold standard for diagnosing recurrence of papillary thyroid carcinoma is histopathological examination (i.e., pathological confirmation of biopsy or surgical resection specimens), which observes the presence of cancer cells under a microscope and combines immunohistochemistry or molecular testing to clarify the nature of the tumor. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a volcano plot of the differential analysis of protein markers in the high-risk and low-risk groups of papillary thyroid carcinoma recurrence in Example 1;

[0071] Figure 2Graphs showing the ROC and OPLS-DA analysis results for the high-risk and low-risk groups for papillary thyroid carcinoma recurrence in Example 1;

[0072] Figure 3 The performance AUC of the model constructed by different marker combinations in Example 2;

[0073] Figure 4 AUC results for models constructed with different hyperparameters in Example 2;

[0074] Figure 5 : is the ROC curve of the combined diagnosis model in the test group in Example 2;

[0075] Figure 6 is the ROC curve of the combined diagnostic model in Example 2 in the validation group;

[0076] Figure 7 This is a diagram for evaluating the diagnostic performance of the three-class combined diagnosis model in Example 3. DETAILED DESCRIPTION

[0077] The present invention will be described in further detail below in conjunction with the accompanying drawings and Examples. It should be noted that the following examples are intended to facilitate understanding of the present invention and do not serve to limit the present invention in any way. The reagents used in this example are all known products and were obtained by purchasing commercially available products.

[0078] Example 1: Screening for biomarkers of recurrence risk in papillary thyroid carcinoma using proteomics

[0079] Plasma samples were collected from patients with papillary thyroid carcinoma after radical surgery. Low-abundance proteins were enriched by immunoaffinity chromatography to remove high-abundance proteins. Protein abundance in the samples was detected by HPLC-MS / MS. The differences in protein abundance between patients with papillary thyroid carcinoma who did not relapse or metastasize within three years and those who experienced relapse or metastasis within three years were analyzed, and the diagnostic performance was analyzed. The specific steps are as follows:

[0080] 1. Sample collection

[0081] Peripheral blood samples (approximately 2 ml) were collected from 100 patients with papillary thyroid carcinoma 2 weeks after surgical treatment. The samples were mixed in vacuum tubes containing EDTA anticoagulant and centrifuged twice at 120 g for 10 minutes at room temperature. The supernatant was collected and the samples were then centrifuged at 360 g for 20 minutes. Platelet samples were then collected in centrifuge tubes and stored at -80°C. The 100 patients included 53 males and 47 females with an average age of 43 years (range, 25-69 years). Forty-three patients had stage I PTC, 51 had stage II PTC, and six had stage III PTC. All patients provided informed consent. Patients with papillary thyroid carcinoma had histopathologically confirmed disease. Inclusion criteria included: (a) no history of other malignancies; and (b) no concurrent malignancies or autoimmune diseases.

[0082] Over the next three years, patients were followed up every three to six months, with regular imaging examinations such as neck ultrasound or CT. Once recurrence or metastasis was detected, pathological histology was required to confirm the diagnosis. A total of nine PTC patients relapsed within three years after surgery, of which three were local recurrences in situ and six were accompanied by lymph node metastasis. Samples stored at -80°C were then taken out and divided into two groups: 91 patient samples without recurrence were classified as a low-risk group for papillary thyroid carcinoma recurrence, and 9 samples with recurrence or metastasis were classified as a high-risk group for papillary thyroid carcinoma recurrence for the screening of proteomic markers.

[0083] 2. Sample processing and enzymatic hydrolysis

[0084] First, plasma samples were centrifuged for 15 minutes at 15,000 g. The supernatant was filtered and subjected to immunoaffinity chromatography to isolate 14 highly abundant proteins. Low-abundance proteins were then concentrated to 350 μL using a 3 kDa cutoff concentrator at 4000 g for 1 hour. The recovered concentrate was then subjected to buffer exchange (AEX-A) using a 7 kDa cutoff desalting column at 1000 g for 2 minutes. Protein concentrations were determined using the BCA assay using AEX-A as a blank. According to sample grouping, 25 μL of TCEP was added to the samples, and the samples were incubated at 37°C for 30 minutes for protein reduction. TMT labeling was then performed by adding the corresponding TMT 16-plex reagent and incubating at room temperature in the dark for 1 hour. The sample was then buffer exchanged using a Zeba column with AEX-A. The TMT 16-plex labeled samples were mixed, and 2 mL of AEX-A was added to the mixed samples, bringing the final volume to 5.5 mL. The samples were filtered through a 0.22 µm filter and separated using a 2D-HPLC system. The collected fractions were freeze-dried, and finally, Trypsin-Lysin C enzyme cocktail was added. The samples were digested by incubation at 37°C for 5 hours, and the digestion reaction was terminated by the addition of 5 μL of 10% TFA. A total of 60 2D-HPLC fractions were used for nanoLC-MS / MS analysis.

[0085] 3. LC-MS / MS data acquisition and database analysis

[0086] DIA analysis was performed using a nanoflow Vanquish Neo system (Thermo Fisher Scientific). Samples separated by nano-HPLC were analyzed by DIA (data-independent) mass spectrometry on an Astral high-resolution mass spectrometer (Thermo Scientific). Detection mode: positive ionization, precursor ion scan range: 380-980 m / z, primary mass spectrometer resolution: 240,000 at 200 m / z, Normalized AGC Target: 500%, Maximum IT: 5 ms. MS2 acquisition mode: DIA, with 299 scan windows, an Isolation Window of 2 m / z, an HCD Collision Energy of 25 eV, a Normalized AGC Target of 500%, and a Maximum IT: 3 ms.

[0087] 4. Data Preprocessing

[0088] Secondary mass spectrometry data were retrieved using Maxquant (v1.6.15.0). The data type is DIA proteomics data based on secondary reporter ion quantification. The secondary spectrum used for quantification requires that the parent ion accounts for more than 75% in the primary spectrum. The database comes from the Homo_sapiens_9606_proteome_gene (release: 2021-10-14, sequence: 20,437) of the Uniprot database, and a common contamination library is added to the database. Contaminating proteins are deleted during data analysis; the enzyme cleavage method is set to Trypsin / P; the number of missed cleavage sites is set to 2; the parent ion mass error tolerance of the First search and Main search is set to 20 ppm and 5 ppm, respectively, and the mass error tolerance of the secondary fragment ion is 20 ppm. The fixed modification is cysteine ​​alkylation, and the variable modification is methionine oxidation and acetylation of the protein N-terminus. The FDR for protein identification and PSM identification is set to 1%.

[0089] 5. Difference Analysis

[0090] A combination of univariate and multivariate statistical analyses was used to screen for differentially expressed proteins between high-risk and low-risk groups for papillary thyroid carcinoma recurrence. Univariate analysis primarily included significance analysis (p-value or FDR value) and fold change analysis of signature molecules across different groups. Multivariate statistical analysis primarily included receiver operating characteristic (ROC) curve analysis and Boruta signature screening based on the random forest algorithm. All statistical analyses were performed using R. Detailed R information is provided in Table 1.

[0091] Table 1. R used in the present invention and related information

[0092]

[0093] The variable importance for the projection (VIP) was calculated to measure the influence and explanatory power of each protein expression pattern on the classification and discrimination of each group of samples. The Wilcoxon rank sum test was further performed to obtain the corrected p value (FDR). According to the conditions of FDR < 0.01 and Fold change > 2, 51 down-regulated proteins and 76 up-regulated proteins were screened (see Figure 1 ).

[0094] In order to evaluate the role of each protein marker in the diagnosis and prediction of recurrence risk of papillary thyroid carcinoma, this example used ROC and Boruta analysis methods to evaluate each protein marker. The results are shown in Figure 2 The horizontal axis represents the AUC obtained from ROC analysis, and the vertical axis represents the -log10 (FDR) calculated by the Wilcoxon test. The size of the dots represents the VIP value obtained from Boruta analysis. Further screening based on VIP > 3 and AUC > 0.6 identified 10 more significant candidate protein biomarkers, as detailed in Table 2.

[0095] Table 2. Differential markers between high-risk and low-risk groups for recurrence of papillary thyroid cancer

[0096]

[0097] Among them, the smaller the FDR value and / or the larger the VIP value, to a certain extent, it indicates that the difference in protein between the high-risk and low-risk groups of papillary thyroid carcinoma recurrence is more significant, and it also indicates that the protein may have a higher diagnostic value.

[0098] Example 2: Construction and validation of a papillary thyroid carcinoma recurrence risk model

[0099] 1. Models constructed using combinations of different markers

[0100] While a single biomarker can differentiate the risk of recurrence after surgery for papillary thyroid cancer, combining multiple biomarkers generally offers greater accuracy in differentiation or prediction. However, a single biomarker that is more accurate in predicting the risk of recurrence after surgery for papillary thyroid cancer may not necessarily play a greater role in the combination when combined with one or more other biomarkers. Furthermore, a greater number of biomarkers does not necessarily equate to a higher predictive accuracy (AUC value) for the combination. Therefore, extensive validation experiments are still necessary.

[0101] In this example, a model was constructed and analyzed for the ten protein markers screened in Example 1: SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB. The study cohort consisted of blood samples collected two weeks after surgical treatment for papillary thyroid carcinoma (PTC). All enrolled patients provided informed consent. Nineteen PTC patients experienced recurrence within three years of surgery (seven with local in situ recurrence and 12 with metastasis). The samples were then divided into two groups: 331 patients without recurrence were assigned to a low-risk group for PTC recurrence, while 19 patients with recurrence or metastasis were assigned to a high-risk group. The samples were then randomly divided into a test group (166 patients in the low-risk group and 10 patients in the high-risk group), and a validation group (165 patients in the low-risk group and 9 patients in the high-risk group).

[0102] In the test group, a combined diagnostic model of multiple protein markers was constructed using a combination of multiple machine learning methods. The predicted probability values ​​were used to estimate the area under the receiver operator characteristic (ROC) curve (AUC) with a 95% confidence interval (CI) to evaluate the discriminatory ability of the multivariate diagnostic model. Using the test group, the Youden index (YI) was calculated to determine the cut-off value for the predicted probability of distinguishing the high-risk group for recurrence of papillary thyroid cancer from the low-risk group for recurrence of papillary thyroid cancer. In addition, the ROC of single markers and different subgroups was constructed and compared. Standard descriptive statistics such as frequency, mean, median, positive predictive value (PPV), negative predictive value (NPV) and standard deviation (SD) were calculated to describe the experimental results of the study population. Statistical analysis was performed using R3.6.1, and a p-value less than 0.05 was considered statistically significant.

[0103] The steps for building the joint diagnosis model are:

[0104] S101: Randomly select 2 to 10 concentration matrices of the 10 protein markers SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB from the samples in the test group as the original training data set.

[0105] S102: Select the generalized linear model (glmnet) algorithm for constructing the prediction model, and the grid search range for optimizing the algorithm's hyperparameters. In this step, the grid search range for model hyperparameter optimization is set for each algorithm as shown in Table 3.

[0106] Table 3. Parameter grid of glmnet algorithm

[0107]

[0108] S103: According to the algorithm and hyperparameter setting range set in step S102, one of the hyperparameter combinations is selected as the parameters for constructing the prediction model.

[0109] S104: Split the original dataset into K subsets using a K-fold cross validation mechanism. To ensure that the ratio of majority class samples to minority class samples in each subset is the same as in the original dataset, a Stratified K-Folds cross validation mechanism is used for data segmentation.

[0110] S105 , according to the K training data subsets obtained by segmentation in step S104 , one of the subsets is selected as a validation set Ddev.

[0111] S106: Merge the training data subsets not selected in step S105 to form a training data pool Dtrain1.

[0112] S107 , building a prediction model based on the selected supervised classification algorithm and hyperparameters according to the training data set Dtrain obtained in step S106 .

[0113] S108: Evaluate the prediction model obtained in step S107 on the validation set Ddev to obtain an AUC value, and store the current prognosis prediction model and the corresponding AUC value in the prediction model pool Pool. Step S108 involves evaluating the prediction model obtained in step S107 on the validation set determined in the current iteration, and storing both the model and the evaluation results in the prediction model pool for future selection and use by the base prediction model. The evaluation mentioned in this step can be an AUC value or other reasonable metric for evaluating model performance.

[0114] S109: Determine whether all subsets have been used as validation sets. Step S109 determines whether all K subsets obtained in step S104 have been used as validation sets and trained on the model. If all subsets have been used as validation sets and training has been completed, proceed to step S110; if any subsets have not been used as validation sets, proceed to step S105. This step ensures that every sample in the original dataset has been used as a validation set, improving model stability and preventing overfitting of the model to a particular subset.

[0115] S110: The average AUC value of all models in the prediction model pool Pool is used as the final performance evaluation value of the combined model. The model parameters and the final performance evaluation AUC value are stored in the optimal model pool Poolbest.

[0116] S111: Determine whether all hyperparameter combinations have been used to construct prediction models. Step S111 determines whether prediction models have been constructed for all algorithms and corresponding hyperparameter combinations obtained in step S102. If all combinations have been used to construct models, step S112 is executed. If any combination has not been used to construct models, step S103 is executed.

[0117] S113 , selecting the model with the largest AUC value from the model set Poolbest obtained in step S112 as the final prediction model for diagnosing the recurrence risk of papillary thyroid carcinoma.

[0118] S114, repeat all the above steps until all combinations of markers are modeled.

[0119] By executing the above model building steps, we obtained the optimal model constructed by all combinations of markers. In order to compare the performance of the models under these different marker combinations, we used the ROC method to evaluate the AUC values ​​of these models in the test group. The results are shown in Table 4 and Figure 3 shown.

[0120] Table 4. Comparison of the area under the ROC curve of the models constructed by different marker combinations in the test group

[0121]

[0122] Table 4 ranks the markers in Table 2 according to the larger VIP value and the smaller FDR value. Starting from the top-ranked marker, the markers are selected in sequence for combination. From 2MP to 8MP, the AUC value, accuracy, and sensitivity of the detection increase with the increase of markers. However, when more markers are added on the basis of 8MP, the diagnostic performance of the constructed model does not continue to increase. The diagnostic efficacy of 8MP is similar to that of 9MP and 10MP, and 8MP requires fewer markers and is less expensive. Therefore, the most preferred model is the model constructed by the combination of 8 markers (ORM1+SERPINB1+CTBS+ SELL+ CD36+DCD+SFTPB+PLIN1).

[0123] 2. Optimization of model parameters

[0124] For the optimal marker combination of ORM1+SERPINB1+CTBS+SELL+CD36+DCD+SFTPB+PLIN1, based on this marker combination, this example analyzed the models constructed under 9 different combinations of glmnet algorithm hyperparameters, and evaluated the model performance by AUC value (AUC was calculated using 10-fold cross-validation method during the modeling process). The results are shown in Table 5 and Figure 4 shown.

[0125] Table 5. AUC of the constructed model under different hyperparameter combinations of the glmnet algorithm

[0126]

[0127] According to Table 5, when the hyperparameter combination of the glmnet algorithm is alpha = 0.55 and lambda = 0.0005, the AUC reaches a maximum value of 0.913.

[0128] The equation for building a model based on the optimal hyperparameter combination is:

[0129]

[0130] Where Y is the predicted value, i represents the i-th biomarker, m represents the number of biomarkers (m=8), Xi represents the detection value of the i-th biomarker (μg / mL), Ki represents the coefficient of the i-th biomarker, and b is a constant of 2.6366832. The coefficients of the eight biomarkers are:

[0131] Table 6. Coefficients of the eight biomarkers in the model

[0132]

[0133] The complete model equation is:

[0134] Y=2.595 SFTPB+2.604 ORM1+2.278 CD36+9.370 DCD+0.237 SELL+5.591 PLIN1+7.668 CTBS+6.238 SERPINB1+2.6366832

[0135] Determination of diagnostic threshold of the combined diagnostic model for papillary thyroid carcinoma:

[0136] ## Setting levels: control = case, case = control

[0137] ## Setting direction: controls < case

[0138] The ROC curve was drawn using the predicted values ​​in the test group, and the optimal diagnostic cutoff value of 0.4823323 was set based on the Youden index. That is, when the predicted value of the diagnostic model is ≤0.4823323, the patient is considered to have a low risk of recurrence of papillary thyroid cancer; when the predicted value of the model is >0.4823323, the patient is considered to have a high risk of recurrence of papillary thyroid cancer. Figure 5As shown: The model has an AUC of 0.913, a sensitivity of 96.4%, and a specificity of 95.1% in the test group.

[0139] 3. Validation of the combined diagnostic model for papillary thyroid carcinoma

[0140] The optimal model constructed was verified in the validation group and the ROC curve was drawn as follows Figure 6 As shown in the figure, the model achieved an AUC of 0.908, a sensitivity of 95.2%, and a specificity of 95.9% in the validation group, which is very close to the diagnostic performance in the test group. This indicates that the papillary thyroid carcinoma recurrence risk prediction model constructed using eight protein markers has good predictive performance and accuracy, and has the best diagnostic efficacy.

[0141] Example 3: Construction and verification of a three-category diagnostic model

[0142] This example attempts to construct a three-category combined diagnostic model for distinguishing the prognosis of papillary thyroid carcinoma (PTC) patients with no recurrence, the prognosis of papillary thyroid carcinoma with in situ recurrence, and the prognosis of papillary thyroid carcinoma with metastasis. The model specifically includes the following steps: (1) construction and screening of the optimal diagnostic model; (2) validation of the effectiveness of the optimal diagnostic model. The specific screening process and results are as follows (in the present invention, the two-classification model in Example 2 uses the AUC value as the evaluation indicator; when a three-classification model is constructed, since multiple categories are involved, the AUC value is usually not applicable. In this example, the diagnostic efficacy of the model is measured using indicators such as sensitivity, specificity, accuracy, and consistency):

[0143] 1. Construction and screening of diagnostic models

[0144] All enrolled patients signed informed consent for a test cohort of 380 patients with papillary thyroid cancer. Twenty of these patients experienced recurrence within three years of surgery (six with local recurrence in situ and 14 with metastasis). These patients were then divided into two groups: 360 patients without recurrence were classified as having a low risk of papillary thyroid cancer recurrence, while the 20 patients with recurrence or metastasis were classified as having a high risk of papillary thyroid cancer recurrence. The patients were then randomly divided into a test group (180 patients in the low-risk group for papillary thyroid cancer recurrence) and a validation group (10 patients in the high-risk group for papillary thyroid cancer recurrence). The validation group included 180 patients in the low-risk group for papillary thyroid cancer recurrence and 10 patients in the high-risk group for papillary thyroid cancer recurrence). This example aims to build on the marker combination of ORM1+SERPINB1+CTBS+SELL+CD36+DCD+SFTPB+PLIN1 screened in Example 2 to further construct a three-category detection model that can effectively differentiate between the papillary thyroid carcinoma prognosis group with no recurrence (low-risk group), the papillary thyroid carcinoma prognosis group with in situ recurrence (in situ recurrence group), and the papillary thyroid carcinoma prognosis group with metastasis (metastasis group). All enrolled patients provided informed consent. Patients with papillary thyroid carcinoma were confirmed by histopathology. Inclusion criteria included: (a) no history of other malignancies; and (b) no concurrent malignancies or autoimmune diseases.

[0145] In this example, LC-MS / MS data acquisition and detection were performed on collected serum samples to obtain the concentrations of eight protein markers: ORM1, SERPINB1, CTBS, SELL, CD36, DCD, SFTPB, and PLIN1. The Shapiro-Wilk test was used to assess normal distribution, and the nonparametric Wilcoxon test was used to analyze differences in blood marker concentrations between the papillary thyroid carcinoma prognosis group (low-risk group), the papillary thyroid carcinoma prognosis group (in situ recurrence group), and the papillary thyroid carcinoma prognosis group (metastasis group). A three-class combined diagnostic model for the eight markers was constructed using a combination of machine learning methods. The area under the receiver operator characteristic (ROC) curve (AUC) was estimated using the predicted probability values ​​with 95% confidence intervals (CI) to assess the discriminatory ability of the multivariate diagnostic model. Using the test set, the Youden index (YI) was calculated to determine the predicted probability cutoff value for distinguishing the low-risk group, in situ recurrence group, and metastasis group. Furthermore, ROCs for individual markers and different subgroups were constructed and compared. Standard descriptive statistics, such as frequency, mean, median, positive predictive value (PPV), negative predictive value (NPV), and standard deviation (SD), were calculated to describe the experimental results of the study population. Statistical analysis was performed using R3.6.1, and a p-value of less than 0.05 was considered statistically significant.

[0146] In this embodiment, in order to construct the optimal three-class joint diagnosis model, after comparing the six algorithms of gradient boosting, naive Bayes, support vector machine, neural network, generalized linear, and discriminant analysis, the gradient boosting method was selected as the best supervised classification algorithm for constructing the prediction model. The grid search range for hyperparameter optimization of the gradient boosting method model is shown in Table 7 below.

[0147] Table 7. Parameter grid search range of gradient boosting method

[0148]

[0149] Through optimization screening in terms of accuracy, consistency, sensitivity, specificity, etc., the optimal parameter combination mode was determined to be: interaction.depth 1, n.trees 100, shrinkage 0.1, n.minobsinnode 10.

[0150] The test and validation groups used two completely different batches of samples. This example only screened markers and constructed models from the test group; the samples from the validation group were only used to verify the diagnostic efficacy of the model. The specific results are shown in Table 8.

[0151] Table 8. Performance evaluation table of the gradient boosting method to build a model to distinguish three categories

[0152]

[0153] As shown in Table 8, the gradient boosting model constructed based on eight protein markers, ORM1, SERPINB1, CTBS, SELL, CD36, DCD, SFTPB, and PLIN1, can be used to predict whether patients with papillary thyroid carcinoma will experience no recurrence, in situ recurrence, or metastasis after surgery. This also demonstrates that the protein markers screened by the present invention can be used to differentiate the risk of recurrence in patients with papillary thyroid carcinoma after surgical treatment, and can also be used to distinguish between in situ recurrence and metastasis when the risk of recurrence is high (patients with both in situ recurrence and metastasis are also included in the metastasis group). However, the diagnostic efficacy for distinguishing between the in situ recurrence group and the metastasis group is still not ideal.

[0154] Combined performance of two- and three-class joint diagnosis models

[0155] To further enhance the diagnostic value of three-category diagnostic models (gradient boosting) constructed using different protein combinations of biomarkers, this example compared the performance of diagnostic models constructed using different protein combinations of biomarkers in a test group, based on the 10 protein markers screened in Example 1. The specific combinations of the different models are shown in Table 9.

[0156] Table 9. Combinations of different diagnostic models

[0157]

[0158] The results are as follows Figure 7 As shown in Table 10, Table 10 is a comparison of the performance indicators of different diagnostic models constructed by the 10 biomarkers screened in Example 1 for three categories. The calculation method of the minimum value, first quartile, median, mean, third quartile and maximum value of accuracy and consistency is as follows: (1) Sort the accuracy or consistency values ​​from small to large; (2) Minimum value: the first value after sorting; (3) First quartile (Q1): multiply the number of data by 0.25. If the result is an integer, take the average of the values ​​at this position and the next position; if it is not an integer, round up to get the position, and the value at this position is Q1; (4) Median: if the number of data is odd, the median is the middle value; if it is even, it is the average of the two middle values; (5) Mean: the sum of all values ​​divided by the number of data; (6) Third quartile (Q3): multiply the number of data by 0.75, and process it in the same way as Q1; (7) Maximum value: the last value after sorting. The minimum and maximum values ​​reflect data extremes, demonstrating the worst and best possible model performance. Quartiles help understand the data's distribution and dispersion. Q1 and below indicate lower performance, while Q3 and above indicate higher performance. The median reflects intermediate performance, and the mean comprehensively reflects the overall average performance. By combining these statistical values, we can gain a comprehensive understanding of the overall performance, distribution characteristics, and stability of the model, providing a strong basis for model selection and optimization.

[0159] Table 10. Performance comparison of diagnostic models based on different protein combination biomarkers

[0160]

[0161] Table 10 shows that for the three-category diagnostic model, the nine-marker joint detection model (9MP), consisting of nine markers, performed best. This clearly demonstrates that the 9MP model, constructed by adding KRT19 to the eight protein markers of ORM1, SERPINB1, CTBS, SELL, CD36, DCD, SFTPB, and PLIN1, significantly improves the diagnostic efficacy of differentiating patients with papillary thyroid carcinoma from those with postoperative recurrence, in situ recurrence, or metastasis. Therefore, the three-category gradient boosting model constructed using these nine protein markers (ORM1 + SERPINB1 + CTBS + SELL + CD36 + DCD + SFTPB + PLIN1 + KRT19) was selected as the optimal joint diagnostic model.

[0162] 3. Diagnostic Performance Measurement and Validation of the Three-Classification Joint Diagnosis Model

[0163] 1. Diagnostic performance measurement of the three-classification joint diagnosis model

[0164] In order to more accurately determine the diagnostic performance and thresholds of the model constructed in this embodiment for different disease classifications, a multi-classification model of the gradient boosting (GBM) algorithm in the model group was used to perform predictive analysis in the test group, and the predicted results were calculated as the predicted probability values ​​of the three categories (low-risk group, in situ recurrence group, and metastasis group). The category with the largest predicted probability value was the final prediction result of the system.

[0165] The meaning and calculation method of each indicator are as follows:

[0166] Results: The three-category combined diagnostic model had an accuracy of 0.78 and a consistency of 0.76 in the test group. The diagnostic sensitivity for the low-risk group was 91.5% and the specificity was 95.9%. The diagnostic sensitivity for the primary recurrence group was 77.8% and the specificity was 76.3%. The diagnostic sensitivity for the metastasis group was 73.2% and the specificity was 72.6%.

[0167] It should be noted that the three-classification joint diagnosis model constructed by gradient boosting is a model constructed by machine learning and cannot fit a specific equation formula like a generalized linear model.

[0168] 2. Validation of the three-classification joint diagnosis model

[0169] The prediction performance of the model built based on the test group was verified in the validation group. The specific results are as follows:

[0170] The accuracy was 0.76, and the consistency was 0.76. The diagnostic sensitivity for the low-risk group was 90.2%, and the specificity was 93.8%. The diagnostic sensitivity for the primary recurrence group was 75.6%, and the specificity was 74.8%. The diagnostic sensitivity for the metastasis group was 72.1%, and the specificity was 71.5%.

[0171] In summary, the three-category combined diagnostic model comprising nine protein markers constructed in this example has good diagnostic value for the low-risk group, in situ recurrence group, and metastasis group.

[0172] All patents and publications cited in this specification are intended to indicate that they are state of the art and that the present invention may be used. All patents and publications cited herein are incorporated by reference in their entirety, as if each publication were specifically incorporated by reference. The invention described herein may be practiced in the absence of any element or elements, limitation or limitations, unless otherwise specified. For example, in each instance, the terms "comprising," "consisting essentially of," and "consisting of" may be replaced with either of the other two terms. The term "a" or "an" herein simply means "one" and does not exclude the inclusion of only one or more. The terms and expressions used herein are intended to be descriptive, not limiting, and are not intended to exclude any equivalent features. However, it is understood that any suitable changes or modifications may be made within the scope of the present invention and the appended claims. It is understood that the embodiments described herein are preferred embodiments and features, and that modifications and variations can be made by persons of ordinary skill in the art based on the spirit of the present invention. Such modifications and variations are considered to be within the scope of the present invention and the scope of the independent and appended claims.

Claims

1. Use of a reagent for detecting a marker for preparing a reagent for predicting whether a patient with papillary thyroid carcinoma will not relapse, relapse in situ, or metastasize within three years after surgery, characterized in that: The markers consist of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1 and KRT19.

2. A kit for predicting whether a patient with papillary thyroid carcinoma will not relapse, relapse in situ, or metastasize within three years after surgery, characterized in that: The kit comprises a detection reagent for the biomarker for use as claimed in claim 1.

3. A biomarker combination for predicting whether a patient with papillary thyroid carcinoma will not relapse, relapse in situ, or metastasize within three years after surgery, characterized in that: The panel consists of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, and KRT19.

4. A system for predicting whether a patient with papillary thyroid carcinoma will not relapse, relapse in situ, or metastasize within three years after surgery, characterized in that: The system includes a data analysis module for analyzing the detection values ​​of markers, wherein the markers consist of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1 and KRT19.

5. The system of claim 4, wherein: The data analysis module uses the detection values ​​of markers of known samples as a training set, and divides patients with papillary thyroid carcinoma into a non-recurrence group, an in situ recurrence group, and a distant metastasis group according to their post-operative conditions. The relationship between the detection values ​​of the non-recurrence group, the in situ recurrence group, and the distant metastasis group is analyzed to construct a model; the model is constructed based on a gradient boosting algorithm; the system also includes a data storage module, a data input interface, and a data output interface; the data storage module is used to store the detection values ​​of biomarkers; the data input interface is used to input the detection values ​​of biomarkers, and the data output interface is used to output prediction results.

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