Biomarker for predicting recurrence risk of papillary thyroid carcinoma and application of biomarker

Abstract

The invention provides a biomarker for predicting the recurrence risk of papillary thyroid carcinoma and application of the biomarker, and a proteomics method is utilized to analyze proteins with significant abundance level difference in blood of two groups of people with papillary thyroid carcinoma recurrence and non-recurrence after papillary thyroid carcinoma patients are subjected to operative treatment, so that the papillary thyroid carcinoma recurrence risk can be predicted, and the papillary thyroid carcinoma recurrence risk can be predicted. According to the method, a biomarker capable of being used for predicting the papillary thyroid carcinoma recurrence risk is screened out, and a multi-marker joint detection model is further constructed, so that the papillary thyroid carcinoma recurrence risk can be accurately, noninvasively and efficiently predicted, and clinical requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of proteomic screening for diagnostic markers of papillary thyroid carcinoma. Specifically, it relates 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 in the endocrine system, accounting for approximately 80% of all thyroid cancer cases.

[0003] Surgical resection is the most core treatment method for PTC patients. However, although PTC is an indolent tumor with a generally good prognosis and a relatively high survival rate, there are still some patients who show recurrence and metastasis after surgery, and often accompanied by lymph node metastasis (LNM), which complicates the postoperative management and increases the risk of recurrence. It is reported that the incidence of central compartment lymph node metastasis in PTC is 30% to 65%, while the incidence of lateral compartment lymph node metastasis is approximately 20% to 30%, but it may be as high as 50% in some more aggressive lesions. Moreover, once recurrence occurs after surgery, 2 / 3 of the patients are often accompanied by iodine resistance and distant metastasis to multiple organs, greatly reducing the quality of life of the patients, and the death risk is significantly correlated with the recurrence risk. If patients who do not benefit from surgical treatment or show progression can be timely predicted for risk and the treatment plan can be adjusted as soon as possible (such as radioactive iodine treatment, adjuvant radiotherapy and chemotherapy, secondary surgical resection, targeted therapy or immunotherapy, etc.), the overall survival rate and quality of life of the patients can be significantly improved. Therefore, finding specific markers for predicting the recurrence risk of papillary thyroid carcinoma and developing new diagnostic methods are the research hotspots and difficulties in the treatment of papillary thyroid carcinoma, and also have important clinical significance and social benefits.

[0004] Proteomics is the science that studies the protein composition, localization, changes, and their interaction rules in cells, tissues, or organisms, including the study of protein expression patterns and proteome functional patterns. With the development of mass spectrometry technology, liquid chromatography-tandem mass spectrometry (LC-MS / MS) has become the most important tool in proteomics research. The development of proteomics is of great significance for finding disease diagnostic markers, screening drug targets, toxicology research, etc., and has thus been widely applied in medical research. Although there have been many articles and patents reporting on the discovery of novel tumor markers in recent years, they have all remained at the laboratory research stage and are rarely used in clinical applications and market promotion. Moreover, in most cases, for the in vitro diagnosis of tumor recurrence risk, a single indicator is far from sufficient. Only by adopting a combined detection form and combining various dimensions of detection can the accuracy of prediction be enhanced. Therefore, finding new markers related to the diagnosis of PTC recurrence risk and constructing a prediction model by combining multiple markers have important clinical value. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides a biomarker for predicting the recurrence risk of papillary thyroid carcinoma and its application. By using proteomics methods, proteins with significantly different abundance levels in the blood of two groups of patients with papillary thyroid carcinoma who have or have not had recurrence after surgical treatment are analyzed to screen out biomarkers that can be used to predict the recurrence risk of papillary thyroid carcinoma, and a multi-marker combined detection model is further constructed, which can accurately, non-invasively, and efficiently predict the recurrence risk of papillary thyroid carcinoma and meet the clinical needs.

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

[0007] The proteomics biomarker provided by the present invention can accurately predict the recurrence risk of papillary thyroid carcinoma patients after surgical treatment, thereby enabling earlier identification of high-risk recurrence populations, helping doctors anticipate in advance whether timely adjustment of treatment plans is needed, reducing the risk of recurrence or metastasis of papillary thyroid carcinoma, and truly benefiting papillary thyroid carcinoma patients.

[0008] The present invention utilizes a proteomics method to collect plasma samples from patients who experience recurrence within a short period of time (e.g., within 1 to 5 years) after surgical treatment of papillary thyroid carcinoma, as well as patients who do not experience recurrence within a short period of time, and analyzes different samples 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, and ultimately 10 differential proteins with a significant correlation with the risk of recurrence of papillary thyroid carcinoma are screened out. These 10 proteins can be used to distinguish patients with recurrent papillary thyroid carcinoma and patients without recurrence, and have a certain diagnostic efficacy, thereby being 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; CRYAB is a protein or amino acid sequence with a UniProt database number of P02511.

[0010] The present invention also surprisingly found that some of the protein markers obtained by proteomics screening are known markers that can be used for other cancers. For example, SELL has been reported to be used for colorectal cancer metastasis prediction, but this screening found that the marker can also be used to predict the risk of recurrence after surgical treatment of papillary thyroid carcinoma. It can be seen that the protein markers of many different cancers are not completely separated or irrelevant. In fact, there are many cross-relationships or influences. Many protein markers can be used for early cancer prediction and prognosis diagnosis. Even many protein markers can be used for prediction and diagnosis of different stages of multiple different cancers. Therefore, there are still many new functions in the field of proteomics that need to be explored, and the market prospects are very broad.

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

[0012] To improve the diagnostic efficacy for the recurrence risk of papillary thyroid carcinoma, it is also necessary to combine different differential proteins to construct a diagnostic model, rank them according to the importance obtained by screening, select different numbers of differential proteins with higher rankings for combination respectively, and finally 8 protein markers are screened. Based on these 8 protein markers, a model is constructed, which has good risk prediction ability in the diagnosis of the recurrence risk of papillary thyroid carcinoma. The data from the detection of clinical papillary thyroid carcinoma samples show that just by using these 8 biomarkers to predict the recurrence risk of papillary thyroid carcinoma, the AUC value can reach 0.913, and the diagnostic performance is good.

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

[0014] It can be understood that the short term in "recurrence within three years after the treatment of papillary thyroid carcinoma" here is not an absolute and unchanging time node. It is just a time node summarized according to current 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, it may be 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. There may be a recurrence of papillary thyroid carcinoma after treatment.

[0015] In some ways, the recurrence of papillary thyroid carcinoma means that after surgical treatment with radical tumor resection, the patient has in-situ or adjacent area recurrence of papillary thyroid carcinoma within three years, or has 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 spinal fluid.

[0017] In some embodiments, the reagent for predicting the recurrence risk of papillary thyroid carcinoma is a detection reagent prepared with this 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, etc.

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

[0019] The present invention relates to biomarkers for screening blood to predict the recurrence risk of papillary thyroid carcinoma. These biomarkers show significant differences in the blood of individuals at high risk and low risk of papillary thyroid carcinoma recurrence. By collecting a blood sample, it is possible to predict or assist in diagnosing the likelihood of papillary thyroid carcinoma recurrence in an individual by detecting these biomarkers in the individual's blood, or to detect these biomarkers in the blood of a group, and then classify the group into individuals at high risk and low risk of papillary thyroid carcinoma recurrence.

[0020] Further, the detection methods include radiological methods, immunological methods, fluorescence methods, flow-through fluorescence methods, latex turbidimetry, biochemical methods, enzymatic methods, hybridization methods, gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, chromatography, chemiluminescence methods, magnetoelectric methods, or optoelectronic conversion methods.

[0021] The presence, absence, or level of these markers here is a relative concept. For example, when comparing the high-risk group and low-risk group of papillary thyroid carcinoma recurrence, the levels of these specific markers are compared relative to the high-risk group and low-risk group of papillary thyroid carcinoma recurrence as a reference. For some markers, the level in the high-risk group of papillary thyroid carcinoma recurrence may be relatively higher than that in the low-risk group, and this increase is statistically significant, such as a significant or highly significant increase. Therefore, when judging these markers, if it is a single marker, if the probability of a certain risk occurrence increases and the level of this marker changes, this change may be a relative increase or a relative decrease, and the difference in this relative increase or decrease is significantly different, and of course, it can also be highly significantly different. So, no matter what means are used for detection, a pre-specified value (cut-off value) can be used as a standard. If the value is higher than this value, it is considered that the level has changed, and such a result can be used for prediction or diagnosis.

[0022] Thus, in some aspects, the biomarker described in the present invention can be obtained by detecting the content of the biomarker in a sample through 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 strip, immunoreaction chip, capillary electrophoresis, infrared spectroscopy, etc. As long as it can be used to detect the content of the protein biomarker in the sample, it can be used for the diagnosis of the high-risk group of recurrent papillary thyroid carcinoma and the low-risk group of recurrent papillary thyroid carcinoma. As long as the content of the protein biomarker in the sample can be detected, it can be used to predict or diagnose the probability of the occurrence of a certain disease. It can be understood that the detection here is for the individual sample, and then compared with the preset standard, and the result of the comparison is used to judge or predict the occurrence status of the disease. For example, it can be used to predict the probability of recurrence of papillary thyroid carcinoma. Such prediction or diagnosis is whether it will occur within a certain time. Of course, such detection can be continuous detection, and the progress of the disease can be inferred from the change in the content of certain substances.

[0023] In some ways, 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 certain biomarker measured in the control sample is 100, and the average peak area measured in the sample of patients in the high-risk group of recurrent papillary thyroid carcinoma is 600, then the abundance of the biomarker in the sample is considered to be 6 times that of the control sample.

[0024] On the other hand, the present invention provides a kit for predicting the recurrence risk of papillary thyroid carcinoma, and the kit includes a detection reagent for the biomarker as described above.

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

[0026] On yet another aspect, the present invention provides a system for predicting the recurrence risk of papillary thyroid carcinoma, and the system includes a data analysis module for analyzing the detection values of biomarkers, and the biomarkers include 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 the treatment of papillary thyroid carcinoma; the recurrence of papillary thyroid carcinoma includes in-situ or adjacent area recurrence of papillary thyroid carcinoma, and metastasis of papillary thyroid carcinoma.

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

[0029] In some embodiments, a combined diagnostic model for predicting the recurrence risk of papillary thyroid carcinoma is constructed by combining multiple machine learning methods, and it is preliminarily confirmed that for any one of the selected novel biomarkers alone, the change in its concentration can be used to distinguish between high-risk and low-risk populations of papillary thyroid carcinoma recurrence, indicating that these biomarkers have extremely high diagnostic value.

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

[0031]

[0032] Among them, 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 2.6366832; the coefficients of the 8 biomarkers are:

[0033]

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

[0035] Furthermore, the system further 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 the biomarkers; the data input interface is used to input the detection values of the 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] On the other hand, the present invention provides the use of a biomarker for preparing a reagent for predicting non - recurrence, in - situ recurrence or distant metastasis in patients with papillary thyroid carcinoma after surgery, and the biomarker includes any one or more of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB.

[0038] The present invention attempts to use the protein biomarkers for predicting recurrence risk to further distinguish between patients with in - situ recurrence and those with metastasis, and finds that each protein biomarker can also be used to distinguish the three - classification of non - recurrence, in - situ recurrence or metastasis in patients with papillary thyroid carcinoma after surgery. The metastasis includes patients with only metastasis and those with both in - situ recurrence and metastasis.

[0039] When using the 8 biomarkers of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, and SERPINB1 to construct a three - classification combined diagnostic model, the accuracy of distinguishing between patients with in - situ recurrence and those with metastasis reaches about 65%.

[0040] Based on the 8 biomarkers, when further adding one biomarker of KRT19 to construct a three - classification combined diagnostic model containing 9 protein biomarkers, its diagnostic efficacy for distinguishing whether patients with papillary thyroid carcinoma will have in - situ recurrence or metastasis after surgical treatment can continue to be improved, and the accuracy reaches about 78%. It can be directly used for the early prediction of non - recurrence, in - situ recurrence or metastasis in patients with papillary thyroid carcinoma after surgery.

[0041] Furthermore, the reagent is used to predict non - recurrence, in - situ recurrence or metastasis in patients with papillary thyroid carcinoma within three years after surgery.

[0042] Furthermore, the reagent is used to detect the content of the biomarker 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, relative abundance or concentration of the biomarker in the body fluid sample.

[0044] On the other hand, the present invention provides a kit for predicting non - recurrence, in - situ recurrence or metastasis in patients with papillary thyroid carcinoma after surgery, and the kit includes a detection reagent for the biomarker as described above.

[0045] In another aspect, the present invention provides a biomarker combination for predicting non - recurrence, in - situ recurrence or metastasis after surgery in patients with papillary thyroid carcinoma, and the combination includes SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS and SERPINB1.

[0046] In another aspect, the present invention provides a system for predicting non - recurrence, in - situ recurrence or metastasis after surgery in patients with papillary thyroid carcinoma. The system includes a data analysis module, and the data analysis module is used to analyze the detection values of biomarkers, and the biomarkers include any one or more of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB.

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

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

[0049] Further, the data analysis module uses the detection values of biomarkers of known samples as a training set. According to the situation after surgery of patients with papillary thyroid carcinoma, it is divided into a non - recurrence group, an in - situ recurrence group and a distant metastasis group, analyzes the relationship between the detection values of the non - recurrence group, the in - situ recurrence group and the distant metastasis group, and constructs a model.

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

[0051] Unlike generalized linear models that can output model formulas and cut - off values, the gradient boosting algorithm directly performs all calculations through machine learning. The detection values can be directly input into the software system, and the prediction results can be directly obtained.

[0052] Further, 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 as follows:

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

[0055] 2. The combined differential diagnosis model of 8 biomarkers constructed by the present invention is convenient, 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 carcinoma and has good application prospects.

[0056] 3. Based on the biomarkers screened that can predict the recurrence risk of papillary thyroid carcinoma, a three-classification model that can simultaneously distinguish between non-recurrence group, in-situ recurrence group, and metastatic group is further constructed, providing a more effective and accurate prediction and diagnosis mode.

[0057] Detailed description

[0058] (1) Diagnosis or detection

[0059] Here, diagnosis or detection refers to the detection or assay of biomarkers in a sample, or the content of the target biomarker, such as absolute content or relative content, and then it is explained whether the individual providing the sample may have or suffer from a certain disease, or the possibility of having a certain disease, based on the presence or quantity of the target biomarker. The meanings of diagnosis and detection here can be interchanged. The result of this detection or the result of the diagnosis cannot be directly used as the direct result of having a disease, but is an intermediate result. If a direct result is to be obtained, other auxiliary means such as pathology or anatomy are still required to confirm having a certain disease. For example, the present invention provides a variety of new biomarkers related to the recurrence risk of papillary thyroid carcinoma, and the change in the content of these biomarkers has a direct correlation with whether the patient belongs to the high-risk population for the recurrence of papillary thyroid carcinoma.

[0060] (2) The connection between the biomarker or biological marker or differential protein and the recurrence risk of papillary thyroid carcinoma

[0061] Biomarker, biological marker, and differential protein have the same meaning in the present invention. Here, the connection means that the appearance or change in the content of a certain biomarker in a sample has a direct correlation with a specific disease. For example, a relative increase or decrease in content indicates that the possibility of having this disease is relatively higher compared to the healthy population.

[0062] If multiple different markers appear simultaneously in a sample or there are relative changes in their contents, it indicates that the likelihood of having this disease is relatively higher compared to the healthy population. That is to say, among the types of markers, some markers have a strong correlation with the disease, some have a weak correlation, and some may even have no correlation with a specific disease. One or more of those markers with a strong correlation can be used as markers for diagnosing the disease, and those with a weak correlation can be combined with the strong markers to diagnose a certain disease, increasing the accuracy of the test results.

[0063] Regarding the numerous biomarkers found in the serum of the present invention, these biomarkers can all be used to distinguish between high-risk and low-risk populations for recurrence of papillary thyroid carcinoma. These markers can be used alone as individual markers for direct detection or diagnosis. Selecting such a marker indicates that the relative change in the content of this marker has a strong correlation with the recurrence risk of papillary thyroid carcinoma. Of course, it can be understood that simultaneous detection of one or more markers with a strong correlation with the recurrence risk of papillary thyroid carcinoma can be selected. Normally understood, in some ways, selecting biomarkers with a strong correlation for detection or diagnosis can achieve a certain standard of accuracy, such as 60%, 65%, 70%, 80%, 85%, 90% or 95% accuracy. Then it can be stated that these markers can obtain an intermediate value for diagnosing a certain disease, but it does not mean that it can directly confirm the presence of a certain disease.

[0064] Of course, it is also possible to select the differential proteins with larger ROC values as diagnostic markers. The so-called strength or weakness is generally calculated and confirmed through some algorithms, such as the contribution rate or weight analysis of the marker to the recurrence risk assessment of papillary thyroid carcinoma. Such calculation methods can include significance analysis (p-value or FDR value) and fold change, and 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). Of course, other methods are also included, such as ROC analysis, etc. Of course, other model prediction methods are also possible. When specifically selecting biomarkers, the differential proteins disclosed in the present invention can be selected, or other existing well-known biomarker combinations can be selected or combined for prediction through model methods.

[0065] (3) Definition of disease terms

[0066] Papillary thyroid carcinoma: Papillary thyroid carcinoma is the most common and has the lowest malignancy. It accounts for about 85% of thyroid cancers. It can occur at any age, but is more common in children or young women (before the age of 40). Some patients had received neck X-ray treatment during childhood. The tumor grows slowly and can be confined within the thyroid gland for several years. The lesion can spread from the primary site to other parts of the gland and cervical lymph nodes through the intrathyroid lymphatic vessels, or can be confined for several years, so its nature is easily overlooked. The incidence of papillary thyroid carcinoma shows an increasing trend year by year. The occurrence of papillary thyroid carcinoma is affected by factors such as hormones, genetics, and environment, such as radiation, goitrogenic substances, iodine deficiency, etc. In addition, Hashimoto's thyroiditis may also lead to the emergence of papillary thyroid carcinoma.

[0067] Surgical resection is the preferred treatment method for papillary thyroid carcinoma. According to the lesion situation of the thyroid tumor, resection of one thyroid lobe plus the isthmus or total thyroidectomy is selected, and central compartment lymph node dissection or cervical lymph node dissection is selected according to the cervical lymph node metastasis situation.

[0068] Recurrence of papillary thyroid carcinoma: It refers to the phenomenon that after a patient completes radical treatment (surgical resection), after a period of clinical disease-free state (i.e., a period without signs of tumor), cancer cell growth or tumor lesions (also known as metastasis) reappear at the primary tumor site or other parts of the body. Recurrence may originate from residual cancer cells that were not completely removed during treatment, or micrometastatic foci that had spread earlier but were not detected. Recurrence of papillary thyroid carcinoma can occur from several months to several years after treatment, but 80% of recurrences occur within 2 - 3 years after surgery, and the recurrence risk is significantly reduced after 5 years. Recurrence of papillary thyroid carcinoma is closely related to tumor stage, treatment standardization, and postoperative management. Although its prognosis is relatively good, some patients may still experience recurrence. Usually, the risk needs to be reduced through regular monitoring, standardized treatment, and long-term follow-up, which is rather cumbersome. The main metastasis route of papillary thyroid carcinoma is lymph node metastasis, and a small number also show distant metastases, such as lung metastasis, bone metastasis, etc. After recurrence or metastasis, treatment plans such as surgery, iodine-131 treatment, or targeted therapy need to be selected according to the specific situation. If detected early, the recurrence situation can be controlled as soon as possible, significantly improving the prognosis.

[0069] (4)The gold standard for the diagnosis of papillary thyroid carcinoma recurrence: is pathological histological examination (i.e., pathological confirmation of biopsy or surgical resection specimens), observing the presence of cancer cells under a microscope, and clarifying the nature of the tumor in combination with immunohistochemistry or molecular detection. Description of the Drawings

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

[0071] Figure 2ROC and OPLS-DA analysis result graphs for the high-risk and low-risk groups of papillary thyroid cancer recurrence in Example 1;

[0072] Figure 3 AUC of the performance of the models constructed with different biomarker combinations in Example 2;

[0073] Figure 4 AUC result graphs of the models constructed with different hyperparameters in Example 2;

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

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

[0076] Figure 7 Diagnostic performance evaluation graph of the three-class combined diagnosis model in Example 3. Detailed implementation manners

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

[0078] Example 1. Screening biomarkers for the recurrence risk of papillary thyroid cancer using proteomics

[0079] By collecting plasma samples from patients after radical surgery for papillary thyroid cancer, enriching low-abundance proteins based on the method of removing high-abundance proteins by immunoaffinity chromatography, detecting the protein abundances in the samples by a high-performance liquid chromatography-mass spectrometry tandem device, analyzing the differences in their abundances between patients with papillary thyroid cancer who did not relapse or metastasize within three years and patients with papillary thyroid cancer who relapsed or metastasized within three years, and analyzing their diagnostic performance. The specific steps are as follows:

[0080] 1. Sample collection

[0081] A total of about 2 ml of peripheral blood samples were collected from 100 patients with papillary thyroid carcinoma 2 weeks after surgical treatment, placed in a vacuum tube containing EDTA anticoagulant, mixed well, centrifuged at 120 g for 10 minutes at room temperature, and the supernatant was taken, repeated twice; then centrifuged at 360 g for 20 minutes. Then the platelet samples were 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 (25 - 69 years), 43 patients with stage I PTC, 51 patients with stage II PTC, and 6 patients with stage III PTC. All enrolled patients signed informed consent forms. Among them, the patients with papillary thyroid carcinoma were all confirmed by pathological histology. Inclusion criteria: (a) no history of other malignancies; (b) no patients with other malignancies or autoimmune diseases combined.

[0082] During the following three years, the patients were followed up every 3 to 6 months, and regular imaging examinations such as neck ultrasound or CT were performed. Once recurrence or metastasis was found, it needed to be confirmed by pathological histology. A total of 9 PTC patients had recurrence within three years after surgery, including 3 cases of in-situ local recurrence and 6 cases with lymph node metastasis. Then the samples stored at -80 °C were taken out, and the samples were divided into two groups: the samples of 91 patients without recurrence were classified into the low-risk group of papillary thyroid carcinoma recurrence, and 9 cases with recurrence or metastasis were classified into the high-risk group of papillary thyroid carcinoma recurrence, for the screening of proteomic markers.

[0083] 2. Sample processing and enzymatic digestion

[0084] First, the plasma sample was centrifuged on a centrifuge for 15 minutes (15,000 g). The supernatant was taken, filtered, and then 14 high-abundance proteins were removed by immunoaffinity chromatography. Then, a concentrator tube with a cut-off molecular weight of 3 kDa was used to concentrate the low-abundance proteins to 350 μL on a centrifuge (4000 g, 1 hour). The concentrated solution was recovered, and a desalting column with a cut-off molecular weight of 7 kDa was used to perform buffer exchange on a centrifuge (1000 g, 2 minutes). The replacement solution was AEX-A (20 mM Tris, 4 M Urea, 3% isopropanol, pH 8.0). Using AEX-A as a blank, the protein concentration in the sample was determined by the BCA method. According to the sample grouping, 25 μL of TCEP was added to the sample, and the sample was incubated at 37 °C for 30 minutes for protein reduction. Then, the corresponding TMT 16-plex reagent was added, and the sample was incubated in the dark at room temperature for 1 hour for the TMT labeling reaction. Subsequently, the Zeba column was used to perform buffer exchange on the sample, and the replacement solution was AEX-A. After mixing the samples labeled with TMT 16-plex, 2 mL of AEX-A was added to the mixed sample, and the final volume was 5.5 mL. The sample was filtered using a 0.22 m filter and the TMT 16-plex labeled sample was separated using a 2D-HPLC system. The collected fractions were freeze-dried, and finally, a mixture of Trypsin-Lysin C enzymes was added, and the sample was incubated at 37 °C for 5 hours for enzymatic digestion. 5 μL of 10% TFA was added to terminate the enzymatic digestion reaction. A total of 60 enzymatically digested 2D-HPLC fractions were used for nanoLC-MS / MS analysis.

[0085] 3. LC-MS / MS Data Acquisition and Database Search Analysis

[0086] DIA analysis was performed using a nanoliter flow Vanquish Neo system (Thermo Fisher) for chromatographic separation. The sample after nanoliter high-performance liquid chromatography separation was analyzed by DIA (data-independent) mass spectrometry using an Astral high-resolution mass spectrometer (Thermo Scientific). Detection mode: positive ion, the precursor ion scan range was 380 - 980 m / z, the resolution of the first-stage mass spectrometry was 240000 at 200 m / z, the Normalized AGC Target was 500%, and the Maximum IT was 5 ms. MS2 used the DIA data acquisition mode, with 299 scan windows set, the Isolation Window was 2 m / z, the HCD Collision Energy was 25 eV, the Normalized AGC Target was 500%, and the Maximum IT was 3 ms.

[0087] 4. Data preprocessing

[0088] The tandem mass spectrometry data was searched using Maxquant (v1.6.15.0). The data type was DIA proteomics data for quantitative analysis based on secondary reporter ions, and the requirement for the secondary spectra used for quantification was that the proportion of precursor ions in the primary spectra was greater than 75%. The database source was Homo_sapiens_9606_proteome_gene from the Uniprot database (release: 2021-10-14, sequence: 20,437), and a common contaminant library was added to the database. Contaminant proteins were removed during data analysis; the digestion method was set to Trypsin / P; the maximum number of missed cleavage sites was set to 2; the mass error tolerances for precursor ions in the First search and Main search were set to 20 ppm and 5 ppm respectively, and the mass error tolerance for secondary fragment ions was 20 ppm. The fixed modification was cysteine alkylation, and the variable modifications were oxidation of methionine and acetylation of the protein N-terminus. The false discovery rates (FDR) for protein identification and PSM identification were both set to 1%.

[0089] 5. Differential analysis

[0090] A combination of univariate analysis and multivariate statistical analysis was used to screen for differential proteins between the high-risk recurrence group and low-risk group of papillary thyroid carcinoma. Univariate analysis mainly included significance analysis (p-value or FDR value) and fold change of characteristic molecules in different groups, and multivariate statistical analysis mainly included receiver operating characteristic curve (ROC) analysis and Boruta feature selection based on the random forest algorithm. All statistical analyses were completed using R, and the specific R-related information is shown in Table 1.

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

[0092]

[0093] The variable importance for the projection (VIP) was calculated to measure the influence intensity and interpretability of the expression patterns of each protein on the classification and discrimination of each group of samples. Further, the Wilcoxon rank sum test was performed to obtain the corrected p-value (FDR). According to the conditions of FDR < 0.01 and Fold change > 2, 51 downregulated proteins and 76 upregulated proteins were screened (see details in Figure 1 ).

[0094] To evaluate the role of each protein biomarker in the diagnosis and prediction of the recurrence risk of papillary thyroid carcinoma, in this example, the ROC and Boruta analysis methods were used to evaluate each protein biomarker, and the results are shown in Figure 2 , where the abscissa is the AUC obtained from the ROC analysis, the ordinate is the -log10(FDR) calculated by the Wilcoxon test, and the size of the points represents the VIP value obtained from the Boruta analysis. Further screening was performed according to VIP > 3 and AUC > 0.6, and a total of 10 more significant candidate protein biomarkers were found, as shown in Table 2 for details.

[0095] Table 2. Differential biomarkers between the high-risk recurrence group and the low-risk recurrence group of papillary thyroid carcinoma

[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 the protein between the high-risk recurrence group and the low-risk recurrence group of papillary thyroid carcinoma is more significant, and at the same time, it also indicates that the protein may have higher diagnostic value.

[0098] Example 2. Construction and verification of the recurrence risk model of papillary thyroid carcinoma

[0099] 1. Models constructed with different biomarker combinations

[0100] Although a single biomarker can also distinguish the recurrence risk of papillary thyroid carcinoma patients after surgery, generally speaking, combining multiple biomarkers can achieve higher accuracy in discrimination or prediction. However, for a single biomarker with higher accuracy in predicting the recurrence risk of papillary thyroid carcinoma patients after surgery, its role in the combination with one or more other biomarkers may not necessarily be greater, and at the same time, it is not the case that the more the number of biomarkers, the higher the prediction accuracy (AUC value) of the combination. Therefore, a large number of verification experiments are still needed.

[0101] In this example, a model was constructed and studied for the 10 protein markers of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB screened in Example 1. The research cohort was blood samples from 350 patients with papillary thyroid carcinoma 2 weeks after surgical treatment. All enrolled patients signed informed consent forms. Among them, 19 PTC patients had recurrence within three years after surgery (7 had local recurrence in situ and 12 had metastases). Thus, they were divided into two groups: the samples of 331 patients without recurrence were classified into the low-risk group of papillary thyroid carcinoma recurrence, and the 19 patients with recurrence or metastasis were classified into the high-risk group of papillary thyroid carcinoma recurrence. They were randomly divided into a test group and a validation group. The test group included 166 samples from the low-risk group of papillary thyroid carcinoma recurrence and 10 samples from the high-risk group of papillary thyroid carcinoma recurrence. The validation group included 165 samples from the low-risk group of papillary thyroid carcinoma recurrence and 9 samples from the high-risk group of papillary thyroid carcinoma recurrence.

[0102] In the test group, a combined diagnostic model of multiple protein markers was constructed using a method that combines multiple machine learning methods. The area under the receiver operator characteristic (ROC) curve (AUC) was estimated using the predicted probability value with a 95% confidence interval (CI) to evaluate the discrimination ability of the multivariate diagnostic model. Using the test group, the Youden index (YI) was calculated to determine the cut-off value for predicting the probability of distinguishing between the high-risk group and the low-risk group of papillary thyroid carcinoma recurrence. In addition, the ROCs of individual markers and different subgroups were constructed and compared. Standard descriptive statistical data 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 R 3.6.1, and a p-value less than 0.05 was considered statistically significant.

[0103] The steps for constructing the combined diagnostic model are as follows:

[0104] S101, among the 10 protein markers of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB in the samples of the test group, a concentration matrix of randomly selected 2 to 10 markers was used as the original training data set.

[0105] S102, the generalized linear model (glmnet) algorithm was selected to construct the prediction model, as well as the grid search range during the hyperparameter optimization process of the algorithm. In this step, the grid search range for hyperparameter optimization of each algorithm was set as shown in Table 3.

[0106] Table 3. Parameter grid of the glmnet algorithm

[0107]

[0108] S103. Select one of the hyperparameter combination methods as the parameters for constructing the prediction model according to the algorithm and the hyperparameter setting range set in step S102.

[0109] S104. Split the original dataset into K subsets according to the 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 that in the original dataset, the StratifiedK-Folds cross-validation mechanism needs to be used for data splitting.

[0110] S105. Select one of the K training data subsets obtained by splitting in step S104 as the validation set Ddev.

[0111] S106. Combine the training data subsets not selected in step S105 to form the training data pool Dtrainl.

[0112] S107. Based on the training dataset Dtrain obtained in step S106, construct a prediction model based on the selected supervised classification algorithm and hyperparameters.

[0113] S108. Based on the prediction model obtained in step S107, evaluate it on the validation set Ddev to obtain the AUC value, and store the current prognosis prediction model and the corresponding AUC value in the prediction model pool Pool. Step S108 is to evaluate the prediction model obtained in step S107 on the validation set determined in the current iteration, and store both the model and the evaluation results in the prediction model pool for future use in selecting the base prediction model. The evaluation mentioned in this step can be the AUC value or other reasonable metrics for evaluating the model performance.

[0114] S109. Determine whether each subset has been used as the validation set. Step S109 is to determine whether the K subsets obtained in step S104 have all been used as the validation set for model training. If all subsets have been used as the validation set and completed training, execute step S110; if there are subsets that have not been used as the validation set, execute step S105. This step ensures that each sample in the original dataset has been used as the validation set, improves the model stability, and prevents the model from overfitting to a certain subset.

[0115] S110. Take the average value of the AUCs of all the models in the obtained prediction model pool Pool as the final performance evaluation value of the model for this combination method. And store the model parameters and the final performance evaluation AUC value in the optimal model pool Poolbest.

[0116] S111. Determine whether prediction models have been constructed for all combinations of hyperparameters. Step S111 is to determine whether prediction models have been constructed for all algorithms and their corresponding hyperparameter combinations obtained in step S102. If models have been constructed for all combinations, execute step S112; if there are combinations for which models have not been constructed, execute step S103.

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

[0118] S114. Repeat all the above steps until modeling has been completed for all combinations of markers.

[0119] Through the execution of the above model construction steps, we obtained the optimal models constructed for all combinations of markers. To compare the performance of the models under these different combinations of markers, 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 as follows.

[0120] Table 4. Comparison of the areas under the ROC curves of models constructed with different combinations of markers in the test group

[0121]

[0122] Table 4 shows that according to the markers with larger VIP values and smaller FDR values in Table 2, the markers are sorted, and starting from the markers with higher rankings, the markers are selected for combination in sequence, from 2MP to 8MP. The detected AUC values, accuracy, and sensitivity all increase as the number of markers increases. However, when the number of markers is further increased based on 8MP, the diagnostic performance of the constructed model does not continue to increase. The diagnostic efficacies of 8MP and 9MP, 10MP are similar, and 8MP requires fewer markers and lower costs. Therefore, the most preferred model is the model constructed by combining 8 markers (ORM1 + SERPINB1 + CTBS + SELL + CD36 + DCD + SFTPB + PLIN1).

[0123] 2. Optimization of model parameters

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

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

[0126]

[0127] It can be seen from Table 5 that when the hyperparameter combination of the glmnet algorithm is alpha = 0.55 and lambda = 0.0005, the AUC reaches the maximum value of 0.913.

[0128] The equation of the model constructed based on the optimal hyperparameter combination is as follows:

[0129]

[0130] Among them, Y is the predicted value, i represents the i-th biomarker, m represents the number of biomarkers (m = 8), Xi represents the measured value of the i-th biomarker (μg / mL), Ki represents the coefficient of the i-th biomarker, and b is the constant 2.6366832; the coefficients of the 8 biomarkers are as follows:

[0131] Table 6. Coefficients of 8 biomarkers in the model

[0132]

[0133] The complete model equation is as follows:

[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 the diagnostic threshold of the combined diagnosis model for papillary thyroid carcinoma:

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

[0137] ## Setting direction: controls < case

[0138] The ROC curve is plotted with the predicted values in the test group, and the optimal diagnostic cut-off value of 0.4823323 is set according to the Youden index value. That is, when the predicted value of the diagnostic model ≤ 0.4823323, it is considered that the risk of recurrence of papillary thyroid carcinoma in the tested person is low; when the model predicted value > 0.4823323, it is considered that the risk of recurrence of papillary thyroid carcinoma in the tested person is high. The results are as follows Figure 5As shown: the AUC of the model in the test group was 0.913, the sensitivity was 96.4%, and the specificity was 95.1%.

[0139] 3. Verification of the combined diagnosis model for papillary thyroid carcinoma

[0140] The optimal model constructed was verified in the validation group, and the ROC curve was plotted as Figure 6 shown. The AUC of the model in the validation group was 0.908, the sensitivity was 95.2%, and the specificity was 95.9%, which was very close to the diagnostic effect in the test group. It can be seen that the recurrence risk prediction model for papillary thyroid carcinoma constructed by the present invention using 8 protein markers has good prediction performance and accuracy, and has the best diagnostic efficacy.

[0141] Example 3. Construction and verification of a three-classification diagnosis model

[0142] In this example, an attempt was made to construct a three-classification combined diagnosis model for distinguishing the group without recurrence of papillary thyroid carcinoma prognosis, the group with in-situ recurrence of papillary thyroid carcinoma prognosis, and the group with metastatic recurrence of papillary thyroid carcinoma prognosis, which specifically included the following processes: (1) construction and screening of the optimal diagnosis model; (2) verification of the effect of the optimal diagnosis model. The specific screening process and results are as follows (in the present invention, the binary classification model in Example 2 uses the AUC value as the evaluation index; when constructing a three-classification model, since multiple categories are involved, the AUC value is usually not applicable, and in this example, indicators such as sensitivity, specificity, accuracy, and consistency are used to measure the diagnostic efficacy of the model):

[0143] I. Construction and screening of the diagnosis model

[0144] For a test cohort of 380 patients with papillary thyroid carcinoma, all enrolled patients signed an informed consent form. Among them, 20 PTC patients had recurrence within three years after surgery (6 had local recurrence in situ and 14 had metastases). Thus, they were divided into two groups: 360 patient samples without recurrence were classified into the low-risk group of papillary thyroid carcinoma recurrence, and 20 patients with recurrence or metastasis were classified into the high-risk group of papillary thyroid carcinoma recurrence. They were randomly divided into a test group and a validation group. The test group included 180 patients in the low-risk group of papillary thyroid carcinoma recurrence and 10 patients in the high-risk group of papillary thyroid carcinoma recurrence; the validation group included 180 patients in the low-risk group of papillary thyroid carcinoma recurrence and 10 patients in the high-risk group of papillary thyroid carcinoma recurrence. In this example, based on the combination of markers ORM1+SERPINB1+CTBS+SELL+CD36+DCD+SFTPB+PLIN1 screened in Example 2, a three-classification detection model was further constructed to effectively distinguish the non-recurrence group (low-risk group), in situ recurrence group (in situ recurrence group), and metastatic group (metastatic group) of papillary thyroid carcinoma prognosis. All enrolled patients signed an informed consent form. Among them, all papillary thyroid carcinoma patients were diagnosed by pathological histology. Inclusion criteria: (a) No history of other malignancies; (b) No patients with other malignancies or autoimmune diseases combined.

[0145] In this example, LC-MS / MS data collection and detection were performed on the collected serum samples to obtain the concentrations of eight protein markers, namely ORM1, SERPINB1, CTBS, SELL, CD36, DCD, SFTPB, and PLIN1. The Shapiro Wilk test was used to evaluate the normal distribution, and the non-parametric Wilcoxon test was used to analyze the differences in blood marker concentrations between the non-recurrence group (low-risk group), in situ recurrence group (in situ recurrence group), and distant metastasis group (metastatic group) of papillary thyroid carcinoma prognosis. A three-classification combined diagnostic model of 8 markers was constructed by combining machine learning methods. The predicted probability value was used to estimate the area under the receiver operator characteristic (ROC) curve (AUC) with a 95% confidence interval (CI) to evaluate the discrimination ability of the multivariate diagnostic model. Using the test group, the Youden index (YI) was calculated to determine the predicted probability cut-off value for distinguishing the low-risk group, in situ recurrence group, and metastatic group. In addition, the ROCs of 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 R 3.6.1, and a p-value less than 0.05 was considered statistically significant.

[0146] In this embodiment, in order to construct an optimal three-class combined diagnostic model, after comparing the models constructed by six algorithms, namely 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 optimizing the hyperparameters of the gradient boosting method is shown in Table 7 below.

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

[0148]

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

[0150] Completely different two batches of samples were used for the test group and the validation group. In this embodiment, the screening of biomarkers and the construction of the model were only carried out in the test group; the samples of 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 for distinguishing three classes by the model constructed by the gradient boosting method

[0152]

[0153] It can be seen from Table 8 that the gradient boosting model constructed based on eight protein biomarkers, namely ORM1, SERPINB1, CTBS, SELL, CD36, DCD, SFTPB, and PLIN1, can be used to predict whether patients with papillary thyroid carcinoma will have no recurrence, in-situ recurrence, or metastasis after surgery. At the same time, it also shows that the protein biomarkers screened by the present invention can be used to distinguish the recurrence risk of patients with papillary thyroid carcinoma after surgical treatment, and can also be used to distinguish whether it is in-situ recurrence or metastasis when the recurrence risk is high (when there is both in-situ recurrence and metastasis, it is also classified into the metastasis group). However, the diagnostic efficacy for distinguishing the in-situ recurrence group and the metastasis group is not ideal.

[0154] II. Combined performance of the three-class combined diagnostic model

[0155] In order to further improve the diagnostic value of the three-class diagnostic model (gradient boosting) constructed by different protein combination biomarkers, in this embodiment, based on the 10 protein biomarkers screened in Example 1, the performance of the diagnostic models constructed by different protein combination biomarkers was compared in the test group. The specific combination forms of different models are shown in Table 9.

[0156] Table 9, Combinatorial forms of different diagnostic models

[0157]

[0158] The results are specifically as Figure 7 shown in Table 10. Table 10 shows the comparison results of performance indicators of different diagnostic models constructed by using the 10 biomarkers screened in Example 1 for three-class classification. The calculation methods for the minimum value, first quartile, median, mean, third quartile, and maximum value of accuracy and consistency are as follows: (1) Sort the values of accuracy or consistency from smallest to largest; (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 not, 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 the processing method is the same as Q1; (7) Maximum value: the last value after sorting. Among them, the minimum value and the maximum value can reflect the extreme situations of the data and show the worst and best performances that the model may exhibit; the quartiles can help understand the distribution range and dispersion degree of the data; below Q1 represents a lower performance level, and above Q3 represents a higher performance level; the median can reflect the performance at the middle level; the mean comprehensively reflects the overall average performance. By synthesizing the above statistical values, the overall situation, distribution characteristics, and stability of the model performance can be comprehensively understood, thus providing a strong basis for model selection and optimization.

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

[0160]

[0161] As can be seen from Table 10, for the three-classification diagnosis model, the nine-marker combined detection model (9MP) composed of nine markers has the best performance. This also clearly shows that on the basis of the eight protein markers of ORM1, SERPINB1, CTBS, SELL, CD36, DCD, SFTPB, and PLIN1, by adding one more marker, KRT19, the constructed 9MP model has a very obvious improvement in the diagnostic efficiency for distinguishing whether patients with papillary thyroid cancer will have no recurrence, in-situ recurrence, or metastasis after surgery. Therefore, the three-classification gradient boosting model constructed with these nine protein markers (ORM1 + SERPINB1 + CTBS + SELL + CD36 + DCD + SFTPB + PLIN1 + KRT19) is used as the best combined diagnostic model.

[0162] III. Determination and Verification of the Diagnostic Performance of the Three-Classification Combined Diagnostic Model

[0163] 1. Determination of the Diagnostic Performance of the Three-Classification Combined Diagnostic Model

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

[0165] Among them, the meanings and calculation methods of each index are as follows:

[0166] Calculation results: The accuracy of the three-classification combined diagnostic model in the test group is 0.78, and the consistency is 0.76. The diagnostic sensitivity for the low-risk group is 91.5%, and the specificity is 95.9%; the diagnostic sensitivity for the in-situ recurrence group is 77.8%, and the specificity is 76.3%; the diagnostic sensitivity for the metastasis group is 73.2%, and the specificity is 72.6%.

[0167] It should be noted that the three-classification combined diagnostic 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. Verification of the Three-Classification Combined Diagnostic Model

[0169] Based on the model constructed with the test group, the predictive performance is verified in the validation group. The specific results are as follows:

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

[0171] In summary, it can be seen that the three-class combined diagnostic model constructed in this embodiment, which includes 9 protein markers, has good diagnostic value for the three classifications of the low-risk group, the in-situ recurrence group, and the metastasis group.

[0172] All patents and publications mentioned in the specification of the present invention indicate that these are publicly known technologies in the art and can be used in the present invention. All patents and publications cited herein are equally listed in the references, the same as each publication is specifically individually referenced. The present invention described herein can be implemented in the absence of any one or more elements, one or more limitations, where such limitations are not specifically stated. For example, in each example herein, the terms "comprising", "consisting essentially of", and "consisting of" can be replaced by any one of the remaining two terms. The so-called "one" herein only means "one", and does not exclude including only one, nor does it exclude including more than two. The terms and expressions used herein are for the purpose of description and are not restrictive, and there is no intention to indicate that the terms and explanations described in this book exclude any equivalent features, but it can be understood that any appropriate changes or modifications can be made within the scope of the present invention and the claims. It can be understood that the embodiments described in the present invention are all preferred embodiments and features, and any person of ordinary skill in the art can make some changes and variations according to the essence described in the present invention, and these changes and variations are also considered to be within the scope of the present invention and the scope limited by the independent claims and the dependent claims.

Claims

1. Use of a marker for preparing a reagent for predicting the recurrence risk of papillary thyroid carcinoma, characterized in that, The biomarker includes any one or more of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB.

2. The use according to claim 1, characterized in that, The biomarker includes SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, and SERPINB1.

3. The use according to claim 2, characterized in that, The recurrence risk refers to recurrence within three years after the treatment of papillary thyroid carcinoma; the recurrence of papillary thyroid carcinoma includes in-situ or adjacent area recurrence of papillary thyroid carcinoma, and metastasis of papillary thyroid carcinoma.

4. The use according to claim 2, characterized in that, The reagent is used to detect the content of a biomarker in a body fluid sample; the body fluid sample includes any one or more of saliva, blood, urine, plasma, serum, and cerebrospinal fluid.

5. The use according to claim 2, characterized in that, The reagent is used to detect the presence, relative abundance, or concentration of a biomarker in a body fluid sample.

6. A kit for predicting the recurrence risk of papillary thyroid carcinoma, characterized in that, A detection reagent for a biomarker including the use according to any one of claims 1 to 5.

7. A biomarker combination for predicting the recurrence risk of papillary thyroid carcinoma, characterized in that, The combination includes SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, and SERPINB1.

8. A system for predicting the recurrence risk of papillary thyroid carcinoma, characterized in that, The system includes a data analysis module, and the data analysis module is used to analyze the detection value of a biomarker, and the biomarker includes any one or more of SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, SERPINB1, KRT19, and CRYAB.

9. The system according to claim 8, wherein The biomarker includes SFTPB, ORM1, CD36, DCD, SELL, PLIN1, CTBS, and SERPINB1; the recurrence risk refers to recurrence within three years after the treatment of papillary thyroid carcinoma; the recurrence of papillary thyroid carcinoma includes in-situ or adjacent area recurrence of papillary thyroid carcinoma, and metastasis of papillary thyroid carcinoma.

10. The system according to claim 9, wherein, The data analysis module uses the detection values of the biomarkers of known samples as a training set, and is divided into a papillary thyroid carcinoma recurrence group and a non-recurrence group of papillary thyroid carcinoma according to whether papillary thyroid carcinoma recurs, analyzes the relationship between the detection values of the papillary thyroid carcinoma recurrence group and the non-recurrence group of papillary thyroid carcinoma, and constructs a model. The equation of the model is: 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 (μg / mL) of the i-th biomarker, Ki represents the coefficient of the i-th biomarker, and b is a constant 2.6366832; the coefficients of the 8 biomarkers are: When Y ≤ 0.4823323, the recurrence risk of papillary thyroid carcinoma in the tested person is low; when Y > 0.4823323, the recurrence risk of papillary thyroid carcinoma in the tested person is high.

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