Diagnostic or prognostic test kit for thyroid cancer and use thereof

Abstract

The application provides a diagnostic or prognostic kit for thyroid cancer and application thereof, the kit comprises a peptide segment combination for detecting thyroid cancer autoantibodies, and the peptide segment is capable of specifically binding to at least one or two of blood autoantibodies PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1 and OBSCN. The kit provided by the application can effectively detect at least one antibody of PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1 and OBSCN, especially NUP153-2 and OBSCN-2, and the combined diagnosis has high diagnostic sensitivity and specificity. These autoantibodies are activated in the process of tumor occurrence and development, and may be produced before the appearance of clinical symptoms of tumor cells, so that the kit has great potential in the early screening of thyroid cancer.

Description

Technical Field

[0001] This invention belongs to the field of medical testing technology, specifically relating to a diagnostic or prognostic test kit for thyroid cancer and its application. Background Technology

[0002] Papillary thyroid carcinoma is a malignant tumor originating from the follicular epithelial cells of the thyroid gland. It has a papillary or follicular structure but exhibits invasive growth. It accounts for approximately 85% to 90% of all thyroid cancers (THCA) and is the most common pathological type of thyroid cancer.

[0003] Survey data shows that thyroid cancer has become the fastest-growing malignant tumor. Since 2000, among all types of cancer, thyroid cancer has ranked first in terms of incidence rate, with all types of thyroid cancer showing an increasing trend. In particular, the incidence rate among women in large cities is nearly three times that of men. According to surveys, the incidence rate of thyroid cancer among urban women ranks fourth among all malignant tumors in women, making it the fastest-growing solid tumor in women, and its harm should not be underestimated.

[0004] Current clinical tests for the thyroid gland include:

[0005] 1) Thyroid hormone testing: T4, T3, FT4, FT3, and TSH are important initial screening tests for determining thyroid function (Recommendation Grade A). However, they are not effective in differentiating between benign and malignant thyroid nodules.

[0006] 2) Thyroid autoantibody detection: TgAb, TPOAb, and TRAb can help diagnose Hashimoto's thyroiditis and atrophic thyroiditis, but their diagnostic efficacy for thyroid cancer is low.

[0007] 3) Thyroid cancer tumor marker detection: Tg, CEA. Tg is a specific protein produced by the thyroid gland, but serum Tg measurement lacks specific value in differentiating between benign and malignant thyroid nodules.

[0008] 4) The diagnosis of THCA mainly relies on pathological diagnosis and ultrasound examination. In pathological diagnosis, fecal non-absorbable biopsy (FNAB) and non-absorbable biopsy (CNB) can be used as the current preoperative diagnostic criteria for thyroid nodules. FNAB is simple to perform and minimally invasive, but its diagnostic accuracy is limited. A high false-negative rate can occur due to insufficient sample collection or atypical cytological characteristics. It is highly dependent on the experience of the operator and pathologist and carries the risk of missed detection. Ultrasound examination, as the preferred method, can help locate the position and size of thyroid masses, but it cannot determine whether a tumor is benign or malignant.

[0009] Given the limitations of existing diagnostic methods and biomarkers, the development of new, more specific, and non-invasive diagnostic biomarkers is of great significance for the early diagnosis and precision treatment of thyroid cancer. Summary of the Invention

[0010] Purpose of the invention

[0011] To address the problems existing in the above-mentioned prior art methods, this invention aims to provide a diagnostic or prognostic test kit for thyroid cancer and its application. The test kit provided by this invention can effectively detect at least one antibody among PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1, and OBSCN, especially the autoantibodies detected by NUP153-2 and OBSCN-2. Combined diagnosis exhibits high diagnostic sensitivity and specificity. These autoantibodies are activated during tumor occurrence and development and may be produced before tumor cells show clinical symptoms, making them of great potential in early thyroid cancer screening.

[0012] Solution

[0013] To achieve the above objectives, the present invention provides the following technical solution:

[0014] In a first aspect, the present invention provides a diagnostic or prognostic test kit for thyroid cancer, comprising a combination of peptides for detecting thyroid cancer autoantibodies, wherein the peptides are peptides that can specifically bind to at least one or two of the blood autoantibodies PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1, and OBSCN.

[0015] Furthermore, the peptide is selected from one, two, or more peptides in the amino acid sequences shown in SEQ ID NO:1 to 42.

[0016] Furthermore, the peptide segment includes one, two, or more of the following peptide segments I) to VII);

[0017] I) A peptide that can specifically bind to antibody ASCC1, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:1-5, 36;

[0018] II) A peptide that can specifically bind to antibody PRKD2, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:6-10, 37;

[0019] III) A peptide that can specifically bind to antibody LAMA3, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:11-15, 38;

[0020] IV) A peptide that can specifically bind to antibody TNIP1, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:16-20, 39;

[0021] V) A peptide that can specifically bind to the antibody OBSCN, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:21-25, 40;

[0022] VI) A peptide that can specifically bind to antibody NUP153, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:26-30, 41;

[0023] VII) A peptide that can specifically bind to the antibody MST1R, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:31-35, 42;

[0024] Optionally, the peptide combination includes the peptides in V) and VI) above;

[0025] Optionally, the peptide combination comprises peptides with amino acid sequences as shown in SEQ ID NO:22, 27.

[0026] Furthermore, it also includes a secondary antibody that specifically binds to blood autoantibodies, the secondary antibody being selected from at least one of IgA, IgM, and IgG.

[0027] Furthermore, it also includes substrates, washing solutions, and blocking solutions for ELISA detection.

[0028] Furthermore, the test sample is blood or serum.

[0029] In a second aspect, an analytical system for the diagnosis or prognosis of thyroid cancer is provided, wherein the input features are selected from the detection kit described in the first aspect to detect the expression level of at least one of the autoantibodies PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1, and OBSCN in the blood.

[0030] Furthermore, the expression level of autoantibodies was determined by detecting the expression levels of the following peptides:

[0031] The expression level of the antibody ASCC1 was detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:1-5, 36.

[0032] And / or, the expression level of the antibody PRKD2 is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:6-10, 37;

[0033] And / or, the expression level of the antibody LAMA3 is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:11-15, 38;

[0034] And / or, the expression level of the antibody TNIP1 is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:16-20, 39;

[0035] And / or, the antibody OBSCN expression level is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:21-25, 40;

[0036] And / or, the expression level of the antibody NUP153 is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:26-30, 41;

[0037] And / or, the expression level of the antibody MST1R is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:31-35, 42.

[0038] Furthermore, the data analysis module stores a prediction model for outputting the probability value of thyroid cancer. The method for constructing the prediction model includes: using the expression level of autoantibodies detected in the peptides in thyroid cancer and healthy samples as input variables, and whether it is thyroid cancer as the dependent variable, obtaining the prediction model through random forest analysis, and testing it through a validation set.

[0039] Thirdly, the invention provides the use of the combination of diagnostic or prognostic biomarkers for thyroid cancer described in the first aspect in the preparation of a kit or analytical system for the diagnosis, efficacy or prognostic assessment, or evaluation of related drugs for thyroid cancer.

[0040] Fourthly, the invention provides the application of a kit or analytical system for preparing a combination of diagnostic or prognostic biomarkers for thyroid cancer described in the first aspect in serum or plasma for the detection of the relative expression levels of such a combination in serum or plasma in the preparation of a kit for the diagnosis, efficacy or prognostic assessment, or evaluation of related drugs for thyroid cancer.

[0041] Beneficial effects

[0042] The detection kit provided by this invention can effectively detect at least one antibody among PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1, and OBSCN, especially the autoantibodies detected by NUP153-2 and OBSCN-2. The combined diagnosis has high diagnostic sensitivity and specificity. These autoantibodies are activated during the occurrence and development of tumors and may be produced before the appearance of clinical symptoms in tumor cells, making them of great potential in the screening of early thyroid cancer. Attached Figure Description

[0043] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative examples are not intended to limit the embodiments. Here, the specific term "illustrative" means "serving as an example, embodiment, or illustration." Any embodiment illustrated as "illustrative" is not necessarily to be construed as superior to or better than other embodiments.

[0044] Figure 1 This is a schematic diagram of the process for verifying PTC serum autoantibody markers using ELISA in Example 1.

[0045] Figure 2 The quality control chart established in Example 1 is shown below; where A: Q30 = 93.8%, Q20 = 97.31%, average base sequencing error rate = 0.01%, B: PCA analysis of disease group and healthy group, Case is disease group and Control is healthy group.

[0046] Figure 3 This describes the survival of thyroid cancer patients at different stages in Example 1.

[0047] Figure 4 This is a schematic diagram of one embodiment of the peptide cleavage method in Example 2.

[0048] Figure 5 This is a box plot of expression of each molecular peptide in Example 2 in the healthy nodule-free group, healthy nodule group, and thyroid cancer group, where stage I is the thyroid cancer group, stage II is the healthy nodule group, and stage III is the healthy nodule-free group; Figure A represents PRKD2-1, PRKD2-2, PRKD2-3, PRKD2-4, and PRKD2-5; Figure B represents MST1R-1, MST1R-2, MST1R-3, MST1R-4, and MST1R-5; and Figure C represents NUP153-1, NUP153-2, and NUP153-3. The diagrams are: NUP153-4, NUP153-5; D represents OBSCN-1, OBSCN-2, OBSCN-3, OBSCN-4, OBSCN-5; E represents TNIP1-1, TNIP1-2, TNIP1-3, TNIP1-4, TNIP1-5; F represents LAMA3-1, LAMA3-2, LAMA3-3, LAMA3-4, LAMA3-5; G represents ASCC1-1, ASCC1-2, ASCC1-3, ASCC1-4, ASCC1-5; and H represents cluster analysis.

[0049] Figure 6This is a box plot of the expression of small molecule peptides OBSCN-2 and NUP153-2 with different secondary antibodies (rabbit anti-human IgG, mouse anti-human IgM121, and mouse anti-human IgA) in the healthy group (a healthy group combining nodule-free and nodule-free groups) and the disease group in Example 2. Figure A shows the expression box plot of OBSCN-2 and NUP153-2 with different secondary antibodies in the healthy normal group (healthy nodule-free group + healthy nodule-containing group) and the disease tumor group (thyroid cancer group); Figure B shows the expression box plot of OBSCN-2 and NUP153-2 with different secondary antibodies in the healthy normal group (healthy nodule-free group + healthy nodule-containing group) and the disease tumor group (thyroid cancer group); Box plots of OBSCN-2 and different secondary antibodies in the healthy NC group (healthy nodule-free group) and TNS group (healthy nodule-containing group); C is the box plot of OBSCN-2 and NUP153-2 and different secondary antibodies in the healthy NC group (healthy nodule-free group) and PCT group (thyroid cancer group, also known as the Tumor group); D is the box plot of OBSCN-2 and NUP153-2 and different secondary antibodies in the healthy PCT group (thyroid cancer group, also known as the Tumor group) and TNS group (healthy nodule-containing group).

[0050] Figure 7 The table shows the ROC curves for the entire amino acid polypeptide chain ASCC1, PRKD2, LAMA3, TNIP1, OBSCN, NUP153, MST1R and individual peptide segments in Example 2. In this table, A represents the ROC curve for the entire amino acid polypeptide chain ASCC1, PRKD2, LAMA3, TNIP1, OBSCN, NUP153, and MST1R; B represents the ROC curve for ASCC1-1, ASCC1-2, ASCC1-3, ASCC1-4, and ASCC1-5; C represents the ROC curve for PRKD2-1, PRKD2-2, PRKD2-3, PRKD2-4, and PRKD2-5; and D represents the ROC curve for LAMA3-1, LAM... ROC curves for A3-2, LAMA3-3, LAMA3-4, and LAMA3-5; E represents the ROC curves for TNIP1-1, TNIP1-2, TNIP1-3, TNIP1-4, and TNIP1-5; F represents the ROC curves for OBSCN-1, OBSCN-2, OBSCN-3, OBSCN-4, and OBSCN-5; G represents the ROC curves for NUP153-1, NUP153-2, NUP153-3, NUP153-4, and NUP153-5; H represents the ROC curves for MST1R-1, MST1R-2, MST1R-3, MST1R-4, and MST1R-5; I represents the HR plot.

[0051] Figure 8 The image shows the ROC curves of the two peptides OBSCN-2 and NUP153-2 from Example 3 binding to different secondary antibodies.

[0052] Figure 9 Here is the 3DPCA diagram for Example 3; where A is the 3DPCA diagram for OBSCN-2, B is the 3DPCA diagram for NUP153-2, and C is the combined 3DPCA diagram for OBSCN-2 and NUP153-2. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprising of," etc., will be understood to include the stated elements or components, and does not exclude other elements or other components.

[0054] Furthermore, to better illustrate the present invention, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that the present invention can be practiced without certain specific details. In some embodiments, materials, elements, methods, and means well known to those skilled in the art are not described in detail in order to highlight the spirit of the invention.

[0055] Unless otherwise specified, the reagents, kits, raw materials, and equipment used in the following examples can all be purchased commercially. Unless otherwise specified, the experimental or detection methods involved in this invention are conventional experimental or detection methods in the art, or can be performed with reference to the corresponding kits or product instructions.

[0056] Tumor autoantibodies are antibodies formed in response to the body's immune response to tumor-associated antigens (TAAs). These autoantibodies are activated during tumor development and progression, and may be produced before tumor cells show clinical symptoms. Antinuclear antibodies (ANA) are a group of autoantibodies that target various nuclear components of eukaryotic cells as antigens. They are primarily IgG, but are also found in other immunoglobulins such as IgM and IgA. It is important to note that ANA is a type of autoantibody.

[0057] A significant advantage of tumor autoantibodies is that they can be detected months or even years before the onset of clinical symptoms. Despite their immense potential in tumor diagnosis, accurately identifying autoantibodies that can effectively diagnose thyroid cancer remains a significant challenge.

[0058] This invention uses a rational experimental design and method to explore unknown thyroid cancer (especially papillary thyroid carcinoma) specific autoantibodies, so as to provide suitable biomarkers for the diagnosis or prognosis of thyroid cancer.

[0059] serum samples

[0060] Serum samples used in the experiment were collected from individuals undergoing health checkups and those newly diagnosed with thyroid cancer at Tangshan Workers' Hospital between May 2023 and June 2024. A total of 200 serum samples were collected in this experiment, including 100 healthy control samples (HC) and 100 samples from papillary thyroid carcinoma (PTC).

[0061] Inclusion criteria: Papillary thyroid carcinoma according to the 2022 guidelines for the diagnosis and treatment of thyroid cancer; serum samples from individuals who had not received medication (3000 rpm, 5 min). The healthy control group consisted of individuals who underwent comprehensive health assessments during health checkups. Serum samples were transported and aliquoted on ice after collection and stored at -80°C for later use.

[0062] Example 1: Determination of PTC serum autoantibody markers using PhIP-seq

[0063] In order to find these unknown papillary thyroid carcinoma-specific autoantibodies, this invention uses PHIP-seq technology to explore unknown papillary thyroid carcinoma-specific autoantibodies.

[0064] PhIP-Seq principles and processes are as follows: Figure 1 PHIP-seq technology constructs peptide libraries using phage display, containing peptide sequences corresponding to proteins or proteomes from various sources (such as humans, mice, bacteria, and viruses). These peptide sequences are reverse-translated into DNA sequences, and after codon optimization and high-throughput synthesis, a phage-displayed peptide library is finally constructed. When samples containing antibodies (such as serum or plasma) are co-incubated with the peptide library, the antibodies specifically bind to the peptides, forming antibody-antigen (phage) complexes. These complexes are enriched through immunoprecipitation, and then subjected to high-throughput sequencing and sequence analysis to obtain the set of peptide sequences recognized by the antibodies in the sample, thereby analyzing the information of the protein antigens.

[0065] Experimental methods:

[0066] The study included a thyroid cancer experimental group (10 samples), a raw library group (a phage display peptide library using PHIP-seq technology, sourced from Shanghai Kangmaxinrui Biotechnology Co., Ltd.), and a healthy control group (10 samples as negative controls). Serum was diluted to 0.2 μg / μL in 96-cell agar plates. An equal volume of phage dilution was added to the serum, and the plates were incubated overnight at 4°C. After incubation, Protein G magnetic beads were added, and the plates were incubated at 4°C for 4 hours. After incubation, the plates were washed three times with a washing buffer, and finally, the magnetic beads were resuspended in sterile water. Sequencing libraries were constructed and high-throughput sequencing was performed.

[0067] Sequencing results analysis:

[0068] 1) Sample Quantitative Results Analysis: First, the sequencing volume of different samples was normalized (the sequencing volume of each sample was proportionally adjusted to 1.25M reads) to obtain the normalized reads for each peptide in each sample. Based on the initial library sequencing abundance of each peptide, i.e., the number of input reads, the signal EF (Enrichment Factor), i.e., Normalized reads / Input reads, was further calculated for different peptides in each sample. Finally, the p-value of each peptide was calculated according to the generalized Poisson distribution model, and a threshold was set based on empty beads. The enriched peptides of each sample were screened according to the set threshold criteria (Normalized reads>15, EF>5, -log(p-value)>2.071).

[0069] 2) Cohort Differential Analysis: The enriched peptides of samples from the corresponding disease cohorts were merged, and the positive peptide data of all samples were separated for cohort analysis. Normalized reads were used as the signal for cohort analysis.

[0070] First, statistical analysis was performed on each peptide, calculating the ratio of the average signal in the disease cohort to the average signal in the control cohort. A t-test was then used to calculate the p-value for statistical analysis, with thresholds set at Fold change > 2 and p-value < 0.05, identifying peptides statistically significant in the disease cohort relative to the control cohort. To eliminate false positives from a single statistical analysis, the positive rates of different peptides in specific cohorts were further screened. First, a threshold was set for each peptide: Cutoff = (mean of the same gene in the control cohort + 1 standard deviation) or NR = the maximum value of 15. The positive rate of samples exceeding the Cutoff in each cohort was calculated. The final positive rate screening criteria were: positive rate >= 30% in the disease cohort and <= 20% in the control cohort, identifying enriched peptides with significant differences in the disease cohort.

[0071] To analyze differences between cohorts at the gene level, peptides that meet the requirement of a single peptide Fold change >= 2 are matched with gene information. Enriched peptides that match the same gene multiple times (>= 2 times) are screened out. The signals of these peptides are averaged on a gene-by-gene basis. Using screening criteria similar to those for differentially expressed peptides (Fold change > 2, p-value < 0.05, Cutoff = (mean of the same gene in the control cohort + 3 times the standard deviation) or the maximum value of NR = 15, positive rate in the disease cohort >= 30%, and in the control cohort <= 20%), genes with significant differences in the disease cohort are obtained.

[0072] 3) Differential analysis of experimental groups

[0073] Differential peptide analysis: For the disease cohort experimental group, the control group served as the control cohort. Based on statistical analysis, enriched peptides showing significant differences in the experimental group were obtained. Further calculation of the positive rate of the disease cohort experimental group resulted in the exclusion of peptides with a positive rate ≥30% in the experimental group and <20% in the control group as target peptides showing significant differences in the disease group. A total of 1098 differentially expressed peptides were obtained. Based on the relationship between the positive rates of the disease and control cohorts, the differentially expressed target peptides were further classified, including those with a positive rate ≥50% in the experimental group and <10% in the control group. Further analysis of these differentially expressed peptides was designated as priority I, while the analysis of the remaining differentially expressed peptides was designated as priority II.

[0074] Quality control establishment

[0075] Sequencing Quality Control: Raw data obtained from the Illumina sequencing platform showed statistically significant differences between the disease group and the healthy group in the PhIP-seq database (P < 0.001), with Q30 = 93.8% and Q20 = 97.31%. The PhIP-seq database can significantly distinguish between the disease group and the healthy group. Quality score distribution along reads is an important indicator for evaluating sequencing data quality. It is an integer mapping of the probability of an error in base calling during sequencing. It reflects the sequencer's confidence in the recognition result of a particular base. A higher Q-score indicates higher reliability of the base recognition, meaning a lower probability of error. Specifically, Q30 represents a base accuracy of 99.9%, meaning that only about 1 base out of every 1000 bases might be misidentified. Results are as follows... Figure 2 .

[0076] Figure 2The results indicate that by analyzing the distribution of the Quality Score, which is around 40, we can determine that the overall quality of the sequencing data is very high. Figure 2 B indicates that PCA analysis of the disease and healthy groups showed that the PhIP-seq database could significantly distinguish between the disease and healthy groups. In the disease group, ID1-ID10 (sample numbers) were concentrated between -500 and 0, and ID10, representing medullary thyroid carcinoma, was significantly different from papillary thyroid carcinoma in the experimental group. This demonstrates that PhIP-seq technology can not only significantly distinguish between the healthy and disease groups, but also differentiate between different types of thyroid cancer within the cancer group.

[0077] PhIP-seq results

[0078] By calculating the positive rate of the samples in the disease cohort experimental group, peptides with a positive rate ≥30% in the disease cohort experimental group and a positive rate ≤20% in the healthy control group were selected as target peptides with significant differences in the disease group. A total of 1098 significantly different peptides were obtained. Compared with the healthy control group, the serum peptide expression levels in the disease experimental group were significantly increased. The differentially expressed peptides were sorted in descending order of fold change, while ensuring that a p-value ≤0.05 was statistically significant. See Table 1 (showing 1-48 and 1097-1098; intermediate peptides are folded).

[0079] Table 1. Statistical table of differentially expressed peptides

[0080]

[0081]

[0082] Note: Fold change: For each peptide, the average NR of the disease cohort / the average NR of the healthy control cohort; p-value: For each peptide, the p-value of the disease cohort signal (NR) and the control cohort signal (NR) after t-test; Cutoff: For each peptide, the threshold for determining the number of positive samples in each cohort. Showing 1-48 and 1097-1098, the intermediate peptide segments are folded.

[0083] Differential analysis of PhIP-seq results

[0084] Further analysis of the positivity rates between the disease and control cohorts revealed 1098 differentially expressed peptides, categorized into two groups. The first group, with a positivity rate ≥50% in the disease cohort and <10% in the control cohort, was designated as Priority I, while the remaining differentially expressed peptides were classified as Priority II. Priority I consisted of 50 amino acid peptides, analyzed using volcano plots, cluster heatmaps, and radar charts. Ultimately, the amino acid peptides PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1, and OBSCN were identified as having significantly higher COR values ​​than other peptides, indicating that these peptides performed exceptionally well across multiple dimensions of comparison, demonstrating significant advantages or high performance.

[0085] Peptides with significant specificity (PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1, and OBSCN) were selected for further research. The sequence information of each peptide chain is as follows:

[0086]

[0087] Survival analysis of PhIP-seq results

[0088] Survival of thyroid cancer patients at different stages, such as Figure 3 As shown, the results indicate that patient survival rates gradually decrease as the disease progresses, with a particularly significant decline in survival rates for patients in advanced stages. Therefore, improving early diagnosis rates and implementing effective treatment measures are crucial for improving the prognosis of thyroid cancer patients.

[0089] Example 2: Validation of PTC serum autoantibody markers using ELISA

[0090] The selected amino acid peptides are cut according to the PhIP-seq detection principle of 20%–70% (e.g., 50%), with each 20 amino acid segment forming a small molecule peptide, which serves as a self-made antigen. The test sample (such as serum, plasma, etc.) containing the antibody to be tested is added to a solid-phase carrier (such as a polystyrene microplate) coated with the self-made antigen. The antibody to be tested in the sample specifically binds to the self-made antigen. The secondary antibody obtained from screening (a specific antibody against the antibody to be tested) is added to form a solid-phase antigen-antibody-secondary antibody complex. The antibody is then detected by the indirect ELISA method.

[0091] 1) Preliminary experiment

[0092] homemade antigen

[0093] The seven peptides with significant differences in the first seven positions (ASCC1, PRKD2, LAMA3, TNIP1, OBSCN, NUP153, MST1R) were cleaved according to the 20-70% (e.g., 50%) PhIP-seq detection principle, resulting in small peptides of 20 amino acids each, which served as the self-made antigen. The principle of the cleavage method is as follows: Figure 4 In actual design or synthesis, slight adjustments may be made, such as 5 to 14 amino acids overlapping at the beginning and end of two adjacent amino acid peptide segments.

[0094] The cleaved small peptides are named sequentially as follows: ASCC1-1, ASCC1-2, ASCC1-3, ASCC1-4, ASCC1-5, PRKD2-1, PRKD2-2, PRKD2-3, PRKD2-4, PRKD2-5, LAMA3-1, LAMA3-2, LAMA3-3, LAMA3-4, LAMA3-5, TNIP1-1, TNIP1-2, TNIP1-3 A total of 35 peptides, namely TNIP1-4, TNIP1-5, OBSCN-1, OBSCN-2, OBSCN-3, OBSCN-4, OBSCN-5, NUP153-1, NUP153-2, NUP153-3, NUP153-4, NUP153-5, MST1R-1, MST1R-2, MST1R-3, MST1R-4, and MST1R-5, constitute 35 self-made antigens. The sequences of each peptide (commissioned by Jier Biochemical (Shanghai) Co., Ltd.) are as follows:

[0095]

[0096]

[0097] 2) Screening for secondary antibodies

[0098] Formulation preparation

[0099] Peptide stock solutions: Dissolve 4 mg of each peptide in 200 μL LDMSO (stock solutions were prepared separately for each of the 35 peptides mentioned above). After complete dissolution, add 800 μL of ultrapure water. Stock solution concentration: 4 mg / mL = 4 μg / μL.

[0100] 50× washing solution includes: 308.8g Tris, 298g NaCl, 112.4mL TWEEN20, 188.8mL HCl (31%), and water to a final volume of 2L; dilute 50 times with water to obtain 1× washing solution.

[0101] The 10× blocking solution consists of: 2g KH₂PO₄, 11.48g anhydrous NaHPO₄, 0.4g KCl, 16g NaCl, 20g Gfs fish collagen, and 1.88mL TWEEN 20. ProClin preservative (1mL / L) may also be added. Filter through a 0.22mm microporous membrane.

[0102] The experimental methods include:

[0103] Dilute the homemade antigen: Take 6 uL of the stock solution and add it to 12 mL of 1×PBS.

[0104] Plate washing: PW-960HUISONG plate washing machine, 150uL / well, 1× washing solution, three times.

[0105] Sealing: Add 200 μL of 1× sealing solution to each well and incubate in an OLABO thermostatic incubator at 37°C for 2 hours.

[0106] Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0107] Sample addition: Dilute three serum samples from the same group into 50 mL blocking buffer, 100 μL per well. Plate washing: Wash three times with 1× washing buffer using a PW-960HUISONG plate washer at 150 μL / well.

[0108] Secondary antibody screening: Add 100uL of secondary antibody to each well and serially dilute (200uL - 100uL mixed with 100uL blocking buffer - 100uL mixed with 100uL blocking buffer, and so on, with a blank control in the last well). Incubate in an OLABO thermostatic incubator at 37℃ for 1 hour.

[0109] Table 2. Serial dilutions of secondary antibodies

[0110]

[0111] Plate washing: PW-960HUISONG plate washing machine, 150uL / well, 1× washing solution, three times.

[0112] Color development: Add TMB color development solution, 100 μL / well, and develop for 15 min in the dark.

[0113] Termination: Add stop solution, 100 μL / well (stop solution consists of 14.28 mL concentrated H2SO4 dissolved in 500 mL pure water).

[0114] Result determination: HSIMM software reading.

[0115] The results of small molecule peptide antigens, serum, and different secondary antibodies (human IgG, mouse anti-human IgG-4-7, mouse anti-human IgG3-23) are as follows: Figure 5 The results showed that the expression levels of antibodies binding to small molecule peptides were significantly increased in the disease group compared to the healthy group (including the nodule-free and nodule-containing healthy groups), and the antibodies also exhibited better classification performance. This demonstrates that all the secondary antibodies we used were effective. It also indirectly indicates that the specific peptides screened in this experiment are effective and reliable.

[0116] Meanwhile, in the disease group, the first three columns showed rabbit anti-human IgG, mouse anti-human IgG-4-7, and mouse anti-human IgG-3-23, with rabbit anti-human IgG being the most significant. Among the first four and fifth columns of mouse anti-human IgM121 and mouse anti-human IgM231 in the disease group, mouse anti-human IgM121 was the most significant. Mouse anti-human IgA, in the last column, was also highly effective.

[0117] The vertical axis represents the dilution levels of the secondary antibody (1 represents 500 ng / ml, and the numbers are numbered sequentially downwards according to the serial dilution). The most effective one is in the first row, with an effective concentration of 500 ng / mL.

[0118] In summary, three effective antibodies were selected: rabbit anti-human IgG, mouse anti-human IgM121, and mouse anti-human IgA, with an effective concentration of 500 ng / mL.

[0119] The classification results of OBSCN-2, NUP153-2 with different secondary antibodies (rabbit anti-human IgG, mouse anti-human IgM121, mouse anti-human IgA) are as follows: Figure 6 The results showed that the disease group and the healthy group could be well distinguished.

[0120] Preliminary screening

[0121] 1) Dilution: Take 2.5 μL of stock solution and add 6 mL of 1X The PBS contains 35 peptides: ASCC1-1, ASCC1-2, ASCC1-3, ASCC1-4, ASCC1-5, PRKD2-1, PRKD2-2, PRKD2-3, PRKD2-4, PRKD2-5, LAMA3-1, LAMA3-2, LAMA3-3, LAMA3-4, LAMA3-5, TNIP1-1, TNIP1-2, TNIP1-3, TNIP1-4, TNIP1-5, OBSCN-1, OBSCN-2, OBSCN-3, OBSCN-4, OBSCN-5, NUP153-1, NUP153-2, NUP153-3, NUP153-4, NUP153-5, MST1R-1, MST1R-2, MST1R-3, MST1R-4, and MST1R-5.

[0122] 2) Plating: The above 35 peptide segments were plated in 96-well plates, 100 μL per well, and incubated overnight (16 h) at 4°C.

[0123] 3) Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0124] 4) Blocking: Add 200uL of blocking solution to each well and incubate in an OLABO thermostatic incubator at 37℃ for 2 hours.

[0125] 5) Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0126] 6) Sample addition: 100 μL of serum per well, incubated in an OLABO thermostatic incubator at 37°C for 1 hour (4 thyroid cancer serum samples, 3 healthy serum samples, and 1 blank sample).

[0127] 7) Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0128] 8) Secondary antibody: Add 100uL of secondary antibody to each well. (Select three antibodies: rabbit anti-human IgG, mouse anti-human IgM121, and mouse anti-human IgA. Dilute the blocking buffer to a concentration of 500ng / mL.) Incubate in an OLABO thermostatic incubator at 37℃ for 1 hour.

[0129] 9) Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0130] 10) Color development: 100 μL of TMB color development solution per well, develop for 15 min in the dark.

[0131] 11) Termination: 100 μL of termination solution per well.

[0132] 12) Result determination: HSIMM software readings.

[0133] Based on the above detection results, a comprehensive analysis of the entire amino acid polypeptide chain (ASCC1, PRKD2, LAMA3, TNIP1, OBSCN, NUP153, MST1R) was performed. ROC curves were constructed and their AUC values ​​were calculated. The results are as follows: Figure 7 A. The peptides were sorted from largest to smallest AUC as follows: MST1R, OBSCN, NUP153, LAMA3, TNIP1, PRKD2. ASCCA, with an AUC of 0.447 (<0.5), was excluded. ROC curves for 35 small peptides were then plotted, and the results are as follows... Figure 7 B~H.

[0134] Then, the 35 small peptides cut into groups of 5 were analyzed one by one, and HR values ​​of genes such as PRKD2, LAMA3, ASCC1, NUP153, TNIP1, and OBSCN were generated using a random forest model. The results are as follows: Figure 7 The results showed that the hazard ratios (HRs) for genes such as PRKD2, LAMA3, ASCC1, NUP153, TNIP1, and OBSCN were all greater than 1, with p values ​​less than 0.05, suggesting that variations in these genes may increase the risk of death in patients. Among them, NUP153 and OBSCN had the highest HR values, reaching 1.622 and 1.685, respectively, indicating that their variations had the most significant impact on survival.

[0135] In summary, from ASCC1-1, ASCC1-2, ASCC1-3, ASCC1-4, ASCC1-5, PRKD2-1, PRKD2-2, PRKD2-3, PRKD2-4, PRKD2-5, LAMA3-1, LAMA3-2, LAMA3-3, LAMA3-4, LAMA3-5, TNIP1-1, TNIP1-2, TNIP1-3, TNIP1-4, TNIP1-5, OBSCN-1, OBSCN-2, OBSCN-3, OB Among a total of 35 peptides, including SCN-4, OBSCN-5, NUP153-1, NUP153-2, NUP153-3, NUP153-4, NUP153-5, MST1R-1, MST1R-2, MST1R-3, MST1R-4, and MST1R-5, differential expression was performed using 4 thyroid cancer serum samples, 3 healthy samples, and 1 blank sample. At the same time, three antibodies, IgA, IgM, and IgG, were used to select two high-quality peptides, OBSCN-2 and NUP153-2, which can significantly distinguish thyroid cancer patients from healthy individuals.

[0136] Example 3: Verifying the diagnostic efficacy of the peptide

[0137] Small molecule peptide antigens were coated onto plates: 100 μL of OBSCN-2 and NUP153-2 peptides were coated onto 96-well plates and incubated overnight (16 h) at 4 °C.

[0138] Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0139] Sealing: Add 200 μL of sealing solution to each well and incubate in an OLABO thermostatic incubator at 37°C for 2 hours.

[0140] Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0141] Sample loading: 15 μL of serum was diluted in 1500 μL of blocking buffer, 100 μL per well, and incubated in an OLABO thermostatic incubator at 37°C for 1 hour. The serum samples included: 94 thyroid cancer serum samples, 2 blank samples, 94 healthy serum samples, 1 blank sample, and 1 medullary thyroid carcinoma sample.

[0142] Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0143] Secondary antibody: Add 100 μL of secondary antibody to each well. (Select three antibodies: rabbit anti-human IgG, mouse anti-human IgM121, and mouse anti-human IgA. Dilute the blocking buffer to a concentration of 500 ng / mL and a volume of 90 mL.) Incubate in an OLABO thermostatic incubator at 37°C for 1 h.

[0144] Plate washing: PW-960HUISONG plate washing machine, 150uL / hole, three washes.

[0145] Color development: 100 μL of TMB colorimetric solution per well, develop for 15 min in the dark.

[0146] Termination: 100 μL of termination solution per well (14.28 mL of concentrated H2SO4 dissolved in 500 mL of pure water).

[0147] Result determination: HSIMM software reading.

[0148] Create and analyze ROC curves.

[0149] Verify the independent AUC results of the peptides.

[0150] Independent AUC results for validation peptides

[0151]

[0152] The OBSCN-2 and NUP153-2 peptides were validated using a large-scale sample study involving 94 serum samples from thyroid cancer patients, 2 control samples, 94 healthy individuals, 1 control sample, and 1 medullary carcinoma patient. ROC curve analysis was used to analyze the individual AUC values ​​of the OBSCN-2 and NUP153-2 peptides against three antibodies: IgA, IgM, and IgG. The results are as follows: Figure 8 The results showed that the AUC value of the independent diagnostic efficacy of OBSCN-2 with IgG antibody was 0.883, and the AUC value of the independent diagnostic efficacy of NUP153-2 with IgM antibody was 0.790. The AUC value of the combined diagnostic efficacy of OBSCN-2 and NUP153-2 with different secondary antibodies (IgA, IgG, IgM) reached 0.91, with sensitivity and specificity of 0.801 and 0.982, respectively. All demonstrated good diagnostic performance.

[0153] And 3DPCA (three-dimensional principal component analysis) was performed, and the results are as follows: Figure 9 3DPCA maps high-dimensional data to three-dimensional space through linear transformation, effectively reducing the dimensionality of the data. This process not only simplifies data processing but also retains most of the important information in the data, making subsequent analysis more efficient. Based on the human eye's perception of three-dimensional space, it was found that elevated disease groups in OBSCN-2 were relatively dispersed and located downstream of PC1 (e.g., Figure 9 A); The healthy control group was relatively concentrated and located upstream of PC1, and the disease group and healthy group could be clearly separated overall. In NUP153-2, the disease group was clearly located in the upper part of PC3, while the healthy control group was located in the lower part of PC3, and the disease group and healthy group could be clearly separated (e.g. Figure 9 B). Joint analysis of OBSCN-2 and NUP153-2 was performed. First, the data from both groups were normalized, removing the top five maximum and minimum values ​​from each group, and removing the baseline blank control. Joint 3DPCA analysis yielded better results: the disease group was more dispersed and located downstream of PC1, while the healthy control group was more concentrated and located upstream of PC1. Overall, the disease and healthy groups could be clearly separated (e.g., Figure 9 C).

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A diagnostic or prognostic test kit for thyroid cancer, characterized in that, It includes a combination of peptides for detecting thyroid cancer autoantibodies, wherein the peptides are peptides that can specifically bind to at least one or two of the blood autoantibodies PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1, and OBSCN.

2. The detection kit according to claim 1, characterized in that, The peptide is selected from one, two or more peptides in the amino acid sequences shown in SEQ ID NO:1 to 42.

3. The detection kit according to claim 1, characterized in that, The peptide segment includes one, two or more of the following peptide segments I) to VII); I) A peptide that can specifically bind to antibody ASCC1, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:1-5, 36; II) A peptide that can specifically bind to antibody PRKD2, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:6-10, 37; III) A peptide that can specifically bind to antibody LAMA3, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:11-15, 38; IV) A peptide that can specifically bind to antibody TNIP1, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:16-20, 39; V) A peptide that can specifically bind to the antibody OBSCN, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:21-25, 40; VI) A peptide that can specifically bind to antibody NUP153, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:26-30, 41; VII) A peptide that can specifically bind to the antibody MST1R, optionally selected from at least one of the amino acid sequences shown in SEQ ID NO:31-35, 42; Optionally, the peptide combination includes the peptides in V) and VI) above; Optionally, the peptide combination comprises peptides with amino acid sequences as shown in SEQ ID NO:22, 27.

4. The detection kit according to any one of claims 1 to 3, characterized in that, It also includes a secondary antibody that can specifically bind to blood autoantibodies, said secondary antibody being selected from at least one, two, or three of IgA, IgM, and IgG; And / or, also includes substrates, wash solutions, and blocking solutions for ELISA detection.

5. The detection kit according to any one of claims 1 to 4, characterized in that, The test sample is blood or serum.

6. An analytical system for the diagnosis or prognosis of thyroid cancer, characterized in that, The input feature is selected from the detection kits described in any one of claims 1 to 5 to detect the expression level of at least one of the autoantibodies PRKD2, LAMA3, MST1R, ASCC1, NUP153, TNIP1, and OBSCN in the blood.

7. The analysis system according to claim 6, characterized in that, The expression level of autoantibodies was determined by detecting the expression levels of the following peptides: The expression level of the antibody ASCC1 was detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:1-5, 36. And / or, the expression level of the antibody PRKD2 is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:6-10, 37; And / or, the expression level of the antibody LAMA3 is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:11-15, 38; And / or, the expression level of the antibody TNIP1 is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:16-20, 39; And / or, the antibody OBSCN expression level is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:21-25, 40; And / or, the expression level of the antibody NUP153 is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:26-30, 41; And / or, the expression level of the antibody MST1R is detected by any one peptide chain in the amino acid sequence shown in SEQ ID NO:31-35, 42.

8. The analysis system according to claim 6 or 7, characterized in that, The data analysis module stores a prediction model for outputting the probability value of thyroid cancer. The method for constructing the prediction model includes: using the expression level of autoantibodies detected in the peptide in thyroid cancer and healthy samples as input variables, and whether it is thyroid cancer as the dependent variable, obtaining the prediction model through random forest analysis, and testing it through a validation set.

9. The use of the combination of diagnostic or prognostic biomarkers for thyroid cancer according to any one of claims 1-5 in the preparation of a kit or analytical system for the diagnosis, efficacy or prognostic assessment, or evaluation of related drugs for thyroid cancer.

10. The use of detecting the relative expression levels of any combination of diagnostic or prognostic biomarkers for thyroid cancer described in any one of 1-5 in serum or plasma in the preparation of kits or analytical systems for the diagnosis, efficacy or prognostic assessment, or evaluation of related drugs for thyroid cancer.

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