A marker group for thyroid cancer screening and diagnosis and use thereof

Abstract

The application discloses a marker group for thyroid cancer screening and diagnosis and application thereof, and belongs to the field of molecular biology technology.The marker group comprises at least four polypeptides shown in sequences of SEQ ID NO.1-35.The marker group constructed by the application has more excellent sensitivity and specificity when used for the auxiliary diagnosis and early screening of thyroid cancer.The auxiliary diagnosis result is partially or completely superior to the diagnosis method of vascular adhesion protein-1 (VAP-1) which is negatively related to the serum thyroid globulin concentration and the ultrasound auxiliary diagnosis model combined with the C-TIRADS guide recommended by the current clinical guide.The early screening result is also partially or completely superior to the common thyroid cancer screening method of CT examination and the detection kit based on the miR-221 / 222 ratio.

Description

Technical Field

[0001] This invention belongs to the field of molecular biology technology, specifically relating to a biomarker group for thyroid cancer screening and diagnosis and its uses. Background Technology

[0002] Thyroid cancer is a common malignant tumor of the endocrine system, including four types: papillary thyroid carcinoma, follicular thyroid carcinoma, medullary thyroid carcinoma, and undifferentiated thyroid carcinoma. Among them, papillary thyroid carcinoma is the most common type. Therefore, accurate diagnosis of thyroid cancer is of great significance for guiding clinical treatment or improving patient prognosis.

[0003] Current initial diagnostic methods primarily rely on ultrasound examination. The gold standard for screening benign and malignant thyroid nodules is percutaneous fine-needle aspiration biopsy (pFNA) or intraoperative frozen section examination. However, given the limited accuracy of ultrasound diagnosis and the fact that pFNA is overly dependent on the diagnostic capabilities of the medical institution's pathology department—which is prone to false negatives due to small tumors, thus delaying patient treatment—some samples cannot be diagnosed, often requiring repeated biopsies or intraoperative frozen section pathology examinations for definitive diagnosis.

[0004] Liquid biopsy, as an emerging diagnostic technique, can overcome the influence of tumor heterogeneity, providing more comprehensive molecular information about tumors and offering strong support for guiding clinical treatment and assessing prognosis. Guidelines indicate that for thyroid nodules that cannot be definitively diagnosed by pFNA, molecular marker detection, such as BRAF mutation, RAS mutation, and RET / PTC rearrangement, can be used to improve diagnostic accuracy.

[0005] However, these biomarkers have the following limitations in practical applications: BRAF mutations have a high detection rate (29%-83%) in papillary thyroid carcinoma (PTC), but positive results can also occur in benign nodules. For example, the BRAF mutation positivity rate in Bethesda classification II (benign) nodules can reach 26%, potentially leading to overdiagnosis. Furthermore, some malignant nodules do not show detectable BRAF mutations, posing a risk of false negatives. The malignancy risk of RAS mutation-positive nodules (including K-RAS, H-RAS, and N-RAS) is 64%-92%, but the risk varies significantly among different subtypes (e.g., H-RAS mutations have a 92% malignancy risk, while N-RAS has a 64% risk), making clinical decision-making difficult. Moreover, gene testing is expensive, potentially increasing the financial burden on patients, especially in indeterminate nodules; the cost-effectiveness of some tests (such as multi-gene combinations) still needs further validation. Summary of the Invention

[0006] In view of the above-mentioned shortcomings in the prior art, the present invention provides a set of biomarkers for thyroid cancer screening and diagnosis and their uses. When used for auxiliary diagnosis and early screening of thyroid cancer, it has excellent sensitivity and specificity and is expected to be applied to the diagnosis and treatment of thyroid cancer.

[0007] To achieve the above objectives, the technical solution adopted by the present invention to solve its technical problem is as follows:

[0008] A biomarker group for thyroid cancer screening and diagnosis includes at least four of the polypeptides shown in SEQ ID NO. 1-35, the specific sequences of which are shown in Table 1.

[0009] Table 1. Peptide Sequences

[0010]

[0011] Among them, the second amino acid K in peptide 5 is modified with Acetyl; the first amino acid Q in peptide 6 is modified with Gln->pyro-Glu, and the sixth amino acid N is modified with Dehydrated; the third amino acid G in peptide 10 is modified with Phospho; the fourth amino acid G in peptide 13 is modified with Phospho; and the fourth amino acid G in peptide 32 is modified with Dehydrated.

[0012] Furthermore, the biomarker set includes the peptides shown in sequences 1 and 2, as well as the following combinations of peptides:

[0013] The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 11 and sequence 12; sequence 30 and sequence 35; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 11, sequence 12 and sequence 20; sequence 9, sequence 30 and sequence 35.

[0014] Furthermore, the biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides:

[0015] The polypeptide combination is one of the following: sequence 3 and sequence 4; sequence 9 and sequence 10; sequence 3, sequence 4 and sequence 9; or sequence 9, sequence 10 and sequence 11.

[0016] Furthermore, the biomarker group includes peptides shown in sequences 1, 10, and 30, as well as peptides shown in sequences 35, 20, 35, and 9, or sequences 20 and 22.

[0017] Furthermore, the biomarker set includes the peptides shown in sequences 5 and 15, as well as the following combinations of peptides:

[0018] The polypeptide combination is one of the following: sequence 25 and sequence 35; sequence 2 and sequence 16; sequence 9, sequence 25 and sequence 35; sequence 2, sequence 16 and sequence 20.

[0019] Furthermore, the biomarker set includes the peptides shown in sequences 4 and 8, as well as the following combinations of peptides:

[0020] The polypeptide combination is sequence 19 and sequence 20; sequence 2 and sequence 6; or sequence 19, sequence 20 and sequence 22.

[0021] Furthermore, the biomarker set includes the polypeptides shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 22.

[0022] Furthermore, the biomarker group includes polypeptides as shown in sequences SEQ ID NO.1~35.

[0023] The use of the above biomarkers in the preparation of formulations for thyroid cancer screening and diagnosis.

[0024] The above biomarkers may be used in basic medical research for non-diagnostic / therapeutic purposes.

[0025] Further, basic medical research includes Western blotting, immunohistochemistry, or flow cytometry.

[0026] The beneficial effects of this invention are:

[0027] The biomarker combination constructed in this invention exhibits superior sensitivity and specificity in the auxiliary diagnosis and early screening of thyroid cancer. Its auxiliary diagnostic results are partially or completely superior to the vascular adhesion protein-1 (VAP-1) diagnostic method, which is negatively correlated with serum thyroglobulin concentration and has a sensitivity and specificity of 66.7% and 77.4%, respectively, recommended in current clinical guidelines, and the ultrasound-assisted diagnostic model combined with the C-TIRADS guidelines (sensitivity and specificity of 96.8% and 63.2%, respectively). Early screening results are also partially or completely superior to the common thyroid cancer screening method, CT scan (sensitivity and specificity of 55.56% and 76.00%, respectively). ] ), and a detection kit based on the miR-221 / 222 ratio (sensitivity and specificity of 75% and 85%, respectively). It holds promise for applications in the diagnosis and treatment of thyroid cancer. Attached Figure Description

[0028] Figure 1 ROC curve for marker combination 1;

[0029] Figure 2 ROC curve for marker combination 2;

[0030] Figure 3 ROC curve for marker combination 3;

[0031] Figure 4 ROC curve for marker combination 4;

[0032] Figure 5 ROC curve for marker combination 5;

[0033] Figure 6 ROC curve for marker combination 6;

[0034] Figure 7 ROC curve for marker combination 7;

[0035] Figure 8 ROC curve for marker combination 8;

[0036] Figure 9 ROC curve for marker combination 9;

[0037] Figure 10 ROC curve for marker combination 10;

[0038] Figure 11 ROC curve for marker combination 11;

[0039] Figure 12 ROC curve for marker combination 12;

[0040] Figure 13 ROC curve for marker combination 13;

[0041] Figure 14 ROC curve for marker combination 14;

[0042] Figure 15 ROC curve for marker combination 15;

[0043] Figure 16 ROC curve for marker combination 16;

[0044] Figure 17 ROC curve for marker combination 17;

[0045] Figure 18 ROC curve for marker combination 18;

[0046] Figure 19 ROC curve for marker combination 19;

[0047] Figure 20ROC curve for marker combination 20;

[0048] Figure 21 ROC curve for marker combination 21;

[0049] Figure 22 ROC curve for marker combination 22;

[0050] Figure 23 ROC curve for marker combination 23;

[0051] Figure 24 ROC curve for marker combination 24;

[0052] Figure 25 ROC curve for marker combination 25;

[0053] Figure 26 ROC curve for marker combination 26. Detailed Implementation

[0054] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0055] The patient samples used in this invention are all from Zhongshan Hospital affiliated with Fudan University and have passed ethical review.

[0056] The experimental methods used in this invention are as follows:

[0057] I. Serum Sample Collection

[0058] 1) Sample type: serum.

[0059] 2) Collection requirements: Fasting is required. Use a coagulation tube to draw 5 mL of venous blood, let it stand for 30 min, centrifuge at 3000 rpm for 15 min, and take out about 1 mL of serum and put it into a cryopreservation tube.

[0060] 3) Sample storage:

[0061] Use on the same day; store at 2-8℃.

[0062] If not used on the same day, store at -20℃ for up to 30 days;

[0063] If stored for an extended period (more than one month), it should be kept at -80°C.

[0064] The freeze-thaw cycle should not exceed 3 times.

[0065] II. Extraction of analytes from serum

[0066] 1) After calibrating the mass spectrometer, turn on the Solid Bio Fully Automated Sample Analysis System SPS1000 / SPS4000, and put in the consumables, matching reagent kits and the sample to be tested;

[0067] Select the procedure method "Concentrated loading";

[0068] Run the program:

[0069] a. Opening a hole;

[0070] b. Take at least 10µL of serum sample and activation reagent, mix them in a 1:1 ratio, and place them in the G-row pre-reserved well for later use;

[0071] c. Clean the custom pipette tip in cleaning reagent 1 and cleaning reagent 2 in sequence. Each time, aspirate at least 10µL of liquid and repeat the aspiration and dispensing process at least 3 times.

[0072] d. Process the serum mixture in the G-row wells using the cleaned custom pipette tips. Aspirate at least 10 µL of solution each time, repeating the process at least three times.

[0073] e. Clean the custom pipette tip after adsorbing the serum mixture using cleaning reagent 3. During cleaning, aspirate at least 10µL of liquid each time, repeating the aspiration and dispensing process at least 3 times.

[0074] f. Transfer no less than 10µL of buffer reagent into the H-row pre-reserved hole, and place the customized pipette tip after using cleaning reagent 3 into the liquid to draw no less than 10µL of liquid. Repeat the suction and aspiration at least 3 times.

[0075] g. Transfer at least 10µL of sample matrix solution into the H-row pre-reserved well to complete sample processing;

[0076] h. Spot 2.0 µL of the solution from well H onto the hydrophobic-coated biochip (Wuxi Pimo Technology Co., Ltd.).

[0077] i. Vacuum drying for 240 seconds.

[0078] The main components of each reagent are shown in Table 2.

[0079] Table 2 Reagent Composition

[0080]

[0081] III. Mass Spectrometry Data Acquisition and Upload

[0082] The hydrophobic coated biochip (Wuxi Pimo Technology Co., Ltd.) was vacuum dried and placed into a mass spectrometer;

[0083] Data acquisition is performed using the pre-defined SP1 voltage (target high voltage), SP2 voltage (pulse high voltage), focusing voltage (lens high voltage), detector voltage (MCP voltage), pulse delay time, acquisition card range, target diameter, laser frequency, calibration method, and laser intensity.

[0084] IV. Quality Control

[0085] 1) After data collection, the data will be uploaded to the "Mass Spectrometry Data Analysis Software";

[0086] 2) The software reads the sample information and signal spectrum, and judges whether the sample and sample preprocessing are qualified according to the quality control model; quality control failure may include a variety of possibilities, including the sample is not a thyroid disease sample, the signal spectrum intensity is not up to standard, etc.

[0087] 3) If the quality control fails, adjust the corresponding parameters according to the quality control results and repeat the serum analyte extraction process;

[0088] 4) If the quality control is qualified, proceed to the next process.

[0089] V. Establishment of Positive Criterion Value and Result Analysis

[0090] The study of positive cutoff values ​​used thyroid cancer samples with clear diagnostic information and normal human samples, covering patients with benign thyroid diseases such as nodular goiter and Hashimoto's thyroiditis. The core algorithm is based on supervised learning of known thyroid cancer sample atlases. Through a series of processes such as smoothing, noise reduction, and baseline removal, characteristic peaks are screened, a classification model is constructed, and the similarity between hormone signal atlases and known hormone signal atlases stored in the software is calculated (Cannataro M, Guzzi PH, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data[J]. 2005.). Finally, the similarity score positive cutoff value of the kit is determined by the Youden index maximization method. When the similarity score < positive cutoff value, the sample test result is negative; when the similarity score ≥ positive cutoff value, the sample test result is positive. When using the maximum similarity score as the positive cutoff value to assist in the diagnosis of thyroid cancer or to perform early screening for thyroid cancer, the sensitivity is calculated as: Sensitivity = (Number of true positives / (Number of true positives + Number of false negatives)) × 100%, and Specificity is calculated as: (Number of true negatives / (Number of true negatives + Number of false positives)) × 100%.

[0091] Example 1: Screening and Identification of Biomarkers

[0092] This invention analyzed 300 normal individuals (147 males (49.0%) and 153 females (51.0%), aged 20 to 65 years, with a mean age of 41.8 ± 12.3 years; specifically, the age distribution was: 63 cases aged 20-29, 78 cases aged 30-39, 85 cases aged 40-49, 58 cases aged 50-59, and 16 cases aged 60-65) and 300 thyroid cancer samples (87 males (29.0%) and 213 females (71.0%), aged 18 to 68 years, with a mean age of 43.5 ± 13.3 years). The participants were aged 0.2 years. The specific age distribution was as follows: 57 cases aged 18-28 years, 83 cases aged 29-38 years, 79 cases aged 39-48 years, 61 cases aged 49-58 years, and 20 cases aged 59-68 years. Time-of-flight mass spectrometry (TOF-MS) was performed. Through first-level mass spectrometry testing, the relative abundance differences of characteristic peak data in normal individuals and thyroid cancer patients were comprehensively considered, along with statistical differences (p<0.05, t-test). A machine learning algorithm (random forest) was used to rank the influence factors for feature selection and to assess the matching degree of data in the database. This resulted in the discovery of 35 blood peptides with diagnostic capabilities for thyroid cancer. The mass-to-charge ratio (m / z), relative abundance, and influence factors of these peptides are shown in Table 1. The relative abundance was normalized based on the normal sample, i.e., ln(mean signal intensity of cancer patient samples / mean signal intensity of normal sample). In machine learning, the selection of characteristic peaks was based on feature importance assessment using ensemble learning. By constructing multiple decision trees, the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction was quantified. The feature importance score, or influence factor, is obtained by calculating the mean reduction in impurity caused by the feature when splitting across all tree nodes (Biau, G., Scornet, E. A random forestguided tour. TEST 25, 197–227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0093] The specific parameters for first-order mass spectrometry are as follows:

[0094] Ionization method: Matrix-assisted laser desorption / ionization (MALDI), with α-cyano-4-hydroxycinnamic acid (CHCA) as the matrix.

[0095] Quality range: 100-4000 Da.

[0096] Resolution: 20000 (full quality range).

[0097] Laser energy: 30-40%.

[0098] Acquisition mode: Positive ion mode.

[0099] Calibration: External quality calibration was performed using the Bruker Peptide Calibration Standard.

[0100] Subsequently, the sequences of these 35 substances in the clinical serum were confirmed using secondary mass spectrometry (MS / MS or TOF / TOF is a peptide identification method recommended by the guidelines of the China Food and Drug Administration and the U.S. Food and Drug Administration). The secondary mass spectrometry data analysis process is as follows:

[0101] Data analysis methods: Mascot software (version 2.8) was used for database searching, with the UniProt Human Proteome Database (released in 2023) as the target database. Search parameters: Enzyme was set to "no digestion", parent ion mass error was allowed ±0.5 Da, fragment ion mass error was allowed ±0.3 Da, fixed modification was cysteine ​​urea methylation, and variable modification was methionine oxidation.

[0102] Sequence confirmation criteria: The confirmation of peptide sequences is based on the matching of fragment ion spectra (b- and y- ions) with theoretical spectra. A Mascot score higher than 30 (p<0.05) is considered significant.

[0103] False positive exclusion: Specificity was verified through reverse database search, and the false positive rate was controlled to below 1%.

[0104] Secondary mass spectrometry parameters:

[0105] Collision-induced dissociation (CID).

[0106] Collision energy: 30 eV.

[0107] Fragment ion mass range: 100-3500 Da.

[0108] Data acquisition: Each sample is scanned at least 1000 times with lasers to improve the signal-to-noise ratio.

[0109] The sequences and specificity of these 35 biomarkers were confirmed by secondary mass spectrometry, ruling out false positives. The specific sequences are shown in Table 1.

[0110] Example 2: Validation of Marker Combinations

[0111] Based on the 35 polypeptide biomarkers identified and confirmed in Example 1 (sequences and mass-to-charge ratios are shown in Table 1), different biomarker combinations were formed as shown in Table 3, and the sensitivity and specificity of the biomarker combinations in the auxiliary diagnosis and early screening of thyroid cancer were verified. The analysis process is as follows: For all validation cohort samples, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain mass spectrometry peak intensity data for all biomarkers in Table 1. The entire detection process was blinded, meaning that the experimental operators were unaware of the sample grouping information. The raw mass spectrometry data were processed by baseline correction, smoothing, and normalization (based on the internal standard peak intensity), and then the peak area or intensity value of each biomarker was extracted. Statistical analysis was performed using R software (version 4.0.2). The preprocessed biomarker intensity data were input into a logistic regression model. For auxiliary diagnostic validation, ten-fold cross-validation was performed using all samples from this cohort (Sun T, Liu J, Yuan H, Li X, Yan H. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm. Front Oncol. 2024 Aug 9;14:1403392. doi: 10.3389 / fonc.2024.1403392. PMID:39184040; PMCID: PMC11341396.). For early screening validation, given the imbalanced sample size, oversampling (SMOTE) was used to process the data before model training and testing (van den Goorbergh R, van Smeden M, Timmerman D, Van Calster B. The harm of class imbalance corrections for riskprediction models: illustration and simulation using logistic regression. JAm Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093.PMID: 35686364; PMCID: PMC9382395.).The data analysis process referenced general guidelines for constructing clinical prediction models (Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993 Apr;39(4):561-77. Erratum in: Clin Chem 1993 Aug;39(8):1589. PMID: 8472349.). Specific calculation results for performance indicators are shown in Table 3, and the corresponding ROC curves are shown below. Figures 1-26 .

[0112] 1) The use of various biomarker combinations for the auxiliary diagnosis of thyroid cancer was validated in 420 patients (122 males (29.0%, age range 19-65 years, mean age 42.8±12.7 years, specifically distributed as follows: 63 cases 19-27 years, 108 cases 28-36 years, 127 cases 37-45 years, 86 cases 46-54 years, 36 cases 55-65 years) and 550 healthy individuals (269 males (48.9%), 281 females (51.1%), age range 21-68 years, mean age 43.2±13.1 years, specifically distributed as follows: 78 cases 21-29 years, 127 cases 30-38 years, 146 cases 39-47 years, 128 cases 48-56 years, 71 cases 57-68 years).

[0113] 2) The early screening efficacy of various biomarker combinations for thyroid cancer was validated using a sample of 300 thyroid cancer patients (87 males (29.0%), 213 females (71.0%), aged 20 to 67 years, with a mean age of 41.6 ± 12.9 years. Specifically, the distribution was as follows: 58 patients aged 20-28, 86 patients aged 29-37, 83 patients aged 38-46, 53 patients aged 47-55, and 20 patients aged 56-67) and 3000 healthy individuals (1468 males (48.9%), 1532 females (51.1%), aged 22 to 70 years, with a mean age of 44.3 ± 13.8 years. Specifically, the distribution was as follows: 423 patients aged 22-30, 587 patients aged 31-39, 623 patients aged 40-48, 698 patients aged 49-57, and 669 patients aged 58-70).

[0114] Table 3 Sensitivity and specificity of biomarker combinations for assisted diagnosis and early screening

[0115]

[0116] (Continued from the table above)

[0117]

[0118] (Continued from the table above)

[0119]

[0120] According to Table 3 and Figures 1-26 The test results show that the diagnostic results of the discrimination peak set (marker combination) selected in this invention are partially or completely superior to the vascular adhesion protein-1 (VAP-1) diagnostic method that is negatively correlated with serum thyroglobulin concentration recommended by current clinical guidelines (sensitivity and specificity are 66.7% and 77.4%, respectively), and the ultrasound-assisted diagnostic model combined with the C-TIRADS guidelines (sensitivity and specificity are 96.8% and 63.2%, respectively).

[0121] Early screening results were also partially or completely superior to CT scans, a common thyroid cancer screening method (sensitivity and specificity were 55.56% and 76.00%, respectively). ] ), and a detection kit based on the miR-221 / 222 ratio (sensitivity and specificity of 75% and 85%, respectively).

[0122] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A biomarker set for thyroid cancer screening and diagnosis, characterized in that, The biomarker group is selected from one of the following combinations of peptides: A) Sequence 1, Sequence 2, Sequence 3 and Sequence 4; B) Sequences 1, 2, 5, and 6; C) Sequence 1, Sequence 2, Sequence 11 and Sequence 12; D) Sequences 1, 2, 30, and 35; E) Sequence 1, Sequence 2, Sequence 3, Sequence 4 and Sequence 5; F) Sequence 1, Sequence 2, Sequence 5, Sequence 6 and Sequence 7; G) Sequence 1, Sequence 2, Sequence 11, Sequence 12 and Sequence 20; H) Sequence 1, Sequence 2, Sequence 9, Sequence 30 and Sequence 35; I) Sequences 2, 16, 5, and 15; J) Sequence 2, Sequence 5, Sequence 15, Sequence 16 and Sequence 20; K) Sequence 2, Sequence 6, Sequence 4 and Sequence 8; L) Sequences 1 to 35; The amino acid sequences of sequences 1 to 35 are shown in SEQ ID NO. 1 to 35; Specifically, the second amino acid K in sequence 5 is modified with Acetyl; the first amino acid Q in sequence 6 is modified with Gln->pyro-Glu, and the sixth amino acid N is modified with Dehydrated; the third amino acid G in sequence 10 is modified with Phospho; the fourth amino acid G in sequence 13 is modified with Phospho; and the fourth amino acid G in sequence 32 is modified with Dehydrated.

2. Use of the reagent for detecting the biomarker group of claim 1 in the preparation of preparations for thyroid cancer screening and diagnosis.

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