Plasma metabolism marker for distinguishing benign thyroid disease from early thyroid cancer and application thereof

Abstract

The invention provides a metabolic marker composition for distinguishing benign thyroid diseases from early thyroid cancer, and provides an auxiliary diagnosis means independent of invasive biopsy by constructing a diagnosis model based on the metabolic marker composition. The metabolic marker combination can realize functional early warning and diagnosis in an earlier stage of thyroid cancer occurrence, solves the problems of insufficient specificity and high false positive rate in distinguishing benign and malignant thyroid lesions in existing imaging examination, has high sensitivity and specificity, can provide a basis for clinical early intervention, and has a good application prospect. The method is suitable for large-scale population screening and repetitive dynamic monitoring of high-risk individuals.

Description

Technical Field

[0001] This invention relates to the field of detection and analysis of metabolic markers, specifically to plasma metabolic markers for differentiating benign thyroid diseases from early thyroid cancer and their applications. Background Technology

[0002] Thyroid cancer (TC) is a malignant tumor originating from the follicular epithelium or parafollicular epithelial cells of the thyroid gland, and it is also the most common malignant tumor of the head and neck. In recent years, the incidence of thyroid cancer has been rising globally. According to the latest global cancer statistics released by the International Agency for Research on Cancer (IARC) of the World Health Organization, new cases of thyroid cancer account for approximately 4.1% of all malignant tumors. In my country, the incidence of thyroid cancer has also been rising continuously, currently ranking third among all malignant tumors, after lung cancer and colorectal cancer. Especially in urban areas, the incidence of thyroid cancer in women has jumped to fourth among all malignant tumors in women, and it is expected to continue to grow at a rate of approximately 20% per year. Although the age-standardized mortality rate of thyroid cancer is relatively low, mainly due to the better prognosis of differentiated thyroid cancers (such as papillary thyroid carcinoma), the rapid increase in incidence still places a huge burden on the medical system in terms of diagnosis and treatment, while also causing widespread public concern and anxiety.

[0003] The core contradiction in the current diagnosis and treatment of thyroid cancer lies in the imbalance between "overdiagnosis" and "accurate differentiation." With the widespread application of high-resolution ultrasound technology, a large number of previously undetectable small thyroid nodules can be detected at an early stage, the vast majority of which are benign lesions. However, ultrasound examination has significant limitations in differentiating between benign and malignant nodules, leading to many benign nodules being misdiagnosed as potentially malignant, resulting in unnecessary invasive examinations such as fine-needle aspiration biopsies, and even overtreatment. This not only causes physical and psychological trauma to patients but also increases the economic burden on healthcare providers. Therefore, how to achieve non-invasive and accurate differentiation between benign thyroid lesions and early thyroid cancer in the large thyroid disease population, avoiding over-intervention in indolent lesions while ensuring early detection of clinically significant malignant lesions, is a key challenge in the current clinical management of thyroid cancer.

[0004] From a pathological perspective, there is a close biological link between benign thyroid diseases and thyroid cancer. The vast majority of thyroid cancers originate from follicular epithelial cells, and their development is a multi-stage, multi-gene-involved, gradual process. Clinical and pathological studies have shown that most thyroid nodules that are benign on imaging or cytology do not become malignant during long-term follow-up. However, some types, such as follicular adenomas, are considered potential precancerous lesions, some of which can progress to follicular carcinoma. In the development of papillary carcinoma, there may also be an evolutionary process from benign nodules gradually developing cellular atypia and eventually becoming malignant. Therefore, from a pathological perspective, establishing a diagnostic method that can accurately distinguish between benign thyroid lesions and early thyroid cancer is of significant clinical importance for early detection of malignant potential and optimizing clinical decision-making.

[0005] Metabolomics, as an important branch of systems biology, can directly reflect the end-phenotype of a living system by qualitatively and quantitatively analyzing the overall changes in small molecule metabolites (molecular weight <1000 Da), and is closely related to the physiological and pathological state of diseases. Tumor cells commonly exhibit metabolic reprogramming (such as the Warburg effect) during their development to meet the energy and biosynthetic precursors required for rapid proliferation. These metabolic changes leave detectable characteristic signals in the metabolic profiles of bodily fluids such as blood and urine. This makes metabolomics a promising candidate for detecting abnormal metabolic changes in the early stages of tumor development, enabling early diagnosis and risk warning.

[0006] Applying metabolomics technology to differentiate between benign and malignant thyroid nodules holds promise for overcoming the limitations of existing diagnostic methods. By combining high-throughput analysis with multivariate statistical and machine learning methods, a set of characteristic metabolic biomarkers that can significantly distinguish between benign thyroid diseases and early-stage thyroid cancer can be screened from patient plasma. This strategy is expected to provide clinicians with an objective, non-invasive, and sensitive auxiliary diagnostic tool, optimizing preoperative assessment and treatment decisions, and promoting the development of thyroid cancer diagnosis and treatment towards precision, minimally invasive procedures, and forward-looking approaches. Summary of the Invention

[0007] The present invention aims to overcome the defects and shortcomings of existing thyroid cancer diagnostic technologies and provide a set of highly specific and sensitive plasma metabolic markers to differentiate between patients with benign thyroid diseases and early thyroid cancer.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] This invention discloses a metabolic marker composition for distinguishing between benign thyroid diseases and early thyroid cancer. The metabolic marker composition comprises: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, and triglycerides 46:1.

[0010] Preferably, the composition further comprises: phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, and phosphatidylcholine 34:4.

[0011] Preferably, the composition further comprises: 2-oxoglutaric acid, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglyceride 56:5, and octyl-L-carnitine.

[0012] Preferably, the composition further comprises: L-piperidinic acid, palmitoleic acid, glycoursodeoxycholic acid, triglyceride 48:1, L-valine-L-serine, and ceramide d41:2.

[0013] This invention discloses a metabolic marker composition for distinguishing between benign thyroid diseases and early thyroid cancer. The composition consists of the following metabolic markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, and triglycerides 46:1.

[0014] Preferably, the composition comprises the following metabolic markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, and phosphatidylcholine 34:4.

[0015] Preferably, the composition comprises the following metabolic markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, phosphatidylcholine 34:4, 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglycerides 56:5, and octyl-L-carnitine.

[0016] Preferably, the composition comprises the following metabolic markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, phosphatidylcholine 34:4, 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglycerides 56:5, octyl-L-carnitine, L-piperidine acid, palmitoleic acid, glycoursodeoxycholic acid, triglycerides 48:1, L-valine-L-serine, and ceramide d41:2.

[0017] This invention discloses a combination of metabolic markers to distinguish between benign thyroid diseases and early thyroid cancer. The combination of metabolic markers includes: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, and triglycerides 46:1.

[0018] Preferably, the combination further comprises: phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, and phosphatidylcholine 34:4.

[0019] Preferably, the combination further comprises: 2-oxoglutaric acid, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglyceride 56:5, and octyl-L-carnitine.

[0020] Preferably, the combination further comprises: L-piperidinic acid, palmitoleic acid, glycoursodeoxycholic acid, triglyceride 48:1, L-valine-L-serine, and ceramide d41:2.

[0021] This invention discloses a combination of metabolic markers to distinguish between benign thyroid diseases and early thyroid cancer. The combination consists of the following metabolite markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, and triglycerides 46:1.

[0022] Preferably, the combination consists of the following metabolite markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, and phosphatidylcholine 34:4.

[0023] Preferably, the combination consists of the following metabolite markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, phosphatidylcholine 34:4, 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglycerides 56:5, and octyl-L-carnitine.

[0024] Preferably, the combination comprises the following metabolite markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, phosphatidylcholine 34:4, 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglycerides 56:5, octyl-L-carnitine, L-piperidine acid, palmitoleic acid, glycoursodeoxycholic acid, triglycerides 48:1, L-valine-L-serine, and ceramide d41:2.

[0025] This invention discloses the use of the described composition in the preparation of a reagent for distinguishing between benign thyroid diseases and early thyroid cancer.

[0026] Preferably, the sample used in the differentiation process is selected from at least one of serum, plasma, or blood.

[0027] This invention discloses the use of the described composition in the preparation of a kit for differentiating benign thyroid diseases from early thyroid cancer.

[0028] Preferably, the sample used in the differentiation process is selected from at least one of serum, plasma, or blood.

[0029] This invention discloses a reagent or kit for distinguishing between benign thyroid diseases and early thyroid cancer, wherein the reagent or kit comprises any of the aforementioned combinations of metabolic markers.

[0030] This invention aims to address the shortcomings of existing imaging examinations in differentiating between benign and malignant thyroid lesions, such as insufficient specificity and high false-positive rates, by constructing a diagnostic model based on this combination of metabolic biomarkers. It provides an auxiliary diagnostic method that does not rely on invasive biopsy. Utilizing the characteristic that metabolic changes precede morphological changes, the metabolic biomarker combination of this invention can achieve functional-level early warning and diagnosis at a more advanced stage of thyroid cancer development, providing a basis for early clinical intervention.

[0031] Compared with the prior art, the present invention has the following significant advantages:

[0032] (1) High accuracy and reliability: Diagnosis is based on a set of screened metabolic markers rather than relying on a single indicator, which can reflect the tumor-specific metabolic disorders at the system level, thereby significantly improving the sensitivity and specificity of differential diagnosis.

[0033] (2) Non-invasive and high compliance: The detection method of the present invention is based on easily obtainable biological samples such as blood. It is non-invasive, greatly reduces the pain and psychological burden of patients, and is more easily accepted. It is suitable for large-scale population screening and repeated dynamic monitoring of high-risk individuals.

[0034] (3) Prospective and early warning capabilities: Since metabolic reprogramming is an early event of cancer, this invention can detect metabolic abnormalities before morphological changes are obvious, thereby achieving an earlier diagnosis than imaging and winning valuable treatment time for patients.

[0035] (4) Easy to standardize and promote: Based on modern high-throughput analysis technology (such as mass spectrometry), the detection method of the present invention is easy to automate and standardize, which is conducive to its promotion and application in clinical laboratories and the formation of stable and reliable diagnostic products. Attached Figure Description

[0036] Figure 1 Validation group: ROC curves distinguishing between benign thyroid disease group and early thyroid cancer group based on 24 metabolic marker combinations.

[0037] Figure 2 Validation group: ROC curves distinguishing between benign thyroid disease group and early thyroid cancer group based on a combination of 18 metabolic markers.

[0038] Figure 3 Validation group: ROC curves distinguishing between benign thyroid disease group and early thyroid cancer group based on a combination of 12 metabolic markers.

[0039] Figure 4 Validation group: ROC curves for distinguishing between benign thyroid disease group and early thyroid cancer group based on a combination of 6 metabolic markers. Detailed Implementation

[0040] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0041] Example 1: Subject Information and Sample Collection

[0042] 1. Subject information:

[0043] 1) Inclusion and exclusion criteria for thyroid cancer patients:

[0044] Inclusion criteria (meeting all of the following conditions):

[0045] (1) Females or males aged 18 years or older;

[0046] (2) Read and fully understand the information, sign the informed consent form, and be able to provide a blood sample for metabolomics testing;

[0047] (3) Patients diagnosed with primary thyroid cancer by biopsy / postoperative pathology or by comprehensive clinical assessment by a clinician, and whose thyroid cancer is classified as stage I according to histopathological staging, are considered to have early-stage thyroid cancer.

[0048] Exclusion criteria (meeting any one of the following):

[0049] (1) During pregnancy or lactation;

[0050] (2) Emergency room or resuscitation required;

[0051] (3) History of blood transfusion within 7 days prior to sampling;

[0052] (4) Has received an organ transplant or allogeneic bone marrow / stem cell transplant;

[0053] (5) History of other malignant tumors within 5 years, or having received any anti-tumor treatment before sampling;

[0054] (6) Multiple primary malignant tumors are present simultaneously.

[0055] 2) Inclusion and exclusion criteria for patients with benign thyroid diseases:

[0056] Inclusion criteria (meeting all of the following conditions):

[0057] (1) Females or males aged 18 years or older;

[0058] (2) Read and fully understand the information, sign the informed consent form, and be able to provide a blood sample for metabolomics testing;

[0059] (3) Individuals who have been ruled out of thyroid cancer by biopsy / postoperative pathology or by comprehensive evaluation by clinicians and have been diagnosed with benign thyroid diseases by biopsy / pathology, including but not limited to thyroid nodules, nodular goiter, thyroiditis, etc.

[0060] Exclusion criteria (meeting any one of the following):

[0061] (1) During pregnancy or lactation;

[0062] (2) Emergency room or resuscitation required;

[0063] (3) History of blood transfusion within 7 days prior to sampling;

[0064] (4) Has received an organ transplant or allogeneic bone marrow / stem cell transplant;

[0065] (5) Any history of malignant tumors or any anti-tumor treatment prior to sampling.

[0066] 2. Sample Information:

[0067] This study collected plasma samples from 215 participants across two medical centers, including 157 participants with early-stage thyroid cancer (TC) and 58 participants with benign thyroid disease (BTD). The TC and BTD samples were randomly assigned to a modeling group and a validation group (Table 1). The modeling group consisted of 118 participants with early-stage thyroid cancer (TC) and 44 participants with benign thyroid disease (BTD). The validation group consisted of 39 participants with early-stage thyroid cancer (TC) and 14 participants with benign thyroid disease (BTD).

[0068] Table 1. Sample Information

[0069] Group Early-stage thyroid cancer (TC) group Benign Thyroid Disease Group (BTD) Modeling Group 118 44 Verification group 39 14 total 157 58

[0070] Example 2 Detection of plasma metabolites

[0071] 1. Reagents: Methanol, acetonitrile, water, acetic acid, isopropanol, and methyl tert-butyl ether of mass spectrometry grade, and formic acid and ammonium acetate of chromatographic (HPLC) grade were all purchased from Sigma-Aldrich, USA.

[0072] 2. Sample preprocessing:

[0073] After being removed from the -80°C freezer, the plasma samples were thawed on ice and vortexed for 10 seconds. Then, 100 μL of plasma was added to 1000 μL of pre-cooled solution (methyl tert-butyl ether: methanol, volume ratio 3:1) and vortexed to obtain the sample extract. Next, 500 μL of a mixture (methanol: water, volume ratio 3:1) was added to the sample extract, followed by sonication, standing, vortexing, and centrifugation to separate the layers, resulting in an organic phase on top and an aqueous phase on the bottom.

[0074] - Organic phase treatment: After sample separation, transfer the upper 500 μL organic phase to a centrifuge tube and dry it using a vacuum concentrator (Speed-Vac). Redissolve the organic phase in 200 μL of a mixed solution (acetonitrile:isopropanol, volume ratio 3:1) and incubate at room temperature for 15 minutes. Vortex the mixture in the centrifuge tube, sonicate for 5 minutes, and centrifuge at room temperature for 5 minutes (12000 rpm). After centrifugation, transfer 180 μL of the supernatant to a 2 mL glass vial for analysis as lipid metabolites using an LC-MS platform.

[0075] - Aqueous phase treatment: After sample separation, transfer the lower 400 μL aqueous phase to a centrifuge tube, add 1100 μL of ice-cold methanol to precipitate proteins, vortex and centrifuge, then transfer 1000 μL of the supernatant to a centrifuge tube, concentrate and dry, add 200 μL of mass spectrometry grade water to the centrifuge tube to reconstitute, and incubate at room temperature for 15 min; after incubation, vortex to mix, sonicate for 5 min, and then centrifuge at 12000 rpm for 5 min at room temperature. Finally, transfer 180 μL of the supernatant to a 2 mL glass sample vial for analysis as aqueous metabolites using an LC-MS platform.

[0076] 3. High-resolution liquid chromatography-mass spectrometry (UHPLC-MS) detection:

[0077] (1) Chromatographic parameters of organic phase metabolites: Organic phase metabolites were separated into small molecules using a Waters ACQUTTY UPLC® BEH C8 (2.1*100mm, 1.7μm) column at a column temperature of 60℃; the liquid chromatography and mass spectrometry systems used were an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively.

[0078] The mobile phase composition was as follows: Mobile phase A was water; mobile phase B was acetonitrile / isopropanol (7 / 3, V / V), both containing 0.1% acetic acid and 10 mmol / L ammonium acetate, with a flow rate of 0.4 mL / min. The separation and elution gradient was as follows: 0–1 min, 55% mobile phase B; 1–4 min, 55–75% mobile phase B; 4–12 min, 75–89% mobile phase B; 12–15 min, 89–100% mobile phase B; 15–19.5 min, 100% mobile phase B; 19.5–19.51 min, 100–55% mobile phase B; 19.51 min–24 min, 55% mobile phase B; the injection volume was 2 μL, and the autosampler temperature was 10 °C.

[0079] (2) Chromatographic parameters of aqueous metabolites: Small molecule separation was performed using a Waters ACQUTTY UPLC® HSS T3 column (2.1*100mm, 1.8μm) at a column temperature of 40℃ for aqueous metabolite analysis; ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific) were used for liquid chromatography and mass spectrometry, respectively.

[0080] The mobile phase composition is as follows: mobile phase A is an aqueous solution containing 0.1% formic acid; mobile phase B is an acetonitrile solution containing 0.1% formic acid, with a flow rate of 0.4 mL / min; the separation and elution gradient is as follows: 0-1 min, 1% mobile phase B; 1-11 min, 1-40% mobile phase B; 11-13 min, 40-70% mobile phase B; 13-15 min, 70-99% mobile phase B; 15-18 min, 99% mobile phase B; 18-19 min, 99-1% mobile phase B; 19-22 min, 1% mobile phase B; the sample injection volume is 3 μL, and the autosampler temperature is 10℃;

[0081] (3) LC-MS Mass Spectrometry Parameters: Full scan and data-dependent acquisition (DDA) were used to acquire first-stage (MS1) and second-stage (MS2) mass spectrometry data for both aqueous and organic metabolites. The full scan mass spectrometry range was 100–1500 Da. The second-stage scan mode (Full MS / dd-MS2) scan range was 100–310, 300–710, and 700–1500 Da. The MS instrument used was an Orbitrap high-resolution mass spectrometer (Thermo Fisher) equipped with an electrospray ionization source (ESI), and data were acquired using both positive and negative ionization modes. The specific parameters are as follows: Automatic Gain Control (AGC) was 3E+6, Maximum Ion Implantation Time (IT) was 200 ms, and the first-stage full scan resolution was 70,000 FWHM (Full Width at Half Maximum) (@200 m / z). In the second-level scan mode (Full MS / dd-MS2), the resolution of the second-level mass spectrometer was 17,500 Å, the quadrupole isolation window width was 1.5 m / z, the AGC was 1E+5, the maximum ion implantation time was 50 ms, and the higher-energy collisional dissociation (HCD) was 30%. Furthermore, the ion spray voltage was 3,500 V in positive mode and 3,000 V in negative mode; the nebulizer gas pressure was 20 psi, the sheath gas temperature was 400 °C, and the sheath gas flow rate was 10 L / min.

[0082] Example 3: Metabolomics Data Preprocessing and Metabolite Identification

[0083] 1. Metabolomics data preprocessing:

[0084] Metabolomics data preprocessing steps include peak extraction, peak alignment, peak filtering, and missing value imputation;

[0085] (1) Convert the raw mass spectrometry data into an mzXML format file and use OpenMS software to perform peak extraction, peak alignment and other processing, thereby converting the mass spectrometry data into a data matrix.

[0086] (2) Apply different filtering criteria to remove the following interfering peaks: (i) isotope peaks; (ii) fragments caused by ionization of the analyte source; (iii) redundant peaks, such as additional low-intensity adducts of the same analyte and redundant derivatives, to ensure the quality of the analytable dataset and remove characteristic peaks with a missing rate of >50% in all samples.

[0087] (3) Missing values ​​were filled using the MICE Forest chain equation. To remove batch effects, normalization was performed using the Normalization Autoencoder (NormAE).

[0088] 2. Identification of metabolites:

[0089] After analyzing the raw data using software, the spectral information of the parent ion and secondary fragment ions of the compound was obtained, such as the mass-to-charge ratio (m / z) of the primary mass spectrometer, the fragments of the secondary ions, and the retention time. The metabolites were then qualitatively identified by comparing the spectral information with that in public databases. Commonly used public metabolite databases include HMDB (www.hmdb.ca), PubChem (https: / / pubchem.ncbi.nlm.nih.gov), MassBank (http: / / www.massbank.jp), MassBank of North America (https: / / massbank.us), and lipid databases such as Lipidmap (https: / / www.lipidmaps.org) and Lipidblast (https: / / fiehnlab.ucdavis.edu / projects / LipidBlast). Based on the initially identified metabolites, final validation was performed using the retention times, MS1 and MS2 mass spectrometry data of standards separated under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are: retention time within 0.1 min, and deviation between theoretical and measured molecular weight of metabolite less than 10 ppm.

[0090] 3. Screening of metabolic biomarkers

[0091] The Least Absolute Shrinkage and Selection Operator (Lasso) regression feature screening method was used to analyze plasma samples from subjects in the benign thyroid disease group and the early thyroid cancer group. Finally, 24 differential metabolites were screened as metabolic markers to distinguish between the benign thyroid disease group and the early thyroid cancer group (Table 2).

[0092] Table 2. 24 metabolic markers used to differentiate between benign thyroid diseases and early thyroid cancer

[0093] Serial Number English name Chinese name HMDB ID Molecular formula 1 Quinolinic acid Quinic acid HMDB0000232 C7H5NO4 2 LysoPC 15:0 Lysophosphatidylcholine 15:0 - C23H48NO7P 3 Indole-3-lactic acid Indole-3-lactic acid HMDB0000671 C11H11NO3 4 Homocysteine homocysteine HMDB0000742 C4H9NO2S 5 TAG 49:0 Triglycerides 49:0 - C52H100O6 6 TAG 46:1 Triglycerides 46:1 - C49H92O6 7 PE 36:4 Phosphatidylethanolamine 36:4 - C41H74NO8P 8 Glucose glucose HMDB0000122 C6H12O6 9 Glutamic acid glutamic acid HMDB0000148 C5H9NO4 10 Citric acid Citric acid HMDB0000094 C6H8O7 11 TAG 58:7 Triglycerides 58:7 - C61H104O6 12 PC 34:4 Phosphatidylcholine 34:4 - C42H76NO8P 13 2-Oxoglutaric acid 2-Oxoglutaric acid HMDB0000208 C5H6O5 14 PC 38:4 Phosphatidylcholine 38:4 - C46H84NO8P 15 PE 34:1 Phosphatidylethanolamine 34:1 - C39H76NO8P 16 O-Acetyl-L-carnitine O-acetyl-L-carnitine HMDB0000201 C9H17NO4 17 TAG 56:5 Triglycerides 56:5 - C59H104O6 18 L-Octanoylcarnitine Sinyl L-carnitine HMDB0000791 C15H29NO4 19 L-Pipecolic acid L-piperidinic acid HMDB0000716 C6H11NO2 20 Palmitoleic acid Palmitoleic acid HMDB0003229 C16H30O2 21 Glycoursodeoxycholic acid Glycineursodeoxycholic acid HMDB0000708 C26H43NO5 22 TAG 48:1 Triglycerides 48:1 - C51H96O6 23 L-Valyl-L-serine L-Valyl-L-serine HMDB0029136 C8H16N2O4 24 Cer d41:2 Ceramide D41:2 - C41H79NO3

[0094] Example 4: Construction of a diagnostic model to differentiate between benign thyroid diseases and early thyroid cancer

[0095] To evaluate the diagnostic efficacy of the selected 24 metabolic biomarker combinations in distinguishing between benign thyroid diseases and early-stage thyroid cancer, multivariate ROC curve analysis was performed on these combinations in the modeling group to construct a diagnostic model for early-stage thyroid cancer. Specifically, three-quarters of the sample data from the benign thyroid disease and early-stage thyroid cancer groups in the modeling group were randomly selected as the training set to construct and train the machine learning classification model; the remaining one-quarter of the samples were used as the test set to validate the discriminative ability of the trained model. Furthermore, a support vector machine (SVM) algorithm was employed, and the stability and reliability of the model were further improved through 1000 randomized cyclic cross-validations. By calculating the average accuracy of the model, a diagnostic model distinguishing between benign thyroid diseases and early-stage thyroid cancer was constructed.

[0096] ROC curve analysis is a plotting of ROC curves based on a series of different binary classification methods (cutoff values ​​or decision thresholds), with sensitivity (true positive rate) on the ordinate and 1-specificity (false positive rate) on the x-axis. The closer the ROC curve is to the upper left corner, the higher the diagnostic accuracy of the biomarker. The diagnostic value of potential biomarkers can be determined by calculating the area under the curve (AUC) for each combination of metabolic biomarkers. The AUC value is a key indicator of model performance. An AUC greater than 0.5, closer to 1, indicates better model performance and better diagnostic accuracy; a value less than 0.5 indicates that the model's performance is close to random guessing, with poor discrimination ability and accuracy. Besides AUC, ROC classification model performance evaluation also includes indicators such as sensitivity and specificity.

[0097] The sensitivity calculation formula is I:

[0098]

[0099] The specificity calculation formula is II:

[0100]

[0101] in:

[0102] TP (True Positive): The number of samples that are actually positive but were correctly predicted as positive.

[0103] TN (True Negative): The number of samples that were actually negative but were correctly predicted as negative.

[0104] FP (False Positive): The number of samples that are actually negative but are incorrectly predicted as positive.

[0105] FN (False Negative): The number of samples that are actually positive but are incorrectly predicted as negative.

[0106] The results showed that the diagnostic model constructed based on combinations of 24 metabolic biomarkers had an AUC of 0.821 (sensitivity: 72.4%, specificity: 81.8%), indicating that the constructed diagnostic model for early thyroid cancer has high diagnostic efficacy. Furthermore, diagnostic models were constructed using combinations of 18, 12, and 6 metabolic biomarkers, respectively, to evaluate the discriminative effect of different combinations of metabolic biomarkers in constructing early thyroid cancer diagnostic models.

[0107] Among them, 18 metabolic markers were identified: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamate, citric acid, triglycerides 58:7, phosphatidylcholine 34:4, 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglycerides 56:5, and octyl-L-carnitine. The ROC results showed an AUC of 0.850 (sensitivity: 82.8%, specificity: 72.7%).

[0108] The 12 metabolic markers were quinic acid, lysophosphatidylcholine (15:0), indole-3-lactic acid, homocysteine, triglycerides (49:0), triglycerides (46:1), phosphatidylethanolamine (36:4), glucose, glutamate, citric acid, triglycerides (58:7), and phosphatidylcholine (34:4). The ROC results showed an AUC of 0.830 (sensitivity: 76.7%, specificity: 81.8%).

[0109] The six metabolic markers used were quinic acid, lysophosphatidylcholine (15:0), indole-3-lactic acid, homocysteine, triglycerides (49:0), and triglycerides (46:1). The ROC results showed an AUC of 0.840 (sensitivity: 72.4%, specificity: 72.7%). These results indicate that the diagnostic models for early thyroid cancer constructed based on combinations of 24, 18, 12, and 6 metabolic markers, respectively, all possess good diagnostic efficacy.

[0110] Example 5: External validation of a diagnostic model for benign thyroid diseases and early thyroid cancer

[0111] To further validate the predictive performance of the diagnostic model for benign thyroid diseases and early thyroid cancer built based on the modeling group, the validation set dataset was used as an unknown sample and placed into the diagnostic model for benign thyroid diseases and early thyroid cancer built using the modeling group. The independent validation performance of the model on unknown datasets outside the modeling set was evaluated. Each sample in the validation set outputs a diagnostic threshold, resulting in a confusion matrix (including true positive, true negative, false positive, and false negative). Sensitivity and specificity can be calculated using formulas. In an ROC curve plot with sensitivity on the ordinate and 1-specificity on the abscissa, the true positive rate and false positive rate values ​​corresponding to each sample in the ROC curve are connected to plot the final ROC curve. The closer the ROC curve is to the upper left corner, the higher the diagnostic accuracy of the metabolic marker. The point on the ROC curve closest to the upper left corner has the largest sum of sensitivity and specificity; this point is called the optimal diagnostic threshold.

[0112] The diagnostic threshold for the prediction model constructed based on a combination of 24 metabolic biomarkers was 0.7267. Table 3 shows the confusion matrix results, confirming the model's good discriminative ability. Among 39 patients with early-stage thyroid cancer, 32 were classified as early-stage thyroid cancer, and 7 were misclassified as having benign thyroid diseases. Among 14 patients with benign thyroid diseases, 11 were classified as having benign thyroid diseases, and 3 were misclassified as having early-stage thyroid cancer. The ROC analysis results of the diagnostic model in the validation group are as follows: Figure 1As shown, sensitivity and specificity were calculated based on the confusion matrix results, and the results showed that AUC=0.848 (sensitivity: 82.1%, specificity: 78.6%).

[0113] In addition, we conducted external validation of diagnostic models constructed using different combinations of metabolic markers, employing 18 metabolic markers (quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamate, citrate, triglycerides 58:7, phosphatidylcholine 34:4, 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglycerides 56:5, and octyl-L-carnitine). The external validation results showed ( Figure 2 AUC=0.833 (Sensitivity: 74.4%, Specificity: 71.4%).

[0114] Using 12 metabolic markers (quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamate, citrate, triglycerides 58:7, phosphatidylcholine 34:4). External validation results showed ( Figure 3 AUC=0.839 (Sensitivity: 76.9%, Specificity: 80%).

[0115] Six metabolic markers were used (quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1). External validation results showed ( Figure 4 The AUC was 0.828 (sensitivity: 74.4%, specificity: 85.7%). These results indicate that the early thyroid cancer diagnostic model constructed using different combinations of metabolic biomarkers also demonstrated good predictive performance in the validation group.

[0116] Table 3. Confusion matrix of 24 metabolic biomarkers in diagnostic models distinguishing between benign thyroid diseases and early thyroid cancer

[0117] Early thyroid cancer group Benign thyroid disease group Early-stage thyroid cancer group (N=39) 32 (TP) 7 (FN) Benign thyroid disease group (N=14) 3 (FP) 11 (TN)

[0118] The present invention has been illustrated through the above embodiments, but the present invention is not limited to the above steps, that is, it does not mean that the present invention must rely on the above steps to be implemented. Those skilled in the art should understand that any improvements or modifications to the present invention should fall within the scope of protection and disclosure of the present invention.

Claims

1. A metabolic marker composition for distinguishing between benign thyroid diseases and early thyroid cancer, characterized in that, The metabolic marker composition comprises: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, and triglycerides 46:

1.

2. The composition according to claim 1, characterized in that, The composition further comprises: phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, and phosphatidylcholine 34:

4.

3. The composition according to claim 2, characterized in that, The composition further comprises: 2-oxoglutaric acid, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglyceride 56:5, and octyl-L-carnitine.

4. The composition according to claim 3, characterized in that, The composition further comprises: L-piperidinic acid, palmitoleic acid, glycoursodeoxycholic acid, triglyceride 48:1, L-valine-L-serine, and ceramide d41:

2.

5. The composition according to claim 4, characterized in that, The composition comprises the following metabolic markers: quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglycerides 49:0, triglycerides 46:1, phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, phosphatidylcholine 34:4, 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglycerides 56:5, octyl-L-carnitine, L-piperidine acid, palmitoleic acid, glycoursodeoxycholic acid, triglycerides 48:1, L-valine-L-serine, and ceramide d41:

2.

6. Use of the composition according to any one of claims 1-5 in the preparation of a reagent for distinguishing between benign thyroid diseases and early thyroid cancer.

7. The use according to claim 6, characterized in that, The samples used in the differentiation process are selected from at least one of serum, plasma, or blood.

8. Use of the composition according to any one of claims 1-5 in the preparation of a kit for distinguishing between benign thyroid diseases and early thyroid cancer.

9. The use according to claim 8, characterized in that, The samples used in the differentiation process are selected from at least one of serum, plasma, or blood.

10. A reagent or kit for differentiating benign thyroid diseases from early thyroid cancer, characterized in that, The reagent or kit comprises the metabolic biomarker composition according to any one of claims 1-5.

Images