CN-122017247-A - Plasma metabolism marker for distinguishing benign thyroid diseases from early thyroid cancers and application of plasma metabolism marker

Abstract

The invention provides a metabolic marker composition for distinguishing benign thyroid diseases from early thyroid cancers, 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 early warning and diagnosis of a functional layer at an earlier stage of thyroid cancer occurrence, solves the problems of insufficient specificity and high false positive rate when the existing imaging examination distinguishes benign and malignant thyroid lesions, has higher sensitivity and specificity, can provide basis for early clinical intervention, and is suitable for large-scale crowd screening and repeated dynamic monitoring of high-risk individuals.

Inventors

• LI YAN
• Hu Juanyuan
• WEN HE
• Cui Huina
• YU SHUQI
• Li Erhan
• PENG XING
• SUN HAIJIAO
• PANG SHUYU
• CHEN WANJUN

Assignees

• 哈尔滨脉图精准技术有限公司

Dates

Publication Date

20260512

Application Date

20260106

Claims (10)

1. 1. A metabolic marker composition for distinguishing benign thyroid diseases from early thyroid cancers is characterized by comprising quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglyceride 49:0 and triglyceride 46:1.
2. 2. The composition of claim 1, wherein the composition further comprises phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglycerides 58:7, and phosphatidylcholine 34:4.
3. 3. The composition of claim 2, wherein the composition further comprises 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglycerides 56:5, and octyl L-carnitine.
4. 4. The composition of claim 3, wherein the composition further comprises L-pipecolic acid, palmitoleic acid, glycoursodeoxycholic acid, triglycerides 48:1, L-valyl-L-serine, and ceramide d41:2.
5. 5. The composition of claim 4, wherein the composition consists of metabolic markers quinic acid, lysophosphatidylcholine 15:0, indole-3-lactic acid, homocysteine, triglyceride 49:0, triglyceride 46:1, phosphatidylethanolamine 36:4, glucose, glutamic acid, citric acid, triglyceride 58:7, phosphatidylcholine 34:4, 2-oxoglutarate, phosphatidylcholine 38:4, phosphatidylethanolamine 34:1, O-acetyl-L-carnitine, triglyceride 56:5, octyl L-carnitine, L-piperidineic acid, palmitoleic acid, glycoursodeoxycholic acid, triglyceride 48:1, L-valyl-L-serine, ceramide d41:2.
6. 6. Use of a composition according to any one of claims 1-5 for the preparation of a reagent for distinguishing benign thyroid disorders from early thyroid cancer.
7. 7. The use according to claim 6, wherein the sample used in the distinguishing process is selected from at least one of serum, plasma or blood.
8. 8. Use of a composition according to any one of claims 1-5 for the preparation of a kit for distinguishing benign thyroid disorders from early thyroid cancer.
9. 9. The use according to claim 8, wherein the sample used in the distinguishing process is selected from at least one of serum, plasma or blood.
10. 10. A reagent or kit for distinguishing benign thyroid disease from early thyroid cancer, comprising the metabolic marker composition of any one of claims 1-5.

Description

Plasma metabolism marker for distinguishing benign thyroid diseases from early thyroid cancers and application of plasma metabolism marker Technical Field The invention relates to the field of detection and analysis of metabolic markers, in particular to a plasma metabolic marker for distinguishing benign thyroid diseases from early thyroid cancers and application thereof. Background Thyroid cancer (Thyroid Cancer, TC) originates from a malignancy of the thyroid follicular epithelium or perifollicular epithelial cells, and is also the most common malignancy of the head and neck. In recent years, the incidence of thyroid cancer has been continuously rising worldwide. Recent global cancer statistics published by the world health organization International cancer research Institute (IARC) show that thyroid cancer is about 4.1% of all malignant tumors. In China, the incidence of thyroid cancer also continuously rises, and the third incidence of all malignant tumors is in existence, and is inferior to lung cancer and colorectal cancer. Especially in urban areas, female thyroid cancer incidence has jumped the fourth of all malignant tumors in females and is expected to continue to grow at a rate of about 20% per year. Although the age-standardized mortality rate of thyroid cancer is relatively low, mainly due to the better prognosis of differentiated thyroid cancer (e.g., papillary carcinoma), the rapid increase in the incidence rate still brings great diagnostic and therapeutic burden to the medical system, and at the same time, induces public widespread attention and anxiety. The core contradiction faced by current diagnosis and treatment of thyroid cancer is the imbalance between "overdiagnosis" and "accurate identification". With the widespread use of high-resolution ultrasound technology, a large number of thyroid micro-nodules, which were previously difficult to detect, are detected early, most of which are benign lesions. However, ultrasound examination has a significant lack of specificity in identifying benign and malignant nodules, resulting in a large number of benign nodules being misjudged as suspicious malignant, and further receiving unnecessary invasive examinations such as fine needle biopsy, even over-treatment. This not only brings physical and psychological trauma to the patient, but also increases the economic burden of medical treatment. Therefore, how to realize noninvasive and accurate distinction of benign thyroid lesions from early thyroid cancers in huge thyroid disease population, not only avoid excessive intervention on inert lesions, but also ensure early discovery of malignant lesions with great clinical significance, and is a key problem of the current clinical management of thyroid cancers. From the pathological development progress, there is a close biological link between benign thyroid diseases and thyroid cancers. Most thyroid carcinomas originate in follicular epithelial cells, and develop in a multi-stage, polygenic progression. Clinical and pathological studies have shown that most thyroid nodules, which are imaging or cytologically judged to be benign, do not undergo malignant changes during long-term follow-up, but that some types such as follicular adenomas are considered to be a potential precancerous lesion, some of which may progress to follicular carcinoma, and that in the development of papillary carcinoma there may also be an evolving process of progressive cellular atypia, ultimately malignant changes, from benign nodules. Therefore, from the aspect of pathological evolution, a diagnosis method capable of accurately distinguishing benign thyroid lesions from early thyroid cancers is established, and the diagnosis method has important clinical significance for early detection of malignant tendency and optimization of clinical decisions. Metabonomics is an important branch of systematic biology, and can directly reflect the final phenotype of a life system by qualitatively and quantitatively analyzing the overall change of small molecular metabolites (molecular weight <1000 Da) in organisms, and is closely related to the physiological and pathological states of diseases. During the development of tumor cells, metabolic reprogramming Cheng Xianxiang (such as the warburg effect) is prevalent to meet the energy and biosynthetic precursors required for their rapid proliferation. These metabolic changes leave a detectable signature in the metabolic profile of body fluids such as blood, urine, etc. The metabonomics is hopeful to capture abnormal metabolic changes in the early stage of tumor development, and early diagnosis and risk early warning are realized. The metabonomics technology is applied to the identification of benign and malignant thyroid nodule, and is hopeful to make up for the shortages of the existing diagnosis method. Through a high-throughput analysis technology and a multi-element statistics and machine learning method, a group of characteristic metabol