Metabolic marker composition for early endometrial cancer diagnosis and application thereof

Abstract

The invention discloses a metabolic marker composition for early endometrial cancer diagnosis and application thereof, and relates to the technical field of biomedicine. The metabolic marker composition for early endometrial cancer diagnosis comprises lysophosphatidyl ethanolamine 16: 0, pyruvic acid, sphingomyelin d32: 1 (d16: 1 / 16: 0) and glycerol. The metabolic marker composition provided by the invention has relatively high sensitivity and specificity when being used for early endometrial cancer diagnosis, has relatively high accuracy, can realize accurate screening of endometrial cancer, and provides important help for prevention of endometrial cancer and reduction of morbidity. Meanwhile, when the metabolic marker composition is used for early endometrial cancer diagnosis, operation is easy, a sample is convenient to obtain, cost is low, invasion is avoided, and a patient can accept detection more conveniently, rapidly and safely.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a metabolic marker composition for diagnosing early endometrial cancer and an application thereof. Background Art

[0002] Endometrial cancer (EC), as one of the three most common malignant tumors in the female reproductive system, has become the malignant tumor with the highest incidence in the female reproductive system, and the age of onset tends to be younger. At present, the screening of endometrial cancer is mainly aimed at high-risk populations, while there is still a lack of effective screening strategies for individuals with medium risk or increased risk. Early diagnosis is the key to improving the cure rate of endometrial cancer patients. However, since the symptoms of the disease are not obvious in the early stages, many patients are often not diagnosed until the disease progresses to the late stage, which not only increases the difficulty of treatment, but also reduces the patient's chance of survival. Therefore, how to effectively improve the screening efficiency of endometrial cancer and its precancerous lesions, ensure early detection, timely diagnosis and rapid treatment measures, has become a major challenge faced by the medical community at home and abroad. Summary of the invention

[0003] Based on the above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a metabolic marker composition for the diagnosis of early endometrial cancer and its application, aiming to provide a marker for the diagnosis of early endometrial cancer with high accuracy, sensitivity and specificity.

[0004] The technical solution of the present invention is as follows:

[0005] In a first aspect of the present invention, a metabolic marker composition for the diagnosis of early endometrial cancer is provided, wherein the metabolic marker composition for the diagnosis of early endometrial cancer comprises lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol.

[0006] Optionally, the metabolic marker composition for the diagnosis of early endometrial cancer consists of lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol.

[0007] Optionally, the metabolic marker composition for the diagnosis of early endometrial cancer also includes at least one of phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine and methylated phosphatidylcholine 30:3e (12:1e / 18:2).

[0008] Optionally, the metabolic marker composition for the diagnosis of early endometrial cancer consists of lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0), glycerol, phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine and methylated phosphatidylcholine 30:3e (12:1e / 18:2).

[0009] Optionally, the metabolic marker composition for early endometrial cancer diagnosis further comprises at least one of lysophosphatidylcholine 20:3, lysophosphatidylcholine 22:4, proline and phenylalanine.

[0010] Optionally, the metabolic marker composition for the diagnosis of early endometrial cancer consists of lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0), glycerol, phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine, methylated phosphatidylcholine 30:3e (12:1e / 18:2), lysophosphatidylcholine 20:3, lysophosphatidylcholine 22:4, proline and phenylalanine.

[0011] The second aspect of the present invention provides a use of the metabolic marker composition for diagnosing early endometrial cancer as described above in the preparation of a product for diagnosing early endometrial cancer.

[0012] Optionally, the sample used by the product for diagnosing early endometrial cancer includes at least one of serum, plasma, blood and dried blood spots.

[0013] Optionally, the product comprises a reagent or a kit.

[0014] Optionally, the kit includes quality control products and / or standards.

[0015] Beneficial effects: The metabolite marker composition provided by the present invention has high sensitivity and specificity when used for the diagnosis of early endometrial cancer, has high accuracy, can achieve accurate screening of endometrial cancer, and provides important help for the prevention and reduction of the incidence of endometrial cancer. At the same time, the metabolite marker composition is simple to operate and easy to obtain samples when used for the diagnosis of early endometrial cancer, has low cost, is non-invasive, and allows patients to be tested more conveniently, quickly, and safely. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a graph showing the results of multivariate ROC curve analysis of 13 metabolic markers in the modeling group in Example 3.

[0017] Figure 2 This is a graph showing the results of multivariate ROC curve analysis of 13 metabolite markers in the validation group in Example 3. DETAILED DESCRIPTION

[0018] The present invention provides a metabolic marker composition for the diagnosis of early endometrial cancer and its application. In order to make the purpose, technical scheme and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] Unless otherwise defined, all technical terms and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0020] Some of the metabolic markers appearing in the following embodiments and examples are explained below.

[0021] Phosphatidylcholine belongs to the glycerol phospholipids. One of the three side chains is phosphatidylcholine, and the other two side chains are fatty acids.

[0022] In phosphatidylcholine 34:4 (16:1 / 18:3), 34:4 refers to the other two fatty acid side chains, which contain 34 carbon atoms and 4 double bonds; 16:1 refers to the fatty acid side chain containing 16 carbon atoms and 1 double bond; 18:3 refers to the fatty acid side chain containing 18 carbon atoms and 3 double bonds.

[0023] Lysophosphatidylethanolamine belongs to the glycerophospholipids. It is the enzymatic hydrolysis product obtained by losing a fatty acid molecule after phosphatidylethanolamine is hydrolyzed under the action of phospholipase. Therefore, it only contains one fatty acid side chain.

[0024] The 16:0 in lysophosphatidylethanolamine 16:0 refers to the fatty acid side chain, which contains 16 carbon atoms and 0 double bonds (ie, does not contain double bonds).

[0025] Methylated phosphatidylcholine belongs to the glycerophospholipids. One of the three side chains is phosphatidylcholine, and the other two side chains are fatty acids.

[0026] Methylated phosphatidylcholine 30:3e (12:1e / 18:2), where 30:3 in 30:3e refers to the other two side chain fatty acids, which contain 30 carbon atoms and 3 double bonds, and the e in 30:3e represents one of the two side chains. The covalent ester bond originally generated by the esterification of one hydroxyl group and one fatty acid molecule is replaced by an ether bond, so there is one less oxygen atom; 12:1 in 12:1e refers to the fatty acid side chain containing 12 carbon atoms and 1 double bond, and the e in 12:1e represents the covalent ester bond originally generated by the esterification of one hydroxyl group and one fatty acid molecule, which is replaced by an ether bond, so there is one less oxygen atom; 18:2 refers to the fatty acid side chain containing 18 carbon atoms and 2 double bonds.

[0027] Sphingomyelin belongs to the class of sphingolipids and contains two fatty acid side chains.

[0028] Sphingomyelin d34:4 (d14:0 / 20:4), in which the d in d34:4 refers to the two hydroxyl groups on the carbon chain of sphingosine, and the 34:4 in d34:4 refers to the 34 carbon atoms and 4 double bonds in the fatty acid side chain; the 14:0 in d14:0 refers to the fatty acid side chain containing 14 carbon atoms and 0 double bonds, and the d in d14:0 refers to the two hydroxyl groups on the carbon chain of sphingosine; 20:4 refers to the fatty acid side chain containing 20 carbon atoms and 4 double bonds.

[0029] Sphingomyelin d32:1 (d16:1 / 16:0), in which the d in d32:1 refers to the two hydroxyl groups on the carbon chain of sphingosine, and the 32:1 in d32:1 refers to the 32 carbon atoms and 1 double bond in the fatty acid side chain; the 16:1 in d16:1 refers to the fatty acid side chain containing 16 carbon atoms and 1 double bond, and the d in d16:1 refers to the two hydroxyl groups on the carbon chain of sphingosine; 16:0 refers to the fatty acid side chain containing 16 carbon atoms and 0 double bonds.

[0030] Lysophosphatidylcholine belongs to the class of glycerophospholipids and contains only one fatty acid side chain.

[0031] The 20:3 in lysophosphatidylcholine 20:3 refers to the fatty acid side chain, which contains 20 carbon atoms and 3 double bonds.

[0032] The 22:4 in lysophosphatidylcholine 22:4 refers to the fatty acid side chain, which contains 22 carbon atoms and 4 double bonds.

[0033] In recent years, metabolomics, as an important branch of systems biology, focuses on studying the changes of all metabolites in biological fluids or tissues. This technology has shown great potential in disease diagnosis, classification, treatment response monitoring and prognosis evaluation. In particular, for endometrial cancer, it is closely related to a variety of metabolic disorders, such as hypertension, diabetes and obesity. With the help of metabolomics technology, by accurately measuring and deeply analyzing the changes in metabolites in plasma, urine or endometrial tissue samples, it is possible to reveal the unique metabolic patterns of early endometrial cancer and identify metabolic biomarkers with important diagnostic value. These potential biomarkers not only help to develop non-invasive early screening methods and increase the probability of finding endometrial cancer, but are also expected to promote the early diagnosis and treatment of the disease, and significantly improve the overall treatment effect and quality of life of patients. Specifically, the present invention is based on metabolomics research, and successfully screens out metabolic markers with significant differences through in-depth analysis and comparison of metabolites in the plasma of early endometrial cancer patients and healthy individuals. The specific combination of these metabolite markers not only provides accurate biological indicators for the early diagnosis of endometrial cancer, but also brings an innovative and efficient detection method to clinical practice. This method has the ability to detect endometrial cancer early, quickly and with high accuracy, which helps to start the treatment process in time and significantly improve the patient's prognosis and quality of life. More specifically, an embodiment of the present invention provides a metabolic marker composition for the diagnosis of early endometrial cancer, wherein the metabolic marker composition for the diagnosis of early endometrial cancer includes lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol.

[0034] The metabolite marker composition provided by the present invention, i.e., the plasma metabolite marker, has high sensitivity and specificity when used for the diagnosis of early endometrial cancer, has high accuracy, can realize accurate screening of endometrial cancer, and provides important help for the prevention and reduction of the incidence of endometrial cancer. At the same time, the metabolite marker composition is simple to operate and easy to obtain samples when used for the diagnosis of early endometrial cancer, has low cost, is non-invasive, and allows patients to be tested more conveniently, quickly, and safely.

[0035] In some embodiments, the metabolic marker composition for the diagnosis of early endometrial cancer is composed of lysophosphatidylethanolamine 16:0, pyruvic acid, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol. The metabolic marker composition for the diagnosis of early endometrial cancer provided in this embodiment can effectively distinguish early endometrial cancer patients from healthy people, has high specificity and sensitivity, high accuracy, and can effectively realize the diagnosis of early endometrial cancer.

[0036] In some embodiments, the metabolic marker composition for early endometrial cancer diagnosis also includes at least one of phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine, and methylated phosphatidylcholine 30:3e (12:1e / 18:2). That is, the metabolic marker composition for early endometrial cancer diagnosis includes lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol, and at least one of phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine, and methylated phosphatidylcholine 30:3e (12:1e / 18:2). The metabolic marker composition for the diagnosis of early endometrial cancer provided in this embodiment can effectively distinguish patients with early endometrial cancer from healthy people, has high specificity and sensitivity, and is highly accurate, and can effectively realize the diagnosis of early endometrial cancer.

[0037] In some embodiments, the metabolic marker composition for the diagnosis of early endometrial cancer is composed of lysophosphatidylethanolamine 16:0, pyruvic acid, sphingomyelin d32:1 (d16:1 / 16:0), glycerol, phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine and methylated phosphatidylcholine 30:3e (12:1e / 18:2). The metabolic marker composition for the diagnosis of early endometrial cancer provided in this embodiment can effectively distinguish early endometrial cancer patients from healthy people, has high specificity and sensitivity, high accuracy, and can effectively realize the diagnosis of early endometrial cancer.

[0038] In some embodiments, the metabolic marker composition for early endometrial cancer diagnosis also includes at least one of lysophosphatidylcholine 20:3, lysophosphatidylcholine 22:4, proline and phenylalanine. That is, the metabolic marker composition for early endometrial cancer diagnosis includes lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol, and at least one of lysophosphatidylcholine 20:3, lysophosphatidylcholine 22:4, proline and phenylalanine. Alternatively, the metabolic marker composition for the diagnosis of early endometrial cancer includes lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0), glycerol, phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine and methylated phosphatidylcholine 30:3e (12:1e / 18:2), and at least one of lysophosphatidylcholine 20:3, lysophosphatidylcholine 22:4, proline and phenylalanine. The metabolic marker composition for the diagnosis of early endometrial cancer provided in this embodiment can effectively distinguish early endometrial cancer patients from healthy people, has high specificity and sensitivity, high accuracy, and can effectively realize the diagnosis of early endometrial cancer.

[0039] In some embodiments, the metabolic marker composition for the diagnosis of early endometrial cancer is composed of lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0), glycerol, phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine, methylated phosphatidylcholine 30:3e (12:1e / 18:2), lysophosphatidylcholine 20:3, lysophosphatidylcholine 22:4, proline and phenylalanine. The metabolic marker composition for the diagnosis of early endometrial cancer provided in this embodiment can effectively distinguish early endometrial cancer patients from healthy people, has high specificity and sensitivity, high accuracy, and can effectively realize the diagnosis of early endometrial cancer.

[0040] The embodiment of the present invention also provides a use of the metabolic marker composition for diagnosing early endometrial cancer as described above in the present invention in the preparation of a product for diagnosing early endometrial cancer. The metabolic marker composition for diagnosing early endometrial cancer provided by the present invention can effectively distinguish early endometrial cancer patients from healthy people, has high specificity and sensitivity, high accuracy, and can effectively realize the diagnosis of early endometrial cancer, and therefore can be used to prepare a product for diagnosing early endometrial cancer.

[0041] In some embodiments, the sample used by the product for diagnosing early endometrial cancer includes at least one of serum, plasma, blood, and dried blood spots.

[0042] In some embodiments, the product comprises a reagent or a kit.

[0043] In some embodiments, the kit includes quality control substances and / or standards.

[0044] The present invention will be further described below by means of specific examples.

[0045] Example 1 Detection and identification of small molecule metabolites in plasma samples

[0046] 1. Subjects’ conditions and sample collection

[0047] The inclusion and exclusion criteria for patients with early endometrial cancer were as follows:

[0048] Inclusion criteria: (1) All subjects obtained written informed consent before the study; (2) Women aged ≥18 years; (3) Patients diagnosed with stage I endometrial cancer (i.e., early endometrial cancer) by biopsy / postoperative pathology or by comprehensive evaluation by clinicians. In other words, all three of the above requirements must be met at the same time.

[0049] Exclusion criteria: (1) pregnancy or lactation; (2) emergency or emergency treatment; (3) history of blood transfusion within 7 days before sampling; (4) people who have received organ transplantation or non-autologous (allogeneic) bone marrow or stem cell transplantation; (5) history of malignant tumor within 5 years or any anti-tumor treatment before sampling; (6) concurrent multiple primary malignant tumors. If any of the above 6 items are met, they will be excluded.

[0050] A total of 78 plasma samples were collected from patients with early endometrial cancer (as the early endometrial cancer group, i.e., the EC(I) group, which only included patients with stage I endometrial cancer) and 123 healthy people (as the healthy control group, i.e., the HC group). Plasma samples were collected in the early morning on an empty stomach and all samples were stored in a -80°C refrigerator.

[0051] The plasma samples of 78 patients with early endometrial cancer were randomly divided into a modeling group and a validation group, and the plasma samples of 123 healthy people were also randomly divided into a modeling group and a validation group (i.e., the samples of the modeling group and the validation group were different). The modeling group included 59 plasma samples from the early endometrial cancer group and 94 plasma samples from the healthy control group; the validation group included 19 plasma samples from the early endometrial cancer group and 29 plasma samples from the healthy control group (Table 1).

[0052] Table 1. Subject sample information

[0053] Early endometrial cancer (EC(I)) group Healthy control (HC) group Number of samples in the modeling group (pieces) 59 94 Number of samples in the validation group (pieces) 19 29 Total (pcs) 78 123

[0054] 2. Reagents

[0055] Methanol, acetonitrile, water, acetic acid, isopropanol, methyl tert-butyl ether of mass spectrometry grade purity, formic acid and ammonium acetate of HPLC grade purity were purchased from Sigma-Aldrich, USA.

[0056] 3. Sample preparation

[0057] 100 μL of plasma was taken and placed in 1000 μL of pre-cooled mixed solution (composed of methyl tert-butyl ether and methanol, with a volume ratio of methyl tert-butyl ether to methanol of 3:1), and vortexed to mix well to obtain a sample extract.

[0058] 500 μL of a mixed solution of methanol and water (the volume ratio of methanol to water is 3:1) was added to the sample extract, ultrasonicated, allowed to stand, vortexed and centrifuged to separate layers. After the sample was separated, the upper layer was the organic phase and the lower layer was the aqueous phase.

[0059] Organic phase: Take 500 μL of the upper organic phase into a centrifuge tube, dry it, add 200 μL of a mixed solution of acetonitrile and isopropanol (the volume ratio of acetonitrile to isopropanol is 3:1), and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube to mix evenly, perform ultrasound-assisted treatment for 5 minutes, and then centrifuge the centrifuge tube at 12000 rpm for 5 minutes at room temperature; take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass injection vial, which is the organic phase material, and detect it on a machine (LC-MS, liquid chromatography-mass spectrometry).

[0060] Aqueous phase: Take 400 μL of the lower aqueous phase to a centrifuge tube, and add 1100 μL of ice methanol to precipitate the protein; after the protein in the centrifuge tube is precipitated, centrifuge the centrifuge tube, transfer 1000 μL of the supernatant to a new centrifuge tube, and dry overnight; add 200 μL of water to the dried centrifuge tube and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube, perform ultrasound-assisted treatment for 5 minutes, and then centrifuge the centrifuge tube at room temperature for 5 minutes; take 180 μL of the supernatant from the centrifuge tube to a 2 mL glass injection vial, which is the aqueous phase material, and detect it on the machine (LC-MS).

[0061] 4. Small molecule metabolite detection

[0062] The organic phase was purified by Waters ACQUTTY BEH C8 1.7 μm 2.1 mm × 100 mm column, the aqueous phase uses Waters ACQUTTY HSS T3 1.8 μm 2.1×100 mm column was used for small molecule separation; liquid chromatography and mass spectrometry both used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0063] The mobile phase parameters of the organic phase to be tested are as follows:

[0064] Mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate (the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 1%); mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile to isopropanol is 7:3, the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 1%), and the separation elution gradient is as follows: 55%-89% mobile phase B from 0 to 12 minutes, and 100% mobile phase B from 12 to 19.5 minutes.

[0065] The mobile phase parameters of the aqueous phase to be tested are as follows:

[0066] Mobile phase A is an aqueous solution containing 0.1% formic acid (the mass content of formic acid is 0.1%); mobile phase B is an acetonitrile solution containing 0.1% formic acid (the mass content of formic acid is 0.1%), and the flow rate is 0.4 mL / min; the separation elution gradient is as follows: 0 minutes for 1% mobile phase B; 13 minutes for 70% mobile phase B, 13.01 minutes for 99% mobile phase B; 18 minutes for 99% mobile phase B; 18.01 minutes for 1% mobile phase B; 22 minutes for 1% mobile phase B. The sample injection volume is 3 μL, and the temperature of the automatic sampler is 10°C.

[0067] The mass spectrometry parameters are as follows:

[0068] Full scan and data dependent acquisition (DDA) were used to obtain the spectral information of primary (MS1) mass spectrum and secondary mass spectrum (MS2) for polar metabolites. The full scan mass spectrum range was 100-1500Da. The secondary scan mode (Full MS / dd-MS2) scan range was 100-310Da, 300-710Da and 700-1500Da. The mass spectrometer was an Orbitrap high-resolution mass spectrometer equipped with an electrospray ionization (ESI) source, and data were collected in positive and negative ionization modes. The specific parameters were as follows: automatic gain control (AGC) was 3E+6 (i.e., 3×10 6), Maximum IT (maximum injection time) is 200 ms, the resolution of the first full scan is 70000 FWHM (@200 m / z), the resolution of the secondary mass spectrometer in the second scan mode (Full MS / dd-MS2) is 17500 FWHM (@200 m / z), the quadrupole window is 1.5 m / z, and the AGC is 1E+5 (i.e. 1×10 5 ), the maximum ion injection time was 50 ms, the relative collision energy (Higher-energy Collisional Dissociation, HCD) was 30 eV, the ion spray voltage was 3500 V in positive mode, 3000 V in negative mode, the nebulizer was 20 psi, the sheath gas temperature was 400 °C, and the sheath gas flow rate was 10 L / min.

[0069] 5. Metabolomics data processing

[0070] a. Extract the peaks of the RAW format files of mass spectrometry into Feature XML format files, reduce the dimension of the original mass spectrometry data, and improve the signal-to-noise ratio;

[0071] b. Using the peak alignment algorithm of OpenMS software, the retention time of the extracted peak format data is corrected and aligned between samples, thereby converting the mass spectrometry data into a data matrix;

[0072] c. Match and filter the isotope peaks in the data matrix obtained in step b, and then replace the abnormal data (0, negative values, background noise, etc.) with vacant values;

[0073] d. Among all the characteristic peaks obtained in step c, those with a detection rate of <80% are eliminated, and those with a detection rate of >80% are filled with the median value of the characteristic peak, and 5% random noise (obeying standard normal distribution) is added;

[0074] e. In order to reduce the difference in metabolite concentrations between samples and make the data distribution more symmetrical, the Normalization Autoencoder (NormAE) was used for homogenization to remove systematic errors such as batch effects.

[0075] 6. Identification of metabolites

[0076] According to the original data, the spectral information of the compound parent ion and secondary fragment ion is obtained after the software is analyzed, such as the mass-to-charge ratio (m / z) of the primary mass spectrum and the fragments of the secondary ion. The metabolites are qualitatively identified by matching the spectral information of the primary and secondary metabolites in the database; commonly used metabolite databases include the Human Metabolite Database (HMDB, www.hmdb.ca), the Organic Small Molecule Bioactivity Database (PubChem, https: / / pubchem.ncbi.nlm.nih.gov), the Mass Spectrum Database (MassBank, http: / / www.massbank.jp; MassBank of North America, https: / / massbank.us) and the Lipid Metabolite Database (Lipidmap, www.lipidmaps.org); based on the metabolites identified in the relevant databases, the metabolites are finally identified according to the retention time, MS1, and MS2 mass spectrometry information when the standard is separated under the same chromatographic column and mass spectrometry conditions. The standard for metabolite identification is that the retention time is within 0.1min difference and the molecular weight error of the metabolite is less than 10ppm.

[0077] Example 2 Screening of metabolic markers for distinguishing early endometrial cancer group from healthy control group

[0078] (1) Perform metabolomics data analysis

[0079] The univariate area under the curve (AUC) value and the variable importance in the projection (VIP) value of the metabolomics data of the EC (I) group and the HC group in the modeling group were combined to distinguish the healthy control group and the early endometrial cancer group, build a diagnostic model, and perform multivariate ROC (Receiver Operating Characteristic) curve analysis. The specific steps include the following:

[0080] The metabolomic data of the modeling group samples (including 59 samples in the EC(I) group and 94 samples in the HC group) were subjected to univariate ROC curve analysis and orthogonal partial least squares discriminant analysis (OPLS-DA). The intersection was taken based on the conditions of univariate AUC value > 0.75 and VIP value > 1.8, and 13 differential metabolites with significant differences between the early endometrial cancer group and the healthy control group were screened out (Table 2).

[0081] Table 2 Metabolic markers used to distinguish early endometrial cancer group from healthy control group

[0082]

[0083]

[0084] Example 3 Construction of a diagnostic model for distinguishing early endometrial cancer group from healthy control group based on a combination of 13 metabolic markers

[0085] In order to evaluate the ability of the screened metabolite markers in distinguishing early endometrial cancer, a multivariate ROC analysis was performed on these 13 metabolite markers. Specifically, 3 / 4 samples were randomly selected from the modeling group sample data as training sets to build and optimize the machine learning classification model. The remaining 1 / 4 of the data was used as a test set to evaluate the classification performance of the model after training. In addition, the support vector machine (SVM) algorithm was used, and 1000 random cycle cross-validations were performed to improve the stability and reliability of the model. Finally, by calculating the average value of the model accuracy, a diagnostic model that can effectively distinguish early endometrial cancer patients from healthy individuals was constructed.

[0086] The ROC curve is a tool used to analyze the relationship between model sensitivity and specificity. It is plotted with sensitivity as the Y-axis and 1-specificity as the X-axis. The performance of the model is evaluated by comparing the area under the curve (AUC): when the AUC value is greater than 0.5, the closer the AUC is to 1, the better the performance of the model and the higher the diagnostic efficiency; conversely, if the AUC is lower than 0.5, it means that the model's predictive ability is poor. The ROC classification prediction model system not only includes the basic ROC curve and AUC indicators, but also involves many important parameters such as sensitivity, specificity, accuracy, and precision.

[0087] The sensitivity is:

[0088]

[0089] Specificity is:

[0090]

[0091] The accuracy is:

[0092]

[0093] The accuracy is:

[0094]

[0095] in,

[0096] TP (True positive): True positive, the number of samples that are actually positive examples that are correctly predicted as positive examples;

[0097] TN (Ture Negative): True negative, the number of samples that are actually negative examples but are correctly predicted as negative examples;

[0098] FP (False Positive): False positive, the number of samples that are actually negative examples but are mistakenly predicted as positive examples;

[0099] FN (False Negative): False negatives, the number of samples that are actually positive examples but are mistakenly predicted as negative examples.

[0100] The results are as follows Figure 1 As shown, AUC = 0.994 (sensitivity = 0.933, specificity = 0.917, accuracy = 0.923, precision = 0.875), indicating that the diagnostic model for distinguishing early endometrial cancer group from healthy control group based on the combination of 13 metabolic markers has a high diagnostic ability.

[0101] In order to further verify the effectiveness of the diagnostic model for distinguishing the early endometrial cancer group from the healthy control group based on the modeling group data, the model was tested using independent validation group data. Specifically, multivariate ROC curve analysis was used to evaluate the independent validation efficacy of the diagnostic model for unknown data sets outside the modeling group. By inputting the samples of the validation group into the diagnostic model established by the modeling group data, the model calculated the corresponding probability value (Probability) based on the measurement results of 13 key metabolite markers in each sample. Using this probability value as the diagnostic threshold, a set of confusion matrices (including true positive, true negative, false positive and false negative information) was generated. Based on this confusion matrix, performance indicators such as the sensitivity, specificity, accuracy and precision of the model can be calculated. In addition, by drawing points with sensitivity as the Y-axis and 1-specificity as the X-axis in the ROC graph, the model performance under different diagnostic thresholds can be intuitively displayed. For each sample, when its probability value is used as the diagnostic threshold, a series of different points can be obtained on the ROC graph, and the curve formed by connecting these points is the ROC curve. On this curve, the point with the best sensitivity and specificity is selected, and the corresponding diagnostic threshold is 0.4463.

[0102] As shown in the confusion matrix results in Table 3, in the diagnostic model constructed based on the above 13 metabolic markers, when the diagnostic threshold was set to 0.4463, among the 19 patients with early endometrial cancer, 17 were accurately identified, while 2 were misjudged as healthy individuals; among the 29 healthy controls, 27 were correctly identified, while 2 were misjudged as early endometrial cancer. Based on the confusion matrix data, the ROC analysis results of the diagnostic model in the validation group were calculated, as shown in Figure 3. Figure 2 As shown. The results showed that AUC = 0.975 (sensitivity = 0.896, specificity = 0.931, accuracy = 0.917, precision = 0.895). The above results show that the diagnostic model constructed for distinguishing the early endometrial cancer group from the healthy control group also showed excellent diagnostic efficacy in the validation group.

[0103] Table 3. Confusion matrix of the diagnostic model used to distinguish early endometrial cancer from healthy people

[0104] Early endometrial cancer Healthy people 19 patients with early endometrial cancer 17(TP) 2(FN) 29 healthy people 2(FP) 27(TN)

[0105] Example 4 Construction of a diagnostic model for distinguishing early endometrial cancer group from healthy control group based on a combination of 9 metabolic markers

[0106] A diagnostic model using a combination of 9 metabolite markers was used to perform ROC curve analysis in the modeling group. The only difference from Example 3 was that the 9 metabolite markers phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0), sphingomyelin d34:4 (d14:0 / 20:4), creatinine, glycerol and methylated phosphatidylcholine 30:3e (12:1e / 18:2) were combined to construct a diagnostic model.

[0107] The results showed that when using 9 metabolic markers, AUC = 0.997 (sensitivity = 0.933, specificity = 0.958, accuracy = 0.949, precision = 0.933). The above results show that the constructed diagnostic models have high diagnostic efficacy and clinical diagnostic significance.

[0108] In order to further verify the effectiveness of the diagnostic model constructed based on the modeling group data to distinguish between the early endometrial cancer group and the healthy control group, it was verified in the validation group, and a multivariate ROC curve analysis was performed. The results were: AUC = 0.976 (sensitivity = 0.895, specificity = 0.966, accuracy = 0.938, precision = 0.944) in the validation group.

[0109] Example 5 Construction of a diagnostic model for distinguishing early endometrial cancer group from healthy control group based on a combination of four metabolic markers

[0110] The diagnostic model using 4 metabolite markers was analyzed by ROC curve in the modeling group. The only difference from Example 3 was that 4 lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol were used. When 4 metabolite markers were used, AUC = 0.967 (sensitivity = 0.933, specificity = 0.864, accuracy = 0.892, precision = 0.824). The above results show that the constructed diagnostic models have high diagnostic efficacy and clinical diagnostic significance.

[0111] In order to further verify the effectiveness of the diagnostic model constructed based on the modeling group data for distinguishing the early endometrial cancer group from the healthy control group, it was verified in the validation group, and a multivariate ROC curve analysis was performed. The results were: AUC = 0.947 (sensitivity = 1, specificity = 0.823, accuracy = 0.889, precision = 0.769) in the validation group. The above results show that the diagnostic model constructed for distinguishing the early endometrial cancer group from the healthy control group also has a good diagnostic effect in the validation group.

[0112] In summary, the present invention provides a metabolic marker composition for the diagnosis of early endometrial cancer and its application. The metabolic marker composition provided by the present invention has high sensitivity and specificity when used for the diagnosis of early endometrial cancer, has high accuracy, can achieve accurate screening of endometrial cancer, and provides important help for the prevention and reduction of the incidence of endometrial cancer. At the same time, the metabolic marker composition is simple to operate and easy to obtain samples when used for the diagnosis of early endometrial cancer, with low cost and non-invasiveness, so that patients can be tested more conveniently, quickly and safely, and is particularly suitable for risk screening of endometrial cancer in remote areas and large populations.

[0113] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A metabolic marker composition for the diagnosis of early endometrial cancer, characterized in that: The metabolic marker composition for diagnosing early endometrial cancer comprises lysophosphatidylethanolamine 16:0, pyruvic acid, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol.

2. The metabolic marker composition for early endometrial cancer diagnosis according to claim 1, characterized in that: The metabolic marker composition for diagnosing early endometrial cancer consists of lysophosphatidylethanolamine 16:0, pyruvic acid, sphingomyelin d32:1 (d16:1 / 16:0) and glycerol.

3. The metabolic marker composition for early endometrial cancer diagnosis according to claim 1, characterized in that: The metabolic marker composition for diagnosing early endometrial cancer also includes at least one of phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine and methylated phosphatidylcholine 30:3e (12:1e / 18:2).

4. The metabolic marker composition for early endometrial cancer diagnosis according to claim 3, characterized in that: The metabolic marker composition for diagnosing early endometrial cancer consists of lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0), glycerol, phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine and methylated phosphatidylcholine 30:3e (12:1e / 18:2).

5. The metabolic marker composition for early endometrial cancer diagnosis according to claim 1 or 3, characterized in that: The metabolic marker composition for early endometrial cancer diagnosis also includes at least one of lysophosphatidylcholine 20:3, lysophosphatidylcholine 22:4, proline and phenylalanine.

6. The metabolic marker composition for early endometrial cancer diagnosis according to claim 5, characterized in that: The metabolic marker composition for diagnosing early endometrial cancer consists of lysophosphatidylethanolamine 16:0, pyruvate, sphingomyelin d32:1 (d16:1 / 16:0), glycerol, phosphatidylcholine 34:4 (16:1 / 18:3), arabinose, sphingomyelin d34:4 (d14:0 / 20:4), creatinine, methylated phosphatidylcholine 30:3e (12:1e / 18:2), lysophosphatidylcholine 20:3, lysophosphatidylcholine 22:4, proline and phenylalanine.

7. Use of the metabolic marker composition for diagnosing early endometrial cancer according to any one of claims 1 to 6 in the preparation of a product for diagnosing early endometrial cancer.

8. The use according to claim 7, characterized in that: The sample used by the product for diagnosing early endometrial cancer includes at least one of serum, plasma, blood and dried blood spots.

9. The use according to claim 8, characterized in that: The product comprises a reagent or a kit.

10. The use according to claim 9, characterized in that: The kit includes quality control products and / or standard products.

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