Screening and application of endometrial cancer plasma metabolism marker composition

Abstract

The invention discloses screening and application of an endometrial cancer plasma metabolism marker composition. Based on metabonomics research, a group of metabolic marker compositions with significant difference are screened out by analyzing and comparing metabolites in plasma of endometrial cancer patients and healthy people. By means of the screened markers, early, rapid and accurate detection of endometrial cancer can be achieved, so that treatment measures can be taken in time, and the survival rate and life quality of patients are improved. The metabolic marker obtained by the invention has the advantages of non-invasive detection, high sensitivity and specificity, high throughput analysis, cost effectiveness and early screening. Early discovery and treatment of diseases such as cancers are facilitated, and prognosis is improved. In practical application, excellent performance and huge potential are shown, and a new direction is provided for disease diagnosis and health management in the future.

Description

Technical Field

[0001] The present invention relates to the field of metabolomics, and in particular to the screening and application of an endometrial cancer plasma metabolic marker composition. Background Art

[0002] Cancer is a leading cause of death worldwide. Endometrial cancer (EC), a type of uterine corpus cancer, is a malignant tumor originating in the endometrium. It is one of the most common cancers of the female reproductive system, particularly prevalent in postmenopausal women. The incidence of endometrial cancer has been increasing in recent years. According to the latest data from the International Agency for Research on Cancer (IARC), there were approximately 417,367 new cases worldwide in 2020, accounting for 4.5% of all female cancers. In China, there were approximately 63,400 new cases of endometrial cancer and 13,500 deaths, with a rate of approximately 9.8 new cases and 1.4 deaths per 100,000 population. Because endometrial cancer often presents with subtle symptoms in its early stages, many patients are diagnosed at an advanced stage, significantly limiting treatment options and compromising prognosis. Therefore, early diagnosis is crucial to improving treatment effectiveness and patient survival. Current diagnostic methods, such as ultrasound and tissue biopsy, have limitations, including invasiveness, high cost, and insufficient sensitivity. Therefore, there is an urgent need to develop new and effective endometrial cancer detection methods to improve treatment efficacy, reduce mortality, and improve patient prognosis.

[0003] With the development of precision medicine, the demand for early diagnosis and treatment of diseases is increasing. Metabolomics, as an emerging "omics" science, can provide comprehensive information about disease status by analyzing metabolites in biological samples, thus opening up new possibilities for the early diagnosis of cancer. As one of the most easily accessible biological samples, changes in the metabolome of plasma can reflect a variety of physiological and pathological states. Plasma metabolomics technology uses highly sensitive detection methods such as mass spectrometry and nuclear magnetic resonance to accurately measure various metabolites in plasma, including amino acids, lipids, carbohydrates, vitamins, and hormones. Changes in these metabolites are closely related to the progression of various diseases. Therefore, plasma metabolomics technology shows great application potential in the diagnosis, classification, treatment response monitoring, and prognosis assessment of diseases. Summary of the Invention

[0004] Based on metabolomics research, this invention screens a panel of significantly different metabolite markers by analyzing and comparing plasma metabolites in endometrial cancer patients and healthy controls. This metabolite marker combination can be used as a biomarker for detecting endometrial cancer, providing a new and effective diagnostic tool for clinical use. This method can achieve early, rapid, and accurate detection of endometrial cancer, thereby facilitating timely treatment and improving patient survival and quality of life.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention discloses a method for screening a composition of plasma metabolic markers for endometrial cancer, comprising the following steps: 1) Collect plasma samples from endometrial cancer patients and healthy controls, and prepare organic and aqueous phases from the samples; 2) Metabolomics data were collected using liquid chromatography and mass spectrometry on the organic and aqueous phases. The organic phase was analyzed using a Waters ACQUTTY UPLC® BEH C8 1.7 µm 2.1*100 mm column, and the aqueous phase was analyzed using a Waters ACQUTTY UPLC® HSS T3 1.8 µm 2.1*100 mm column. 3) After analyzing the collected data, the metabolite spectrum information is obtained and the metabolites are qualitatively identified after matching with common databases; 4) In the modeling group, the metabolomics data of endometrial cancer patients and healthy controls were subjected to univariate ROC analysis and OPLS-DA analysis; 5) The intersection data of the univariate ROC analysis value and the OPLS-DA analysis value were taken to obtain differential metabolites, where the intersection was a univariate ROC analysis AUC value > 0.79 and an OPLS-DA analysis VIP value > 1.8.

[0006] Preferably, the mass spectrometry conditions are as follows: automatic gain control (AGC) of 3E+6, Maximum IT of 200 ms, a full scan resolution of 70,000 FWHM (@200 m / z), in the secondary scan mode (Full MS / dd-MS2), the secondary mass spectrometry resolution is 17,500, the quadrupole window is 1.5 m / z, the AGC is 1E+5, the maximum ion injection time is 50 ms, the relative collision energy (HCD) is 30 eV, the ion spray voltage is 3,500 V in positive mode and 3,000 V in negative mode, the nebulizer is 20 psi, the sheath gas temperature is 400°C, and the sheath gas flow rate is 10 L / min.

[0007] Preferably, the organic phase liquid chromatography conditions are as follows: mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate; mobile phase B is an acetonitrile-isopropanol (7:3 v / v) solution containing 0.1% acetic acid and 1% ammonium acetate, 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.

[0008] Preferably, the aqueous liquid chromatography conditions are: 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 elution gradient is as follows: 0 min, 1% mobile phase B; 13 min, 70% mobile phase B, 13.01 min, 99% mobile phase B; 18 min, 99% mobile phase B; 18.01 min, 1% mobile phase B; 22 min, 1% mobile phase B.

[0009] The present invention discloses plasma metabolic markers of endometrial cancer, which include glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid and lysophosphatidylethanolamine 20:4.

[0010] Preferably, the metabolic markers further include: lysophosphatidylcholine 22:5, pyruvic acid and erythrosine.

[0011] Preferably, the metabolic markers further include: arabinose and L-glutamine.

[0012] Preferably, the metabolic markers consist of glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid and lysophosphatidylethanolamine 20:4.

[0013] Preferably, the metabolic markers consist of glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid, lysophosphatidylethanolamine 20:4, lysophosphatidylcholine 22:5, pyruvic acid and erythrosine.

[0014] Preferably, the metabolic markers consist of glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid, lysophosphatidylethanolamine 20:4, lysophosphatidylcholine 22:5, pyruvic acid, erythritol, arabinose and L-glutamine.

[0015] The present invention discloses an endometrial cancer plasma metabolic marker composition, which comprises glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid and lysophosphatidylethanolamine 20:4.

[0016] Preferably, the metabolic marker composition further comprises: lysophosphatidylcholine 22:5, pyruvic acid and erythrosine acid.

[0017] Preferably, the metabolic marker composition further comprises: arabinose and L-glutamine.

[0018] Preferably, the metabolic marker composition consists of glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid and lysophosphatidylethanolamine 20:4.

[0019] Preferably, the metabolic marker composition consists of glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid, lysophosphatidylethanolamine 20:4, lysophosphatidylcholine 22:5, pyruvic acid and erythrosine.

[0020] Preferably, the metabolic marker composition consists of glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid, lysophosphatidylethanolamine 20:4, lysophosphatidylcholine 22:5, pyruvic acid, erythric acid, arabinose and L-glutamine.

[0021] The present invention discloses use of the metabolic marker in preparing a reagent and / or a kit for detecting endometrial cancer.

[0022] The present invention discloses a kit for detecting endometrial cancer, which comprises any one of the metabolic marker compositions.

[0023] The present invention discloses use of the metabolic marker composition in preparing a reagent and / or a kit for detecting endometrial cancer.

[0024] The present invention discloses a kit for detecting endometrial cancer, which comprises any one of the metabolic markers.

[0025] Preferably, the kit further comprises quality control substances and / or standard substances.

[0026] Compared to existing technologies, the present invention brings innovation to disease diagnosis through metabolomics technology, with the following advantages: 1) Non-invasive detection: only blood samples are required, which is safe and easy to accept; 2) High sensitivity and specificity: Accurately detect small metabolic changes and achieve early diagnosis; 3) High-throughput analysis: Multiple metabolites can be analyzed simultaneously, suitable for large-scale screening; 4) Cost-effectiveness: Reduced resource consumption and improved screening efficiency; 5) Early screening advantage: Contributes to the early detection and treatment of diseases such as cancer, improving prognosis. In summary, the metabolomics disease diagnosis technology of the present invention not only achieves technological innovation, but also demonstrates excellent performance and huge potential in practical applications, providing a new direction for future disease diagnosis and health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 .Multivariate ROC curve analysis of 9 metabolic markers in the modeling group.

[0028] Figure 2 Multivariate ROC curve analysis of 9 metabolic markers in the validation group. DETAILED DESCRIPTION

[0029] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0030] Example 1 Subject Conditions and Sample Collection

[0031] The inclusion and exclusion criteria for patients with endometrial cancer were as follows: Inclusion criteria: (1) All subjects obtained written informed consent before participating in the study; (2) Women aged ≥18 years; (3) Patients diagnosed with endometrial cancer by biopsy / postoperative pathology or through comprehensive evaluation by clinicians.

[0032] 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) multiple primary malignant tumors at the same time.

[0033] Plasma samples were collected from 92 patients with endometrial cancer (EC) and 123 healthy controls (HC). All plasma samples were collected in the early morning on an empty stomach and stored in a -80°C freezer.

[0034] Example 2 Detection, Identification, and Metabolite Marker Screening of Small Molecule Metabolites in Plasma Samples

[0035] (1) Reagents: Methanol, acetonitrile, water, acetic acid, isopropanol, and methyl tert-butyl ether of mass spectrometry grade, and formic acid and ammonium acetate of HPLC grade were purchased from Sigma-Aldrich, USA.

[0036] (2) Sample preparation: Take 100 μL of plasma, place it in 1000 μL of pre-cooled (methyl tert-butyl ether: methanol, volume ratio 3:1) solution, vortex to mix the extracted blood sample, and obtain the sample extract; add 500 μL of (methanol: water, volume ratio 3:1) solution to the sample extract, sonicate, let it stand, vortex and centrifuge to separate the layers.

[0037] Organic phase: After the sample is separated, 500 μL of the upper layer is transferred to a centrifuge tube as the organic phase. After the organic phase is dried, 200 μL of (acetonitrile:isopropanol, volume ratio 3:1) is added and incubated at room temperature for 15 min. After incubation, the centrifuge tube is vortexed and ultrasonically treated for 5 min. The centrifuge tube is then centrifuged at room temperature for 5 min (12,000 rpm). 180 μL of the supernatant from the centrifuge tube is transferred to a 2 mL glass injection vial as the organic phase for LC-MS analysis. Aqueous phase: During sample stratification, remove 400 μL of the lower aqueous phase to a centrifuge tube and add 1100 μL of ice-cold methanol to precipitate the protein. After the protein in the centrifuge tube is precipitated, centrifuge the tube and 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 min. After incubation, vortex the centrifuge tube, ultrasonically treat it for 5 min, and then centrifuge it at room temperature for 5 min (12000 rpm). Take 180 μL of the supernatant from the centrifuge tube and transfer it to a 2 mL glass injection vial. This is the aqueous phase and is detected by LC-MS.

[0038] (3) Small molecule metabolite detection: A Waters ACQUTTY UPLC® BEH C8 1.7 µm 2.1*100 mm column was used for the organic phase, and a Waters ACQUTTY UPLC® HSS T3 1.8 µm 2.1*100 mm column was used for the aqueous phase for small molecule separation. The liquid chromatography and mass spectrometry were both performed using an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0039] The mobile phase parameters of the organic phase to be tested are as follows: Mobile phase A was an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate; mobile phase B was an acetonitrile-isopropanol (7:3 v / v) solution containing 0.1% acetic acid and 1% ammonium acetate. The separation elution gradient was as follows: 55%-89% mobile phase B from 0 to 12 minutes and 100% mobile phase B from 12 to 19.5 minutes.

[0040] The mobile phase parameters of the aqueous analyte are as follows: Mobile phase A was an aqueous solution containing 0.1% formic acid; mobile phase B was an acetonitrile solution containing 0.1% formic acid, with a flow rate of 0.4 mL / min. The separation elution gradient was as follows: 0 min, 1% mobile phase B; 13 min, 70% mobile phase B; 13.01 min, 99% mobile phase B; 18 min, 99% mobile phase B; 18.01 min, 1% mobile phase B; 22 min, 1% mobile phase B. The sample injection volume was 3 μL, and the autosampler temperature was 10°C. The mass spectrometry parameters are as follows: Polar metabolites were analyzed using full scan and data-dependent acquisition (DDA) to acquire primary (MS1) and secondary (MS2) mass spectrometric information. The full scan mass spectrum range was 100–1500 Da. The secondary scan mode (Full MS / dd-MS2) scan ranges were 100–310, 300–710, and 700–1500 Da. The mass spectrometer was an Orbitrap high-resolution mass spectrometer equipped with an electrospray ionization (ESI) source, acquiring data in both positive and negative ionization modes. The specific parameters are as follows: automatic gain control (AGC) of 3E+6, MaximumIT of 200 ms, full scan resolution of 70,000 FWHM (@200 m / z), full MS / dd-MS2 resolution of 17,500, quadrupole window of 1.5 m / z, AGC of 1E+5, maximum ion injection time of 50 ms, higher-energy collisional dissociation (HCD) of 30 eV, ion spray voltage of 3,500 V in positive mode and 3,000 V in negative mode, nebulizer of 20 psi, sheath gas temperature of 400°C, and sheath gas flow rate of 10 L / min.

[0041] (4) Metabolomics data processing The RAW format files downloaded from the mass spectrometer were extracted and converted into FeatureXML format files to reduce the dimensionality of the original mass spectrometry data and improve the signal-to-noise ratio. The peak alignment algorithm of OpenMS software was used to correct and align the retention times of the extracted peak format data between samples, thereby converting the mass spectrometry data into a data matrix. The isotope peaks in the data matrix obtained in step 2 were matched and filtered, and abnormal data (0, negative values, background noise, etc.) were replaced with blank values. Among all the characteristic peaks obtained in step 3, those with a detection rate <80% were eliminated, and those with a detection rate >80% were filled with the median value of the characteristic peak and 5% random noise (obeying the standard normal distribution) was added. In order to reduce the differences in metabolite concentrations between samples and make the data distribution more symmetrical, the normalization autoencoder (NormAE) was used for homogenization processing to remove systematic errors such as batch effects.

[0042] (5) Identification of metabolites After software analysis of the raw data, the spectral information of the compound precursor ion and secondary fragment ions, such as the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragmentation of the secondary ions, is obtained. Metabolites are qualitatively identified by matching the spectral information of primary and secondary metabolites with those in databases. Commonly used 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 Lipidmap (www.lipidmaps.org). Metabolites identified based on these databases are finally identified based on the retention time, MS1, and MS2 mass spectra of the standard separated on the same chromatographic column and mass spectrometry conditions. Metabolite identification criteria are retention time differences within 0.1 min and molecular weight errors within 10 ppm.

[0043] (6) Metabolomics data analysis The metabolomics data of EC and HC group samples were analyzed by combining the univariate area under the curve (AUC) value and the variable importance in the projection (VIP) value to distinguish the healthy group from the endometrial cancer group. A diagnostic model was constructed, and multivariate ROC (Receiver Operating Characteristic) curve analysis was performed.

[0044] Subject Information: The samples used in both the modeling and validation groups were actual samples collected independently, and the two groups were distinct. The modeling group included 71 plasma samples from the EC group and 95 plasma samples from the HC group; the validation group included 21 plasma samples from the EC group and 28 plasma samples from the HC group (Table 1). Metabolic marker screening and diagnostic model construction were first performed using the modeling group samples, and the validation group samples were then used for verification. Table 1. Subject information Endometrial cancer (EC) Healthy people (HC) Modeling group sample size 71 95 Number of samples in the validation group 21 28 total 92 123 Metabolic marker screening for distinguishing endometrial cancer group from healthy group: The metabolomic data of the EC and HC modeling groups were analyzed using univariate ROC analysis to obtain AUC values, and OPLS-DA analysis was used to obtain VIP values. Then, the differential metabolites between the healthy group and the endometrial cancer group were screened using the intersection of AUC value > 0.79 and VIP value > 1.8. Finally, a total of 9 differential metabolites were obtained (Table 2).

[0045] Table 2. Nine metabolic markers for distinguishing healthy and cervical cancer patients Metabolic Markers-Chinese Metabolic Markers-English AUC VIP glutamate Glutamic acid 0.822 2.089 Lysophosphatidylcholine 22:5 LysoPC 22:5 0.804 1.957 Lysophosphatidylethanolamine 20:4 LysoPE 20:4 0.806 2.127 Phosphatidylcholine 34:4 (16:1 / 18:3) PC34:4 (16:1 / 18:3) 0.819 2.066 Pyruvate Pyruvic acid 0.804 1.957 Erythropic acid Erythronic acid 0.799 2.046 L-Glutamine L-Glutamine 0.798 1.875 arabinose Arabinose 0.792 1.970 Hippuric acid Hippuric acid 0.791 2.023

[0046] Example 3 Construction of a diagnostic model for distinguishing endometrial cancer and healthy subjects To evaluate the ability of the selected metabolite markers to distinguish endometrial cancer, we performed a multivariate receiver operating characteristic (ROC) analysis on these nine metabolite markers. Three-quarters of the sample data from the modeling group were randomly selected as the training set to construct and optimize the machine learning classification model. The remaining one-quarter of the data served as the test set to evaluate the classification performance of the trained model. Furthermore, we employed the support vector machine (SVM) algorithm and performed 1000 randomized recurrent cross-validation cycles to improve the model's stability and reliability. Ultimately, by calculating the average model accuracy, we successfully constructed a diagnostic model that can effectively distinguish endometrial cancer patients from healthy individuals.

[0047] The ROC curve is a tool used to analyze the relationship between model sensitivity and specificity. It is plotted with sensitivity on the Y-axis and 1-specificity on the X-axis. Model performance 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 model's performance and the higher its diagnostic efficacy. Conversely, if the AUC is less than 0.5, it indicates poor predictive ability. The ROC classification prediction model system not only includes the basic ROC curve and AUC indicators, but also involves multiple important parameters such as sensitivity, specificity, accuracy, and precision.

[0048] The sensitivity is:

[0049] Specificity is:

[0050] The accuracy is:

[0051] The accuracy is:

[0052] in, TP (True positive): True positive, the number of samples that are actually positive examples that are correctly predicted as positive examples; TN (Ture Negative): True negative, the number of samples that are actually negative examples that are correctly predicted as negative examples; FP (False Positive): False positives, the number of samples that are actually negative examples but are mistakenly predicted as positive examples; FN (False Negative): False negatives, the number of samples that are actually positive but are mistakenly predicted as negative; The results are as follows Figure 1 As shown in the figure, AUC = 0.995 (sensitivity = 0.875, specificity = 1, accuracy = 0.929, precision = 0.857), indicating that the diagnostic model for distinguishing endometrial cancer from healthy groups based on the combination of 9 metabolic markers has a high diagnostic ability.

[0053] In addition, receiver operating characteristic (ROC) curve analyses were performed on the diagnostic models using different combinations of metabolite markers. These analyses included a combination of seven metabolite markers (glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid, lysophosphatidylethanolamine 20:4, lysophosphatidylcholine 22:5, pyruvate, and erythritol) and a combination of four metabolite markers (glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid, and lysophosphatidylethanolamine 20:4). The results showed that the AUC for the seven metabolite markers was 0.992 (sensitivity = 0.944, specificity = 0.956, accuracy = 0.951, precision = 0.944); and the AUC for the four metabolite markers was 0.979 (sensitivity = 0.888, specificity = 0.916, accuracy = 0.904, precision = 0.917). The above results show that the constructed diagnostic models have high diagnostic efficacy and clinical diagnostic significance.

[0054] Example 4 Validation of a diagnostic model for distinguishing endometrial cancer from healthy subjects

[0055] To further validate the diagnostic model constructed based on the modeling data to distinguish endometrial cancer from healthy controls, the model was tested using data from an independent validation set. Multivariate receiver operating characteristic (ROC) curve analysis was used to evaluate the independent validation performance of the diagnostic model on an unknown dataset outside the modeling set. Samples from the validation set were fed into the diagnostic model constructed using the modeling data. The model calculated a probability value (probability) based on the measurements of nine key metabolites in each sample. Using this probability value as the diagnostic threshold, a confusion matrix (containing information such as true positives, true negatives, false positives, and false negatives) was generated. Based on this confusion matrix, performance metrics such as model sensitivity, specificity, accuracy, and precision were calculated. Furthermore, by plotting points on an ROC plot with sensitivity on the Y-axis and 1-specificity on the X-axis, model performance at different diagnostic thresholds can be visualized. For each sample, a series of distinct points are generated on the ROC plot when its probability value is used as the diagnostic threshold. The curve formed by connecting these points is the ROC curve. The optimal sensitivity and specificity combination corresponds to a diagnostic threshold of 0.5511.

[0056] As shown in the confusion matrix results in Table 3, in the diagnostic model constructed based on the above 9 metabolic markers, when the diagnostic threshold was set to 0.5511, among the 21 endometrial cancer patients, 19 were accurately identified, while 2 were misclassified as healthy individuals; among the 28 healthy control groups, 27 were correctly identified, and only 1 was misclassified as 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 2The results showed an AUC of 0.990 (sensitivity = 0.905, specificity = 0.964, accuracy = 0.939, and precision = 0.95). These results demonstrate that the diagnostic model constructed for distinguishing endometrial cancer from healthy controls also exhibited excellent diagnostic efficacy in the validation group.

[0057] Table 3. Confusion matrix of the diagnostic model used to distinguish endometrial cancer from healthy subjects Endometrial cancer Healthy Group 21 cases of endometrial cancer 19 (TP) 2 (FN) 28 healthy subjects 1 (FP) 27 (TN) In addition, the diagnostic models based on different combinations of metabolite markers were validated in the validation group, and multivariate ROC curve analysis was performed. The results showed that the combination of the seven metabolite markers, glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid, lysophosphatidylethanolamine 20:4, lysophosphatidylcholine 22:5, pyruvate, and erythritol, had an AUC of 0.988 (sensitivity = 0.952, specificity = 0.964, accuracy = 0.959, precision = 0.952) in the validation group; the combination of the four metabolite markers, glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid, and lysophosphatidylethanolamine 20:4, had an AUC of 0.969 (sensitivity = 0.857, specificity = 0.892, accuracy = 0.877, precision = 0.857) in the validation group. The above results show that the diagnostic model constructed to distinguish endometrial cancer from the healthy group also has a good diagnostic effect in the validation group.

[0058] In summary, this invention uses advanced metabolomics techniques based on plasma samples to accurately detect small molecule metabolites in plasma, successfully screening a set of specific metabolic markers for distinguishing endometrial cancer patients from healthy individuals. Based on these markers, we further utilized machine learning algorithms to construct an efficient and accurate diagnostic model that can effectively identify endometrial cancer, which is of great significance for early prevention of the disease and reducing its incidence. In addition, this method has the advantages of being non-invasive and convenient for sampling, making it particularly suitable for endometrial cancer risk screening in remote areas and large populations.

[0059] It should be understood that the scope of application of the present invention is not limited to the above examples. A person skilled in the art may reasonably make improvements or modifications based on the above description. All such improvements and modifications are deemed to be within the scope of protection of the appended claims.

Claims

1. A method for screening a composition of plasma metabolic markers for endometrial cancer, characterized in that: The steps include: 1) Collect plasma samples from endometrial cancer patients and healthy subjects, and prepare organic and aqueous phases of the samples respectively; 2) Use liquid chromatography and mass spectrometry to collect metabolomics data for the organic phase and the aqueous phase. The organic phase uses a Waters ACQUTTY UPLC ® BEH C8 1.7 µm 2.1*100 mm column, and the aqueous phase uses a Waters ACQUTTY UPLC ® HSS T3 1.8 µm 2.1*100 mm column; 3) After analyzing the collected data, the spectral information of the metabolites is obtained, and the metabolites are qualitatively identified after matching with the commonly used databases; 4) In the modeling group, the metabolomics data of endometrial cancer patients and healthy subjects were subjected to univariate ROC analysis and OPLS-DA analysis; 5) The intersection data of the univariate ROC analysis value and the OPLS-DA analysis value were taken to obtain differential metabolites, wherein the intersection was the univariate ROC analysis AUC value > 0.79 and the OPLS-DA analysis VIP value > 1.

8.

2. The screening method according to claim 1, characterized in that The mass spectrometry conditions were as follows: automatic gain control (AGC) of 3E+6, Maximum IT of 200 ms, full scan resolution of 70,000 FWHM (@200 m / z), in full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer was 17,500, the quadrupole window was 1.5 m / z, AGC was 1E+5, the maximum ion injection time was 50 ms, the relative collision energy (HCD) was 30 eV, the ion spray voltage was 3,500 V in positive mode and 3,000 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.

3. The screening method according to claim 1, characterized in that The organic phase liquid chromatography conditions are as follows: mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate; mobile phase B is an acetonitrile-isopropanol (7:3 v / v) solution containing 0.1% acetic acid and 1% ammonium acetate, and the separation elution gradient is as follows: 0-12 minutes is 55%-89% mobile phase B, 12-19.5 minutes is 100% mobile phase B.

4. The screening method according to claim 1, characterized in that The aqueous phase liquid chromatography conditions are 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, and the flow rate is 0.4 mL / min; the separation elution gradient is as follows: 0 min, 1% mobile phase B; 13 min, 70% mobile phase B, 13.01 min, 99% mobile phase B; 18 min, 99% mobile phase B; 18.01 min, 1% mobile phase B; 22 min, 1% mobile phase B.

5. The metabolite marker composition obtained by the screening method according to claim 1, characterized in that: The metabolic marker composition includes: glutamate, phosphatidylcholine 34:4 (16:1 / 18:3), hippuric acid and lysophosphatidylethanolamine 20:

4.

6. The metabolic marker composition according to claim 5, characterized in that: The metabolic marker composition further comprises: lysophosphatidylcholine 22:5, pyruvic acid and erythrosine acid.

7. The metabolic marker composition according to claim 6, characterized in that: The metabolic marker composition further includes: arabinose and L-glutamine.

8. Use of the metabolite marker composition according to any one of claims 5 to 7 in the preparation of a reagent and / or kit for detecting endometrial cancer.

9. A kit for detecting endometrial cancer, characterized in that: The kit comprises the metabolic marker composition according to any one of claims 5-7.

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

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