Plasma metabolic markers for detecting various gynecological tumors and their applications

Abstract

This invention provides a combination of metabolic biomarkers for distinguishing between healthy individuals and gynecological tumors. Based on metabolomics research, this invention analyzes and compares metabolites in the plasma of gynecological tumor patients and healthy individuals to screen for a set of metabolic biomarker combinations with significant differences. Based on the characteristics of plasma metabolites, this invention constructs a diagnostic model with high sensitivity and specificity. This method can be used for risk screening of three types of gynecological tumors: ovarian cancer, cervical cancer, and endometrial cancer. It is non-invasive, easy to obtain samples, and has high subject compliance, making it suitable for large-scale population screening, especially in remote areas with limited medical resources. It also avoids over-testing and reduces testing costs.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of metabolomics, in particular to plasma metabolic markers for detecting multiple gynecological tumors and application thereof. BACKGROUND

[0002] The three most common gynecological malignant tumors in female population are ovarian cancer, cervical cancer and endometrial cancer. These tumors are highly harmful, especially in the uterus and ovaries. Cervical cancer and endometrial cancer have early clinical symptoms, and most patients are in the early stage, with a 5-year survival rate of more than 80%. However, once recurrence and metastasis occur, the prognosis is poor, and the 5-year survival rate is less than 20%. In contrast, ovarian cancer is highly malignant and difficult to diagnose, with a mortality rate of the first place, and about 70% of patients are in the advanced stage at the time of diagnosis, with a 5-year survival rate of only 39%, which is the highest mortality rate among gynecological tumors. Therefore, screening and diagnosis of multiple gynecological tumors are of great importance.

[0003] Currently, the detection methods for the above three gynecological malignant tumors in clinical practice mainly determine the size, location and stage of the tumor through histopathological examination combined with imaging examination. However, most of the examination methods are invasive, resulting in reduced patient compliance. Although imaging examination avoids invasiveness, it itself has radiation hazards, especially for pregnant women, and is not suitable for development. Moreover, these conventional examination methods are only suitable for patients who have clinical symptoms and need further examination, and are not convenient for people who are older, at higher risk and need regular screening, especially in areas with poor medical conditions and scarce medical resources. Therefore, regular screening of multiple gynecological tumor risks has become a major problem for women.

[0004] In view of the current situation, the application of metabolomics in disease diagnosis provides a new perspective, which identifies disease states by analyzing the changes of metabolites, and provides a practical alternative method for clinical diagnosis. Metabolomics analyzes the relationship between metabolites and physiological and pathological changes of the body by accurately quantifying small molecule metabolites in the body, studies the occurrence and development of diseases, finds biomarkers for diseases, and predicts disease prognosis, etc. The sensitivity and specificity of diagnosis are ideal. Therefore, based on metabolomics technology, the present application provides a combination of metabolic markers for detecting plasma samples based on metabolomics. This method has high sensitivity and specificity, and can realize the risk screening of ovarian cancer, cervical cancer and endometrial cancer at one time, avoiding one-by-one examination of multiple sites at the initial screening stage, avoiding excessive diagnosis and treatment, significantly reducing the detection cost, and improving the detection efficiency. SUMMARY

[0005] The technical problem to be solved by this invention is to overcome the defects and deficiencies of the prior art and provide a method for constructing a highly sensitive and specific diagnostic model for multiple gynecological malignancies based on metabolomics detection of combinations of metabolic markers in plasma samples. This method enables risk screening of three high-risk gynecological tumors in women, including ovarian cancer, cervical cancer, and endometrial cancer, and provides important assistance for risk screening of gynecological tumors in women.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] This invention discloses a combination of metabolic markers for distinguishing between healthy individuals and gynecological tumors, including ovarian cancer, cervical cancer, and endometrial cancer. The combination of metabolic markers includes: sphingomyelin d38:2 (d20:1 / 18:1), L-glutamate, ceramide d35:1 (d18:1 / 17:0), and sphingomyelin d41:2 (d18:1 / 23:1).

[0008] Preferably, the combination of metabolic markers further includes: phosphatidylcholine 32:2, hippuric acid and ceramide d40:2 (d18:2 / 22:0).

[0009] Preferably, the combination of metabolic markers further includes: L-glutamine, lysophosphatidylethanolamine 16:0, benzoic acid, and pyruvate.

[0010] Preferably, the combination of metabolic markers further includes: sphingomyelin d38:2 (d20:1 / 18:1), L-glutamate, ceramide d35:1 (d18:1 / 17:0), sphingomyelin d41:2 (d18:1 / 23:1), phosphatidylcholine 32:2, hippuric acid, and ceramide d40:2 (d18:2 / 22:0).

[0011] Preferably, the combination of metabolic markers further includes: sphingomyelin d38:2 (d20:1 / 18:1), L-glutamate, ceramide d35:1 (d18:1 / 17:0), sphingomyelin d41:2 (d18:1 / 23:1), phosphatidylcholine 32:2, hippuric acid, ceramide d40:2 (d18:2 / 22:0), L-glutamine, lysophosphatidylethanolamine 16:0, benzoic acid, and pyruvate.

[0012] Preferably, the metabolic markers consist of sphingomyelin d38:2 (d20:1 / 18:1), L-glutamic acid, ceramide d35:1 (d18:1 / 17:0), sphingomyelin d41:2 (d18:1 / 23:1), phosphatidylcholine 32:2, hippuric acid, ceramide d40:2 (d18:2 / 22:0), L-glutamine, lysophosphatidylethanolamine 16:0, benzoic acid, and pyruvate.

[0013] This invention discloses a metabolic biomarker composition for distinguishing between healthy individuals and gynecological tumors, including ovarian cancer, cervical cancer, and endometrial cancer. The composition comprises: sphingomyelin d38:2 (d20:1 / 18:1), L-glutamic acid, ceramide d35:1 (d18:1 / 17:0), sphingomyelin d41:2 (d18:1 / 23:1), phosphatidylcholine 32:2, hippuric acid, ceramide d40:2 (d18:2 / 22:0), L-glutamine, lysophosphatidylethanolamine 16:0, benzoic acid, and pyruvate.

[0014] This invention discloses the use of the aforementioned combination of metabolic markers in the preparation of reagents / kits for detecting gynecological tumors, namely ovarian cancer, cervical cancer, and endometrial cancer.

[0015] This invention discloses a kit for detecting gynecological tumors, the kit comprising the aforementioned combination of metabolic markers, wherein the gynecological tumors are ovarian cancer, cervical cancer, and endometrial cancer.

[0016] Preferably, the kit also includes quality control products and / or standards.

[0017] This invention discloses a method for screening metabolic markers associated with gynecological tumors, including ovarian cancer, cervical cancer, and endometrial cancer, comprising the following steps:

[0018] 1) Collect plasma samples from gynecological cancer patients and healthy individuals, and prepare organic and aqueous phases from the samples respectively;

[0019] 2) Metabolomics data were acquired from the organic and aqueous phases using liquid chromatography and mass spectrometry. The organic phase was analyzed using a Waters ACQUTTY UPLC® BEH C8 1.7µm 2.1*100mm column, and the aqueous phase was analyzed using a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column.

[0020] 3) After parsing the collected data, obtain the spectral information of metabolites, and then perform qualitative analysis of the metabolites by matching them with commonly used databases;

[0021] 4) In the modeling group, metabolomics data from gynecological tumor patients and healthy individuals were subjected to univariate ROC analysis and OPLS-DA analysis to obtain univariate AUC and VIP values, respectively.

[0022] 5) Obtain the differential metabolites by taking the intersection data of univariate ROC analysis values ​​and OPLS-DA analysis values. The intersection is defined as a univariate ROC analysis AUC value > 0.7 and an OPLS-DA analysis VIP value > 1.8.

[0023] Preferably, the mass spectrometry conditions are:

[0024] The Full MS mode has a resolution of 70,000, a scan range of 100-1500 m / z, an AGC of 3E+6, and a maximum IT of 200 ms. In Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5 m / z, the AGC is 1E+5, the maximum ion implantation time is 50 ms, and the HCD relative collision energy is 30 eV.

[0025] 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 for 0-12 minutes, and 100% mobile phase B for 12-19.5 minutes.

[0026] Preferably, the aqueous 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. The separation elution gradient is as follows: 0-13 minutes for 1%-70% mobile phase B, and 13-18 minutes for 99% mobile phase B.

[0027] Compared with existing technologies, this invention constructs a diagnostic model with high sensitivity and specificity based on the characteristics of plasma metabolites. This method can be used for risk screening of three gynecological tumors: ovarian cancer, cervical cancer, and endometrial cancer. It is non-invasive, easy to obtain samples, and has strong subject compliance. It is suitable for large-scale population screening and is more suitable for remote areas with scarce medical resources. It also avoids over-examination and reduces examination costs. Attached Figure Description

[0028] Figure 1 Multivariate ROC curve analysis of 11 key biomarkers that distinguish between healthy and cancer groups in the modeling group.

[0029] Figure 2 Multivariate ROC curve analysis of 11 key biomarkers that distinguish between healthy and cancer groups in the validation group. Detailed Implementation

[0030] 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.

[0031] Example 1: Subject Information and Sample Collection

[0032] 1. Subject Information

[0033] 1) Sample inclusion criteria:

[0034] Participants must meet all of the following inclusion criteria to be eligible to participate in this study:

[0035] (1) Females aged ≥18 years;

[0036] (2) Read and fully understand the information, sign the informed consent form, and be able to provide a blood sample for metabolomics testing;

[0037] (3) Cancer group: diagnosed by biopsy / postoperative pathology or by comprehensive clinical evaluation by clinicians as ovarian cancer, primary cervical cancer or endometrial cancer.

[0038] 2) Sample exclusion criteria:

[0039] Subjects who meet any of the following exclusion criteria are ineligible to participate in this study:

[0040] (1) During pregnancy or lactation;

[0041] (2) Emergency room visit or resuscitation required;

[0042] (3) History of blood transfusion within 7 days prior to sampling;

[0043] (4) People who have received organ transplants or have previously received non-autologous (allogeneic) bone marrow or stem cell transplants;

[0044] (5) History of malignant tumor within 5 years or any anti-tumor treatment before sampling;

[0045] (6) Simultaneous co-occurrence of multiple primary malignant tumors.

[0046] 1) Subject information

[0047] This study collected plasma samples from 408 participants across two medical centers, including 123 healthy control (HC) samples and 285 cancer samples. The cancer samples included 98 cervical cancer samples, 92 endometrial cancer samples, and 95 ovarian cancer samples. Specifically, the plasma samples used for modeling consisted of 94 healthy controls and 220 cancer samples (77 cervical cancer samples, 72 endometrial cancer samples, and 71 ovarian cancer samples); the plasma samples used for validation consisted of 29 healthy controls and 65 cancer samples (21 cervical cancer samples, 20 endometrial cancer samples, and 24 ovarian cancer samples) (Table 1).

[0048] Table 1: Subject Information

[0049] Healthy control group (HC) Cancer group Number of people in the modeling team 94 220 Number of people in the verification group 29 65 total 123 285

[0050] Example 2: Detection of plasma metabolites and screening of metabolic biomarkers

[0051] 1. Plasma metabolite detection

[0052] 1) Reagents:

[0053] Methanol, acetonitrile, water, acetic acid, and isopropanol of mass spectrometry grade purity, and formic acid, ammonium acetate, and methyl tert-butyl ether of chromatographic (HPLC) grade purity were all purchased from Sigma-Aldrich, USA.

[0054] 2) Sample preparation:

[0055] Take 100 μL of plasma and 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 stand, vortex and centrifuge to separate the layers. The upper layer is the organic phase and the lower layer is the aqueous phase.

[0056] - Organic phase: After the sample is separated into layers, take 500 μL of the upper organic phase into a centrifuge tube, dry it, add 200 μL of (acetonitrile:isopropanol, volume ratio 3:1), and incubate at room temperature for 15 minutes; after incubation, vortex the centrifuge tube, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm); take 180 µL of the supernatant from the centrifuge tube into a 2 mL glass vial, which is the organic phase test solution, and perform LC-MS detection.

[0057] - Aqueous phase: After sample separation, transfer the lower 400 μL aqueous phase to a centrifuge tube and add 1100 μL of ice-cold methanol to precipitate proteins. After protein precipitation, centrifuge the tube and transfer 1000 μL of the supernatant to a new centrifuge tube, then dry overnight. Add 200 μL of water to the dried centrifuge tube and incubate at room temperature for 15 minutes. After incubation, vortex the mixture, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm). Transfer 180 μL of the supernatant from the centrifuge tube to a 2 mL glass vial as the aqueous phase test solution. Analyze using LC-MS.

[0058] 3) Detection of small molecule metabolites:

[0059] For small molecule separation, the organic phase was separated using a Waters ACQUTTY UPLC® BEH C8 1.7µm 2.1*100mm column, and the aqueous phase was separated using a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column. The liquid chromatography and mass spectrometry systems used were the ACQUITY UPLC I-Class liquid chromatography system (Waters) and the Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0060] The mobile phase parameters are as follows:

[0061] Organic phase test solution mobile phase parameters – 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. The separation elution gradient is as follows: 55%-89% mobile phase B for 0-12 minutes, and 100% mobile phase B for 12-19.5 minutes.

[0062] Mobile phase parameters for the aqueous test solution: Mobile phase A is an aqueous solution containing 0.1% formic acid; Mobile phase B is an acetonitrile solution containing 0.1% formic acid. The separation and elution gradient is as follows: 0-13 minutes for 1%-70% mobile phase B, and 13-18 minutes for 99% mobile phase B.

[0063] The mass spectrometry parameters are as follows:

[0064] Mass spectrometry data were acquired using Full MS and Full MS / dd-MS2 (each with both positive and negative modes). The parameters used by QExactive were as follows: Full MS mode had a resolution of 70,000, a scan range of 100-1500 m / z, an AGC of 3E+6, and a maximum IT of 200 ms; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer was 17,500, the quadrupole window was 1.5 m / z, the AGC was 1E+5, the maximum ion implantation time was 50 ms, and the HCD relative collision energy was 30 eV.

[0065] 2. Metabolomics data preprocessing and metabolite identification

[0066] 1) Metabolomics data processing:

[0067] (1) Extract peaks from the RAW format file of the mass spectrometer and convert it into a FeatureXML format file to reduce the dimensionality of the original mass spectrometry data and improve the signal-to-noise ratio; (2) Use the peak alignment algorithm of OpenMS software to correct and align the retention time of the extracted peak format data between samples, thereby converting the mass spectrometry data into a data matrix; (3) Match and filter the isotope peaks in the data matrix obtained in step 2, and replace abnormal data (0, negative values, background noise, etc.) with missing values; (4) Remove the feature peaks with a detection rate of <80% from all the feature peaks obtained in step 3, fill the median value of the feature peak with a detection rate of >80%, and add 5% random noise (following a standard normal distribution); (5) In order to reduce the difference in metabolite concentration between samples and make the data distribution more symmetrical, use a normalization autoencoder (NormAE) for normalization processing to remove systematic errors such as batch effects.

[0068] 2) Identification of metabolites:

[0069] After analyzing the raw data using software, the spectral information of the primary precursor ion (MS1) and secondary fragment ion (MS2) of the compound is obtained. This information, such as the mass-to-charge ratio (m / z) of the primary mass spectrometer and the fragment ion data, is matched with the spectral information of primary and secondary metabolites in public databases to qualitatively identify the metabolites. Commonly used metabolite databases include the Human Metabolite Database (HMDB, www.hmdb.ca), the Metabolomics Database (Metlin, metlin.scripps.edu), the Mass Spectrometry Database (www.massbank.jp), and the Lipid Map Database (Lipidmap, www.lipidmaps.org). Metabolites identified based on these databases are then finally validated using retention times, MS1, and MS2 mass spectrometry data obtained when separated from standards under the same chromatographic column and mass spectrometry conditions. The criteria for metabolite identification are a retention time difference within 0.1 min and a theoretical and measured molecular weight difference of less than 10 ppm.

[0070] 3. Data Analysis

[0071] 1) Screening of metabolic biomarkers to distinguish between healthy and cancer groups

[0072] Univariate ROC analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed on the data from the modeling group samples to obtain univariate AUC and VIP values. Using a combination of univariate AUC > 0.7 and VIP > 1.8 as screening criteria, the intersection of these criteria was taken, ultimately identifying 11 differentially expressed metabolites (Table 2) that significantly contributed to the differences between groups, serving as important metabolic biomarkers distinguishing between healthy and cancer groups.

[0073] Table 2: 11 Important Metabolic Markers that Differentiate Between Healthy and Cancer Groups

[0074] Logo - English Logo - Chinese Univariate AUC value VIP value 1 L-Glutamine L-glutamine 0.84 2.33 2 SM d38:2 (d20:1 / 18:1) Sphingomyelin d38:2 (d20:1 / 18:1) 0.79 1.87 3 LysoPE 16:0 Lysophosphatidylethanolamine 16:0 0.79 1.85 4 Benzoic acid benzoic acid 0.79 2.04 5 Pyruvic acid Pyruvic acid 0.79 1.87 6 L-Glutamic acid L-glutamic acid 0.79 2.09 7 Cer d35:1 (d18:1 / 17:0) Ceramide d35:1 (d18:1 / 17:0) 0.77 1.95 8 PC 32:2 Phosphatidylcholine 32:2 0.78 1.94 9 Hippuric acid hippuric acid 0.78 2.05 10 Cer d40:2 (d18:2 / 22:0) Ceramide d40:2(d18:2 / 22:0) 0.77 1.82 11 SM d41:2 (d18:1 / 23:1) Sphingomyelin d41:2 (d18:1 / 23:1) 0.77 1.83

[0075] Example 3: Construction of a diagnostic model to distinguish between healthy and cancer groups

[0076] To validate the diagnostic efficacy of the 11 selected metabolic biomarkers in distinguishing between healthy and cancer groups, multivariate ROC curve analysis was performed on these 11 biomarkers in the modeling group. Specifically, three-quarters of the sample data from the healthy and cancer groups in the modeling group were randomly used as the training set, and one-quarter as the test set. A support vector machine (SVM) was used for randomized iterations of 1000 times, and the diagnostic model for distinguishing between healthy and cancer groups was constructed by statistically analyzing the average accuracy of the final model.

[0077] ROC curves are a method for studying the relationship between model sensitivity and specificity. Sensitivity is plotted on the ordinate, and 1-specificity on the x-axis. The evaluation criterion is the area under the curve (AUC). An AUC greater than 0.5, and closer to 1, indicates better model performance and diagnostic effectiveness. An AUC less than 0.5 indicates poor model accuracy. ROC classification prediction models, in addition to common parameters such as the receiver operating characteristic (ROC) curve and AUC, also include sensitivity and specificity.

[0078] Sensitivity is:

[0079]

[0080] Specificity is:

[0081]

[0082] in,

[0083] TP (True positive): The number of samples that are actually positive but were correctly predicted as positive.

[0084] TN (True Negative): The number of samples that are actually negative but were correctly predicted as negative.

[0085] FP (False Positive): The number of samples that are actually negative but are incorrectly predicted as positive.

[0086] FN (False Negative): The number of samples that are actually positive but are incorrectly predicted as negative.

[0087] The results are as follows Figure 1As shown, AUC=0.994 (sensitivity=0.964, specificity=0.958), indicating that the constructed diagnostic model has high diagnostic efficacy.

[0088] In addition, ROC curve analysis was performed on diagnostic models with different combinations of metabolic markers in the modeling group. Seven metabolic markers were used: sphingomyelin d38:2 (d20:1 / 18:1), L-glutamate, ceramide d35:1 (d18:1 / 17:0), phosphatidylcholine 32:2, hippuric acid, ceramide d40:2 (d18:2 / 22:0) and sphingomyelin d41:2 (d18:1 / 23:1), and four metabolic markers were used: sphingomyelin d38:2 (d20:1 / 18:1), L-glutamate, ceramide d35:1 (d18:1 / 17:0) and sphingomyelin d41:2 (d18:1 / 23:1). The results showed that when using 7 metabolic biomarkers, the AUC was 0.973 (sensitivity = 0.891, specificity = 0.958); when using 4 metabolic biomarkers, the AUC was 0.969 (sensitivity = 0.964, specificity = 0.875). These results indicate that the constructed diagnostic models all possess high diagnostic efficacy and clinical diagnostic significance.

[0089] 3) Validation of diagnostic models used to differentiate between healthy and cancer groups

[0090] To further validate the effectiveness of the diagnostic model for distinguishing between healthy and cancer groups, built based on the modeling group data, validation group data was used to validate the model. Specifically, multivariate ROC curve analysis was performed to evaluate the independent validation performance of the diagnostic model on unknown datasets outside the modeling group dataset. After the validation group samples were placed into the diagnostic model constructed by the modeling group, the probability value was output based on the detection data of 11 important metabolic markers distinguishing between healthy and cancer groups for each sample. Using the probability value of each sample as the diagnostic threshold, a confusion matrix (including true positive, true negative, false positive, and false negative) was obtained. Sensitivity and specificity can be calculated using formulas, and a point can be marked on the ROC analysis graph with sensitivity on the ordinate and 1-specificity on the abscissa. Similarly, when the probability value of each sample is used as the diagnostic threshold, multiple different points are obtained in the ROC analysis graph. Connecting these points will produce an ROC curve. Figure 2 Among them, the point with the best sensitivity and specificity was selected, and the diagnostic threshold at this point was 0.8340.

[0091] As shown in Table 3, the confusion matrix results indicate that, based on the 11 metabolic biomarkers, the diagnostic model, with a diagnostic threshold of 0.8340, correctly identified 64 out of 65 cancer patients and misidentified 1 as a healthy individual; among 29 healthy subjects, 26 were correctly identified and 3 were misidentified as cancer patients. The ROC analysis results of the diagnostic model in the validation group are as follows: Figure 2 As shown, sensitivity and specificity were calculated based on the confusion matrix results, with an AUC of 0.984 (sensitivity = 0.985, specificity = 0.897). These results indicate that the constructed diagnostic model for distinguishing between healthy and cancer groups also demonstrates good diagnostic performance in the validation group.

[0092] Table 3: Confusion matrix of diagnostic models used to distinguish between healthy and cancer groups

[0093] Cancer Group healthy subjects 65 cancer patients 64 (TP) 1 (FN) 29 healthy subjects 3 (FP) 26 (TN)

[0094] In addition, diagnostic models with different combinations of metabolic biomarkers were validated in the validation group. Multivariate ROC curve analysis showed that the combination of the seven metabolic biomarkers—sphingomyelin d38:2 (d20:1 / 18:1), L-glutamate, ceramide d35:1 (d18:1 / 17:0), phosphatidylcholine 32:2, hippuric acid, ceramide d40:2 (d18:2 / 22:0), and sphingomyelin d41:2 (d18:1 / 23:1)—had an AUC of 0.965 (sensitivity = 0.923, specificity = 0.759) in the validation group. The combination of four metabolic biomarkers—sphingomyelin d38:2 (d20:1 / 18:1), L-glutamate, ceramide d35:1 (d18:1 / 17:0), and sphingomyelin d41:2—also achieved a high AUC of 0.965 (sensitivity = 0.923, specificity = 0.759). The (d18:1 / 23:1) combination achieved an AUC of 0.955 (sensitivity = 0.846, specificity = 0.897) in the validation group. These results indicate that the constructed diagnostic model for distinguishing between healthy and cancerous groups also demonstrated good diagnostic performance in the validation group.

[0095] The present invention has been illustrated through the above embodiments, but the present invention is not limited to the above process steps, that is, it does not mean that the present invention must rely on the above process steps to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions of the raw materials used in the present invention, additions of auxiliary components, and selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A combination of metabolic markers for differentiating between healthy and gynecological tumors, the gynecological tumors being ovarian cancer, cervical cancer and endometrial cancer, characterized in that, The metabolic marker combination consists of the following metabolic markers: Sphingomyelin d38:2 (d20:1 / 18:1), L-glutamic acid, Ceramide d35:1 (d18:1 / 17:0), and Sphingomyelin d41:2 (d18:1 / 23:1).

2. The combination of metabolic markers according to claim 1, characterized in that, The metabolic marker combination consists of the following metabolic markers: Sphingomyelin d38:2 (d20:1 / 18:1), L-glutamic acid, Ceramide d35:1 (d18:1 / 17:0), Sphingomyelin d41:2 (d18:1 / 23:1), Phosphatidylcholine 32:2, Hippuric acid, and Ceramide d40:2 (d18:2 / 22:0).

3. The combination of metabolic markers according to claim 2, characterized in that, The metabolic marker combination consists of the following metabolic markers: Sphingomyelin d38:2 (d20:1 / 18:1), L-glutamic acid, Ceramide d35:1 (d18:1 / 17:0), Sphingomyelin d41:2 (d18:1 / 23:1), Phosphatidylcholine 32:2, Hippuric acid, Ceramide d40:2 (d18:2 / 22:0), L-glutamine, Lysophosphatidylethanolamine 16:0, Benzoic acid, and Pyruvic acid.

4. Use of the metabolic marker combination of any one of claims 1-3 in the preparation of a reagent and / or a kit for detecting gynecological tumors, which are ovarian cancer, cervical cancer, and endometrial cancer.

5. A kit for detecting gynecological tumors, characterized by, The kit comprises the metabolic marker combination of any one of claims 1-3, which is for detecting gynecological tumors, which are ovarian cancer, cervical cancer, and endometrial cancer.

6. The kit of claim 5, wherein The kit further comprises a quality control and / or a standard.

7. A screening method for metabolic markers associated with gynecological tumors, such as ovarian cancer, cervical cancer and endometrial cancer, according to claims 1-3, characterized by, The kit further comprises a quality control and / or a standard. The kit further comprises a quality control and / or a standard. 1) Collecting plasma samples of gynecological tumor patients and healthy people, and preparing organic phase and aqueous phase of the samples respectively, comprising: taking 100 μL of plasma, placing it in 1000 μL of pre-cooled methyl tert-butyl ether:methanol solution with a volume ratio of 3:1, vortexing the extracted blood sample to obtain a sample extract; adding 500 μL of methanol:water solution with a volume ratio of 3:1 to the sample extract, ultrasonicating, standing, vortexing, and centrifuging to separate the layers, the upper layer being the organic phase and the lower layer being the aqueous phase; 2) Metabolomics data acquisition of organic phase and aqueous phase using liquid chromatography and mass spectrometry, the organic phase uses Waters ACQUTTY UPLC ® BEH C8 1.7µm 2.1*100mm column, the aqueous phase uses Waters ACQUTTY UPLC ® HSS T3 1.8µm 2.1*100mm column, wherein the organic phase liquid chromatography conditions are: mobile phase A is 0.1% acetic acid and 1% ammonium acetate aqueous solution; mobile phase B is 0.1% acetic acid and 1% ammonium acetate solution of volume ratio of 7:3 of acetonitrile-isopropyl alcohol, 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; the aqueous phase liquid chromatography conditions are: mobile phase A is 0.1% formic acid aqueous solution; mobile phase B is 0.1% formic acid acetonitrile solution; the separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, 13-18 minutes is 99% mobile phase B; the mass spectrometry conditions are: Full MS mode resolution is 7 million, scanning range is 100-1500m / z, AGC is 3E+6, Maximum IT is 200 milliseconds; in Full MS / dd-MS2 mode, the resolution of secondary mass spectrometry is 1.75 million, the quadrupole rod window is 1.5 m / z, AGC is 1E+5, the maximum ion injection time is 50 ms, and the HCD relative collision energy is 30eV; 3) After analyzing the collected data, the spectrum information of metabolites is obtained, and the metabolites are qualitatively matched with common databases; 4) In the modeling group, the metabolomics data of gynecological tumor patients and healthy people samples are subjected to univariate ROC analysis and OPLS-DA analysis, and univariate AUC value and VIP value are obtained respectively; 5) Take the intersection data of univariate ROC analysis value and OPLS-DA analysis value to obtain differential metabolites, the intersection is univariate ROC analysis AUC value>0.7 and OPLS-DA analysis VIP value>1.8.

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