Plasma metabolism marker for detecting various gynecological tumors and application thereof

Abstract

The invention provides a metabolic marker combination for distinguishing healthy and gynecological tumors. On the basis of metabonomics research, metabolites in plasma of gynecological tumor patients and healthy people are analyzed and compared, and a group of metabolic marker compositions with significant difference are screened out. According to the method, a diagnosis model with high sensitivity and high specificity is constructed based on metabolite characteristics of plasma, and the method can be used for carrying out risk screening on three gynecological tumors, ovarian cancer, cervical cancer and endometrial cancer, has the characteristics of non-invasiveness, convenience in sample acquisition, high subject compliance and the like, is suitable for large-scale population screening, and has wide application prospects. The method is more suitable for remote areas with shortage of medical resources, excessive examination is avoided, and the examination cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of metabolomics, and in particular to plasma metabolic markers for detecting various gynecological tumors and applications thereof. Background Art

[0002] The three most common gynecological malignancies in women are ovarian cancer, cervical cancer, and endometrial cancer. These tumors are very harmful, especially in the uterus and ovaries. The clinical symptoms of cervical cancer and endometrial cancer appear early, and most patients are in the early stage, with a good prognosis and a 5-year survival rate of more than 80%. However, once recurrence and metastasis occur, the prognosis is poor, with a 5-year survival rate of less than 20%. In contrast, ovarian cancer is highly malignant and difficult to diagnose. Its incidence ranks third and its mortality rate ranks first. About 70% of patients are diagnosed in the late stage, and the 5-year survival rate is only 39%, the highest mortality rate among gynecological tumors. Therefore, screening and diagnosis of various gynecological tumors are crucial.

[0003] At present, the clinical detection methods for the above three types of gynecological malignancies are mainly to determine the size, location and stage of the tumor through histopathological examination combined with imaging examination. However, most of the examination methods are invasive, which leads to reduced patient compliance. Although imaging examinations avoid invasiveness, the methods themselves have radiation hazards, especially for pregnant women, and they are not suitable for use. Moreover, these conventional examination methods are only suitable for patients who have clinical symptoms and need further examination. It is inconvenient for older people with higher risks who need regular screening, especially those in areas with poor medical conditions and scarce medical resources. Therefore, regular screening for the risks of various gynecological tumors has become a major problem that plagues women.

[0004] In view of the current situation, the application of metabolomics in disease diagnosis provides a new perspective. It identifies disease states by analyzing changes in metabolites, providing a practical alternative method for clinical diagnosis. Metabolomics accurately quantifies and characterizes small molecule metabolites in the body, analyzes the relationship between metabolites and physiological and pathological changes in the body, studies the occurrence and development of diseases, searches for disease biomarkers, predicts disease prognosis, etc., and has ideal diagnostic sensitivity and specificity. Therefore, based on metabolomics technology, the present invention provides a metabolite marker combination based on metabolomics detection of plasma samples. This method has high sensitivity and specificity, and can achieve risk screening for three diseases, ovarian cancer, cervical cancer, and endometrial cancer, in one examination, avoiding one-by-one examination of multiple sites in the early stages of screening, avoiding excessive diagnosis and treatment, significantly reducing detection costs, and improving detection efficiency. Summary of the invention

[0005] The technical problem to be solved by the patent of this invention is to overcome the defects and shortcomings of the above-mentioned prior art, provide a metabolite marker combination based on metabolomics detection in plasma samples, construct a high-sensitivity and high-specificity diagnostic model for multiple gynecological malignancies, and realize risk screening of three high-risk gynecological tumors in the female population, including ovarian cancer, cervical cancer and endometrial cancer, providing important assistance for risk screening of gynecological tumors in women.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention discloses a metabolic marker combination for distinguishing between health and gynecological tumors, wherein the gynecological tumors are ovarian cancer, cervical cancer and endometrial cancer, and the metabolic marker combination comprises: 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).

[0007] Preferably, the metabolic marker combination further includes: phosphatidylcholine 32:2, hippuric acid and ceramide d40:2 (d18:2 / 22:0).

[0008] Preferably, the metabolic marker combination further comprises: L-glutamine, lysophosphatidylethanolamine 16:0, benzoic acid and pyruvic acid.

[0009] Preferably, the metabolic marker combination 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).

[0010] Preferably, the metabolic marker combination 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 pyruvic acid.

[0011] Preferably, the metabolic markers consist of 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 pyruvic acid.

[0012] The present invention discloses a metabolic marker composition for distinguishing between health and gynecological tumors, wherein the gynecological tumors are ovarian cancer, cervical cancer and endometrial cancer, and 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 pyruvic acid.

[0013] The present invention discloses the use of the metabolic marker combination in preparing a reagent or a kit for detecting gynecological tumors, wherein the gynecological tumors are ovarian cancer, cervical cancer and endometrial cancer.

[0014] The invention discloses a kit for detecting gynecological tumors, the kit comprising the metabolic marker combination, and the gynecological tumors are ovarian cancer, cervical cancer and endometrial cancer.

[0015] Preferably, the kit further comprises quality control substances and / or standard substances.

[0016] The present invention discloses a method for screening metabolic markers related to gynecological tumors, wherein the gynecological tumors are ovarian cancer, cervical cancer and endometrial cancer, comprising the following steps: 1) Collect plasma samples from gynecological tumor patients and healthy subjects, and prepare organic phase and aqueous phase 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 1.7µm 2.1*100mm column, and the aqueous phase uses a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm 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 gynecological tumor patients and healthy subjects were subjected to univariate ROC analysis and OPLS-DA analysis to obtain univariate AUC values ​​and VIP values, respectively; 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 > 0.7 and the OPLS-DA analysis VIP value > 1.8.

[0017] Preferably, the mass spectrometry conditions are: The resolution of Full MS mode is 70,000, the scanning range is 100-1500m / z, the AGC is 3E+6, and the Maximum IT is 200 milliseconds. 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 injection time is 50 ms, and the HCD relative collision energy is 30eV.

[0018] Preferably, the organic phase liquid chromatography conditions are: 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.

[0019] Preferably, the aqueous phase 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. The separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, 13-18 minutes is 99% mobile phase B.

[0020] Compared with the existing technology, the present invention constructs a diagnostic model with high sensitivity and high specificity based on the metabolite characteristics of plasma. 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 tight medical resources. It can avoid excessive examinations and reduce examination costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 .Multivariate ROC curve analysis of 11 important markers for distinguishing healthy and cancer groups in the modeling group.

[0022] Figure 2 .Multivariate ROC curve analysis of 11 important markers for distinguishing healthy and cancer groups in the validation group. DETAILED DESCRIPTION

[0023] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0024] Example 1 Subject conditions and sample collection 1. Subjects 1) Sample inclusion criteria: Subjects must meet all of the following inclusion criteria to be eligible for this study: (1) Females aged ≥18 years; (2) Read and fully understand, sign the informed consent form, and be able to provide blood samples for metabolomics testing; (3) Cancer group: Patients were diagnosed with ovarian cancer, primary cervical cancer, or endometrial cancer by biopsy / postoperative pathology or through comprehensive evaluation by clinicians.

[0025] 2) Sample exclusion criteria: Subjects who meet any of the following exclusion criteria are not eligible to participate in this study: (1) Pregnancy or lactation; (2) Emergency or emergency treatment required; (3) History of blood transfusion within 7 days before sampling; (4) People who have received organ transplant or non-autologous (allogeneic) bone marrow or stem cell transplant; (5) A history of malignant tumor within 5 years or any anti-tumor treatment before sampling; (6) Multiple primary malignant tumors coexisting simultaneously.

[0026] 1) Subjects This study collected plasma samples from 408 subjects through two medical centers, including 123 healthy control (HC) samples and 285 cancer group samples, of which the cancer group samples included 98 cervical cancer samples, 92 endometrial cancer samples and 95 ovarian cancer samples. The plasma samples used for the modeling group were 94 healthy control group samples and 220 cancer group samples (including 77 cervical cancer samples, 72 endometrial cancer samples and 71 ovarian cancer samples); the plasma samples used for the validation group were 29 healthy control group samples and 65 cancer group samples (including 21 cervical cancer samples, 20 endometrial cancer samples and 24 ovarian cancer samples) (Table 1).

[0027] Table 1: Subjects Healthy control group (HC) Cancer Modeling team number 94 220 Number of people in the verification group 29 65 total 123 285 Example 2 Detection of plasma metabolites and screening of metabolic markers 1. Plasma metabolite detection 1) Reagents: Methanol, acetonitrile, water, acetic acid, isopropanol of mass spectrometry grade purity, formic acid, ammonium acetate and methyl tert-butyl ether of HPLC grade purity were purchased from Sigma-Aldrich, USA.

[0028] 2) Sample preparation: Take 100 μL of plasma, place it in 1000 μL of pre-cooled (methyl tert-butyl ether: methanol, volume ratio of 3:1) solution, vortex mix the extracted blood sample to obtain the sample extract; add 500 μL of (methanol: water, volume ratio of 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.

[0029] - Organic phase: After the sample is layered, take 500μL of the upper organic phase to a centrifuge tube. After drying, add 200μL of (acetonitrile:isopropanol, volume ratio 3:1) 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 (12000 rpm). Take 180μl of the supernatant from the centrifuge tube and transfer it to a 2 mL glass injection vial as the organic phase test solution for LC-MS detection.

[0030] - Aqueous phase: After the sample is layered, take 400μL of the lower aqueous phase to a centrifuge tube, and add 1100μL of ice methanol to precipitate the protein; after the protein in the centrifuge tube is precipitated, centrifuge the centrifuge tube, take 1000μL of the supernatant and transfer it to a new centrifuge tube, and dry it overnight; add 200μL of water to the dried centrifuge tube and incubate it at room temperature for 15 minutes; after incubation, vortex the centrifuge tube, treat it with ultrasound for 5 minutes, and then centrifuge the centrifuge tube at room temperature for 5 minutes (12000rpm); take 180μL of the supernatant from the centrifuge tube to a 2 mL glass injection vial as the aqueous phase test solution. Detected by machine (LC-MS).

[0031] 3) Small molecule metabolite detection: The organic phase used a Waters ACQUTTY UPLC ® BEH C8 1.7µm 2.1*100mm column, and the aqueous phase used a Waters ACQUTTYUPLC ® HSS T3 1.8µm 2.1*100mm column for small molecule separation; the liquid chromatography and mass spectrometer used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0032] The mobile phase parameters are as follows: Mobile phase parameters for organic phase test solution – 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: 0-12 minutes, 55%-89% mobile phase B, 12-19.5 minutes, 100% mobile phase B.

[0033] Mobile phase parameters for aqueous analytes – 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, 1%-70% mobile phase B, 13-18 minutes, 99% mobile phase B.

[0034] The mass spectrometry parameters are as follows: The mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each including positive and negative modes). The parameters used by QExactive are as follows: the resolution of the Full MS mode is 70,000, the scanning range is 100-1500 m / z, the AGC is 3E+6, and the Maximum IT is 200 milliseconds. In the Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometry is 17,500, the quadrupole window is 1.5 m / z, the AGC is 1E+5, the maximum ion injection time is 50 ms, and the HCD relative collision energy is 30 eV.

[0035] 2. Metabolomics data preprocessing and metabolite identification 1) Metabolomics data processing: (1) Extract the peaks from the RAW format file downloaded from the mass spectrometer and convert it into a FeatureXML format file to reduce the dimension of the original mass spectrometry data and improve the signal-to-noise ratio; (2) Use the peak alignment algorithm of the OpenMS software to calibrate 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 then replace the abnormal data (0, negative values, background noise, etc.) with vacant values; (4) Among all the characteristic peaks obtained in step 3, 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 the standard normal distribution); (5) In order to reduce the difference in metabolite concentrations between samples and make the data distribution more symmetrical, the normalization autoencoder (NormAE) is used for homogenization processing to remove systematic errors such as batch effects.

[0036] 2) Identification of metabolites: According to the original data, the spectral information of the primary parent ion (MS1) and secondary fragment ion (MS2) of the compound is obtained after software analysis, such as the mass-to-charge ratio (m / z) of the primary mass spectrum and the fragments of the secondary ion. The spectral information of the primary and secondary metabolites in the public database is matched 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 Spectrum Database (www.massbank.jp) and the Lipid Metabolite Database (Lipidmap, www.lipidmaps.org). Based on the metabolites identified in the relevant databases, the metabolites are finally verified 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 theoretical value and measured value of the metabolite molecular weight are less than 10ppm.

[0037] 3. Data Analysis 1) Screening of metabolic markers for distinguishing healthy and cancer groups The univariate AUC value and VIP value were obtained by performing univariate ROC analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) on the data of the modeling group samples. The univariate AUC value>0.7 and VIP value>1.8 screening conditions were combined and the intersection was taken. Finally, a total of 11 differential metabolites that contributed significantly to the differences between the groups were identified (Table 2) as important metabolic markers for distinguishing the healthy and cancer groups.

[0038] Table 2: 11 important metabolic markers that distinguish healthy and cancer groups Landmarks-English Landmarks-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 Pyruvate 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 Example 3 Construction of a diagnostic model for distinguishing healthy and cancer groups In order to verify the diagnostic effect of the 11 screened metabolite markers in distinguishing the healthy and cancer groups, a multivariate ROC curve analysis was performed on the above 11 metabolite markers in the modeling group. Specifically, 3 / 4 of the sample data of the healthy group and the cancer group in the modeling group were randomly used as training sets (training), and 1 / 4 was used as a test set (test) for learning, and the machine learning support vector machine (SVM) was used for random loop iteration 1000 times. By statistically calculating the average value of the final model accuracy, a diagnostic model for distinguishing the healthy and cancer groups was constructed.

[0039] The ROC curve is a method to study the relationship between model sensitivity and specificity. It uses sensitivity as the ordinate and 1-specificity as the abscissa. The evaluation basis is to compare the area under the curve (AUC). When the AUC is greater than 0.5, the closer the AUC is to 1, the better the model performance is, indicating a better diagnostic effect. If it is less than 0.5, it means that the model accuracy is poor. In addition to common parameters such as the receiver operating curve (ROC) and the area under the curve (AUC), the ROC classification prediction model also includes sensitivity and specificity.

[0040] The sensitivity is:

[0041] Specificity is:

[0042] 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 but are correctly predicted as negative examples; FP (False Positive): False positive, 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 examples but are mistakenly predicted as negative examples.

[0043] The results are as follows Figure 1 As shown, AUC = 0.994 (sensitivity = 0.964, specificity = 0.958), the results show that the constructed diagnostic model has high diagnostic efficacy.

[0044] In addition, ROC curve analysis was performed on the diagnostic models of different metabolite marker combinations in the modeling group, using seven metabolite markers, 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 metabolite markers, 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 7 metabolic markers were used, AUC = 0.973 (sensitivity = 0.891, specificity = 0.958); when 4 metabolic markers were used, AUC = 0.969 (sensitivity = 0.964, specificity = 0.875). The above results indicate that the constructed diagnostic models have high diagnostic efficacy and clinical diagnostic significance.

[0045] 3) Validation of diagnostic models for distinguishing healthy from cancer groups In order to further verify the effectiveness of the diagnostic model for distinguishing between healthy and cancer groups based on the modeling group data, the validation group data was used to verify the above diagnostic model. A multivariate ROC curve analysis was specifically performed to evaluate the independent verification effect of the diagnostic model on unknown data sets other than the modeling group data set. After the validation group samples were placed in the diagnostic model constructed by the modeling group, the corresponding probability value (Probability) will be output based on the detection data of the 11 important metabolic markers that distinguish between the healthy and cancer groups of each sample. Based on the probability value of each sample as the diagnostic threshold, a set of confusion matrices (including true positives, true negatives, false positives and false negatives) are obtained. According to the formula, the sensitivity and specificity can be calculated, and a point can be marked in the ROC analysis diagram with sensitivity (Sensitivity) as the vertical coordinate and 1-specificity (1-Specificity) as the horizontal coordinate. Similarly, when the probability value of each sample is used as the diagnostic threshold, multiple different points are obtained in the ROC analysis diagram. These points are linked to draw the ROC curve diagram ( Figure 2 ). Among them, the point with the best sensitivity and specificity is selected, and the diagnostic threshold at this time is 0.8340.

[0046] As shown in the confusion matrix results in Table 3, in the diagnostic model constructed based on the above 11 metabolic markers, with a diagnostic threshold of 0.8340, among the 65 cancer patients, 64 were correctly identified as cancer patients, and 1 was misidentified as a healthy person; among the 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 shown in Figure 2 As shown in the figure, the sensitivity and specificity were calculated based on the results of the confusion matrix, and the results showed that AUC = 0.984 (sensitivity = 0.985, specificity = 0.897). The above results show that the diagnostic model constructed to distinguish between healthy and cancer groups also has a good diagnostic effect in the validation group.

[0047] Table 3: Confusion matrix of the diagnostic model for distinguishing healthy from cancer groups Cancer Group Healthy subjects 65 cancer patients 64 (TP) 1 (FN) 29 healthy subjects 3 (FP) 26 (TN) In addition, the diagnostic models of different combinations of metabolite markers were validated in the validation group, and multivariate ROC curve analysis was performed. The results showed that the above seven metabolite markers, 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 four metabolite markers, 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 (d18:1 / 23:1) combination had an AUC of 0.955 (sensitivity = 0.846, specificity = 0.897) in the validation group. The above results show that the diagnostic model constructed to distinguish between healthy and cancer groups also has a good diagnostic effect in the validation group.

[0048] The present invention illustrates the process method of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned process steps, that is, it does not mean that the present invention must rely on the above-mentioned process steps to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of the raw materials selected by the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A metabolic marker combination for distinguishing between health and gynecological tumors, wherein the gynecological tumors are ovarian cancer, cervical cancer and endometrial cancer, characterized in that: The metabolic marker combination includes: 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 metabolic marker combination according to claim 1, characterized in that: The metabolic marker combination further includes: phosphatidylcholine 32:2, hippuric acid and ceramide d40:2 (d18:2 / 22:0).

3. The metabolic marker combination according to claim 2, characterized in that: The metabolic marker combination further includes: L-glutamine, lysophosphatidylethanolamine 16:0, benzoic acid and pyruvic acid.

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

5. A kit for detecting gynecological tumors, characterized in that: The kit comprises the metabolic marker combination according to any one of claims 1 to 3, and the gynecological tumors are ovarian cancer, cervical cancer and endometrial cancer.

6. The kit according to claim 5, characterized in that The kit also includes quality control products and / or standard products.

7. A method for screening metabolic markers associated with gynecological tumors, wherein the gynecological tumors are ovarian cancer, cervical cancer and endometrial cancer, characterized in that: The steps include: 1) Collect plasma samples from gynecological tumor patients and healthy subjects, and prepare organic phase and aqueous phase 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 1.7µm 2.1*100mm column, and the aqueous phase uses a Waters ACQUTTY UPLC ® HSS T3 1.8µm 2.1*100mm 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 gynecological tumor patients and healthy subjects were subjected to univariate ROC analysis and OPLS-DA analysis to obtain univariate AUC values ​​and VIP values, respectively; 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 the univariate ROC analysis AUC value > 0.7 and the OPLS-DA analysis VIP value > 1.

8.

8. The screening method according to claim 7, characterized in that The mass spectrometry conditions are: The resolution of Full MS mode is 70,000, the scanning range is 100-1500m / z, the AGC is 3E+6, and the Maximum IT is 200 milliseconds. 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 injection time is 50 ms, and the HCD relative collision energy is 30eV.

9. The screening method according to claim 7, 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.

10. The screening method according to claim 7, 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; The separation elution gradient was as follows: 1%-70% mobile phase B from 0 to 13 minutes and 99% mobile phase B from 13 to 18 minutes.

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