Metabolic marker combination for distinguishing health from cervical cancer and application of metabolic marker combination

Abstract

The invention provides a metabolic marker combination for distinguishing health from cervical cancer. On the basis of metabonomics research, metabolites in plasma of cervical cancer patients and healthy people are analyzed and compared to screen out a group of metabolic marker compositions with significant difference; comprising pyroglutamic acid, L-glutamine, phosphatidylcholine 34: 4, threonic acid, benzoic acid, lysophosphatidylethanolamine 16: 0, arabinose, lysophosphatidylcholine 20: 4 and lysophosphatidylethanolamine 22: 6, a high-sensitivity and high-specificity cervical cancer diagnosis model is further constructed, and accurate screening of cervical cancer is achieved. Important help is provided for preventing cervical cancer and reducing the morbidity. The method disclosed by the invention has the characteristics of non-invasiveness, convenience in sample acquisition, strong subject compliance and the like, is suitable for large-scale population screening, and is more suitable for remote areas with shortage of medical resources.

Description

Technical Field

[0001] The present invention relates to the field of metabolomics, and in particular to a metabolic marker combination for distinguishing health from cervical cancer and an application thereof. Background Art

[0002] Cervical cancer (CC), also known as cervical cancer, is a malignant tumor of the cervical mucosa associated with human papillomavirus (HPV), which can be prevented through HPV vaccination and screening. According to the 2020 Global Cancer Burden Report released by the International Agency for Research on Cancer (IARC), there were approximately 604,000 new cases of cervical cancer in 2020 worldwide, with nearly 110,000 new cases in China; there were approximately 340,000 deaths worldwide, of which nearly 60,000 were in China, accounting for 1 / 6 of the global number of cases. Therefore, cervical cancer is an important public health issue worldwide and needs to be given sufficient attention.

[0003] For cervical cancer, early detection and treatment are key. Cervical cancer screening may detect pre-invasive lesions. Common screening and diagnostic methods mainly include cervical cytology combined with HPV testing, cervical biopsy, imaging examination and serum tumor marker screening. Cytology combined with HPV testing uses invasive means to obtain cells from the cervix for cell diagnosis and HPV testing. This examination can detect cervical precancerous lesions and cervical cancer cells. In cervical biopsy, the doctor determines the abnormal site based on the colposcopy, removes the tissue, makes pathological sections, and performs microscopic histological diagnosis. This is the gold standard for confirming cervical cancer and precancerous lesions. Imaging examinations mainly include vaginal ultrasound, CT and magnetic resonance imaging (MRI). By making full use of imaging diagnosis, the size, positional relationship and invasion range of the tumor can be determined, and further understanding can be obtained whether the tumor has metastasized and the site of metastasis. Serum tumor marker screening. Tumor markers for cervical cancer are usually checked by checking serum squamous cell carcinoma antigen (SCC), cytokeratin 19 fragment (CYFRA21-1), alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA) and cancer antigen markers (CA199, CA125), etc. These tests are helpful for the diagnosis and follow-up of cervical cancer. A combination of 2 or 3 of the above screening methods can determine the scope of the lesion, the size of the cervical tumor, the condition of the uterus and adnexa, and provide a reference for determining the tumor stage. However, most of the above screening methods are invasive and traumatic, which will bring certain psychological pressure and mental anxiety to patients, and have low sensitivity and specificity. For example, cytology combined with HPV testing is easily affected by the cell collection process, inflammation levels and hormonal changes, and HPV infection does not necessarily lead to the occurrence of cervical cancer, especially for those infected with low-risk HPV, so the test results are prone to false positives or false negatives. Although serum tumor marker screening avoids invasive examinations, its specificity is not high because serum tumor markers are not specific to cervical cancer and may also be elevated in other types of tumors or non-tumor diseases. In addition, the levels of serum tumor markers may be affected by a variety of factors, such as inflammation, infection, and drug use, which may cause fluctuations in marker levels and thus affect the accuracy of the test results. Therefore, there is an urgent need for an accurate, convenient, economical, and large-scale population-applicable cervical cancer screening method.

[0004] Metabolomics studies the dynamic changes of endogenous metabolites in organisms by qualitatively and quantitatively analyzing small molecule metabolites in body fluids or tissues. In recent years, metabolomics technology has developed rapidly in the field of disease diagnosis and has broad prospects. By comparing the differences in metabolic spectra between healthy people and diseased patients, specific metabolites related to the disease can be discovered. These metabolites, as potential biomarkers, are helpful for early diagnosis and differential diagnosis of the disease and have high diagnostic sensitivity and specificity. Therefore, this patent constructs a cervical cancer diagnosis model with high sensitivity, specificity and accuracy based on metabolomics data, providing effective assistance for the diagnosis of cervical cancer. 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 in plasma samples based on metabolomics detection, construct a high-sensitivity and high-specificity cervical cancer diagnosis model, realize accurate screening of cervical cancer, and provide important help for the prevention of cervical cancer and reduction of incidence.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions: The invention discloses a metabolic marker combination for distinguishing health from cervical cancer. The metabolic marker combination comprises pyroglutamic acid, threonic acid, benzoic acid and lysophosphatidylethanolamine 22:6.

[0007] Preferably, the metabolic marker combination further includes: phosphatidylcholine 34:4, lysophosphatidylethanolamine 16:0 and lysophosphatidylcholine 20:4.

[0008] Preferably, the metabolic marker combination further comprises: L-glutamine and arabinose.

[0009] Preferably, the metabolic marker combination further includes: pyroglutamic acid, L-glutamine, phosphatidylcholine 34:4, threonic acid, benzoic acid, lysophosphatidylethanolamine 16:0, arabinose, lysophosphatidylcholine 20:4 and lysophosphatidylethanolamine 22:6.

[0010] Preferably, the metabolite marker combination consists of the following metabolites: pyroglutamic acid, L-glutamine, phosphatidylcholine 34:4, threonic acid, benzoic acid, lysophosphatidylethanolamine 16:0, arabinose, lysophosphatidylcholine 20:4 and lysophosphatidylethanolamine 22:6.

[0011] The invention discloses use of the metabolic marker combination in preparing a reagent or a kit for detecting cervical cancer.

[0012] The present invention discloses a kit for detecting cervical cancer, wherein the kit comprises the metabolic marker combination according to any one of claims 1 to 5.

[0013] Preferably, the kit further comprises quality control substances and / or standard substances.

[0014] The present invention discloses a method for screening cervical cancer metabolic markers, comprising the following steps: 1) Collect plasma samples from cervical cancer 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 cervical cancer 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 > 0.8 and the OPLS-DA analysis VIP value > 2.0.

[0015] Preferably, the mass spectrometry conditions are: Full MS mode resolution is 70,000, scanning range is 100-1500m / z, AGC is 3E+6, Maximum IT is 200 milliseconds; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometry is 17,500, the quadrupole 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.

[0016] 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.

[0017] 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.

[0018] Compared with the existing technology, the present invention constructs a cervical cancer diagnosis model with high sensitivity, high specificity and high accuracy based on the metabolite characteristics of plasma. This method 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Multivariate ROC curve analysis of 9 important markers for distinguishing HC vs CC in the modeling group.

[0020] Figure 2 Multivariate ROC curve analysis of 9 significant markers for distinguishing HC vs CC in the validation group. DETAILED DESCRIPTION

[0021] 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.

[0022] Example 1 Subject Condition 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) Cervical cancer group: confirmed by biopsy / postoperative pathology or clinically diagnosed as primary cervical cancer through comprehensive evaluation by clinical physicians.

[0023] 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.

[0024] 3) Subjects This study collected plasma samples from 221 subjects through two medical centers, including 123 healthy control (HC) samples and 98 cervical cancer (CC) samples. The plasma samples used for the modeling group were 88 healthy control group samples and 70 cervical cancer group samples; the plasma samples used for the validation group were 35 healthy control group samples and 28 cervical cancer group samples (Table 1).

[0025] Table 1. Subjects Healthy control group (HC) Cervical cancer (CC) Modeling team number 88 70 Number of people in the verification group 35 28 total 123 98 Example 2 Detection and screening of plasma metabolites 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.

[0026] 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.

[0027] - 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.

[0028] - 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).

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 4) Metabolomics data preprocessing and metabolite identification 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.

[0034] 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.

[0035] 5) Data analysis Metabolic marker screening for distinguishing healthy from cervical cancer: After metabolite detection and annotation of the above samples, a total of 461 metabolites were obtained. The data of the modeling group were subjected to univariate ROC analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) to obtain univariate AUC values ​​and VIP values. Using the univariate AUC value>0.8 and VIP value>2.0 screening conditions combined, a total of 9 differential metabolites that contributed significantly to the differences between the groups were finally identified (Table 2), as important metabolic markers for distinguishing between health and cervical cancer.

[0036] Table 2. 9 important metabolic markers for distinguishing health from cervical cancer Landmarks-English Landmarks-Chinese Univariate AUC value VIP Value 1 Pyroglutamic acid Pyroglutamate 0.87 2.58 2 L-Glutamine L-Glutamine 0.86 2.33 3 PC 34:4 Phosphatidylcholine 34:4 0.84 2.26 4 Threonic acid Threonic acid 0.82 2.12 5 Benzoic acid benzoic acid 0.82 2.01 6 LysoPE 16:0 Lysophosphatidylethanolamine 16:0 0.82 2.03 7 Arabinose Arabinose 0.81 2.06 8 LysoPC 20:4 Lysophosphatidylcholine 20:4 0.81 2.24 9 LysoPE 22:6 Lysophosphatidylethanolamine 22:6 0.80 2.03 Example 3 Construction of a diagnostic model for distinguishing between healthy and cervical cancer In order to verify the diagnostic effect of the 9 selected metabolites in distinguishing healthy people from cervical cancer, a multivariate ROC curve analysis was performed on the above 9 metabolites in the modeling group. Specifically, 3 / 4 of the sample data of the HC group and CC 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 healthy people from cervical cancer was constructed.

[0037] 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.

[0038] The sensitivity is:

[0039] Specificity is:

[0040] 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.

[0041] The results are as follows Figure 1 As shown, AUC = 0.985 (sensitivity = 0.833, specificity = 0.910), the results show that the constructed diagnostic model has high diagnostic efficacy.

[0042] In addition, the diagnostic models of different combinations of metabolites were analyzed by ROC curves in the modeling group. Seven metabolites were used, including pyroglutamic acid, phosphatidylcholine 34:4, threonic acid, benzoic acid, lysophosphatidylethanolamine 16:0, lysophosphatidylcholine 20:4 and lysophosphatidylethanolamine 22:6, and four metabolites were used, including pyroglutamic acid, threonic acid, benzoic acid and lysophosphatidylethanolamine 22:6. The results showed that when seven metabolites were used, AUC = 0.989 (sensitivity = 0.941, specificity = 0.955); when four metabolites were used, AUC = 0.977 (sensitivity = 0.944, specificity = 0.910). The above results show that the constructed diagnostic models have high diagnostic efficacy and clinical diagnostic significance.

[0043] Example 4 Validation of a diagnostic model for distinguishing between healthy and cervical cancer.

[0044] In order to further verify the effectiveness of the diagnostic model for distinguishing between health and cervical cancer 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 9 important metabolic markers for distinguishing between health and cervical cancer for each sample. Based on the probability value of each sample as the diagnostic threshold, a set of confusion matrices (including true positive, true negative, false positive and false negative) 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 0.5189.

[0045] As shown in the confusion matrix results in Table 3, in the diagnostic model constructed based on the above 9 metabolic markers, with a diagnostic threshold of 0.5189, among the 28 cervical cancer patients, 25 were correctly diagnosed as cervical cancer and 3 were misdiagnosed as healthy people; among the 35 healthy subjects, 33 were correctly diagnosed and 2 were misdiagnosed as cervical cancer. The ROC analysis results of the diagnostic model in the validation group are shown in Figure 3. Figure 2As shown in the figure, the sensitivity and specificity were calculated based on the results of the confusion matrix, and the results showed that AUC = 0.991 (sensitivity = 0.893, specificity = 0.943). The above results show that the diagnostic model constructed for distinguishing between healthy and cervical cancer also has a good diagnostic effect in the validation group.

[0046] Table 3. Confusion matrix of the diagnostic model used to distinguish between healthy and cervical cancer Cervical cancer Healthy subjects 28 cases of cervical cancer 25 (TP) 3 (FN) 35 healthy subjects 2 (FP) 33 (TN) In addition, the diagnostic model of different combinations of metabolite markers was verified in the validation group, and multivariate ROC curve analysis was performed. The results showed that the combination of the above 7 metabolite markers, pyroglutamic acid, phosphatidylcholine 34:4, threonic acid, benzoic acid, lysophosphatidylethanolamine 16:0, lysophosphatidylcholine 20:4 and lysophosphatidylethanolamine 22:6, had an AUC of 0.987 (sensitivity = 0.893, specificity = 0.943) in the validation group; the 4 metabolite markers, pyroglutamic acid, threonic acid, benzoic acid and lysophosphatidylethanolamine 22:6, had an AUC of 0.979 (sensitivity = 0.929, specificity = 0.800) in the validation group. The above results show that the diagnostic model constructed for distinguishing between health and cervical cancer also has a good diagnostic effect in the validation group.

[0047] 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 cervical cancer, characterized in that: The metabolic marker combination includes: pyroglutamic acid, threonic acid, benzoic acid and lysophosphatidylethanolamine 22:

6.

2. The metabolic marker combination according to claim 1, characterized in that: The metabolic marker combination further includes: phosphatidylcholine 34:4, lysophosphatidylethanolamine 16:0 and lysophosphatidylcholine 20:

4.

3. The metabolic marker combination according to claim 2, characterized in that: The metabolic marker combination further includes: L-glutamine and arabinose.

4. Use of the metabolic marker combination according to any one of claims 1 to 3 in the preparation of a reagent and / or kit for detecting cervical cancer.

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

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 of cervical cancer, characterized in that: The steps include: 1) Collect plasma samples from cervical cancer 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 cervical cancer 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 > 0.8 and the OPLS-DA analysis VIP value > 2.

0.

8. The screening method according to claim 7, characterized in that The mass spectrometry conditions 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.

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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