Metabolic marker composition for early diagnosis of cervical cancer as well as screening method and application of metabolic marker composition

Abstract

The invention discloses a metabolic marker composition for early diagnosis of cervical cancer as well as a screening method and application of the metabolic marker composition. The metabolic marker composition for early diagnosis of cervical cancer comprises pyroglutamic acid, lysophosphatidylcholine 20: 3, phosphatidylcholine 32: 2 and pyruvic acid. The metabolic marker composition provided by the invention has relatively high sensitivity and specificity when being used for early-stage cervical cancer diagnosis, can realize accurate screening of early-stage cervical cancer, and provides important help for prevention of cervical cancer and reduction of morbidity. Meanwhile, when the metabolic marker composition is used for early-stage cervical cancer diagnosis, operation is easy, a sample is convenient to obtain, cost is low, invasion is avoided, a patient can receive detection more conveniently, rapidly and safely, and the metabolic marker composition is more suitable for large-scale population screening and regular tracking in areas with shortage of medical resources.

Description

Technical Field

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

[0002] Cervical cancer (CC) is a malignant tumor that occurs in the vaginal part of the cervix and the cervical canal. It is one of the most common gynecological malignancies, with a high morbidity and mortality rate, and is one of the main factors threatening women's life and health. The early symptoms of cervical cancer are not obvious and are generally not easy to detect, but as the disease progresses, symptoms such as frequent pain in the lower abdomen, vaginal bleeding, and abnormal vaginal discharge may appear. When these conditions occur and people go to the hospital for treatment, it is usually in the late stage. The treatment effect of late-stage cervical cancer is usually worse than that of early-stage cancer, because late-stage cancer may have spread to other parts outside the cervix, such as parauterine tissue, pelvic wall, vagina or distant organs, which seriously affects the recovery effect and increases the cost of treatment. It also brings certain physical and psychological pain to patients during the treatment process. Therefore, screening for early cervical cancer becomes particularly important.

[0003] Human papillomavirus (HPV) is a common sexually transmitted virus. There are many different types of it, some of which are closely related to the occurrence of cervical cancer. Studies have shown that almost all cases of cervical cancer are related to HPV infection. Specifically, when women are infected with high-risk HPV, the virus may persist in the cervix and cause abnormal cell proliferation, which then develops into cervical cancer. Therefore, HPV screening combined with cytology screening is currently the main detection method for cervical cancer screening and is widely used for early screening and regular follow-up of cervical cancer. Although HPV testing has a high sensitivity for high-risk HPV infection, it may also result in false negative results due to low viral load or insufficient sampling of the virus. The specificity of HPV testing is also relatively low, because HPV infection does not necessarily lead to cervical cancer, especially for low-risk HPV infections. In addition, HPV screening, as an invasive detection method, will reduce patient compliance, and HPV testing is usually more expensive, which may increase the burden on the medical system, especially in areas with limited resources. The high cost may prevent some people from undergoing regular HPV testing.

[0004] Therefore, there is an urgent need for a non-invasive screening technology with high sensitivity, high specificity, convenience and economy for the screening and prevention of early cervical cancer. Summary of the invention

[0005] Based on the above-mentioned deficiencies of the prior art, the purpose of the present invention is to provide a metabolic marker composition for the diagnosis of early cervical cancer and its screening method and application, aiming to solve the problems that the existing methods for diagnosing early cervical cancer have low specificity, invasiveness and high cost.

[0006] The technical solution of the present invention is as follows:

[0007] In a first aspect of the present invention, a metabolic marker composition for the diagnosis of early cervical cancer is provided, wherein the metabolic marker composition for the diagnosis of early cervical cancer comprises pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvate.

[0008] Optionally, the metabolic marker composition for early cervical cancer diagnosis further comprises at least one of lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4 and hippuric acid.

[0009] Optionally, the metabolic marker composition for early cervical cancer diagnosis also includes at least one of 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4 and L-valine.

[0010] Optionally, the metabolic marker composition for early diagnosis of cervical cancer consists of pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2, pyruvic acid, lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4, hippuric acid, 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4 and L-valine.

[0011] The second aspect of the present invention provides a method for screening the metabolic marker composition for early cervical cancer diagnosis as described above, which comprises the following steps:

[0012] The plasma of patients with early cervical cancer and healthy people is extracted respectively to obtain extracts;

[0013] Using a chromatography-mass spectrometer to detect the organic phase and the aqueous phase in the extract to obtain detection data;

[0014] Processing the detection data, and then identifying metabolites to obtain metabolomics data;

[0015] The metabolomics data are analyzed to screen and obtain the metabolite marker composition for early cervical cancer diagnosis.

[0016] Optionally, the step of analyzing the metabolomics data to screen and obtain the metabolite marker composition for early cervical cancer diagnosis specifically includes:

[0017] The metabolomics data were subjected to univariate receiver operating characteristic curve analysis and orthogonal partial least squares discriminant analysis to screen out the metabolite marker composition for early cervical cancer diagnosis.

[0018] Optionally, the area under the receiver operating characteristic curve greater than 0.75 and the VIP value greater than 1.8 are used as screening conditions to screen out a metabolic marker composition for the diagnosis of early cervical cancer.

[0019] The third aspect of the present invention provides a use of the metabolic marker composition for early cervical cancer diagnosis as described above in the preparation of a product for diagnosing early cervical cancer.

[0020] Optionally, the sample used by the product for diagnosing early cervical cancer includes at least one of serum, plasma, blood and dried blood spots.

[0021] Optionally, the product comprises a reagent or a kit.

[0022] Beneficial effects: The metabolite marker composition provided by the present invention has high sensitivity and specificity when used for the diagnosis of early cervical cancer, can realize accurate screening of early cervical cancer, and provides important help for the prevention of cervical cancer and the reduction of the incidence rate. At the same time, the metabolite marker composition is simple to operate and easy to obtain samples when used for the diagnosis of early cervical cancer, with low cost and non-invasiveness, so that patients can be tested more conveniently, quickly and safely, and is more suitable for large-scale population screening and regular follow-up in areas with tight medical resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a multivariate ROC curve analysis diagram of 11 metabolic markers used to distinguish between the healthy control group and the early cervical cancer group in the modeling group of Example 3.

[0024] Figure 2 This is a multivariate ROC curve analysis diagram of 11 metabolic markers for distinguishing the healthy control group from the early cervical cancer group in the validation group of Example 3. DETAILED DESCRIPTION

[0025] The present invention provides a metabolic marker composition for early cervical cancer diagnosis and a screening method and application thereof. To make the purpose, technical scheme and effect of the present invention clearer and more specific, the present invention is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] Unless otherwise defined, all technical terms and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0027] Some of the metabolic markers appearing in the following embodiments and examples are explained below.

[0028] Lysophosphatidylcholine belongs to the class of glycerophospholipids and contains only one fatty acid side chain.

[0029] The 20:3 in lysophosphatidylcholine 20:3 refers to the fatty acid side chain, which contains 20 carbon atoms and 3 double bonds.

[0030] The 22:4 in lysophosphatidylcholine 22:4 refers to the fatty acid side chain, which contains 22 carbon atoms and 4 double bonds.

[0031] The 22:5 in lysophosphatidylcholine 22:5 refers to the fatty acid side chain, which contains 22 carbon atoms and 5 double bonds.

[0032] Lysophosphatidylethanolamine belongs to the glycerophospholipids. It is the enzymatic hydrolysis product obtained by losing a fatty acid molecule after phosphatidylethanolamine is hydrolyzed under the action of phospholipase. Therefore, it only contains one fatty acid side chain.

[0033] The 20:4 in lysophosphatidylethanolamine 20:4 refers to the fatty acid side chain, which contains 20 carbon atoms and 4 double bonds.

[0034] Methylphosphatidylcholine is formed by the methylation reaction of phosphatidylcholine and contains two fatty acid side chains.

[0035] In methylphosphatidylcholine 30:3e, 30:3 refers to the two fatty acid side chains, which contain 30 carbon atoms and 3 double bonds. e represents one of the two fatty acid side chains. The covalent ester bond originally formed by the esterification of one hydroxyl group and one fatty acid molecule is replaced by an ether bond, so there is one less oxygen atom.

[0036] Phosphatidylcholine belongs to the glycerol phospholipids. One of the three side chains is phosphatidylcholine, and the other two side chains are fatty acids.

[0037] The 32:2 in phosphatidylcholine 32:2 refers to the additional two fatty acid side chains, which contain 32 carbon atoms and two double bonds.

[0038] Metabolomics technology is widely used in the fields of early disease diagnosis, personalized medicine, new drug development and targeted discovery. In recent years, it has developed rapidly in the field of disease diagnosis and has broad prospects. Metabolomics can discover biomarkers related to diseases by analyzing changes in metabolites in body fluids (such as blood and urine). Changes in these biomarkers often appear earlier than traditional clinical symptoms, which is more helpful for early diagnosis of diseases. Therefore, the present invention is based on metabolomics data to obtain early cervical cancer diagnostic markers with high sensitivity and high specificity, which provides effective help for the diagnosis of early cervical cancer, and achieves early screening, early detection and early treatment. Specifically, an embodiment of the present invention provides a metabolite marker composition for early cervical cancer diagnosis, wherein the metabolite marker composition for early cervical cancer diagnosis includes pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvate.

[0039] In the embodiment of the present invention, the metabolic marker composition for the diagnosis of early cervical cancer containing 4 metabolic markers has significant differences between early cervical cancer patients and healthy people, and can effectively distinguish early cervical cancer patients from healthy people. The metabolic marker composition provided in the embodiment of the present invention has high sensitivity and specificity when used for the diagnosis of early cervical cancer, can achieve accurate screening of early cervical cancer, and provide important help for the prevention of cervical cancer and the reduction of incidence. At the same time, the metabolic marker composition is simple to operate and easy to obtain samples when used for the diagnosis of early cervical cancer, with low cost and non-invasiveness, so that patients can be tested more conveniently, quickly and safely, and is more suitable for large-scale population screening and regular follow-up in areas with tight medical resources.

[0040] In some embodiments, the metabolic marker composition for early diagnosis of cervical cancer also includes at least one of lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4 and hippuric acid. That is to say, in this embodiment, the metabolic marker composition for early diagnosis of cervical cancer includes pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid, and at least one of lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4 and hippuric acid. The metabolic marker composition for early diagnosis of cervical cancer provided in this embodiment can effectively distinguish early cervical cancer patients from healthy people, has high specificity and sensitivity, high accuracy, and can effectively realize the diagnosis of early cervical cancer.

[0041] In some embodiments, the metabolic marker composition for early cervical cancer diagnosis further includes at least one of 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4 and L-valine. That is, the metabolic marker composition for the diagnosis of early cervical cancer includes pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid, and at least one of 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4 and L-valine; or, the metabolic marker composition for the diagnosis of early cervical cancer includes pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid, and at least one of lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4 and hippuric acid, and at least one of 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4 and L-valine. The metabolic marker composition for early cervical cancer diagnosis provided in this embodiment can effectively distinguish early cervical cancer patients from healthy people, has high specificity and sensitivity, high accuracy, and can effectively realize the diagnosis of early cervical cancer.

[0042] In some embodiments, the metabolic marker composition for early cervical cancer diagnosis is composed of pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2, pyruvic acid, lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4, hippuric acid, 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4 and L-valine. The metabolic marker composition for early cervical cancer diagnosis provided in this embodiment can effectively distinguish early cervical cancer patients from healthy people, has high specificity and sensitivity, high accuracy, and can effectively realize the diagnosis of early cervical cancer.

[0043] The present invention also provides a method for screening the metabolic marker composition for early cervical cancer diagnosis as described above, which comprises the following steps:

[0044] S1. Extracting plasma from patients with early cervical cancer and healthy people respectively to obtain extracts;

[0045] S2, using a chromatography-mass spectrometer to detect the organic phase and the aqueous phase in the extract to obtain detection data;

[0046] S3, processing the detection data, and then identifying metabolites to obtain metabolomics data;

[0047] S4. Analyze the metabolomics data and screen to obtain the metabolite marker composition for early cervical cancer diagnosis.

[0048] This embodiment detects and identifies small molecule metabolites in the plasma of early cervical cancer patients and healthy people based on metabolomics technology, and screens out a specific metabolite marker composition for early cervical cancer diagnosis, which has high sensitivity and specificity when used for early cervical cancer diagnosis. In addition, the metabolite marker composition for early cervical cancer diagnosis has high specificity and sensitivity when used for early cervical cancer diagnosis, high accuracy, and enables patients to be tested more conveniently and safely, and is suitable for large-scale screening and testing of the general population.

[0049] In step S4, in some embodiments, the step of analyzing the metabolomics data to screen and obtain the metabolite marker composition for early cervical cancer diagnosis specifically includes:

[0050] The metabolomics data were subjected to univariate receiver operating characteristic curve analysis and orthogonal partial least squares discriminant analysis to screen out the metabolite marker composition for early cervical cancer diagnosis.

[0051] In some embodiments, the area under the receiver operating characteristic curve greater than 0.75 and the VIP value greater than 1.8 are used as screening conditions to screen out a metabolic marker composition for the diagnosis of early cervical cancer.

[0052] An embodiment of the present invention also provides a use of the metabolic marker composition for early cervical cancer diagnosis as described above in the preparation of a product for diagnosing early cervical cancer.

[0053] In some embodiments, the sample used by the product for diagnosing early cervical cancer includes at least one of serum, plasma, blood, and dried blood spots, but is not limited thereto.

[0054] In some embodiments, the product comprises a reagent or a kit.

[0055] The present invention will be further described below by means of specific examples.

[0056] Example 1 Detection and identification of small molecule metabolites in plasma samples

[0057] 1. Subjects’ conditions and sample collection

[0058] Inclusion criteria for early cervical cancer samples:

[0059] Subjects must meet all of the following inclusion criteria:

[0060] (1) Females aged ≥18 years;

[0061] (2) read and fully understand, sign the informed consent form, and be able to provide blood samples for metabolomics testing;

[0062] (3) Patients diagnosed with primary cervical cancer by biopsy / postoperative pathology or through comprehensive evaluation by clinicians, and based on clinical staging information, patients with stage I and stage II cervical cancer were combined into the early cervical cancer group.

[0063] Exclusion criteria for early cervical cancer samples:

[0064] Subjects with early cervical cancer were excluded if they met any of the following exclusion criteria:

[0065] (1) Pregnancy or lactation;

[0066] (2) Emergency or emergency treatment required;

[0067] (3) history of blood transfusion within 7 days before sampling;

[0068] (4) People who have received organ transplantation or non-autologous (allogeneic) bone marrow or stem cell transplantation;

[0069] (5) A history of malignant tumor within 5 years or any anti-tumor treatment before sampling;

[0070] (6) Multiple primary malignant tumors coexisting simultaneously.

[0071] A total of 204 plasma samples were collected from two medical centers, including 123 plasma samples from the healthy control (HC) group and 81 plasma samples from patients with early cervical cancer (eCC, only stage I and stage II samples). Plasma samples were collected in the early morning on an empty stomach, and all collected plasma samples were stored in a -80°C refrigerator.

[0072] 123 plasma samples from healthy controls were randomly divided into a modeling group and a validation group, and 81 plasma samples from early cervical cancer were randomly divided into a modeling group and a validation group (i.e., the samples of the modeling group and the validation group were different). The modeling group included 93 healthy controls (HC) and 55 early cervical cancer (eCC) groups; the validation group included 30 healthy controls (HC) and 26 early cervical cancer (eCC) groups (Table 1).

[0073] Table 1. Subjects

[0074] Healthy control (HC) group Early cervical cancer (eCC) group Modeling team number (persons) 93 55 Number of people in the verification group (persons) 30 26 Total (person) 123 81

[0075] 2. Reagents

[0076] Methanol, acetonitrile, water, acetic acid and 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.

[0077] 3. Sample preparation

[0078] 100 μL of plasma was taken and placed in 1000 μL of pre-cooled mixed solution (composed of methyl tert-butyl ether and methanol, with a volume ratio of methyl tert-butyl ether to methanol of 3:1), and vortexed to mix well to obtain a sample extract.

[0079] 500 μL of a mixed solution of methanol and water (the volume ratio of methanol to water is 3:1) was added to the sample extract, ultrasonicated, allowed to stand, vortexed and centrifuged to separate layers. After the sample was separated, the upper layer was the organic phase and the lower layer was the aqueous phase.

[0080] Take 500 μL of the upper organic phase into a centrifuge tube, dry it, add 200 μL of a mixed solution of acetonitrile and isopropanol (the volume ratio of acetonitrile to isopropanol is 3:1), and incubate at room temperature for 15 minutes; after incubation, vortex mix the centrifuge tube, perform ultrasound-assisted treatment for 5 minutes, and then centrifuge the centrifuge tube at 12000 rpm for 5 minutes at room temperature; take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass injection vial as the organic phase test solution, and detect it on a machine (LC-MS, liquid chromatography-mass spectrometry).

[0081] Take 400 μL of the lower aqueous phase into a centrifuge tube, and add 1100 μL of ice methanol to precipitate the protein; after the protein in the centrifuge tube is precipitated, centrifuge the centrifuge tube, take 1000 μL of the supernatant and transfer it to a new centrifuge tube, and dry it overnight; add 200 μL of water to the dried centrifuge tube and incubate it at room temperature for 15 minutes; after incubation, vortex the centrifuge tube to mix, ultrasonically treat it for 5 minutes, and then centrifuge the centrifuge tube at room temperature for 5 minutes; take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass injection vial as the aqueous phase test liquid, and detect it on the machine (LC-MS).

[0082] 4. Small molecule metabolite detection

[0083] The organic phase was analyzed using 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 was used for small molecule separation; ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific) were used for liquid chromatography and mass spectrometry.

[0084] The mobile phase parameters are as follows:

[0085] Mobile phase parameters of the organic phase test liquid: mobile phase A is an aqueous solution containing 0.1% acetic acid and 1% ammonium acetate (the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 1%); mobile phase B is a mixed solution of acetonitrile and isopropanol containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile to isopropanol is 7:3, the mass content of acetic acid is 0.1%, and the mass content of ammonium acetate is 1%), and the separation elution gradient is as follows: 0-12 minutes is 55%-89% mobile phase B, and 12-19.5 minutes is 100% mobile phase B.

[0086] Mobile phase parameters of aqueous test liquid: Mobile phase A is an aqueous solution containing 0.1% formic acid (mass content of formic acid is 0.1%); Mobile phase B is an acetonitrile solution containing 0.1% formic acid (mass content of formic acid is 0.1%). The separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, 13-18 minutes is 99% mobile phase B.

[0087] The mass spectrometry parameters are as follows:

[0088] Mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each with positive and negative modes). The parameters used by QExactive were as follows:

[0089] In Full MS mode, the resolution is 70,000, the scan range is 100-1500 m / z (m / z is the ratio of ion mass to charge number), and the AGC (automatic gain control) is 3E+6 (i.e. 3×10 6 ), Maximum IT (maximum injection time) is 200 milliseconds;

[0090] In Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5 m / z, and the AGC is 1E+5 (i.e., 1×10 5 ), the maximum ion injection time is 50 milliseconds, and the HCD (high energy collision dissociation) relative collision energy is 30 eV.

[0091] 5. Metabolomics data preprocessing and metabolite identification

[0092] (1) Metabolomics data processing

[0093] a. Extract the peaks of the RAW format files of mass spectrometry into FeatureXML format files, reduce the dimension of the original mass spectrometry data, and improve the signal-to-noise ratio;

[0094] b. Using the peak alignment algorithm of OpenMS software, the retention time of the extracted peak format data is corrected and aligned between samples, thereby converting the mass spectrometry data into a data matrix;

[0095] c. Match and filter the isotope peaks in the data matrix obtained in step b, and then replace the abnormal data (0, negative values, background noise, etc.) with vacant values;

[0096] d. Among all the characteristic peaks obtained in step c, those with a detection rate of <80% are eliminated, and those with a detection rate of >80% are filled with the median value of the characteristic peak, and 5% random noise (obeying standard normal distribution) is added;

[0097] e. In order to reduce the difference in metabolite concentrations between samples and make the data distribution more symmetrical, the Normalization Autoencoder (NormAE) was used for homogenization to remove systematic errors such as batch effects.

[0098] (2) Identification of metabolites

[0099] 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 are matched with the spectral information of the primary and secondary metabolites in the public database 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 criteria for metabolite identification are 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.

[0100] Example 2 Screening of metabolic markers for distinguishing healthy and early cervical cancer

[0101] First, the data of the modeling group (including the HC group and the eCC group, with 93 people in the HC group and 55 people in the eCC group) were subjected to univariate ROC (receiver operating characteristic curve) analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) to obtain the univariate AUC value (i.e., the area under the univariate ROC curve) and VIP value (variable importance value). Secondly, the univariate AUC value>0.75 combined with the VIP value>1.8 was used as the screening condition, and finally 11 metabolic markers that contributed significantly to the difference between the groups were identified (Table 2), as important metabolic markers for distinguishing healthy and early cervical cancer.

[0102] Table 2. 11 important metabolic markers for distinguishing healthy and early cervical cancer

[0103]

[0104]

[0105] Example 3: Construction of a diagnostic model for distinguishing between healthy and early cervical cancer using 11 metabolic markers

[0106] In order to verify the diagnostic effect of the 11 selected metabolites in distinguishing healthy and early cervical cancer, a multivariate ROC curve analysis was performed on the above 11 metabolites in the modeling group. Specifically, 3 / 4 of the sample data of the HC group and the eCC 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 and early cervical cancer was constructed.

[0107] 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.

[0108] The sensitivity is:

[0109]

[0110] Specificity is:

[0111]

[0112] in,

[0113] TP (True positive): True positive, the number of samples that are actually positive examples that are correctly predicted as positive examples;

[0114] TN (Ture Negative): True negative, the number of samples that are actually negative examples but are correctly predicted as negative examples;

[0115] FP (False Positive): False positive, the number of samples that are actually negative examples but are mistakenly predicted as positive examples;

[0116] FN (False Negative): False negatives, the number of samples that are actually positive examples but are mistakenly predicted as negative examples.

[0117] The results are as follows Figure 1 As shown, AUC = 0.984 (sensitivity = 0.929, specificity = 0.957), the results show that the constructed diagnostic model has high diagnostic efficacy.

[0118] In order to further verify the effectiveness of the diagnostic model for distinguishing between healthy and early cervical cancer based on the modeling group data, the above diagnostic model was verified using the validation group data. A multivariate ROC curve analysis was specifically performed to evaluate the independent verification effect of the diagnostic model on the unknown data set (i.e., the validation group data set) 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) was output according to the detection data of the 11 important metabolic markers for distinguishing between healthy and early cervical cancer of each sample. A set of confusion matrices (including true positive, true negative, false positive, and false negative) was obtained based on the probability value of each sample as the diagnostic threshold. According to the formula, the sensitivity and specificity can be calculated, and a point can be marked in the ROC analysis diagram with sensitivity as the ordinate and 1-specificity as 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 diagram. Connecting these points will draw a ROC curve diagram. Among them, the point with the best sensitivity and specificity is selected, and the diagnostic threshold at this time is 0.4583.

[0119] 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 0.4583, among the 26 patients with early cervical cancer, 24 were diagnosed as early cervical cancer, and 2 were misjudged as healthy people; among the 30 healthy subjects, 26 were correctly diagnosed, and 4 were misjudged as early cervical cancer; the ROC analysis results of the diagnostic model in the validation group are shown in Figure 3. Figure 2 As shown, based on the results of the confusion matrix, the sensitivity, specificity, precision and accuracy were calculated, and the results showed that AUC = 0.973 (sensitivity = 0.923, specificity = 0.867). The above results show that the diagnostic model constructed for distinguishing between healthy and early cervical cancer also has a good diagnostic effect in the validation group.

[0120] Table 3. Confusion matrix of the diagnostic model used to distinguish between healthy and early cervical cancer

[0121] Early cervical cancer Healthy subjects 26 cases of early cervical cancer 24(TP) 2(FN) 30 healthy subjects 4(FP) 26(TN)

[0122] Example 4: Construction of a diagnostic model for distinguishing between healthy and early cervical cancer using 7 metabolic markers

[0123] A diagnostic model using a combination of 7 metabolite markers was performed, and ROC curve analysis was performed in the modeling group. The only difference from Example 3 was that 7 metabolite markers, namely pyroglutamate, lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4, hippuric acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvate, were used to construct a diagnostic model.

[0124] The results showed that when 7 metabolic markers were used, AUC = 0.978 (sensitivity = 0.923, specificity = 0.917).

[0125] In order to further verify the effectiveness of the diagnostic model for distinguishing between healthy and early cervical cancer based on the modeling group data, it was verified in the validation group and a multivariate ROC curve analysis was performed. The results showed that in the validation group, AUC = 0.965 (sensitivity = 0.923, specificity = 0.867).

[0126] Example 5: Construction of a diagnostic model for distinguishing between healthy and early cervical cancer using four metabolic markers

[0127] A diagnostic model using a combination of four metabolite markers was used, and ROC curve analysis was performed in the modeling group. The only difference from Example 3 was that the four metabolite markers, namely pyroglutamate, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvate, were used in combination to construct a diagnostic model.

[0128] The results showed that when four metabolic markers were used, AUC = 0.969 (sensitivity = 0.929, specificity = 0.870).

[0129] In order to further verify the effectiveness of the diagnostic model for distinguishing between healthy and early cervical cancer based on the modeling group data, it was verified in the validation group and a multivariate ROC curve analysis was performed. The results showed that in the validation group, AUC = 0.958 (sensitivity = 0.885, specificity = 0.867).

[0130] The above results show that the constructed diagnostic models have high diagnostic efficacy and clinical diagnostic significance. The above-constructed diagnostic model for distinguishing between healthy and early cervical cancer also has a good diagnostic effect in the validation group.

[0131] In summary, the present invention provides a metabolic marker composition for early cervical cancer diagnosis and its screening method and application. The metabolic marker composition has high sensitivity and specificity when used for early cervical cancer diagnosis, can achieve accurate screening of early cervical cancer, and provide important help for the prevention and reduction of cervical cancer incidence. At the same time, the metabolic marker composition is simple and easy to obtain samples when used for early cervical cancer diagnosis, with low cost and non-invasiveness, so that patients can be tested more conveniently, quickly and safely, and is more suitable for large-scale population screening and regular follow-up in areas with tight medical resources.

[0132] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A metabolic marker composition for early diagnosis of cervical cancer, characterized in that: The metabolic marker composition for early cervical cancer diagnosis comprises pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2 and pyruvic acid.

2. The metabolic marker composition for early cervical cancer diagnosis according to claim 1, characterized in that: The metabolic marker composition for early cervical cancer diagnosis also includes at least one of lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4 and hippuric acid.

3. The metabolic marker composition for early cervical cancer diagnosis according to claim 1 or 2, characterized in that: The metabolic marker composition for early cervical cancer diagnosis also includes at least one of 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4 and L-valine.

4. The metabolic marker composition for early cervical cancer diagnosis according to claim 1, characterized in that: The metabolic marker composition for early cervical cancer diagnosis consists of pyroglutamic acid, lysophosphatidylcholine 20:3, phosphatidylcholine 32:2, pyruvic acid, lysophosphatidylcholine 22:5, lysophosphatidylethanolamine 20:4, hippuric acid, 2,3,4-trihydroxybutyric acid, methylphosphatidylcholine 30:3e, lysophosphatidylcholine 22:4 and L-valine.

5. A method for screening a metabolic marker composition for early cervical cancer diagnosis according to any one of claims 1 to 4, characterized in that: The steps include: The plasma of patients with early cervical cancer and healthy people is extracted to obtain extracts; Using a chromatography-mass spectrometer to detect the organic phase and the aqueous phase in the extract to obtain detection data; Processing the detection data, and then identifying metabolites to obtain metabolomics data; The metabolomics data are analyzed to screen and obtain the metabolite marker composition for early cervical cancer diagnosis.

6. The screening method according to claim 5, characterized in that The steps of analyzing the metabolomics data and screening to obtain the metabolite marker composition for early cervical cancer diagnosis specifically include: The metabolomics data were subjected to univariate receiver operating characteristic curve analysis and orthogonal partial least squares discriminant analysis to screen out the metabolite marker composition for early cervical cancer diagnosis.

7. The screening method according to claim 6, characterized in that The area under the receiver operating characteristic curve greater than 0.75 and the VIP value greater than 1.8 were used as screening conditions to screen out a metabolic marker composition for the diagnosis of early cervical cancer.

8. Use of the metabolic marker composition for early cervical cancer diagnosis according to any one of claims 1 to 4 in the preparation of a product for diagnosing early cervical cancer.

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

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

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